Leukemia Stem Cells: Methods and Protocols [1st ed. 2021] 9781071608098

Table of contents :
Preface
Contents
Contributors
Part I: Introductory Reviews
Chapter 1: Introduction and Classification of Leukemias
1 Introduction
2 Classification of Leukemias
2.1 Stratification Based on Histology and Surface Phenotype
2.2 Stratification Based on Cytogenetics and Molecular Analyses
2.3 Next-Generation Stratification
3 Re-writing our Understanding of Hematopoiesis
4 The Principle of Leukemia
5 The Nature of Leukemia Cells
6 Concluding Remarks
References
Chapter 2: Leukemia Stem Cells: Concept and Implications
1 Introduction
2 The Unmet Need of Cancer Cures
3 Tumors Are Heterogeneous Tissues: The Road to Leukemia Stem Cell Discovery
4 The Leukemia-Initiating Cell and the Preleukemic Stem Cell
5 Implications of the Existence of LSCs
5.1 Implications for Leukemia Treatment
5.2 Implications for Leukemia Research
5.2.1 Implications for Research with Human Samples
5.2.2 Implications for Leukemia Modeling in Animals
6 Conclusions
References
Chapter 3: Leukemia Stem Cell Drug Discovery
1 Introduction
2 From Conventional Chemotherapy to Targeted Approaches
3 Cancer Stem Cells: The Hidden Hand Behind Cancer
4 CSC-Based Animal Models of Cancer: Essential Tools in Translational Medicine
5 Current Mouse Models of Human Cancer
6 How to Model Human Cancer in Mice?
7 Conclusions
References
Part II: Protocols
Chapter 4: Analysis and Isolation of Mouse Leukemic Stem Cells
1 Introduction
2 Materials
3 Methods
3.1 Isolation of HSCs
3.1.1 Collecting BM Cells
3.1.2 Enrichment by MACS
3.1.3 Antibody Staining
3.1.4 Sorting of HSCs
3.2 Methods for Isolation of LSCs from Mice
3.2.1 Isolate LSCs from MLL-AF9-Induced AML Mice
3.2.2 Isolate LSCs from ICN-1-Induced T-ALL Mice
3.2.3 Isolate LSCs from N-Myc-Induced B-ALL Mice
4 Notes
References
Chapter 5: Mass Cytometry of Hematopoietic Cells
1 Introduction
2 Materials
2.1 Thawing and Viability Treatment of Cells
2.2 Stimulation and Fixation of Cells
2.3 Barcoding of Fixed Cells
2.4 Staining Fixed Cells
2.5 CyTOF Acquisition
3 Methods
3.1 Thawing and Viability Treatment of Cryopreserved Bone Marrow Mononuclear Cells
3.2 Stimulation and Fixation of Bone Marrow Mononuclear Cells
3.3 Barcoding of Fixed Cells
3.4 Staining Fixed Cells
3.5 CyTOF Acquisition
4 Notes
References
Chapter 6: PCR Technology to Identify Minimal Residual Disease
1 Introduction
2 Materials
3 Methods
3.1 PCR Screening of IG/TR Rearrangements
3.1.1 PCR Mixes
3.1.2 PCR Mix Composition for IGH, IGK, TRD, and TRG (see Note 2)
3.1.3 PCR Mix Composition for TRA
3.1.4 PCR Mix Composition for TRB
3.1.5 PCR Set up
3.1.6 PCR Amplification Conditions and Verification of PCR Amplification
3.2 Assessment of Clonality and Sequencing of Clonal IG/TR Rearrangements
3.2.1 Homo-Heteroduplex Gel Electrophoresis (See Also Note 3)
3.2.2 Sequencing and Identification of Clonal Patient-Specific Variable Regions
3.3 Sequence Interpretation and Design of Allele-Specific Oligonucleotides for RQ-PCR
3.4 RQ-PCR Sensitivity Testing and MRD Monitoring
3.5 MRD Monitoring by RQ-PCR
3.5.1 DNA Quantification Using a Reference Gene (Albumin)
3.5.2 MRD Quantification in Follow-up Samples
3.5.3 Interpretation of MRD Results
3.6 Reporting MRD Values to Clinicians
4 Notes
References
Chapter 7: Next-Generation Sequencing Technology to Identify Minimal Residual Disease in Lymphoid Malignancies
1 Introduction
2 Materials
2.1 1st PCR
2.2 Purification of TRB-VJ and TRB-DJ PCR Products by Gel Extraction
2.3 2nd PCR
2.4 Purification of Subpools by Gel Extraction
2.5 Preparation of the Final Pool for Sequencing, and Sequencing
3 Methods
3.1 1st PCR
3.2 Purification of TRB-VJ and TRB-DJ PCR Products by Gel Extraction
3.3 2nd PCR
3.4 Purification of Subpools by Gel Extraction
3.5 Preparation of the Final Pool for Sequencing, and Sequencing
3.6 Bioinformatic Analysis of the Data
4 Notes
References
Chapter 8: Genomic Inverse PCR for Screening of Preleukemic Cells in Newborns (GIPFEL Technology)
1 Introduction
2 Materials
2.1 CD19+ Enrichment
2.2 DNA Isolation
2.3 Restriction Enzyme Digest
2.4 Ligation
2.5 Exonuclease Digest
2.6 Ethanol Precipitation
2.7 PCR/qPCR Reactions
2.8 Gel Electrophoresis
2.9 Gel Extraction
2.10 Sanger Sequencing
3 Methods
3.1 CD19+ Enrichment
3.2 DNA Isolation
3.3 Restriction Enzyme Digest
3.4 Cleanup of Restriction Enzyme Digest
3.5 Ligation
3.6 Exonuclease Digest
3.7 Ethanol Precipitation
3.8 Pre-amplification PCR
3.9 Semi-Nested Real-Time PCR
3.10 Demultiplexed Semi-Nested Real-Time PCR (See Note 19)
3.11 Demultiplexed Semi-Nested PCR
3.12 Agarose Gel-Electrophoresis
3.13 Gel Extraction
3.14 Sanger Sequencing
4 Notes
References
Chapter 9: Single-Cell Transcriptomic Analysis of Hematopoietic Cells
1 Introduction
2 Materials
2.1 Tissue Dissociation
2.2 SmartSeq2
2.2.1 Cell Lysis
2.2.2 Reverse Transcription
2.2.3 Amplification
2.2.4 Cleaning and Quality Control
2.2.5 Library Preparation
2.2.6 Library Cleaning and Quality Control
2.3 10x Genomics
2.3.1 Gel Bead-in-EMulsions (GEM) Generation and Barcoding (See Note 4)
2.3.2 Post GEM-RT Cleanup and cDNA Amplification
2.3.3 3′ Gene Expression Library Construction
2.4 Library Quantification, Pooling, and Sequencing
3 Methods
3.1 Tissue Dissociation
3.2 SmartSeq2
3.2.1 Cell Lysis
3.2.2 Reverse Transcription
3.2.3 Amplification
3.2.4 Cleaning and Quality Control
3.2.5 Library Preparation
3.2.6 Library Cleaning and Quality Control
3.3 10x Genomics
3.3.1 GEM Generation and Barcoding
3.3.2 Post GEM-RT Cleanup and cDNA Amplification
3.3.3 3′ Gene Expression Library Construction
3.4 Library Quantification, Pooling, and Sequencing
3.4.1 Library Quantification
3.4.2 Sequencing
4 Notes
References
Chapter 10: In-Depth Mass Spectrometry-Based Single-Cell and Nanoscale Proteomics
1 Introduction
2 Materials
2.1 Chip Fabrication
2.2 Cell Culture Solutions
2.3 Single-Cell Selection and Cell Lysis
2.4 Cell Digestion and Sample Collection
2.5 Liquid Chromatography
2.5.1 Preparation of Analytical, Split Flow, and Solid-Phase-Extraction (SPE) Columns
2.5.2 Etching and Connecting Emitter Tip
2.5.3 Liquid Chromatography-Mass Spectrometry Setup
2.5.4 Other Accessories
2.5.5 Buffers
3 Methods
3.1 Fabrication of Nanowell Chip
3.2 Fabrication of Cover Plate
3.3 Cell Culture and Harvesting
3.4 Single-Cell Selection and Cell Lysis Using nanoPOTS
3.5 Cell Digestion and Sample Collection
3.6 Packing of Analytical, Split, and SPE Columns
3.6.1 Fabricate Frit Using the Frit Kit
3.6.2 Packing the Analytical and Split Columns
3.6.3 Packing the SPE
3.7 Preparing a Chemically Etched Fused Silica Capillary nanoESI Emitter (See Note 1)
3.8 LC-MS Setup
4 Notes
References
Chapter 11: In Vivo Clonal Analysis of Aged Hematopoietic Stem Cells: Single-Cell Transplantation
1 Introduction
2 Materials
2.1 Isolation of Bone Marrow Cells
2.2 Staining HSCs in the Bone Marrow (See Note 2)
2.3 Single-Cell Sorting of HSCs
2.4 Single-Cell Transplantation of Sorted HSCs
2.5 Peripheral Blood Analysis
3 Methods
3.1 Isolation of Bone Marrow Cells
3.2 Staining HSCs in the Bone Marrow
3.3 Single-Cell Sorting of HSCs
3.4 Single-Cell Transplantation of Sorted HSCs
3.5 Peripheral Blood Analysis
4 Notes
References
Chapter 12: Experimental Competitive Bone Marrow Transplant Assays
1 Introduction
2 Materials
2.1 Preparation of Recipient Mice
2.2 Preparation of BM Cells
2.3 Flow Cytometry for HSC Equivalents
2.4 Preparation of Bone Marrow Cells for Transplant
2.5 Bone Marrow Transplants
2.6 Peripheral Blood Analysis
2.7 Analysis of BM Reconstitution
3 Methods
3.1 Preparation of Recipient Mice
3.2 Preparation of Donor and Competitor Bone Marrow (BM) Cell Samples
3.3 Flow Cytometry Staining to Establish Donor Cells HSC Equivalents
3.4 Preparation of the Bone Marrow Cells for Transplant
3.5 Bone Marrow Transplants
3.6 Peripheral Blood Analysis
3.7 Analysis of BM Reconstitution
3.8 Secondary and Tertiary Grafts
4 Notes
References
Chapter 13: Human T-ALL Xenografts
1 Introduction
2 Materials
2.1 Leukemic Blasts Enrichment by Density Centrifugation
2.2 Phenotypic Characterization by Flow Cytometry
2.3 Cryopreservation
2.4 Lentiviral Transduction
2.5 Mouse Immunodeficient Strains and Irradiation
2.6 Cell Inoculation
2.6.1 Retro-Orbital Injection
2.6.2 Intravenous Injection
2.6.3 Subcutaneous Injection
2.7 Therapeutic Treatment of T-ALL Xenotransplanted Mice
2.7.1 Intraperitoneal Administration
2.7.2 Oral Gavage Administration
2.8 Xenograft Monitoring and Mouse Analysis
2.8.1 Body Weight Measurement
2.8.2 Peripheral Blood Extraction
2.8.3 Bone Marrow Aspiration
2.8.4 Bioluminescence Imaging (IVIS)
2.8.5 Subcutaneous Tumor Measurement and Analysis
2.8.6 Mice Euthanasia and Organ Extraction
3 Methods
3.1 T-ALL Sample Preparation, Characterization, and Lentiviral Transduction
3.1.1 Enrichment of Leukemic Blasts from Peripheral Blood or Bone Marrow Samples by Density Centrifugation
3.1.2 Phenotypic Characterization by Flow Cytometry
3.1.3 Cryopreservation
3.1.4 Lentiviral Transduction
3.2 T-ALL Xenotransplantation
3.2.1 Mouse Immunodeficient Strains and Irradiation
3.2.2 Cell Inoculation
3.3 Therapeutic Treatment of T-ALL Xenotransplanted Mice
3.3.1 Intraperitoneal Administration
3.3.2 Oral Gavage Administration
3.4 Xenograft Monitoring and Mouse Analysis
3.4.1 Body Weight Measurement
3.4.2 Peripheral Blood Extraction
3.4.3 Bone Marrow Aspiration
3.4.4 Bioluminescence Imaging (IVIS)
3.4.5 Subcutaneous Tumor Measurement and Analysis
3.4.6 Mice Euthanasia and Organ Extraction for Flow Cytometry Analysis
4 Notes
References
Chapter 14: An Experimental and Computational Protocol to Study Cell Proliferation in Human Acute Myeloid Leukemia Xenografts
1 Introduction
2 Materials
2.1 Generation of AML Patient-Derived Xenografts
2.2 In Vivo Label-Retaining Assay
2.3 Flow Cytometry: Data Acquisition and Analysis
2.4 Computational Analysis of Cell Proliferation with ProCell
Box 1 ProCell Installation
3 Methods
3.1 Generation of AML Patient-Derived Xenografts (Fig. 1, Steps 1 and 2)
3.2 In Vivo Label-Retaining Assay (Fig. 1, Steps 3-5)
3.3 Flow Cytometry: Data Acquisition and Analysis (Fig. 1, Steps 6 and 7)
3.4 Computational Analysis of Cell Proliferation with ProCell (Fig. 1, Steps 8 and 9)
4 Notes
References
Chapter 15: Leukemic Stem Cell Culture in Cytokine-Free Medium
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Flow Cytometry
3 Methods
3.1 Primary Human AML Cell Culture
3.2 Flow Cytometry
4 Notes
References
Chapter 16: Ex Vivo Expansion of Adult Hematopoietic Stem and Progenitor Cells with Valproic Acid
1 Introduction
2 Materials
2.1 Isolation of CD34+ Cells from UCBs
2.2 Cytokines, Antibodies, and Kits
2.3 Cryopreservation
3 Methods
3.1 Density Gradient Isolation of Mononuclear Cells
3.2 CD34+ Cell Purification from UCBs (See Note 13)
3.3 Ex Vivo Expansion of Purified UCB-CD34+ Cells with VPA
3.4 Antibody Staining for Flow Cytometry Analysis
3.5 Flow Cytometry Acquisition and Data Analysis
3.6 Cryopreservation of Purified UCB-CD34+ Cells or Ex Vivo-Expanded Cells with VPA
4 Notes
References
Chapter 17: Isolation, Culture, and Manipulation of Human Cord Blood Progenitors
1 Introduction
2 Materials
2.1 Human Mononuclear Cells Isolation from Cord Blood
2.2 CD34+ HSPCs Cell Isolation
2.3 CD34+ HSPCs Cells Cryopreservation
2.4 CD34+ HSPCs Culture
2.5 CD34+ HSPCs Transduction and Sorting
2.6 Colony-Forming Unit Assay
2.7 Cell Proliferation Assay
2.8 Mice Transplantations and Follow-Up
2.9 Analysis of Human Engraftment in Mice
3 Methods
3.1 Cord Blood Samples Collection and Mononuclear Cells Isolation
3.2 CD34+ Cell Isolation
3.3 CD34+ Cells Cryopreservation
3.4 CD34+ HSPCs Culture
3.5 CD34+ HSPCs Transduction and Sorting
3.6 Colony-Forming Unit Assay
3.7 Cell Proliferation Assay
3.8 Mice Transplantations and Follow-Up
3.9 Analysis of Human Engraftment in Mice
4 Notes
References
Chapter 18: Lentiviral Transduction for Optimal LSC/HSC Manipulation
1 Introduction
2 Materials
2.1 Lentiviral Preparation
2.2 HSC Transduction
3 Methods
3.1 Transfection of 293T Cells for Viral Packaging/Concentration of Viral Supernatants
3.2 Lentiviral Transduction of Purified Hematopoietic Stem Cells
4 Notes
References
Chapter 19: Characterizing the In Vivo Role of Candidate Leukemia Stem Cell Genes
1 Introduction
2 Materials
2.1 Virus Production
2.2 Dissection and Isolation of Bone Marrow
2.3 Viral Transduction
2.4 Irradiation
2.5 Intravenous Injections
2.6 Peripheral Blood Analysis
2.7 Postmortem Exam
3 Methods
3.1 Production of Retroviral Particles for AML Oncogenic Driver
3.2 Production of Lentiviral Particles to Manipulate Expression of Candidate LSC Genes
3.3 Preparation of Bone Marrow Samples for Transduction
3.4 Viral Transduction of Lineage-Depleted Bone Marrow Cells
3.5 Irradiation and Intravenous Injection into the Lateral Tail Vein of Recipient Mice
3.6 Collection of Peripheral Blood
3.7 Analysis of Peripheral Blood by Hemocytometer and FACS
3.8 Monitoring Disease Development
3.9 Postmortem Examination of Spleen, Liver, and Bone Marrow of Diseased Mice
4 Notes
References
Chapter 20: Clonal Analysis of Patient-Derived Samples Using Cellular Barcodes
1 Introduction
2 Materials
2.1 Transfection
2.2 Transduction
2.3 DNA Isolation
2.4 PCR
2.5 Agarose Gel
2.6 PCR Product Purification
2.7 PCR Product Quality Control
3 Methods
3.1 Considerations for Barcode Library Production
3.2 Transfection
3.2.1 Day-7: Thaw HEK293FT Cells
3.2.2 Day 0: Plate HEK293FT Cells
3.2.3 Day 2: Transfection
3.2.4 Day 3: Medium Change
3.2.5 Day 4: Harvest Virus
3.3 Transduction of a Cell Line: Quality Control of Produced Virus
3.3.1 Day-7: Thaw SupB15 Cells
3.3.2 Day-6: Refresh Culture Medium
3.3.3 Day 0: Pre-coat Wells with RetroNectin
3.3.4 Day 1: Transduction
3.3.5 Day 2: Remove Virus
3.3.6 Day 3: Determine Transduction Efficiency
3.4 Transduction of Patient-Derived B-ALL Cells
3.4.1 Day 0: Pre-coat Wells with RetroNectin
3.4.2 Day 1: Thaw and Transduce Patient-Derived B-ALL Cells
3.4.3 Day 2: Remove Virus
3.4.4 Day 3: Determine Transduction Efficiency
3.5 Barcode Retrieval by Next-Generation Sequencing
3.5.1 Isolation of Genomic DNA
3.5.2 Barcode Amplification by Standard PCR
3.5.3 Barcode Amplification by Nested PCR
3.5.4 PCR Product Cleanup of Pooled Samples
3.5.5 Quality Control of Purified Barcode Sequences
3.6 Data Processing
3.6.1 Pre-filtering
3.6.2 Check for Multiple Integrations per Cell
3.6.3 Removing Sequencing Noise
3.6.4 Counting Clones
4 Notes
References
Chapter 21: Arrayed Molecular Barcoding of Leukemic Stem Cells
1 Introduction
2 Materials
2.1 Lentivirus Production
2.2 Isolation of Leukemia Cells from Mice
2.2.1 Isolation of c-Kit+ Cells from Bone Marrow
2.2.2 Lentiviral Transduction and Titration
2.2.3 Arrayed Screen
2.2.4 In Vivo Readout of LSCs and Genomic DNA Isolation
2.2.5 Preparation of Libraries and Sequencing
3 Methods
3.1 Production of Lentiviruses Containing Barcode Sequences
3.2 Isolation of Leukemic Cells from Mice
3.3 Test of Transduction Efficacy in Target Cells
3.4 Performing Screens Using Cells Labeled with Arrayed Molecular Barcodes
3.4.1 Barcoding of c-Kit+ Leukemia Cells
3.4.2 Arrayed Ex Vivo Screening
3.4.3 In Vivo Readout
3.5 DNA Preparation and Sequencing
3.5.1 Isolation of Genomic DNA
3.5.2 PCR Amplification of Barcodes
3.5.3 Indexing of the Samples
3.5.4 Sequencing and Analysis
4 Notes
References
Chapter 22: In Vivo Generation of Leukemic Stem Cells by HSC Targeting by Transgenesis
1 Introduction
2 Materials
2.1 Equipment
2.2 Reagents
3 Methods
3.1 Preparation of the Transgene
3.1.1 Expansion of the Plasmid Containing the Oncogene
3.1.2 Restriction Enzyme Digestion
3.1.3 Agarose Gel-Electrophoresis
3.1.4 Gel Extraction
3.1.5 Subcloning of the Oncogene cDNA into the Plasmid Containing the Promoter
3.1.6 Expansion of the Transgene Plasmid Prior to Purification
3.1.7 Transgene Purification
3.2 Transgene Pronuclear Microinjection
3.3 Genotyping and Analysis of Founders
4 Notes
References
Chapter 23: In Situ Hematopoietic Stem Cell Imaging
1 Introduction
2 Materials
2.1 Mouse Preparation
2.2 Whole Mount Immunostaining
3 Methods
3.1 Preparation of Whole Mount Sternal Tissue
3.2 Immunostaining of HSCs in Whole Mount Tissue
3.3 Sample Orientation for Microscopy
3.4 Microscopy
3.5 Analysis
4 Notes
References
Chapter 24: A Genome Editing System for Therapeutical Targeting of Stem Cells
1 Introduction
2 Materials
2.1 Cell Culture
2.2 RNP Preparation
2.3 Electroporation
2.4 Mice Transplantation and Sacrifice
2.5 Antibodies
2.6 PCR-Based Analysis of Editing Efficiency
3 Methods
3.1 Cell Thawing
3.2 Cell Electroporation
3.3 Xenotransplantation in Immunodeficient Mice
3.4 Analysis of Engraftment and Multilineage Differentiation of Edited HSCs
3.5 Measurement of Editing Frequency by Sanger Sequencing and TIDE Analysis
3.6 Colony-Forming Cell Assay
4 Notes
References
Chapter 25: Method for the Generation of Induced Hematopoietic Stem Cells
1 Introduction
1.1 Hematopoietic Stem Cells
1.2 Strategies for Reprogramming into HSCs
2 Materials
2.1 Mice
2.2 Primary Hematopoietic Cell Sorting
2.3 Cells
2.4 Cloning
2.5 Lentivirus Production
2.6 Transplantation and Peripheral Blood Sampling upon Transplantation
3 Methods
3.1 Identification of Potential Candidate Genes for Reprogramming
3.2 Cloning
3.3 Production of Lentiviral Vectors
3.4 Isolation of Primitive Hematopoietic Progenitors
3.5 Transduction with Lentiviral Vectors
3.6 Competitive Repopulation Assay
References
Chapter 26: Modeling Leukemia Stem Cells with Patient-Derived Induced Pluripotent Stem Cells
1 Introduction
2 Materials
2.1 Equipment
2.2 Disposables
2.3 Cells and Vectors
2.4 Media Components and Reagents
2.5 Reagent Setup
3 Methods
3.1 Reprogramming of BM/PB MNCs
3.2 Expansion of iPSCs and Establishment of Lines
3.3 Cryopreservation of Cells
3.4 Hematopoietic Differentiation
4 Notes
References
Chapter 27: High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells
1 Introduction
1.1 Considerations, Technologies, Principles
1.2 Benchmarking of Endothelial Cells and Extraction of Cells from Patients
2 Materials
2.1 hiPSC-EC Differentiation
2.2 Isolation of Mononuclear Cells from Bone Marrow Samples
3 Methods
3.1 hiPSC-EC Differentiation
3.2 Experiment Setup
3.3 Cell Plating
3.4 Cell Culture
3.5 Cell Fixation
3.6 Cell Immunostaining
3.7 Image Segmentation
3.7.1 Image Acquisition
3.7.2 Find Nuclei
3.7.3 Filter Image
3.7.4 Find Cytoplasm
3.7.5 Select Population
3.7.6 Find Image
3.7.7 Select Region
3.7.8 Modify Population
3.7.9 Find Spots
3.7.10 Calculate Properties
3.7.11 Select Population
3.7.12 Define Results
3.8 Multidimensional Reduction
3.9 Isolation of Mononuclear Cells from Bone Marrow Samples
4 Notes
4.1 Notes for Cell Benchmarking Protocol
4.2 Image Acquisition
4.3 Image Segmentation
4.4 Image Analysis
4.5 Object Dimensions
4.6 Texture Features
4.7 Data Exploration and Visualization
4.8 Data Transformation
4.9 Data Modeling, Clustering, Classification, and Dynamics
References
Chapter 28: Bioinformatic Methods to Identify Mutational Signatures in Cancer
1 Introduction
2 Downloading the Input Data
3 Quick Start Guide for Mutational Signatures Analysis with SigProfilerExtractor
3.1 Prerequisites
3.2 Installing SigProfilerExtractor
3.3 Installing a Reference Genome
3.4 Performing Signatures Extraction
3.5 Interpreting Signatures Extraction Results
4 Detailed Protocol of Mutational Signature Extraction Using SigProfilerExtractor
4.1 Software Implementation
4.2 Methods and Materials to Extract Signatures Using Python Platform
4.2.1 Required Operating System
4.2.2 Required Tools
4.2.3 Software Installation
4.2.4 Data Preparation for Extracting Mutational Signatures
Box 1 Description of SigProfilerExtractor´s Parameters in a Python Environment
4.2.5 Extracting Mutational Signatures from a Matrix
4.2.6 Extraction of Signatures from Mutational Catalogs
4.2.7 Detailed Explanation of Results
5 Methods and Materials to Extract Signatures Using R Platform
5.1 Required Operating System
5.2 Required Tools
5.3 Software Installation
5.4 Installation of Packages
5.5 Data Preparation for Extracting Mutational Signatures
Box 2 Description of SigProfilerExtractor´s Parameters in an R Environment
5.5.1 Extraction of Signatures from Mutational Catalogs
6 Notes
References
Index

Citation preview

Methods in Molecular Biology 2185

César Cobaleda Isidro Sánchez-García Editors

Leukemia Stem Cells Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Leukemia Stem Cells Methods and Protocols

Edited by

César Cobaleda Immune System Development and Function Unit, Centro de Biologia Molecular “Severo Ochoa” (CSIC/UAM), Madrid, Spain

Isidro Sánchez-García IBMCC, Laboratory 13, CSIC/Universidad de Salamanca, Salamanca, Spain

Editors Ce´sar Cobaleda Immune System Development and Function Unit Centro de Biologia Molecular “Severo Ochoa” (CSIC/UAM) Madrid, Spain

Isidro Sa´nchez-Garcı´a IBMCC, Laboratory 13 CSIC/Universidad de Salamanca Salamanca, Spain

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-0809-8 ISBN 978-1-0716-0810-4 (eBook) https://doi.org/10.1007/978-1-0716-0810-4 © Springer Science+Business Media, LLC, part of Springer Nature 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Any new theoretical advance or groundbreaking discovery in science is always faced with initial rejection and opposition, and the cancer stem cell (CSC) theory has not been different. Indeed, even today, more than two decades since the first unambiguous demonstration of their presence in tumors with the advent of flow cytometry and animal transplantations, all the consequences of their existence are far from being taken up to the practice in either the clinic or even in basic or translational cancer research. This inertia of the previous models of cancer, and the reluctance to accept and integrate the CSC theory, could have been understandable in the early years, since (1) the CSC model exposed the reason behind the frustratingly frequent failure of current cancer therapies and (2) cancer studies based on analyzing the tumor mass as a whole lost a large part of their importance. This reluctance today is indefensible. The existence of CSCs supposes a true change of paradigm in our understanding of cancer, but it will only have a real impact when we properly assimilate its implications and apply this knowledge to both cancer research and cancer treatment. To this aim, the CSC hypothesis should be incorporated from a bottomup perspective in the design of both basic cancer investigations and therapy development projects. This means that acknowledging the CSC-based nature of tumors should be the starting point for the design of new research and therapeutical approaches. These should replace the top-bottom procedures used still too often today, in which the old paradigms are still assumed, ignoring the fact that they are not useful anymore under the postulates of the CSC theory. The work that pioneered the demonstration of the CSC hypothesis was performed in the hematopoietic system, and leukemia stem cells (LSCs) were in fact the first CSCs identified. Actually, still today, both normal hematopoietic stem cells (HSCs) and LSCs are the best understood normal/malignant stem cell pair in the human and mouse organisms, and their study keeps leading the way in this field of research, with results that can very often be extrapolated to other tissues and their respective tumors. The advances in LSC research are being helped by the amazing array of new technological developments that are allowing us to study leukemias with a depth that we could not have imagined just a decade ago. The present volume, part of the Methods in Molecular Biology series, aims to provide a comprehensive hands-on manual covering all the techniques involved in the cellular and molecular identification and characterization of both normal hematopoietic and leukemic stem cells, both from human patients and from mouse models of human leukemia. Also, the book covers the most frequently used experimental approaches for the generation of such stem cell-based models of human leukemia. In order to help the less expert reader to become familiar with the concepts discussed in the protocol chapters, we have also included a review section at the beginning of the book, to provide the reader with quick-reference introductory chapters about the classification and basic biology of human leukemias, the cancer stem cell concept, and about how this concept has important implications for the way we understand, treat, and model leukemia.

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Preface

Finally, we would like to thank the series editor, Professor John Walker, for giving us the opportunity to work on this project and for his support and guidance during the process. And, last but not least, we would also like to thank all the authors, whose contributions and commitment have made this book possible, for their cooperative effort and understanding throughout the reviewing and editing process. Madrid, Spain Salamanca, Spain

Ce´sar Cobaleda Isidro Sa´nchez-Garcı´a

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

INTRODUCTORY REVIEWS

1 Introduction and Classification of Leukemias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geoffrey Brown 2 Leukemia Stem Cells: Concept and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isidro Sa´nchez-Garcı´a and Ce´sar Cobaleda 3 Leukemia Stem Cell Drug Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a

PART II

v ix

3 25 39

PROTOCOLS

4 Analysis and Isolation of Mouse Leukemic Stem Cells . . . . . . . . . . . . . . . . . . . . . . . Fang Dong, Haitao Bai, and Hideo Ema 5 Mass Cytometry of Hematopoietic Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Astraea Jager, Jolanda Sarno, and Kara L. Davis 6 PCR Technology to Identify Minimal Residual Disease . . . . . . . . . . . . . . . . . . . . . . Giovanni Cazzaniga, Simona Songia, and Andrea Biondi 7 Next-Generation Sequencing Technology to Identify Minimal Residual Disease in Lymphoid Malignancies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michaela Kotrova, Nikos Darzentas, Christiane Pott, ¨ ggemann and Monika Bru 8 Genomic Inverse PCR for Screening of Preleukemic Cells in Newborns (GIPFEL Technology) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Hein, Arndt Borkhardt, and Ute Fischer 9 Single-Cell Transcriptomic Analysis of Hematopoietic Cells . . . . . . . . . . . . . . . . . . Paulina M. Strzelecka, Anna M. Ranzoni, and Ana Cvejic 10 In-Depth Mass Spectrometry-Based Single-Cell and Nanoscale Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yiran Liang, Thy Truong, Ying Zhu, and Ryan T. Kelly 11 In Vivo Clonal Analysis of Aged Hematopoietic Stem Cells: Single-Cell Transplantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kyomi J. Igarashi and Ryo Yamamoto 12 Experimental Competitive Bone Marrow Transplant Assays . . . . . . . . . . . . . . . . . . Roxann He´tu-Arbour, Sarah Bouali, and Krista M. Heinonen 13 Human T-ALL Xenografts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patricia Fuentes, Marı´a L. Toribio, and Sara Gonza´lez-Garcı´a

vii

51 65 77

95

113 135

159

181 195 215

viii

14

15 16

17 18 19 20 21 22

23

24 25 26

27

28

Contents

An Experimental and Computational Protocol to Study Cell Proliferation in Human Acute Myeloid Leukemia Xenografts . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thalia Vlachou, Marco S. Nobile, Chiara Ronchini, Daniela Besozzi, and Pier Giuseppe Pelicci Leukemic Stem Cell Culture in Cytokine-Free Medium . . . . . . . . . . . . . . . . . . . . . Xiaolei Liu and Peter S. Klein Ex Vivo Expansion of Adult Hematopoietic Stem and Progenitor Cells with Valproic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luena Papa, Mansour Djedaini, Manisha Kintali, Christoph Schaniel, and Ronald Hoffman Isolation, Culture, and Manipulation of Human Cord Blood Progenitors. . . . . . Cristina Prieto, Damia Romero-Moya, and Rosa Montes Lentiviral Transduction for Optimal LSC/HSC Manipulation. . . . . . . . . . . . . . . . Gustavo Mostoslavsky Characterizing the In Vivo Role of Candidate Leukemia Stem Cell Genes . . . . . Yu Wei Zhang, Julian Mess, and Nina Cabezas-Wallscheid Clonal Analysis of Patient-Derived Samples Using Cellular Barcodes . . . . . . . . . . Sabrina Jacobs, Leonid V. Bystrykh, and Mirjam E. Belderbos Arrayed Molecular Barcoding of Leukemic Stem Cells. . . . . . . . . . . . . . . . . . . . . . . Marion Chapellier and Marcus J€ a ra˚s In Vivo Generation of Leukemic Stem Cells by HSC Targeting by Transgenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ as Carolina Vicente-Duen In Situ Hematopoietic Stem Cell Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aparna Venkatraman, Sarah E. Smith, Sandra Pinho, Meng Zhao, Linheng Li, and Paul Frenette A Genome Editing System for Therapeutical Targeting of Stem Cells . . . . . . . . . Giacomo Frati and Annarita Miccio Method for the Generation of Induced Hematopoietic Stem Cells . . . . . . . . . . . . Leonid Olender, Klil Levy, and Roi Gazit Modeling Leukemia Stem Cells with Patient-Derived Induced Pluripotent Stem Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andre´ G. Deslauriers, Andriana G. Kotini, and Eirini P. Papapetrou High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lorenzo Veschini, Heba Sailem, Disha Malani, Vilja Pieti€ a inen, Ana Stojiljkovic, Erika Wiseman, and Davide Danovi Bioinformatic Methods to Identify Mutational Signatures in Cancer . . . . . . . . . . S. M. Ashiqul Islam and Ludmil B. Alexandrov

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

241

259

267

281 299 307 317 345

361 373

383 399

411

423

447 475

Contributors LUDMIL B. ALEXANDROV • Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA HAITAO BAI • State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China MIRJAM E. BELDERBOS • Department of Ageing Biology and Stem Cells, European Research Institute for the Biology of Ageing (ERIBA), University Medical Center Groningen (UMCG), University of Groningen, Groningen, The Netherlands; Princess Ma´xima Center for Pediatric Oncology, Utrecht, The Netherlands DANIELA BESOZZI • Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy ANDREA BIONDI • Department of Medicine and Surgery, University of Milan Bicocca, Monza, Italy; Pediatrics, Ospedale San Gerardo/Fondazione MBBM, Monza, Italy ARNDT BORKHARDT • Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital Medical Faculty, Heinrich-Heine-University, Du¨sseldorf, Germany SARAH BOUALI • Institut National de la Recherche Scientifique, Laval, QC, Canada GEOFFREY BROWN • Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK MONIKA BRU¨GGEMANN • Unit for Hematological Diagnostics, Medical Department II, University Hospital Schleswig-Holstein, Kiel, Germany LEONID V. BYSTRYKH • Department of Ageing Biology and Stem Cells, European Research Institute for the Biology of Ageing (ERIBA), University Medical Center Groningen (UMCG), University of Groningen, Groningen, The Netherlands NINA CABEZAS-WALLSCHEID • Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany; Centre for Integrative Biological Signalling Studies (CIBSS), Freiburg, Germany GIOVANNI CAZZANIGA • Centro Ricerca Tettamanti, Fondazione Tettamanti, Pediatrics, Monza, Italy; Department of Medicine and Surgery, University of Milan Bicocca, Monza, Italy MARION CHAPELLIER • Division of Clinical Genetics, Lund University, Lund, Sweden CE´SAR COBALEDA • Immune System Development and Function Unit, Centro de Biologia Molecular “Severo Ochoa” (CSIC/UAM), Madrid, Spain ANA CVEJIC • Wellcome Trust—Medical Research Council, Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Cambridge, UK DAVIDE DANOVI • Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King’s College London, London, UK

ix

x

Contributors

NIKOS DARZENTAS • Unit for Hematological Diagnostics, Medical Department II, University Hospital Schleswig-Holstein, Kiel, Germany KARA L. DAVIS • Department of Pediatrics, Bass Center for Childhood Cancer and Blood Disorders, Stanford University, Stanford, CA, USA ANDRE´ G. DESLAURIERS • Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Biotech Research and Innovation Center, University of Copenhagen, København, Denmark; Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA MANSOUR DJEDAINI • Division of Hematology/Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA FANG DONG • State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China HIDEO EMA • Department of Regenerative Medicine, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China UTE FISCHER • Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital Medical Faculty, Heinrich-Heine-University, Du¨sseldorf, Germany GIACOMO FRATI • Imagine Institute, Paris, France PAUL FRENETTE • Institute for Stem Cell and Regenerative Medicine Research, Albert Einstein College of Medicine, Bronx, NY, USA PATRICIA FUENTES • Interactions with the Environment Program, Immune System Development and Function Unit, Centro de Biologı´a Molecular Severo Ochoa, CSICUAM, Madrid, Spain ROI GAZIT • The Shraga Segal Department for Microbiology, Immunology, and Genetics, Faculty of Health Sciences, National Institute for Biotechnology in the Negev, The BenGurion University of the Negev, Beer-Sheva, Israel SARA GONZA´LEZ-GARCI´A • Interactions with the Environment Program, Immune System Development and Function Unit, Centro de Biologı´a Molecular Severo Ochoa, CSICUAM, Madrid, Spain DANIEL HEIN • Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children’s Hospital Medical Faculty, Heinrich-Heine-University, Du¨sseldorf, Germany KRISTA M. HEINONEN • Institut National de la Recherche Scientifique, Laval, QC, Canada ROXANN HE´TU-ARBOUR • Institut National de la Recherche Scientifique, Laval, QC, Canada RONALD HOFFMAN • Division of Hematology/Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA KYOMI J. IGARASHI • Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA S. M. ASHIQUL ISLAM • Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

Contributors

xi

SABRINA JACOBS • Department of Ageing Biology and Stem Cells, European Research Institute for the Biology of Ageing (ERIBA), University Medical Center Groningen (UMCG), University of Groningen, Groningen, The Netherlands ASTRAEA JAGER • Department of Pediatrics, Bass Center for Childhood Cancer and Blood Disorders, Stanford University, Stanford, CA, USA MARCUS JA€ RA˚S • Division of Clinical Genetics, Lund University, Lund, Sweden RYAN T. KELLY • Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA; Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA MANISHA KINTALI • Division of Hematology/Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA PETER S. KLEIN • Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA ANDRIANA G. KOTINI • Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA MICHAELA KOTROVA • Unit for Hematological Diagnostics, Medical Department II, University Hospital Schleswig-Holstein, Kiel, Germany KLIL LEVY • The Shraga Segal Department for Microbiology, Immunology, and Genetics, Faculty of Health Sciences, National Institute for Biotechnology in the Negev, The BenGurion University of the Negev, Beer-Sheva, Israel YIRAN LIANG • Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA LINHENG LI • Stowers Institute for Medical Research, Kansas City, MO, USA XIAOLEI LIU • Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA DISHA MALANI • Institute for Molecular Medicine Finland-FIMM, Helsinki Institute for Life Science-HiLIFE, University of Helsinki, Helsinki, Finland JULIAN MESS • Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany; Spemann Graduate School for Biology and Medicine (SGBM), Freiburg, Germany; Centre for Integrative Biological Signalling Studies (CIBSS), Freiburg, Germany ANNARITA MICCIO • Imagine Institute, Paris, France ROSA MONTES • GENYO Centre for Genomics and Oncological Research, PfizerUniversidad de Granada—Junta de Andalucı´a. PTS Granada, Granada, Spain GUSTAVO MOSTOSLAVSKY • Section of Gastroenterology, Department of Medicine, Center for Regenerative Medicine (CReM), Boston University School of Medicine, Boston, MA, USA MARCO S. NOBILE • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands LEONID OLENDER • The Shraga Segal Department for Microbiology, Immunology, and Genetics, Faculty of Health Sciences, National Institute for Biotechnology in the Negev, The Ben-Gurion University of the Negev, Beer-Sheva, Israel LUENA PAPA • Division of Hematology/Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA EIRINI P. PAPAPETROU • Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Black Family Stem Cell Institute, Icahn School of

xii

Contributors

Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA PIER GIUSEPPE PELICCI • Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy VILJA PIETIA€ INEN • Institute for Molecular Medicine Finland-FIMM, Helsinki Institute for Life Science-HiLIFE, University of Helsinki, Helsinki, Finland SANDRA PINHO • Department of Pharmacology, University of Illinois at Chicago, Chicago, IL, USA CHRISTIANE POTT • Unit for Hematological Diagnostics, Medical Department II, University Hospital Schleswig-Holstein, Kiel, Germany CRISTINA PRIETO • VIB Center for Cancer Biology, Leuven, Belgium; KU Leuven Center for Human Genetics, Leuven, Belgium ANNA M. RANZONI • Wellcome Trust—Medical Research Council, Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Cambridge, UK DAMIA ROMERO-MOYA • Department of Anatomy, University of California, San Francisco, CA, USA CHIARA RONCHINI • Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy HEBA SAILEM • The Institute of Biomedical Engineering, Oxford, UK ISIDRO SA´NCHEZ-GARCI´A • Experimental Therapeutics and Translational Oncology Program, Instituto de Biologı´a Molecular y Celular del Ca´ncer, CSIC/Universidad de Salamanca and Institute of Biomedical Research of Salamanca (IBSAL), Salamanca, Spain JOLANDA SARNO • Department of Pediatrics, Bass Center for Childhood Cancer and Blood Disorders, Stanford University, Stanford, CA, USA CHRISTOPH SCHANIEL • Department of Pharmacological Sciences, Mount Sinai Institute for System Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Cell, Developmental and Regenerative Biology, Black Family Stem Cell Institute, Mount Sinai Institute for System Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA SARAH E. SMITH • Stowers Institute for Medical Research, Kansas City, MO, USA SIMONA SONGIA • Centro Ricerca Tettamanti, Fondazione Tettamanti, Pediatrics, Monza, Italy ANA STOJILJKOVIC • Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland PAULINA M. STRZELECKA • Wellcome Trust—Medical Research Council, Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK; Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome Trust Sanger Institute, Cambridge, UK; Department of Haematology, Oncology and Tumor Immunology, Charite´-Universit€ a tsmedzin, Berlin, Germany ´ MARIA L. TORIBIO • Interactions with the Environment Program, Immune System Development and Function Unit, Centro de Biologı´a Molecular Severo Ochoa, CSICUAM, Madrid, Spain THY TRUONG • Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA APARNA VENKATRAMAN • Stowers Institute for Medical Research, Kansas City, MO, USA

Contributors

xiii

LORENZO VESCHINI • Academic Centre of Reconstructive Science, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK CAROLINA VICENTE-DUEN˜AS • Institute of Biomedical Research of Salamanca (IBSAL), CSIC-Universidad de Salamanca, Salamanca, Spain THALIA VLACHOU • Department of Experimental Oncology, IEO, European Institute of Oncology, IRCCS, Milan, Italy ERIKA WISEMAN • Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King’s College London, London, UK RYO YAMAMOTO • Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA YU WEI ZHANG • International Max Planck Research School for Molecular and Cellular Biology (IMPRS-MCB), Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany MENG ZHAO • Institute of Hematology, Key Laboratory of Stem Cells and Tissue Engineering, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China YING ZHU • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA

Part I Introductory Reviews

Chapter 1 Introduction and Classification of Leukemias Geoffrey Brown Abstract Classifying the hematological malignancies by assigning cells to their normal counterpart and describing the nature of disease progression are entirely reliant on an accurate picture for the development of the multifarious types of blood and immune cells. In recent years, our understanding of the complex relationships between the various hematopoietic stem cell-derived cell lineages has undergone substantial revision. There has been similar progress in how we describe the nature of the “target” cells that genetic insults transform to give rise to the hematological malignancies. Here I describe how both longstanding and new information has influenced classifying, for diagnosis, the hematological malignancies. Key words Leukemia, Classification, Hematopoiesis, Stem cells

1

Introduction This book examines the technological advances in the fields of normal and leukemia stem cell biology that have allowed our concepts of the biology of the hematological malignancies to undergo revolutionary changes. These rapidly moving fields of research have provided ways of describing more precisely the nature of the hematological malignancies. Improved diagnosis and stratification is of great value to patients regarding the treatments they presently receive and the future possibilities of targeted and personalized therapies. The subtyping of hematological malignancies also provides an essential framework for epidemiological studies. By way of introduction, this chapter examines how diagnosing a type of leukemia and our understanding of the etiology of this group of diseases have undergone refinement as technologies have advanced. The four major types of hematological malignancies are leukemia, multiple myeloma, Hodgkin lymphoma, and Non-Hodgkin lymphoma. Leukemia cells mainly locate in the blood, multiple myeloma cells in the bone marrow, and Hodgkin and

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

3

4

Geoffrey Brown

Table 1 The main hematological malignancies Malignancy

Cell lineage

Diagnostic codea

Mainly located in the blood Myeloid leukemia Lymphoid leukemia

Myeloid cells B and T lymphocytes

C92–94 C91

Mainly located in the bone marrow Myeloproliferative disorders Myelodysplastic syndromes Multiple myeloma

Myeloid cells All blood cells Plasma cells

M9950/3, 996_/3, 9975/3 M998_/3 C90

Mainly located in lymph nodes Hodgkin lymphoma Non-Hodgkin lymphoma

Reed-Sternberg cell B and T lymphocytes

C81 C82–85, C96

a

According to the International Classification of Diseases 10th edition

non-Hodgkin lymphoma cells in the lymph nodes. The four major types of leukemia are acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL). Myeloid and lymphoid denote the involvement of these cell types in the disease, whereby lymphoid cells include B and T lymphocytes and innate lymphoid cells, and myeloid cells the rest of the blood and immune cells that arise from the hematopoietic stem cell (HSC). Whether a leukemia is lymphoid or myeloid is usually straightforward and central to the choice of chemotherapy. The Reed-Sternberg cell that is characteristic of Hodgkin lymphoma is a bizarre-looking large mono- or multinucleated cell that has a B cell origin in the vast majority of cases [1]. Acute and chronic denote whether the malignant cells grow rapidly or more slowly. In addition, there are myeloproliferative disorders, restricted to myeloid cells, and myelodysplastic syndromes, involving all blood cells (Table 1). However, the classification and precise nature of the hematological malignancies is much more complex than the major types shown in Table 1, as they are substantially heterogeneous. For example, the leukemias have at least 200 different acquired molecular abnormalities. As required by the Department of Health in the UK, most diagnostic services use an integrated reporting system of histopathology/cell surface phenotyping, to provide information on the cell lineage affiliation and the differentiation status of malignant cells, and genetics/molecular analysis, to provide information on genomic aberrations. In addition, newer molecular technologies around genome-wide studies contribute to classification, providing newer insight to the biology of the hematopoietic malignancies and an era of new therapeutic targets.

Introduction and Classification of Leukemias

2

5

Classification of Leukemias

2.1 Stratification Based on Histology and Surface Phenotype

From the introduction (by Ehrlich in 1879) of the differential staining of the leukocytes, assigning a patient’s hematological malignancy to a cell type relied on histopathology. Subsequently, investigators developed polyclonal antisera to cell surface proteins and used these as markers to characterize a patient’s leukemia cells. A forerunner was the discovery of the common acute lymphoblastic leukemia antigen (cALLA, now CD10) leading to the identification of common acute lymphoblastic leukemia (cALL) [2, 3]. At the same time, the definition of B and T lymphocytes in man, by the use of polyclonal antisera to surface antigens, led to the description of B-acute lymphoblastic leukemia (B-ALL), T-acute lymphoblastic leukemia (T-ALL), and “null” or unclassified ALL [4–6]. The importance of describing the morphological and phenotypic heterogeneity of the cells from patients with hematological malignancies was to allow the tailoring of a chemotherapeutic protocol to a well-defined subset of patients and the subtyping of the acute lymphoblastic leukemias had a dramatic impact on therapeutic outcomes. In the early 70s, the success rate in curing children with cALL was very poor and the tailoring of treatment and new drugs has led to a cure rate of 85%. The advent of monoclonal antibodies provided panels of cell surface markers for the various types of blood cells and their precursors. Markers for the latter cells were important because of the impaired differentiation seen in acute leukemia. The panels of markers added precision to stratifying the leukemias, as outlined in the chapter about mass cytometry. Analyses of the surface phenotype of leukemia cells also advanced ascribing each leukemia to its normal counterpart. Investigators used a bifurcating fate map for the development of the blood and immune cells that arose in the 1980s to assign a leukemia to a cell lineage, denoting the differentiation status of the leukemia cells. Figure 1 depicts an early version of a tree-like lineage map for hematopoiesis; a plethora of more recent tree-like models differ in the depiction of the relationships between the various myeloid cells ( [7, 8] and reviewed in [9]). In “classic” maps, a HSC makes an immediate and irrevocable decision to give rise to either a common lymphoid progenitor (CLP) [10], which generates B and T lymphocytes and innate lymphoid cells, or a common myeloid progenitor (CMP) [11], which generates the remaining blood cells types. The progeny of CLPs and CMPs continue to move stepwise, through serial fate decisions, to the various end-cell types. The cases of ALL classified as cALL, which are 76% of cases in children and 50% of adult cases, exhibit neither a B nor T lymphocyte phenotype. However, cells from around one-third of cALL patients have cytoplasmic μ chain of the immunoglobulin molecule and investigation of the expression of cALLA by bone marrow cells

6

Geoffrey Brown

Fig. 1 A hierarchical tree model of hematopoiesis. The tenet of the model is the existence of hematopoietic stem cells that give rise to the common myeloid progenitor and common lymphoid progenitor. Downstream progenitors were identified regarding the types of colony-forming cell (CFC) derived from bone marrow cells in assays using semisolid media. BFU-E burst-forming unit erythroid, GM-CFC granulocyte/macrophage colonyforming cell, Eo-CFC eosinophil colony-forming cell, meg-CFC megakaryocyte colony-forming cell, mast-CFC mast cell colony-forming cell, pro-B B lymphocyte committed cell, pro-T T lymphocyte committed cell

revealed expression by B lymphocyte progenitor cells. Hence, investigators concluded that a B cell committed/pre-B cell is the “target” cell for the cALL subclass of ALL. The positioning of cALL within the hierarchy of B lymphocyte development is shown in Fig. 2, which depicts a “who’s who” of some hematopoietic malignancies as seen in the 1980s [12]. A B-lymphocyte precursor cell is the “target” cell in the case of cALL and a suggestion is that this cell has adopted a stem cell-like program, as seen for granulocyte/macrophage precursors transformed by an oncogenic fusion protein [13, 14]. Granulocyte/macrophage precursors transformed via the introduction of the MLL-AF9 fusion protein reactivate genes that are highly expressed in normal hematopoietic stem cells and that are associated with self-renewal. These leukemia cells maintained the identity of the progenitor cell from which they arose ( [13] and see later). In the 1980s, investigators viewed the acute and chronic myeloid leukemias as a disease of HSCs, whereby the cells that initiate these leukemias are inherently stem cell-like and they self-renew but fail to differentiate appropriately (Fig. 2). Patients with chronic myeloid leukemia present with an abundance of relatively mature granulocytic cells. However and in 1977, Fialkow and colleagues

Introduction and Classification of Leukemias

7

Fig. 2 The positioning of leukemia cell types within the hematopoietic hierarchy. The red arrows designate the “target” cells and the hatched lines the extent of cell differentiation to maturation arrest. The figure is a modified version of the schema provided in Ref. 12. ALL acute lymphoblastic leukemia, cALL common acute lymphoblastic leukemia, AML acute myeloid leukemia, CML, chronic myeloid leukemia, NHL non-Hodgkin lymphoma, CLL chronic lymphocytic leukemia, cut. cutaneous

showed that a HSC is transformed in CML. CML is typified by the presence of the t(9:22) chromosomal translocation giving rise a short 22 chromosome, termed the Philadelphia chromosome. This translocation is present in most of the differentiated cell types of a patient’s cells that, in addition to revealing the origin of CML, showed that normal HSCs give rise to at least granulocytes, erythrocytes, platelets, and monocytes/macrophages [15]. Cells at different hierarchical levels of development (Fig. 2), identified often by the use of surface markers, are a potential target for transformation leading to a hematological malignancy. As can be seen, some leukemias and lymphomas are assigned to mature B and T lymphocytes regarding their cellular origin. Mature lymphocytes are a target for malignant transformation because of their special properties of long life and an extensive proliferative capacity. There was the need to arrive at a consensus on how best to stratify hematological malignancies. To achieve this, investigators combined cytomorphology with cytochemistry to determine the nature of a patient’s leukemia cells [16]. An example, published in 1976, is the French-American-British (FAB) classification system that, based on morphological criteria, classified ALL into three subtypes (L1–L3), AML into eight subtypes (M0–M7), and the myelodysplastic syndromes. Pathologists and clinicians used this

8

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system for almost 20 years and astute microscopy and experience in reviewing the stained smears were paramount to the successful use of the system. A testament to the exactness of the FAB classification is that the subtypes of leukemia described some 30 years ago are largely those we now define by genetic analysis. Accordingly, conventional microscopy still plays a realistic role in diagnosing a patient’s leukemia. Clinicians now obtain a more comprehensive identity for leukemia cells by the combined use of cytomorphology, cytochemistry, and multi-parameter flow cytometry. Immunophenotypic analysis, by flow cytometry, allows clinicians to classify most cases of leukemia (>95%) as myeloid versus lymphoid. Even so, some leukemias have a complex and ambiguous phenotype and show no affiliation to a particular hematopoietic cell lineage or cross-lineage antigen expression, including B lymphocyte/T lymphocyte, B lymphocyte/myeloid, T lymphocyte/myeloid, and a combination of these three lineages. These mixed phenotypes add a level of complexity that we do not fully understand, other than the suggestion of promiscuous gene expression by leukemia cells. 2.2 Stratification Based on Cytogenetics and Molecular Analyses

Hematological malignancies, and other cancers, are essentially genetic diseases and particular cytogenetic abnormalities associate almost exclusively with a type of malignancy. Clinicians have therefore used the nature and number of chromosomal abnormalities, as detected by metaphase chromosomal G-band analysis and interphase fluorescence in situ hybridization (FISH), to categorize leukemias/lymphomas. In addition to aiding diagnosis, cytogenetic aberrations are important prognostic factors regarding a patient’s response to therapy; Table 2 shows some of the chromosomal abnormalities observed in childhood ALL and their relationship to prognosis. The International Classification of Disease (ICD) exists in parallel to any kind of classification used for the hematological

Table 2 Some of the chromosomal abnormalities in childhood ALL Malignancy B lymphocyte lineage Good prognosis—ETV6-RUNX1 (20%), Hyperdiploid >50 (25%), TCF3-PBX1 (4%) Poor prognosis—BCR-ABL1-like (9%), CRLF2 (4%), other MLL rearrangements (4%), ERG (3%), BCR-ABL1 (2%), dic(9;20) (2%), iAMP21 (2%), MLL-AFF1 (2%), hypodiploid 20% in PB. BM cells are flushed out into a tube with 3 mL PBE buffer using a 1 ml syringe with a 27G needle. 3. Filter BM cells through a 200-hole screen mesh to create single-cell suspension. Place the cells on ice and count the cell number.

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Fig. 1 Gates for sorting CD201+150+48 Lineage Sca1+c-Kit+ (ESLAMLSK) cells. Briefly, CD150+CD48 cells were gated from Lineage cells. CD201+ cells were gated from Lineage CD150+CD48 cells. Sca-1+c-Kit+ cells were gated from CD201+CD150+CD48 Lineage cells

Table 2 Lineage antibodies cocktail for staining LSCs from AML mice Antibody

Clone number

Volume (μL)

Gr-1-biotin

RB6-8C5

1

B220-biotin

RA3-6B2

1

Ter119-biotin

TER-119

1

CD3-biotin

145-2C11

0.5

CD4-biotin

RM4–5

0.25

CD8-biotin

53–6.7

0.25

IL-7R-biotin

A7R-34

1

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

4. Prepare an antibody cocktail according to the published LSC phenotypic markers L-GMP and L-CD34+/-LSK cells (L-CD34+ and L-CD34 LSK cells). L-GMP cells: IL-7R Lineage Sca-1 c-Kit+CD34+FcγRII/III+GFP+ [13]. L-CD34+/-LSK cells: Lineage Sca-1+c-Kit+CD34+GFP+ and Lineage Sca-1+c-Kit+CD34 GFP+ cells (see Note 4). CD11b antibody is excluded from the Lineage cocktail because LSCs in AML mouse partially express myeloid cell marker CD11b. Biotinylated anti-interleukin-7 receptor (IL-7R) antibody is included in the lineage cocktail for the analysis of LSCs in AML mice according to the originally published paper [13]. 5. Calculate the volume of antibodies according to the cell number. Table 2 lists the volume of antibodies used for 1  107 cells.

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Table 3 List of antibodies for sorting L-GMP and L-CD34+/-LSK cells from AML mice Antibody

Clone number

Volume (μL)

Streptavidin-APC-Cy7

–

1

c-kit-APC

2B8

1

Sca-1-PE-Cy7

E13–161.7

1

CD34-BV421

RAM34

3

FcγRII/III-PE

23

1

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

6. Add the lineage antibody cocktail into a cell suspension of about 1  107 cells/100 μL. Vortex cells gently and stain for 30 min on ice while protecting from light. 7. Add 1 mL PBE buffer to the cells and centrifuge at 283  g at 4  C for 5 min. 8. Discard the suspension and resuspend the cells in 100 μL PBE buffer. Add other antibodies described in Table 3 and stain cells for 1 h on ice while protecting from light. 9. Stain the cells separately with each antibody in Table 3 as a single color control. Use some cells without antibody staining as a negative control. 10. Add 1 mL PBE buffer to the cells and centrifuge cells at 283  g at 4  C for 5 min. 11. Discard PBE and resuspend the cells using the suitable volume of PBE buffer (for instance 107 cells/500 μL buffer). 12. Set up the flow cytometer for cell sorting. 13. Load the cell tube and set the flow rate at 5000–7000 events/ second. 14. Use the negative control and the single-color control to set up the voltage for each antibody. 15. Record the cells for each single-color control and set appropriate compensation between different fluorophores. 16. Collect cells using the sorting gates as shown in Fig. 2. Use FSC-W and FSC-H gating to exclude doublets and then gate GFP+ leukemia cells. L-GMP and L-CD34+/-LSK are sorted. 3.2.2 Isolate LSCs from ICN-1-Induced T-ALL Mice

1. Establish the Intercellular domain of Notch1 (ICN-1)-induced T-ALL mouse model through transduction of BM HSPCs with ICN-1-GFP retrovirus [16]. Monitor the symptoms of

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Fig. 2 Gates for sorting of LSCs in AML mice. GFP+ cells were first gated, and then Lineage IL-7R cells were gated. Lineage Sca-1 c-Kit+ cells were further gated based on the expression of CD34 and FcγRII/III. CD34+FcγRII/III+ Lineage Sca-1 c-Kit+ GFP+ leukemic cells were further gated to obtain L-GMP. For the gating of L-CD34+/-LSK, GFP+ Lineage IL-7R cells were gated. Lin Sca-1+c-Kit+ (LSK) cells were further gated based on the expression of CD34 to get L-CD34+/-LSK cells. L-LSK cells may be simply used as LSCs because LSCs were detected in both CD34+LSK and CD34 LSK cells [6]

Table 4 Lineage antibodies cocktail for staining LSCs from T-ALL mice Antibody

Clone number

Volume (μL)

CD11b-biotin

M1/70

1

Gr-1-biotin

RB6-8C5

1

B220-biotin

RA3-6B2

1

Ter119-biotin

TER-119

1

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

sickness and measure the percentage of leukemia cells (GFP+ cells) in the PB of mice by using flow cytometry. 2. Sacrifice the mice to obtain BM cells from femora, tibiae, and iliac crests when the frequency of GFP+ cells is >20% in PB. BM cells are flushed out into a tube with 3 mL PBE buffer using a 1 mL syringe with a 27G needle. 3. Filter the whole BM cells through a 200-hole screen mesh to obtain single-cell suspension. Place the cell suspension on ice and count the cell number. 4. Prepare an antibody cocktail according to the LSC phenotypic markers: L-LSK cells. CD3/CD4/CD8 antibodies are excluded from the lineages cocktail for the analysis of T-ALL mice because LSCs in T-ALL mice partially express T cell markers CD3/4/8. 5. Calculate the volume of antibodies according to the cell number. Table 4 lists the volume of antibodies required for 1  107 cells.

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Table 5 List of antibodies for sorting L-LSK cells from T-ALL mice Antibody

Clone number

Volume (μL)

Streptavidin-APC-Cy7

–

1

c-kit-APC

2B8

1

Sca-1-PE-Cy7

E13–161.7

1

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

6. Add the lineage antibody cocktail to 100 μL cell suspension of about 1  107 cells. Vortex cells gently and stain for 30 min on ice while protecting from light. 7. Add 1 mL PBE buffer to the cells and centrifuge for 5 min at 283  g at 4  C. 8. Discard the supernatant and resuspend the cells in 100 μL PBE buffer. Add other antibodies as described in Table 5. Stain cells for 30 min on ice while protecting from light. 9. Stain the cells separately with an individual antibody in Table 5 for single-color control. Use some cells without antibody staining as a negative control. 10. Add 1 mL PBE buffer to the cells and centrifuge at 283  g at 4  C for 5 min. Discard PBE and resuspend the cells using the suitable volume of PBE buffer (for instance 107 cells/500 μL buffer). 11. Set up the flow cytometer for cell sorting. 12. Load the cell tube and set the flow rate at 5000–7000 events/ second. 13. Use the negative control and single-color control to set up the voltage for each antibody. 14. Record the cells for each single-color control and set appropriate compensation between different fluorophores. 15. Collect cells with the sorting gates as shown in Fig. 3. Use FSC-W and FSC-H gating to exclude doublets, and then gate GFP+ leukemia cells. LSK cells are sorted. 3.2.3 Isolate LSCs from N-Myc-Induced B-ALL Mice

1. Establish the N-Myc-induced B-ALL mouse model through transduction of BM HSPCs with N-Myc-GFP retrovirus [17]. Monitor the symptoms of sickness and measure the percentage of GFP+ leukemic cells in the PB of mice by using flow cytometry. 2. Sacrifice the mice to obtain BM cells from femora, tibiae, and iliac crests when the frequency of GFP+ cells is >20% in PB. BM

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Fig. 3 Gates for sorting of LSCs in T-ALL mice. GFP+ cells were first gated, and then Lineage cells were gated. Sca-1+c-Kit+ cells were gated from Lineage cells to obtain L-LSK cells

Table 6 Lineage antibodies cocktail for staining LSCs from B-ALL mice Antibody

Clone number

Volume (μL)

CD11b-biotin

M1/70

1

Gr-1-biotin

RB6-8C5

1

Ter119-biotin

TER-119

1

CD3-biotin

145-2C11

0.5

CD4-biotin

RM4–5

0.25

CD8-biotin

53–6.7

0.25

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

cells are flushed out into a tube with 3 mL PBE buffer using a 1 mL syringe with a 27G needle. 3. Filter BM cells through a 200-hole screen mesh to obtain single-cell suspension. Place the cell suspension on ice and count the cell number. 4. Prepare an antibody cocktail based on the LSC phenotypic markers: GFP+ CD34+/-Lineage Sca-1+ c-Kit+ (see Note 5). B220 antibody is excluded from the lineage cocktail for the analysis of B-ALL mice because only part of LSCs in B-ALL mice express B cell marker B220. 5. Calculate the volume of antibodies. Table 6 lists the volume of antibodies needed for 1  107 cells. 6. Add the lineage antibody cocktail into cell suspension of about 1  107 cells/100 μL. Vortex cells gently and stain cells for 30 min on ice while protecting from light. 7. Add 1 mL PBE buffer to the cells and centrifuge the cell suspension for 5 min at 283  g at 4  C.

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Table 7 List of antibodies for sorting L-CD34+/-LSK cells from B-ALL mice Antibody

Clone number

Volume (μL)

Streptavidin-APC-Cy7

–

1

c-kit-APC

2B8

1

Sca-1-PE-Cy7

E13–161.7

1

CD34-BV421

RAM34

3

This table shows the information about antibodies and the volume of each antibody for staining 1  107 cells/100 μL

Fig. 4 Gates for sorting of LSCs in B-ALL mice. GFP+ cells were first gated, and then Lineage cells were gated. CD34 cells or CD34+ cells were gated from Lineage cells. Sca-1+c-Kit+ double positive cells were gated from Lineage CD34 cells or Lineage CD34+ cells to obtain L-CD34 LSK cells or L-CD34+LSK cells

8. Discard PBE and resuspend the cells in 100 μL PBE buffer. Add other antibodies as described in Table 7. Stain cells for 1 h on ice while protecting from light. 9. Stain the cells with an individual antibody in Table 7 as singlecolor controls. Use some cells without antibody staining as a negative control. 10. Add 1 mL PBE buffer into the cells and centrifuge cell suspension for 5 min at 283  g at 4  C. 11. Discard PBE and resuspend the cells using the suitable volume of PBE buffer (for instance 107 cells/500 μL buffer). 12. Set up the flow cytometer for cell sorting. 13. Load the sample tube and set the flow rate at 5000–7000 events/second. 14. Use the negative control and single-color controls to set up the voltage for each color. 15. Collect data for each single-color control and set appropriate compensation between different fluorescent dyes. 16. Collect the cells with the sorting gates as shown in Fig. 4. Use FSC-W and FSC-H gate to exclude doublets, and then gate GFP+ leukemia cells. L-CD34+/-LSK cells are sorted.

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Notes 1. Red blood cells are lysed with ACS solution (Stem Cell Technology) at 4  C for 5 min, and cells are counted after stained with Trypan blue solution to distinguish live cells from dead cells. Typically 1  108 mononuclear cells can be collected from the femora, tibiae, and iliac crests of a 7- to 10-week-old mouse. 2. The filter is required before cell passage through the column. Otherwise dead cells or cell clots can block the column. 3. To increase the enrichment efficiency, the same volume of PBE buffer should be applied one more time to the column to flush out the magnetically labeled cells. 4. AML LSCs exist in IL-7R Lineage Sca1 c-Kit+CD34+FcγRII/III+GFP+ cells (L-GMP) and both CD34+LSK and CD34 LSK cells (L-CD34+/-KSL cells). You may use LSK cells for LSCs. 5. B-ALL LSCs exist in both CD34+LSK and CD34 LSK cells (L-CD34+/-KSL cells) (our unpublished data). You may use LSK cells for LSCs.

Acknowledgments This work was supported by the grant from the National Natural Science Foundation of China (81670105). References 1. Bonner WA, Hulett HR, Sweet RG, Herzenberg LA (1972) Fluorescence activated cell sorting. Rev Sci Instrum 43(3):404–409. https://doi.org/10.1063/1.1685647 2. Pei W, Feyerabend TB, Rossler J, Wang X, Postrach D, Busch K, Rode I, Klapproth K, Dietlein N, Quedenau C, Chen W, Sauer S, Wolf S, Hofer T, Rodewald HR (2017) Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548 (7668):456–460. https://doi.org/10.1038/ nature23653 3. Morrison SJ, Scadden DT (2014) The bone marrow niche for haematopoietic stem cells. Nature 505(7483):327–334. https://doi. org/10.1038/nature12984 4. Ganuza M, McKinney-Freeman S (2017) Hematopoietic stem cells under pressure. Curr Opin Hematol 24(4):314–321. https://

doi.org/10.1097/MOH. 0000000000000347 5. Lapidot T, Sirard C, Vormoor J, Murdoch B, Hoang T, Caceres-Cortes J, Minden M, Paterson B, Caligiuri MA, Dick JE (1994) A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367(6464):645–648. https://doi.org/10. 1038/367645a0 6. Dong F, Bai H, Wang X, Zhang S, Wang Z, Xie M, Zhang S, Wang J, Hao S, Cheng T, Ema H (2019) Mouse acute leukemia develops independent of self-renewal and differentiation potentials in hematopoietic stem and progenitor cells. Blood Adv 3(3):419–431. https:// doi.org/10.1182/bloodadvances. 2018022400 7. Wang J, Liu Z, Zhang S, Wang X, Bai H, Xie M, Dong F, Ema H (2019) Lineage marker expression on mouse hematopoietic stem cells.

Isolation of Mouse LSCs Exp Hematol 76(13–23):e12. https://doi. org/10.1016/j.exphem.2019.07.001 8. Ema H, Morita Y, Yamazaki S, Matsubara A, Seita J, Tadokoro Y, Kondo H, Takano H, Nakauchi H (2006) Adult mouse hematopoietic stem cells: purification and single-cell assays. Nat Protoc 1(6):2979–2987. https://doi.org/ 10.1038/nprot.2006.447 9. Osawa M, Hanada K, Hamada H, Nakauchi H (1996) Long-term lymphohematopoietic reconstitution by a single CD34-low/negative hematopoietic stem cell. Science 273 (5272):242–245. https://doi.org/10.1126/ science.273.5272.242 10. Kent DG, Dykstra BJ, Eaves CJ (2016) Isolation and assessment of single long-term reconstituting hematopoietic stem cells from adult mouse bone marrow. Curr Protoc Stem Cell Biol 38:2A 4 1–2A 4 24. https://doi.org/10. 1002/cpsc.10 11. Gur-Cohen S, Itkin T, Chakrabarty S, Graf C, Kollet O, Ludin A, Golan K, Kalinkovich A, Ledergor G, Wong E, Niemeyer E, Porat Z, Erez A, Sagi I, Esmon CT, Ruf W, Lapidot T (2015) PAR1 signaling regulates the retention and recruitment of EPCR-expressing bone marrow hematopoietic stem cells. Nat Med 21(11):1307–1317. https://doi.org/10. 1038/nm.3960 12. Meyer C, Hofmann J, Burmeister T, Groger D, Park TS, Emerenciano M, Pombo de Oliveira M, Renneville A, Villarese P, Macintyre E, Cave H, Clappier E, MassMalo K, Zuna J, Trka J, De Braekeleer E, De Braekeleer M, Oh SH, Tsaur G, Fechina L, van der Velden VH, van Dongen JJ, Delabesse E, Binato R, Silva ML, Kustanovich A, Aleinikova O, Harris MH, Lund-Aho T, Juvonen V, Heidenreich O, Vormoor J, Choi WW, Jarosova M, Kolenova A, Bueno C, Menendez P, Wehner S, Eckert C, Talmant P, Tondeur S, Lippert E, Launay E, Henry C, Ballerini P, Lapillone H, Callanan MB, Cayuela JM, Herbaux C, Cazzaniga G, Kakadiya PM, Bohlander S, Ahlmann M, Choi JR, Gameiro P, Lee DS, Krauter J, Cornillet-Lefebvre P, Te Kronnie G, Schafer BW, Kubetzko S, Alonso CN, Zur Stadt U, Sutton R, Venn NC,

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Izraeli S, Trakhtenbrot L, Madsen HO, Archer P, Hancock J, Cerveira N, Teixeira MR, Lo Nigro L, Moricke A, Stanulla M, Schrappe M, Sedek L, Szczepanski T, Zwaan CM, Coenen EA, van den Heuvel-Eibrink MM, Strehl S, Dworzak M, Panzer-GrumayerR, Dingermann T, Klingebiel T, Marschalek R (2013) The MLL recombinome of acute leukemias in 2013. Leukemia 27(11):2165–2176. https://doi.org/10.1038/leu.2013.135 13. Krivtsov AV, Twomey D, Feng Z, Stubbs MC, Wang Y, Faber J, Levine JE, Wang J, Hahn WC, Gilliland DG, Golub TR, Armstrong SA (2006) Transformation from committed progenitor to leukaemia stem cell initiated by MLL-AF9. Nature 442(7104):818–822. https://doi.org/10.1038/nature04980 14. Lu Z, Xie J, Wu G, Shen J, Collins R, Chen W, Kang X, Luo M, Zou Y, Huang LJ, Amatruda JF, Slone T, Winick N, Scherer PE, Zhang CC (2017) Fasting selectively blocks development of acute lymphoblastic leukemia via leptinreceptor upregulation. Nat Med 23(1):79–90. https://doi.org/10.1038/nm.4252 15. Cheng H, Hao S, Liu Y, Pang Y, Ma S, Dong F, Xu J, Zheng G, Li S, Yuan W, Cheng T (2015) Leukemic marrow infiltration reveals a novel role for Egr3 as a potent inhibitor of normal hematopoietic stem cell proliferation. Blood 126(11):1302–1313. https://doi.org/10. 1182/blood-2015-01-623645 16. Hu X, Shen H, Tian C, Yu H, Zheng G, XuFeng R, Ju Z, Xu J, Wang J, Cheng T (2009) Kinetics of normal hematopoietic stem and progenitor cells in a Notch1-induced leukemia model. Blood 114(18):3783–3792. https://doi.org/10.1182/blood-2009-06227843 17. Sugihara E, Shimizu T, Kojima K, Onishi N, Kai K, Ishizawa J, Nagata K, Hashimoto N, Honda H, Kanno M, Miwa M, Okada S, Andreeff M, Saya H (2012) Ink4a and Arf are crucial factors in the determination of the cell of origin and the therapeutic sensitivity of Myc-induced mouse lymphoid tumor. Oncogene 31(23):2849–2861. https://doi.org/10. 1038/onc.2011.462

Chapter 5 Mass Cytometry of Hematopoietic Cells Astraea Jager, Jolanda Sarno, and Kara L. Davis Abstract Mass cytometry is now a well-established method that enables the measurement of 40–50 markers (generally proteins but transcripts are also possible) in single cells. Analytes are detected via antibodies tagged with heavy metal and detected by using a time-of-flight mass spectrometer. Over the past decade, mass cytometry has proven to be a valuable method for immunophenotyping hematopoietic cells with remarkable precision in both healthy and malignant scenarios. This chapter explains in detail how to profile hematopoietic cells by using this high-dimensional multiplexed approach. Key words Mass cytometry, CyTOF, Hematopoiesis, Single-cell, High-dimensional, Multiplex, Immunophenotyping, Leukemia

1

Introduction Hematopoiesis is the process by which blood cells develop through the acquisition of defined phenotypes as a result of coordinated and cell-specific gene expression. The understanding of the molecular and regulatory mechanisms involved in the differentiation of hematopoietic cells has been fundamental to study the pathogenesis of human immunological disorders and hematological malignancies. While murine hematopoiesis has been one the most studied and understood stem cell differentiation systems [1, 2], the exact identities and differentiation paths of different cell types as well as signaling pathways involved in human lymphopoiesis remain vague. Human hematopoiesis is thought to be a hierarchical process, where a rare population of multipotent hematopoietic stem cells (HSCs), under precise transcriptional control, make differentiation decisions resulting in myeloid or lymphoid specific populations defined by the expression of particular cell surface proteins. The expression of cell surface proteins, also called “cluster of differentiation” (CD) proteins, has been widely studied by using fluorescence-activated cell sorting (FACS). In the past, the limited number of markers that can be simultaneously measured by FACS

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Fig. 1 CyTOF experimental workflow. Summary of samples processing steps for mass cytometry analysis of bone marrow mononuclear cells (BMMC). (1) Frozen BMMCs are thawed, transferred to a 15 mL tube and cisplatin viability stain is performed as described in the text. Transfer cells in 1 mL cluster tubes, and after 30 min of resting at 37  C, cells can be perturbed with pervanadate (as positive control of phosphorylation status), cytokines or drugs according to the experimental need. After the perturbations, cells are fixed with PFA 1.6% and can be barcoded using Palladium-based mass tag and combined in one single FACS tube. (2) Cells are stained in two different steps. For the surface staining, cells are treated first for 10 min with Fc Blocking to prevent nonspecific binding of the antibodies, and then they are stained for 30 min with the cocktail mix of

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(about 10–12) prevented a comprehensive overview of the complex populations comprising human hematopoietic development from a single sample. Although newer instruments enable measurement of up to 30 markers by 5-laser FACS, auto-fluorescence and compensation make this experimentally complex. The development of new techniques for single-cell analysis including single-cell RNA sequencing and mass cytometry has led to insights into the understanding of the perceived hematopoietic hierarchy. These techniques now support a model of hematopoiesis as a continuous process, lacking the traditional boundaries of cell populations belonging to different hierarchical levels [3]. With this concept of hematopoiesis, mass cytometry (Cytometry by Time-Of-Flight, CyTOF) has proven to be a useful tool to study the continuum of transitional stages within the different blood lineages [4, 5]. This technology enables the measurement of ~50 markers simultaneously per single cell, including cell surface proteins, phosphosignaling molecules, transcription factors, epigenetic modifications, and cell fate markers [4, 6, 7]. The reporter antibodies, used to detect the markers of interest, are conjugated to heavy metal isotopes instead of fluorophores as in flow cytometry, and their unique mass signatures are detected by a time-of-flight mass spectrometer. This overcomes the limitation of spectral overlap that limits analysis of many parameters via flow cytometry while maintaining a similar sensitivity. Coupling mass cytometry with algorithms able to evaluate single cell relationships, it has been possible to uncover the phenotypic development and functional states of immune cells in either the human or murine hematopoietic system [5, 8]. Our group recently leveraged the knowledge of healthy B-cell development and the high-dimensional information obtained by mass cytometry to overcome the inherent heterogeneity of cancer cells and to find, in diagnostic leukemic samples, populations associated with treatment failure [9]. Here we describe how to perform highthroughput analysis of healthy and malignant hematopoietic cells using mass cytometry. An overview of the experimental workflow is shown in Fig. 1. ä Fig. 1 (continued) metal-conjugated antibodies, previously prepared. Next, cells are permeabilized with methanol 100% for 10 min at 4  C and then stained for 30 min with intracellular metal-conjugated antibodies mix. (3) Once cells are ready for CyTOF acquisition, they are introduced in the instrument by using a nebulizer that converts single cells into droplets which are injected into an inductively coupled plasma torch. Here single cells are vaporized, atomized, and ionized, and the resulted ion cloud then enters into a quadrupole that filters out the lower masses. In the time-of-flight (TOF) module, ions in the cloud are ordered from light to heavy mass and accelerated to the detector that measures the quantity of each isotope for each individual cell. (4). Data analysis can be performed using different tools; some examples are shown, such as histogram overlay to measure markers expression in different populations, heatmaps to look at changes under different conditions (helpful for signaling analysis) or dimension reduction analysis (i.e., t-SNE), useful to perform phenotypic characterization of the populations present in the samples

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Materials Materials should be kept sterile for the thawing and stimulation steps, while cells are alive. Non-sterile materials may be used for the barcoding and staining steps. Clean materials are used throughout all preparations.

2.1 Thawing and Viability Treatment of Cells

1. Cryopreserved Bone Marrow Mononuclear cells. 2. 37  C Water bath. 3. 15 mL Conical tubes. 4. Heparin solution 500: 10,000 U/mL heparin in ultrapure water. Dissolve 50 mg of heparin in 1 mL of sterile ultrapure water to obtain a concentration of 10,000 U/mL. Store at 4  C. 5. Cisplatin 1000 stock: 25 mM cisplatin in DMSO. Dissolve 25 mg of cisplatin in 3.3 mL of DMSO to obtain a concentration of 25 mM. Store at 80  C. 6. Thawing media: RPMI 1640 supplemented with 10% FBS, 1 Penicillin-Streptomycin, 1 glutamine, 0.025 U/mL benzonase, 20 U/mL heparin [9]. Add 450 mL of RPMI 1640, 50 mL of FBS, 5 mL of 100 Penicillin-Streptomycin, and 5 mL of 200 mM glutamine together; mix well and filter through a 0.2 μM filter. Add 5 μL of 25KU benzonase and 100 μL of 500 heparin solution per 50 mL of RPMI 1640 supplemented with 10% FBS, 1 Penicillin-Streptomycin, 1 glutamine. 7. Centrifuge at room temperature, buckets for 15 mL conical tubes. 8. Automated cell counters or hemocytometer. 9. Viability-media: RPMI 1640 supplemented with 25 μM cisplatin final concentration [10]. Add 10 μL of Cisplatin 1000 stock to 10 mL of RPMI 1640. 10. Cluster tubes and cluster tube rack. 11. Single channel manual pipets and filter tips. 12. Serological pipets and pipet aid. 13. Trypan Blue.

2.2 Stimulation and Fixation of Cells

1. Multichannel pipets and filter pipet tips. 2. Appropriate stimulation or drug treatments for specific cell type of interest (see Note 1). 3. 16% Paraformaldehyde, filtered (see Note 2). 4. 37  C 5% CO2 cell culture incubator.

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5. Centrifuge at 4C, buckets for 96-well plates. 6. Cell Staining Media (CSM): PBS with 0.5% BSA and 0.02% sodium azide. 7. Vortex. 2.3 Barcoding of Fixed Cells

1. 2 mL deep well blocks. 2. Multichannel pipets and filter tips. 3. 96-Well plate aspirator. 4. Palladium barcoding reagent, 20 plex [11] (see Note 3). 5. CyTOF PBS, Calcium and Magnesium free (see Note 4). 6. Saponin buffer: CyTOF PBS with 0.02% saponin and 0.02% sodium azide (see Note 5). 7. CSM. 8. Single channel manual pipets and filter tips. 9. FACS tubes.

2.4 Staining Fixed Cells

1. CSM. 2. Fc Receptor blocking solution. 3. Metal-conjugated antibodies, divided by surface and intracellular localization (see Note 6). 4. 0.1μM Spin filters. 5. Microcentrifuge. 6. 100% Ice-cold (90% while dispensing samples and reagents into the nanowells of the nanoPOTS chip. Alternatively, cells can be sorted directly into nanowells in the nanoPOTS chip using fluorescence-activated cell sorting [17]. 1. Pulled 35-μm-i.d. capillary tips: protect the center of a 20-cmlong capillary (i.d. 200 μm, o.d. 360 μm) with two metal protectors leaving a gap of 1 mm. Heat the middle 1 mm of the capillary using a butane torch and pull both ends to make 2 tips [30]. 2. Treat a regular glass slide with 1% BSA for 1 h. Wash the slide with water and air dry. 3. Prepare cell lysis buffer: mix 20 μL of 1% DDM with 30 μL of 50 mM ABC buffer and 50 μL of PBS. Add 1 μL of 500 mM DTT to reach a final concentration of 5 mM. 4. Use the nanoPOTS robotic nanoliter liquid handler and a tapered capillary to dispense 100 nL of cell lysis buffer into each well. 5. Connect the capillary tip to the syringe on the nanoPOTS robot. Mount a BSA-treated slide to the SVM340 microscope. 6. Add ~500 μL of cell suspension to the BSA-treated slide and wait for ~1 min to allow cells to settle. 7. Use the SVM340 microscope to locate a cell of interest. Use the nanoPOTS robot to aspirate 10 nL of air, then position the tip over a cell of interest and aspirate 5 nL of cell suspension including the targeted single cell. Later, move the tip to a well on the chip and dispense 10 nL of cell suspension and air (Fig. 4). Repeat to fill other nanowells with cells. 8. Place the cover slide over the nanoPOTS chip. Clamp the cover slide to the chip using binder clips. 9. Incubate the chip in a humid chamber at 70  C for 1 h. 3.5 Cell Digestion and Sample Collection

1. Cool the chip to the room temperature. 2. Prepare 30 mM IAA by diluting 10 μL of 300 mM IAA with 90 μL 50 mM ABC buffer. 3. Remove the cover slide and mount the chip at the same position. Add 50 nL of 30 mM IAA to each well. Seal the chip and place in a humidified chamber at room temperature in the dark for 30 min. 4. Dilute 5 μL of 0.1 ng/nL lys-C solution to 100 μL with 50 mM ABC. 5. Mount the chip on the nanoPOTS platform. Add 50 nL of lys-C solution into each well. Seal the chip and incubate it in a humidified chamber at 37  C for 4 h.

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Fig. 4 Schematic of single-cell selection from a dilute suspension on a slide using a capillary tip

6. Dilute 5 μL of 0.2 ng/nL trypsin solution to 200 μL with 50 mM ABC. 7. Mount the chip in the nanopipetting system and add 50 nL of trypsin solution into each well. Seal the chip and incubate it in a humidified chamber at 37  C for 10 h (see Note 11). 8. Cool the chip to room temperature. Mount the chip on the nanopipetting system and dispense 50 nL of 0.1% TFA. Replace the cover and incubate for 60 min at room temperature. 9. Connect a new, clean untapered capillary (i.d. 200 μm, o.d. 360 μm) to the syringe on the nanopipetting robot. Use the capillary to aspirate all the liquid from one well, then aspirate 200 nL of 0.1% FA to wash the well twice. Collect all the washing solution in the same capillary. 10. Cut the capillary and seal both ends with parafilm. Label the capillary and store at 4  C before analyzing with LC-MS. 3.6 Packing of Analytical, Split, and SPE Columns 3.6.1 Fabricate Frit Using the Frit Kit

Fused-silica capillaries should be polished using the capillary polishing station before use.

1. Combine 20 μL Kasil 1 and 60 μL Kasil 1624 from the Frit kit in a microcentrifuge tube and mix well by vortexing. Add 20 μL

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Fig. 5 Diagram of the MS-188 Haskel pump with a set of HiP valves and Tee. The Haskel pump converts air pressure to liquid pressure at nominal ratio of 188. Peak horsepower is at 75% nominal ratio
drive pressure, i.e., 188:1 pump @ 100 psi air drive peaks at 100
188
0.75 psi ¼ 14,100 psi. Transfer metal tube diameter as well fittings/ferrules size are flexible. In this setup, 1/1600 tubes are used

of formamide into the mixture and vortex thoroughly until the cloudy precipitate dissolves. 2. Cut a 60-cm-long, 30-μm-i.d. capillary and polish both ends. 3. Dip one end of the capillary column into the frit solution and remove quickly. The solution is drawn a few mm into the capillary through capillary action. 4. Allow the capillary to sit at room temperature for at least 20 min to allow the frit solution to become semi-solidified. 5. Bake the capillary at 100  C for at least 4 h. 6. Cut the fritted end so the frit is about 2 mm long, then repolish the end. 3.6.2 Packing the Analytical and Split Columns

1. Set up the MS-188 Haskel pump as illustrated in Fig. 5. Prior to packing, the pump should be rinsed with a few 100 mL of the packing solution. 2. Use the 380-μm-o.d. PEEK tubing sleeve to seal the open end of the baked capillary column with frit at the other end to the stainless-steel vessel made from the Swagelok fitting (acting as a slurry chamber), into which the C18 packing material and the stir bar have been placed. 3. Secure the Swagelok fitting on the stir plate. 4. Prepare the slurry solution in another vial by mixing 20 mg of packing media in 1 mL of acetonitrile.

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5. Inject the slurry solution in the Swagelok fitting by syringe and seal the fitting. 6. Open the Nitrogen tank and then the HiP valve (the relief valve remains closed). Turn on the stir plate. Gradually increase the packing pressure from 500 to 8000 psi (see Note 12). 7. Stop the nitrogen gas flow and close the HiP valve when the column length reaches 50 cm. Turn off the stir plate. 8. Condition the capillary column at 8000 psi for 10 min in an ultrasonic bath, then leave overnight to depressurize. 9. Disconnect the capillary column. 10. Make sure the fritted end is polished. Repolish as needed before connecting to the emitter tip. 11. Repeat the process (frit fabrication and packing) for another 75-μm-i.d. capillary of the same length to serve as a split column. 12. Clean the Swagelok fitting and flush the pump line when done. 3.6.3 Packing the SPE

1. Prepare another 50-cm-long, 50-μm-i.d. packed capillary column using the method described above. 2. Cut the column into 5-cm-long segments. 3. Cut and polish an 8-cm-long, 50-μm-i.d. empty capillary. Fabricate a frit at one end as described above. 4. Connect the open end of the 8-cm empty capillary to the 5-cm packed capillary using the 50-μm bore stainless-steel Union and PEEK fittings. Then connect the 5-cm packed capillary to the MS-188 Haskel pump system using the PEEK sleeve as described above. 5. Use the pump to push the packing material from the packed capillary to the empty one. Start the pressure at 1500 psi and then ramp it to 3000 psi. 6. After the packing material has been transferred to the empty capillary, maintain the pressure at 3000 psi for a few minutes and then close the valve and stop the pump. 7. Depressurize the SPE column and disconnect the capillary. 8. Connect the open end of the capillary to a nitrogen gas tank for drying. 9. Mark the location where the packing material is packed to (about 5 cm long). 10. Prepare another frit solution. 11. Connect the fritted end of the capillary to a vacuum pump. 12. Quickly dip and remove the open end of the capillary into the frit solution while the vacuum pump is on. Observe the movement of the frit solution under a microscope. Stop the vacuum pump before the frit solution moves too far into the packing bed.

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13. Disconnect the capillary from the vacuum pump and let it sit at room temperature for at least 20 min. 14. Bake the capillary at 100  C for at least 4 hours. 15. Cut frit end so that it is about 2 mm long and then polish as needed. 16. Use the PEEK fitting to connect the capillary with stainless steel unions at each end of the capillary. 3.7 Preparing a Chemically Etched Fused Silica Capillary nanoESI Emitter (See Note 1)

A 10-μm-i.d., 360-μm-o.d. fused-silica capillary is connected to a syringe filled with DI water. Water is pumped through the capillary at ~0.1 μL/min to protect the inside of the capillary from being etched. Nanostrip 2X is first used to remove the outer polymer coating at 100  C, and the end of the capillary is then rinsed in distilled water. HF is then used to etch a finely tapered tip on the fused silica capillary (FSC) that is suitable for supporting a stable electrospray in the nanoflow regime. A description of the etch process has been published [31]. The end of the capillary exposed to HF is rinsed with water upon completion and all chemicals are stored appropriately. 1. Fill a syringe with ~50 μL of HPLC-grade H2O. Attach to the capillary and connect the syringe to the syringe pump. 2. Open the Nanostrip 2X and heat it to 100  C using the block heater in the fume hood. 3. Lower the FSC until 10 mm are submerged in the Nanostrip. Set the flow rate of the capillary to 0.1 μL/min. Smaller i.d. tubing does not need as high of a flow rate to prevent internal etching. 4. Wait ~15 min, or until the polyimide coating has been removed (the submerged capillary will be clear). Remove the capillary and dip it into distilled water to rinse. 5. Turn off the heater and do not replace the lid on the Nanostrip 2X until the temperature has dropped to 8 One charge state only Preferred On 30.0 s

An electrospray potential of 1.9 kV is applied at the source, and the ion transfer tube is set at 250  C for desolvation. The ion funnel RF level is set to 45

4. Connect the inlet of the SPE to one end of the sample storage capillary in the same manner. 5. Connect the other end of the sample storage capillary to line 6 (Fig. 7). 6. Set the flow rate at 1 μL/min for 10 min (see Note 16). 7. Stop the flow, then disconnect the SPE. Reconnect the SPE to line 6 in a reverse direction (tail-head). Set the flow rate at 1 μL/min for 5 mins to flush out any debris. 8. Reduce the flow rate to 300 nL/min (switch valve remains at position A), then disconnect the SPE. 9. Connect the SPE to the analytical column as illustrated in Fig. 7. 10. Switch the valve to position B. A higher flow rate (300 nL/ min) rather than the regular (250 nL/min) is used for pressure testing. Make sure there is no leaking/clogging. 11. Connect the high voltage cable between the head of the SPE and the MS to apply voltage. 12. Start the MS data acquisition and LC gradient elution program to collect data. 13. After finishing the analysis, move the emitter tip away from the MS inlet and switch the MS to standby. Avoid heating the tip when there is no flow for a long period.

4

Notes 1. Buffered HF mixture (or “buffered oxide etchant, BOE” at 6:1 ratio) is preferred for chip etching. For safety, put on safety goggles and wear double gloves when working with HF. Make sure all HF operations are performed in a fume hood with calcium gluconate gel on hand in case of skin exposure. Use only compatible plastic containers made of HDPE or polypropylene for HF solutions. Read through the SDS. 2. Nanostrip is highly caustic. Appropriate safety measures should be taken and it should be stored in glass containers with vented lids. 3. Photomask and chip fabrication are typically performed in cleanroom, or at least a clean lab area with yellow light only. For wells larger than 200 μm, high-resolution plastic transparency films are also acceptable. 4. Dispensed droplets on the chip may evaporate rapidly. Unused wells such as the smaller wells shown in Fig. 1a can be filled with water to reduce evaporation of sample-containing wells.

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5. The AZ 400 K developer can be reused several times, but the time required for developing will gradually increase with repeated use. As the photoresist is developed, the color of the exposed features will change, and once completed, only the underlying chromium layer will remain. Usually this takes 30 s– 1 min. 6. The CEP-200 micro-chrome etchant can be reused several times. Usually chromium removal will take 1–2 min and completion can be observed visually. 7. PFDS is a silane-based blocking agent, it reacts with the –OH group on the glass surface. The glass must be very dry and clean before treatment. Make sure there are no bubbles in the PFDS solution on the chip to ensure uniform coating. 8. For single-cell selection, usually ~5–10 nL of the cell suspension is aspirated. In order to better image the single cells and select only the targeted single cells of interest, a very dilute cell suspension is preferred. Alternatively, fluorescence-activated cell sorting may be used for sample introduction into nanowells. 9. Less than 5 nL of solution can be reproducibly withdrawn using a 10 μL syringe and a high-quality syringe pump. If smaller volume is required, subnanoliter volumes can be withdrawn using a 1 μL syringe. Similar cell selection and sample preparation protocols may be performed using a pulled capillary tip connected with an infuse/withdraw syringe pump under a microscope. 10. Fluorescence-activated cell sorting (FACS) can also be used for single-cell isolation on the chip [17]. If nanoliter pipetting capabilities are not available, an alternative method is to use conventional micropipettes interfaced with a chip having 3-mm-diameter wells for nanoscale proteomics. Using this method, samples comprising as few as 25 cells have been analyzed [28]. 11. Depending on cell number, 0.5 to 2 ng of enzymes are added. 12. Liquid pressure of 500–8000 psi is achieved with an air input pressure of 3.5–56.7 psi on the Haskel air amplifier pump. The ramping rate depends on the length of the column. The maximum pressure should be around 8000 psi. Periodically observing the movement of the packing material (with a microscope) is recommended. Increase the pressure when the packing material packing rate becomes slow. 13. With repeated use, the HF will lose strength due to the water flowing in from the capillary and reaction of the HF. This will cause the etching to take longer until it is replaced with fresh HF. Record the time that it takes to complete the etching

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process after each run, then use that for the expected time for the next run. 14. Flow rate depends on the characteristics of the analytical, split, and SPE columns. The total flow rate provided by the nanoLC pump may need to be adjusted, and the observed pressure may vary. 15. Organic matter deposited on the emitter tip might reduce the electrospray stability. Cleaning the tip by immersing the tip in Nanostrip 2X followed by water (while having a flow on) may restore an emitter that is performing poorly. 16. Keep track of the pressure of the system to notice if leaking or clogging occurs. References 1. Povinelli BJ, Rodriguez-Meira A, Mead AJ (2018) Single cell analysis of normal and leukemic hematopoiesis. Mol Asp Med 59:85–94. https://doi.org/10.1016/j.mam.2017.08. 006 2. Potter N, Miraki-Moud F, Ermini L, Titley I, Vijayaraghavan G, Papaemmanuil E, Campbell P, Gribben J, Taussig D, Greaves M (2019) Single cell analysis of clonal architecture in acute myeloid leukaemia. Leukemia 33 (5):1113–1123. https://doi.org/10.1038/ s41375-018-0319-2 3. Chung W, Eum HH, Lee HO, Lee KM, Lee HB, Kim KT, Ryu HS, Kim S, Lee JE, Park YH, Kan ZY, Han W, Park WY (2017) Singlecell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun 8(1):1–2. https://doi. org/10.1038/ncomms15081 4. Darmanis S, Sloan SA, Croote D, Mignardi M, Chernikova S, Samghababi P, Zhang Y, Neff N, Kowarsky M, Caneda C, Li G, Chang SD, Connolly ID, Li YM, Barres BA, Gephart MH, Quake SR (2017) Single-cell RNA-Seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep 21(5):1399–1410. https://doi.org/10. 1016/j.celrep.2017.10.030 5. Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, Hovestadt V, Escalante LE, Shaw ML, Rodman C, Gillespie SM, Dionne D, Luo CC, Ravichandran H, Mylvaganam R, Mount C, Onozato ML, Nahed BV, Wakimoto H, Curry WT, Iafrate AJ, Rivera MN, Frosch MP, Golub TR, Brastianos PK, Getz G, Patel AP, Monje M, Cahill DP, Rozenblatt-Rosen O, Louis DN, Bernstein BE, Regev A, Suva ML (2017) Decoupling genetics, lineages, and microenvironment

in IDH-mutant gliomas by single-cell RNA-seq. Science 355(6332):eaai8478. https://doi. org/10.1126/science.aai8478 6. Tian Q, Stepaniants SB, Mao M, Weng L, Feetham MC, Doyle MJ, Yi EC, Dai HY, Thorsson V, Eng J, Goodlett D, Berger JP, Gunter B, Linseley PS, Stoughton RB, Aebersold R, Collins SJ, Hanlon WA, Hood LE (2004) Integrated genomic and proteomic analyses of gene expression in mammalian cells. Mol Cell Proteomics 3(10):960–969. https:// doi.org/10.1074/mcp.M400055-MCP200 7. Vogel C, Abreu RD, Ko DJ, Le SY, Shapiro BA, Burns SC, Sandhu D, Boutz DR, Marcotte EM, Penalva LO (2010) Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol 6:400. https:// doi.org/10.1038/msb.2010.59 8. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M (2011) Global quantification of mammalian gene expression control. Nature 473 (7347):337–342. https://doi.org/10.1038/ nature10098 9. Akbani R, Ng PKS, Werner HMJ, Shahmoradgoli M, Zhang F, Ju ZL, Liu WB, Yang JY, Yoshihara K, Li J, Ling SY, Seviour EG, Ram PT, Minna JD, Diao LX, Tong P, Heymach JV, Hill SM, Dondelinger F, Stadler N, Byers LA, Meric-Bernstam F, Weinstein JN, Broom BM, Verhaak RGW, Liang H, Mukherjee S, Lu YL, Mills GB (2014) A pan-cancer proteomic perspective on the cancer genome atlas. Nat Commun 5:14. https:// doi.org/10.1038/ncomms4887 10. Witze ES, Old WM, Resing KA, Ahn NG (2007) Mapping protein post-translational

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modifications with mass spectrometry. Nat Methods 4(10):798–806. https://doi.org/ 10.1038/nmeth1100 11. Silva AMN, Vitorino R, Domingues MRM, Spickett CM, Domingues P (2013) Posttranslational modifications and mass spectrometry detection. Free Radic Biol Med 65:925–941. https://doi.org/10.1016/j.fre eradbiomed.2013.08.184 12. Mnatsakanyan R, Shema G, Basik M, Batist G, Borchers CH, Sickmann A, Zahedi RP (2018) Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry. Expert Rev Proteomics 15 (6):515–535. https://doi.org/10.1080/ 14789450.2018.1483340 13. Thygesen C, Boll I, Finsen B, Modzel M, Larsen MR (2018) Characterizing diseaseassociated changes in post-translational modifications by mass spectrometry. Expert Rev Proteomics 15(3):245–258. https://doi.org/ 10.1080/14789450.2018.1433036 14. Fienberg HG, Nolan GP (2014) Mass cytometry to decipher the mechanism of nongenetic drug resistance in cancer. In: Fienberg HG, Nolan GP (eds) High-dimensional single cell analysis: mass cytometry, multi-parametric flow cytometry and Bioinformatic techniques, Current topics in microbiology and immunology, vol 377, pp 85–94. https://doi.org/10.1007/ 82_2014_365 15. Di Palma S, Bodenmiller B (2015) Unraveling cell populations in tumors by single-cell mass cytometry. Curr Opin Biotechnol 31:122–129. https://doi.org/10.1016/j.copbio.2014,07. 004 16. Zhu Y, Piehowski PD, Zhao R, Chen J, Shen YF, Moore RJ, Shukla AK, Petyuk VA, Campbell-Thompson M, Mathews CE, Smith RD, Qian WJ, Kelly RT (2018) Nanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cells. Nat Commun 9:882. https://doi.org/10. 1038/s41467-018-03367-w 17. Zhu Y, Clair G, Chrisler WB, Shen YF, Zhao R, Shukla AK, Moore RJ, Misra RS, Pryhuber GS, Smith RD, Ansong C, Kelly RT (2018) Proteomic analysis of single mammalian cells enabled by microfluidic Nanodroplet sample preparation and ultrasensitive NanoLC-MS. Angew Chem-Int Edit 57(38):12370–12374. https://doi.org/10.1002/anie.201802843 18. Zhu Y, Dou MW, Piehowski PD, Liang YR, Wang FJ, Chu RK, Chrisler WB, Smith JN, Schwarz KC, Shen YF, Shukla AK, Moore RJ, Smith RD, Qian WJ, Kelly RT (2018) Spatially resolved proteome mapping of laser capture microdissected tissue with automated sample

transfer to Nanodroplets. Mol Cell Proteomics 17(9):1864–1874. https://doi.org/10.1074/ mcp.TIR118.000686 19. Zhu Y, Podolak J, Zhao R, Shukla AK, Moore RJ, Thomas GV, Kelly RT (2018) Proteome profiling of 1 to 5 spiked circulating tumor cells isolated from whole blood using Immunodensity enrichment, laser capture microdissection, nanodroplet sample processing, and ultrasensitive nanoLC-MS. Anal Chem 90 (20):11756–11759. https://doi.org/10. 1021/acs.analchem.8b03268 20. Zhu Y, Zhao R, Piehowski PD, Moore RJ, Lim S, Orphan VJ, Pasa-Tolic L, Qian WJ, Smith RD, Kelly RT (2018) Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples. Int J Mass Spectrom 427:4–10. https://doi.org/ 10.1016/j.ijms.2017.08.016 21. Dou MW, Zhu Y, Liyu A, Liang YR, Chen J, Piehowski PD, Xu KR, Zhao R, Moore RJ, Atkinson MA, Mathews CE, Qian WJ, Kelly RT (2018) Nanowell-mediated two-dimensional liquid chromatography enables deep proteome profiling of < 1000 mammalian cells. Chem Sci 9(34):6944–6951. https:// doi.org/10.1039/c8sc02680g 22. Liang YR, Zhu Y, Dou MW, Xu KR, Chu RK, Chrisler WB, Zhao R, Hixson KK, Kelly RT (2018) Spatially resolved proteome profiling of 0.5% (see Subheading 3.4.2).

3.3.1 Intraperitoneal Administration

1. Load the syringe with up to 200 μL of the desired compound suspension. For monoclonal antibody (mAb) treatment, endotoxin-free solutions of purified mAb (1 mg/mL in PBS) sterilized by filtration should be used (10 mg/Kg) (see Note 20). 2. Restrain the mouse by the scruff method. Expose the ventral side of the animal, tilting the head down at a slight angle. Insert the sterile needle at a 30
angle, bevel up, in the lower right or left quadrant of the abdomen and inject the material as shown in [29].

3.3.2 Oral Gavage Administration

Gavaging is used to dose an animal with a specified volume of material directly into its stomach. 1. Weigh the animal and calculate the maximum volume that can be administered orally (up to 10 mL/kg). 2. Measure the distance from the oral cavity to the end of the xiphoid process (caudal point of the sternum) with the feeding needle/tube on the outside of the restrained animal. This will be the distance the needle will be inserted into the esophagus. Mark this distance on the needle using a permanent marker (see Note 21). 3. Fill the syringe with the appropriate volume of material and attach the needle. 4. Restrain the animal by the scruff. 5. Place the tip of the needle into the animal mouth and slide gently past the back and left of the tongue. The needle should slide easily down the esophagus, if properly placed. DO NOT FORCE. If any resistance is met, remove the needle and reinsert. 6. Once the needle is properly placed, administer the material. 7. This procedure is detailed in UBC animal care guidelines [30].

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Body weight loss is used as an indicator of disease progression in T-ALL xenotransplanted mice. The endpoint of the experiment is established once 15–20% weight losses are reached compared with measurements at the starting point. Measure body weight by placing each mouse independently on an electronic weighing scale once a week at the beginning of the experiment. When weight loss is starting to be appreciated, more frequent measurements (every 2–3 days) are recommended. Mouse peripheral blood is obtained from the submandibular vein by using needles instead of razors, without the use of anesthesia [31]. 1. Prepare 1.5-mL tubes containing 100 μL of heparin sodium solution and one sterile needle per mouse. 2. Quickly, poke the mouse in the submandibular vein with the needle and collect blood into the tube. When enough blood is obtained, press the vein with a small piece of ethanol-soaked cotton to stop bleeding. Mix blood with heparin to avoid coagulation by inverting the tube 4–5 times. Keep at RT. Alternative bleeding methods such as retro-orbital, distal tail or cardiac puncture can also be used [32]. 3. Mouse erythrocyte depletion is required for further flow cytometry analysis. Add 1 mL of ELB-I to each blood-containing tube and incubate for 5 min at RT. Centrifuge at 5000  g for 10 min at 4
C. Discard supernatant and wash twice with 1 mL of cold PBS or SB. Resuspend in 200 μL of SB, transfer to a p96-well plate and proceed to label the sample for flow cytometry (see Subheading 3.1.2).

3.4.3 Bone Marrow Aspiration

Serial sampling of mouse tibia BM facilitates longitudinal studies of BM composition and T-ALL engraftment over time without requiring mouse sacrifice. 1. Place the mouse in the induction chamber connected to anesthesia equipment that vaporizes isoflurane at 3–4% in O2. 2. When completely anesthetized, remove the mouse from the induction chamber and place it with its face upside down and its nose inside a funnel-shaped cone, which functions as an anesthesia mask, connected with the equipment. For maintenance, isoflurane concentrations should be 1.25–1.75% (see Note 22). Anesthetized animal must be closely monitored during the procedure to assure that it is maintained in the proper anesthetic plane (see Note 23). 3. Most common anesthetic complication is hypothermia. Keep the mouse on a warming blanket to prevent hypothermia

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Fig. 2 Schematic representation of intra-tibial bone marrow aspiration procedure. The needle is introduced through the trabecular bone, in the proximal end of the tibia and it is slowly placed into the epiphysis cavity. Successful aspiration is confirmed visually by the appearance of blood in the top of the needle in the base of the syringe

during the procedure. A protected warming device can be also placed in the cage. 4. Disinfect the entire leg containing the tibia that will undergo aspiration with 70% ethanol-soaked cotton pieces. 5. Fill the syringe with 200–500 μL of PBS and reserve. 6. Keep the tibia bent from the femur by pushing the tibia with either the ring finger or the fifth finger in a 45
angle. This allows exposing the proximal end of the tibia. The syringe is held using the thumb and the index finger (Fig. 2). 7. Insert the needle through the trabecular bone and turn it clockwise and counterclockwise while pushing it slowly into the epiphysis cavity. Confirm the correct positioning of the needle by gently moving the syringe laterally (Fig. 2). 8. Inject 50 μL of PBS and gently pull the needle plunger back, creating negative pressure, while moving the needle back and forth within the tibia cavity. Successful aspiration will be confirmed visually by the appearance of blood in the top of the needle in the base of the syringe (see Note 24). 9. Remove the needle and syringe and transfer the aspirated sample to a 1.5-mL tube prefilled with 500 μL of PBS. For most applications, BM samples should be kept on ice until further processing. For flow cytometry, resuspend in 200 μL of SB,

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transfer to p96-well plates, and proceed as indicated in Subheading 3.1.2. 10. Following completion of the aspiration procedure, remove the mouse from the anesthesia and keep it on a warming blanket until fully recovered (see Note 25). Ensure it is able to ambulate and reach food and water. Observe the mouse for signs of distress or infection post procedure in the next 24 h. Signs include: constant bleeding, anemia, or lethargy. If any of these signs are seen post procedure, the animal(s) should be euthanized. 11. Analgesic ibuprofen is administered in the drinking water at 0.2 mg/mL, providing a daily dose of approximately 40 mg/ kg ibuprofen with average daily water consumption of 5–6 mL for 2 days (see Note 26). An illustrative example for femoral BM aspiration in live mice can be visualized in [33]. 3.4.4 Bioluminescence Imaging (IVIS)

This protocol allows monitoring T-ALL cells in live animals using bioluminescent images (summarized in Fig. 3). T-ALL cells should be transduced with a luciferase-reporter vector (see Subheading 3.1.4) (Fig. 3a, b) that leads to production of the luciferase enzyme (Firefly), which oxidizes the substrate D-Luciferin with the consequent emission of photons (see Note 27). 1. Before starting mouse manipulation, initialize the IVIS imaging system and allow the CCD camera to reach the operating temperature (see manual instructions of IVIS Lumina II Caliper Life Science) [34]. 2. Load the syringe with D-Luciferin solution (15 mg/mL) and inject 150 mg/Kg mouse body weight (10 mL/Kg) intraperitoneally (see Subheading 3.3.1). 3. After 5 min, place the mouse in a dark anesthesia chamber connected to anesthesia equipment that vaporizes isoflurane at 3–4% in O2. 4. Remove anesthetized mice from the chamber and place them inside the IVIS machine on the warming tray with their heads inside the funnel-shaped cone (Fig. 3c). Keep a constant isoflurane concentration of 1.5% during imaging procedures. 5. Take images after 5 min of exposures starting face up, and then, 5 more min in the exposure face down (see Note 28). 6. After capturing and saving the images, remove mice from the anesthesia and keep on a warming blanket until fully recovered. 7. The IVIS imaging system expresses the bioluminescent signal in photons per seconds and displays it as an intensity map. Draw a region of interest (ROI) of identical size over each

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Fig. 3 Schematic representation of sequential steps of T-ALL xenograft in vivo bioluminescence imaging. (a) Generation of stable luciferase-expressing human T-ALL cells by transduction with lentiviral vectors containing the Firefly luciferase gene. (b) Intravenous injection of luciferase-expressing T-ALL cells into sublethally irradiated immunodeficient mice. (c) Image acquisition is performed using an IVIS Lumina II spectrum imaging system. (d) Pseudo-color images of mice transplanted with luciferase-expressing human T-ALL cells at the indicated weeks post-transplant. (e) Quantification of bioluminescent signals from ventral and dorsal mouse exposures expressed as Average Radiance (Avg Rad: photons/s/cm2/sr) at the indicated weeks posttransplant. Imaging analysis is performed using the Living Image® Software 3.2

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mouse (Fig. 3d). The photon flux emitted by the luciferaseexpressing cells is measured as an Average radiance (photons/ s/cm2/sr) (Fig. 3e). Imaging analysis is performed using the Living Image® Software 3.2 [34]. 3.4.5 Subcutaneous Tumor Measurement and Analysis

To establish tumor growth, monitor tumor appearance weekly and, once tumors are detectable, measure every 2–3 days. Use a digital caliper when available (or a manual one in its place). 1. Annotate both the width (shorter measure) and length (longer measure) of the tumor. Tumor volume is calculated using the modified ellipsoidal formula: Tumor volume ¼ 1/2 (length  width2). Mice should be sacrificed when tumors reach a diameter > 17 mm. 2. Isolate the tumor removing as much skin as possible using scissors and tweezers. Cut it in small pieces and transfer to 40 μm cell strainers inserted into a 6-well plate well. 3. Rinse with PBS. Mechanically disaggregate using tweezers and 5-mL syringe embolus. Recover flow-through, dilute with PBS, and filter using 70 μm cup filters. 4. Centrifuge at 500  g for 5 min, discard supernatant, and resuspend cellular pellet in an adequate volume for cell counting. Proceed to flow cytometry analysis (see Subheading 3.1.2).

3.4.6 Mice Euthanasia and Organ Extraction for Flow Cytometry Analysis

Mice should be euthanized when they present advanced symptoms of disease and reach established humane endpoints (i.e., tumor diameter >17 mm, body weight loss >15–20%, labored respiration) (see Note 29). Carbon dioxide chamber is the most common method of euthanasia for rodents. T-ALL cells usually colonize BM and peripheral lymphoid (thymus, spleen, lymph nodes) and nonlymphoid organs (liver, brain, kidney). Organ extraction must be done under sterile conditions as grafting T-ALL cells can be used for serial transplantations. Protocols and videos for the extraction of different organs can be found in [35]. The protocol described below is indicated for flow cytometry analysis of T-ALL cells colonizing different mice organs. Alternatively, immunohistochemistry techniques are useful to characterize leukemic grafting cells. 1. Extract the organ of interest, cut it into small pieces using tweezers and scissors, and transfer to 40 μm cell strainers inserted into a 6-well plate well. Proceed as described in Subheading 3.4.5 for isolated subcutaneous tumors. 2. Highly irrigated organs such as spleen and liver should be depleted of erythrocytes prior to flow cytometry. Thus, after centrifugation, resuspend the cellular pellet in ELB-II and incubate at RT for 30 s-1 min (see Note 30). Dilute tenfold with cold PBS, filter through 70 μm cup filters, and centrifuge

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at 500  g for 5 min. Discard supernatant and resuspend in an adequate volume for cell counting. Proceed to flow cytometry analysis (see Subheading 3.1.2).

4

Notes 1. Frozen T-ALL samples are kept in liquid N2 containers until use. 2. See also Lymphoprep manufacturer’s instructions for different blood sample volumes. Lymphoprep solution must be protected from light and pre-warmed to RT (i.e., 15–25
C) prior to use. Centrifugation must be done at RT (i.e., 20
C), with no brake in order to avoid density gradient disruption. 3. The antibody combinations shown in Table 1 are recommended for analysis in a FACS Canto II cytometer (BD Biosciences). Alternative antibody combinations may be used for other flow cytometers. 4. We highly recommend flow cytometry of fresh samples because T-ALL cells are quite sensitive to fixation. 5. Lentiviral supernatants must be titrated to determine the number of transducing units/mL [36], which would be beneficial for an optimal T-ALL transduction. 6. We normally use OP9-GFP [21] or OP9-DL4 [37] stromal cells as feeders for T-ALL cell cultures. OP9 cell lines must be cultured as indicated [38]. 7. RetroNectin® can also be incubated for 2 h at 37
C the same day of transduction. We use 1–2  105 T-ALL cells per RetroNectin®-coated well of 24-well plates. Retronectin®-coated 6-well plates are used for higher T-ALL cell numbers (0.5–1  106 cells/well). 8. For transduction of primary T-ALL cells, we routinely use a multiplicity of infection (MOI) of 10–20 (10–20 transducing units/cell), but optimal transduction doses should be determined according to the sensitivity or resistance of leukemic cells to lentivirus infection. Using too high MOIs may lead to increased cell death. This protocol normally results in 10–40% of primary T-ALL transduced cells. When a 100% transduced population is required for PDX assays, cell sorting must be performed prior to transplantation. For T-ALL cell lines, a MOI of 5–10 can lead to a > 80% transduction efficiency.

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9. Dose-survival curves should be performed to determine the maximal sublethal dose that facilitates BM T-ALL engraftment. An important consideration is that NSG and SCID mice display the scid side effect (radiation sensitivity and increased toxicity with genotoxic drugs) [23]. 10. Proliferating mouse cells die within the next 24 h after irradiation and they recover afterward, thus preventing human cell engraftment in the BM after this period. 11. Injection of >106 cells/mouse could result in local tumor growth. No less than 105 cells/mouse is recommended for injection of thawed cells, owing to decreased viability. Injected volume should not exceed 200 μL. 12. In addition to using the inhalant anesthetic, we recommended to place a drop of ophthalmic anesthetic (0.5% proparacaine hydrochloride ophthalmic solution) on the eye that will receive the injection. This provides additional procedural and postprocedural analgesia. 13. Slow needle withdrawing prevents the injectable to follow the needle path out. There should be little or no bleeding. 14. Temperature should not exceed 25–30
C at the level of the animal. Remove the mouse from the heat source immediately should any change in respiration rate or excessive salivation is observed. 15. For tail vein injection, mice should be older than 6 weeks because at younger ages the vessels are not thick enough for injection. 16. Injected volume should not exceed 100 μL. Cell numbers will depend on the engraftment potential and viability of the particular T-ALL sample. 17. Since injection needs accurate manipulation of the needle, holding the mouse is a critical part of the procedure. Several restraint devices are useful for holding mice for longer periods of time. We recommend a rotating injector device (Braintree Scientific) in which the tail can be rotated. This feature provides for an easy access to lateral vein in the tail. 18. DO NOT ASPIRATE, as it will cause the vein to collapse. If any swelling at the injection site or resistance to injection occurs, remove the needle and reinsert it slightly above the initial injection site. Penetration caused by excessively deep insertion is quite common, because the vessel wall is located just beneath the skin surface. Once the needle tip is under the skin, it is very important to pull back the syringe slightly during

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insertion to confirm the blood will flow back and then start the injection without moving the needle tip. 19. Cell numbers will depend on the injected T-ALL cell line. For leukemia cell lines such as Jurkat or HPB-ALL, 1–2  106 cells/mouse should develop subcutaneous tumors with a latency of 2–3 weeks. When cell lines with unknown latency periods are required, initial cell numbers must be previously assayed. 20. If the antibody is commercial, it should be azide-free. Laboratory purified antibodies should be prepared in endotoxin-free (95%. 6. Seed cells at 5  106 viable cells per well in 1 mL of culture medium (described above) in a 12-well plate (see Note 4). 7. Incubate at 37  C with 5% CO2 in air and  95% humidity for up to 10–14 days without changing the medium or splitting cells. Add fresh 3 μM CHIR-99021 (final concentration) and 10 nM Rapamycin (final concentration) every 2 days and check the cell viability by trypan blue, also every 2 days (see Note 5). 8. Harvest cells for analysis (see Note 6). 3.2

Flow Cytometry

Flow cytometry (FCM) is typically performed on day 9 of culture using CD34 and CD15 to monitor the number and differentiation status of leukemic stem and progenitor cells (see Note 7). Approximately 2  105 cells are needed per sample for FCM. 1. Precool centrifuge to 4  C. 2. Gently filter about 2  105 cells through 40 μm strainer and transfer the samples into 1.5 mL Eppendorf tube. 3. Centrifuge at 240  g for 6 min at 4  C. 4. Discard the supernatant. Suspend the cells in 1 mL of ice-cold FCM staining buffer. 5. Calculate the concentration and viability of cell suspension using trypan blue. 6. Centrifuge at 600  g for 5 min at 4  C. 7. Prepare a master mix of antibodies diluted in staining buffer according to manufacturer’s instructions (Table 3). 8. Resuspend the cells with 50 μL antibody mix. Incubate for 30 min on ice in the dark. 9. Prepare beads for controls and compensation (see Note 8). 10. Wash the cell/antibody mix by directly adding 1 mL of staining buffer and centrifuge at 600  g for 5 min at 4  C.

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Table 3 Mix of antibodies used to stain surface antigens Antibody

Per one sample (μL)

CD34-PE-Cy7

0.5

CD15-FITC

0.5

Total staining buffer per sample

50

Fig. 2 Gating strategy for the elimination of debris (a), dead cells (b), and to distinguish CD34+CD15 subpopulation on day 9 of culture (c)

11. Discard supernatant. Suspend the cells in 500 μL of PBS containing 3 μM DAPI. Transfer the samples to FACS tube. 12. Incubate in the dark for 10 min at RT and proceed to flow cytometry. 13. Analyze the data using an appropriate software. Debris is excluded by delimiting a region in the FSC/SSC histogram as shown in Fig. 2a. Among the remaining events, dead cells are excluded by gating events negative for DAPI (Fig. 2b). Then, gate CD34+CD15 subpopulation according to Fig. 2c. Note that the frequency of CD34+CD15 varies from patient to patient.

4

Notes 1. Compared to quick-thaw protocols that use a 37  C water bath, our method reduces cell clumping without compromising cell viability and yield. 2. If cells start to clump, add 100 μg DNase I Solution per mL of cell suspension, gently mix, and incubate at room temperature (15–25  C) for 10 min. 3. We use a hemocytometer with trypan blue dye to determine cell number and percentage of viable cells. Viable fraction is

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Fig. 3 Variation of primary AML culture viability with cell density. Viability of cultured primary AML cells in cytokine-free medium at the indicated cell densities for 2 days. The viable fraction was determined by trypan blue staining

calculated as the number of live cells/(live cells + dead cells). Percentage of CD34+CD15 cells can also be measured using flow cytometry at this step. The number and/or concentration of viable CD34+CD15 cells is then calculated by multiplying the total number of viable cells by the percentage of CD34+CD15 cells in the sample. 4. Cell density affects viability. We assessed the viability after culturing primary AML cells in cytokine-free medium at different cell densities for 2 days (Fig. 3). The viable fraction was determined by trypan blue staining. In the present protocol, we culture cells at a density of 5  106 cells per mL. 5. Culture length could be modified based on the objective and requirements of the experiments. Note that variability between samples from different patients is expected and may also be observed with different aliquots from the same patient. While we observe cell loss throughout the culture period, cells cultured with CHIR-99021 alone show substantially enhanced viability. Cells cultured with CHIR-99021 and Rapamycin show improved viability compared to control medium lacking either drug but lower viability than observed with CHIR99021 alone. 6. In the present protocol, we analyze cells by immunophenotyping with the cell surface markers CD34 and CD15 using flow cytometry (Fig. 1). Colony forming assay with MethoCult H4435 (Stem Cell) can also be performed to determine the functional potential of HSPCs. However, the gold standard to measure functional LSCs is the in vivo repopulating assay to determine their ability to engraft immunocompromised mice. 7. Additional surface markers (CD45, CD45RA, CD90) can also be used to better characterize the HSPC population.

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8. We use compensation beads as a single-stained compensation control. Compbeads are stained with antibodies conjugated with each fluorochrome used and analyzed on the flow cytometer with the same acquisition settings as samples to eliminate fluorescence overlap. If the analysis involves more dyes (e.g., the panel CD34, CD15, CD45, CD45RA, CD90), we suggest using the Fluorescence-Minus-One (FMO) controls [9]. References 1. Pearce DJ, Taussig D, Zibara K, Smith LL, Ridler CM, Preudhomme C, Young BD, Rohatiner AZ, Lister TA, Bonnet D (2006) AML engraftment in the NOD/SCID assay reflects the outcome of AML: implications for our understanding of the heterogeneity of AML. Blood 107(3):1166–1173. https://doi.org/10. 1182/blood-2005-06-2325 2. Woiterski J, Ebinger M, Witte KE, Goecke B, Heininger V, Philippek M, Bonin M, Schrauder A, Rottgers S, Herr W, Lang P, Handgretinger R, Hartwig UF, Andre MC (2013) Engraftment of low numbers of pediatric acute lymphoid and myeloid leukemias into NOD/SCID/IL2Rcgammanull mice reflects individual leukemogenecity and highly correlates with clinical outcome. Int J Cancer 133 (7):1547–1556. https://doi.org/10.1002/ijc. 28170 3. van Gosliga D, Schepers H, Rizo A, van der Kolk D, Vellenga E, Schuringa JJ (2007) Establishing long-term cultures with self-renewing acute myeloid leukemia stem/progenitor cells. Exp Hematol 35(10):1538–1549. https://doi. org/10.1016/j.exphem.2007.07.001 4. Klco JM, Spencer DH, Lamprecht TL, Sarkaria SM, Wylie T, Magrini V, Hundal J, Walker J, Varghese N, Erdmann-Gilmore P, Lichti CF, Meyer MR, Townsend RR, Wilson RK, Mardis ER, Ley TJ (2013) Genomic impact of transient low-dose decitabine treatment on primary AML cells. Blood 121(9):1633–1643. https://doi. org/10.1182/blood-2012-09-459313

5. Griessinger E, Anjos-Afonso F, Pizzitola I, Rouault-Pierre K, Vargaftig J, Taussig D, Gribben J, Lassailly F, Bonnet D (2014) A niche-like culture system allowing the maintenance of primary human acute myeloid leukemia-initiating cells: a new tool to decipher their chemoresistance and self-renewal mechanisms. Stem Cells Transl Med 3(4):520–529. https://doi.org/10.5966/sctm.2013-0166 6. Pabst C, Krosl J, Fares I, Boucher G, Ruel R, Marinier A, Lemieux S, Hebert J, Sauvageau G (2014) Identification of small molecules that support human leukemia stem cell activity ex vivo. Nat Methods 11(4):436–442. https:// doi.org/10.1038/nmeth.2847 7. Bhavanasi D, Wen KW, Liu X, Vergez F, DanetDesnoyers G, Carroll M, Huang J, Klein PS (2017) Signaling mechanisms that regulate ex vivo survival of human acute myeloid leukemia initiating cells. Blood Cancer J 7(12):636. https://doi.org/10.1038/s41408-017-0003-1 8. Huang J, Nguyen-McCarty M, Hexner EO, Danet-Desnoyers G, Klein PS (2012) Maintenance of hematopoietic stem cells through regulation of Wnt and mTOR pathways. Nat Med 18 (12):1778–1785. https://doi.org/10.1038/ nm.2984 9. Roederer M (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45(3):194–205

Chapter 16 Ex Vivo Expansion of Adult Hematopoietic Stem and Progenitor Cells with Valproic Acid Luena Papa, Mansour Djedaini, Manisha Kintali, Christoph Schaniel, and Ronald Hoffman Abstract Umbilical cord blood (UCB) units provide an alternative source of human hematopoietic stem cells (HSCs) for patients who require allogeneic stem cell transplantation but lack a matched donor. However, the limited number of HSCs within each UCB unit remains a major challenge for their use in regenerative medicine and HSC transplantation in adults. Efficient expansion of human HSCs in ex vivo cultures initiated with CD34+ cells isolated from UCBs can overcome this limitation. The method described here utilizes a deacetylase inhibitor, valproic acid (VPA), to rapidly expand to a high degree the numbers of functional HSCs and committed progenitors (HPCs). The expanded HSCs are capable of establishing both short-term and long-term multilineage hematopoietic reconstitution. This highly reproducible and simple protocol can be also applied to expansion of both HSCs and HPCs from different sources including the bone marrow and peripheral blood. Key words Ex vivo expansion, Valproic acid (VPA), Hematopoietic stem cells (HSCs), Umbilical cord blood (UCB), Hematopoietic progenitor cells (HPCs), Cryopreservation

1

Introduction Umbilical cord blood (UCB) collections are excellent sources of primitive hematopoietic stem cells (HSCs) and are used for stem cell transplantation in patients with hematological malignancies or genetic blood disorders who require allogeneic stem cell transplantation but lack a matched donor. The major limitation for UCB collections as single grafts in adult patients, however, is the limited number of HSCs. This limitation can be overcome by ex vivo expansion of CD34+ cells purified from an UCB unit (UCB-CD34+). Most of the previously reported ex vivo culture systems result in the loss of stem cell properties [1]. Our strategy using VPA successfully expands ex vivo the number of functional HSCs [2, 3] with properties that are highly reminiscent to those of

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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the un-manipulated HSCs with long-term (LT) repopulating potential [2, 4]. The method provided here utilizes a combination of a cytokine cocktail (composed of stem cell factor (SCF), FMS-like tyrosine kinase 3 ligand (FLT3L), interleukin 3 (IL3), and thrombopoietin (TPO)) and treatment with VPA. The VPA strategy results in expansion of the number of primitive HSCs present in a UCB-CD34+ cell fraction due to a combination of cellular reprogramming and proliferation [1, 2]. The cellular reprogramming of UCB-CD34+ cells is accompanied by the acquisition of a phenotypic, transcriptomic, and mitochondrial profile that characterizes un-manipulated LT-HSCs [2]. These ex vivo-expanded cells are capable of establishing long-term multilineage hematopoietic reconstitution in sublethellay irradiated nonobese diabetic gamma (NSG) mice [3]. Initial data from our clinical trial using VPA-expanded allogeneic UCB grafts (NCT03885947) have shown multilineage donor cell reconstitution with especially rapid T cell and platelet engraftment in patients with hematological malignancies. Ex vivo expansion with VPA is reproducible and can be employed for both HSC and HPC expansion not only from UCB-CD34+ cells but also from CD34+ cells purified from bone marrow as well as peripheral blood. The VPA expansion protocol is rapid and requires only 7 days of ex vivo culture in order to expand the number of HSCs minimizing both the time of manipulation and the frequency of contamination [1]. This protocol is not costly, as it does not require expensive and specialized devices or fed-batched cultures that are continuously supplemented with fresh cytokines. This method also holds a great potential to overcome the loss of functional HSCs associated with gene editing and modification of autologous HSCs [5]. Importantly, the VPA ex vivo-expanded cells can be effectively cryopreserved without affecting either their cellular viability or their engraftment potential after thawing. Cryopreservation of expanded cells using the technique described here is suitable for long-term storage and beneficial for the commercialization of the expanded stem cell product and the shipment to transplant centers.

2

Materials

2.1 Isolation of CD34+ Cells from UCBs

1. Umbilical cord blood. 2. SepMate-50 mL tubes (Stemcell Technologies). 3. Ficoll-Paque density gradient media. 4. Room temperature 1 phosphate-buffered saline (PBS): 5. Cold (4  C) 1 PBS.

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6. 7.5% Bovine serum albumin (BSA) solution. 7. 0.5 M Ethylene diamine tetra-acetic acid (EDTA). 8. PBE Buffer: To prepare PBE (1 PBS-0.5% BSA-1 mM EDTA) buffer for isolation of UCB-CD34+ cells, mix 465 mL 1 PBS with 2 mL of 0.5 M EDTA and 33 mL of 7.5% bovine serum albumin (BSA). Store PBE buffer at 4  C (see Note 1). 9. Centrifuge with variable acceleration and brake speed. 10. Cellometer Auto 2000 (Nexcelom). 11. Cellometer counting slides. 12. Acridine orange/propidium iodide (AO/PI) staining solution for live/dead. 13. AutoMACS Pro Separator. 14. Flow Cytometer. 15. Humidified incubator (set to 5% CO2, 37  C). 2.2 Cytokines, Antibodies, and Kits

1. 100 mM valproic acid (VPA) stock solution. To prepare 100 mM VPA, dissolve 20 mg of valproic acid sodium salt powder (Millipore-Sigma Cat # P4543) in 1.2 mL of culture media supplemented with cytokines as described in Subheading 3.3, step 1. To treat cells with 1 mM VPA, distribute 15 μL of 100 mM VPA to cells in 1.5 mL of culture medium supplemented with cytokines (see Subheading 3.3, step 3). 2. Anti-hCD34 MicroBead Kit. 3. AbC™ Total Compensation capture bead (human). 4. Penicillin-Streptomycin. 5. StemSpan™ SFEM II (Serum-Free Expansion Medium) culture medium. 6. Recombinant Human Flt-3 Ligand Protein. 7. Recombinant Human IL-3 Protein. 8. Recombinant Human SCF Protein. 9. Recombinant Human Thrombopoietin Protein (TPO). 10. Anti-human CD34 Antibody (581), APC. 11. CD34 Monoclonal Antibody (4H11), APC. 12. CD34 Monoclonal Antibody (8G12), APC. 13. Anti-human CD90 (Thy-1) Antibody (5E10), FITC. 14. Anti-human CD201 (RCR-401), PE. 15. Anti-human CD49f Antibody (GoH3), PerCP-Cy5.5. 16. Anti-human CD45RA Antibody 506 (or brilliant Violet 510).

(HI100),

eFluor

17. Anti-human CD133 Antibody (TMP4), Super Bright 600.

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18. Anti-human CD33 Antibody (WM-53), Alexa Fluor 700. 19. Anti-human CD38 Antibody (HB7), Super Bright 702. 20. Super Bright Staining Buffer. 21. Human FcR blocking reagent. 22. Flow Cytometer. 2.3

Cryopreservation

1. CryoMed—Model 7450. 2. CryoTube Vials (1.8 mL, sterile). 3. CryoStor® CS10.

3

Methods

3.1 Density Gradient Isolation of Mononuclear Cells

1. Distribute the entire “fresh” UCB unit into a 75 cm2 flask. Dilute the UCB with 1 PBS at room temperature by mixing 1 part of 1 PBS to 2 parts of UCB (see Note 2). 2. Prepare SepMate tubes. One tube per each 35 mL of diluted UCB. 3. Add 15 mL of Ficoll-Paque per SepMate tube (see Notes 3 and 4), (Fig. 1a). 4. Add the diluted UCB sample (35 mL) along the wall of the SepMate tube (see Notes 5 and 6), (Fig. 1b). 5. Set centrifuge settings to low acceleration and break speed and centrifuge tubes at 400  g for 20 min. Mononuclear cells (MNCs) locate in the buffy coat (white layer) between the plasma (top) and red blood cell layer (bottom) (Fig. 1c, d). 6. Aspirate 2/3 of the plasma without disturbing the buffy coat layer. Pour the top layer that contains the enriched MNCs into a new 50 mL tube (see Notes 7 and 8) (Fig. 1e). 7. Add cold PBS until the volume reaches 50 mL in each tube. 8. Set centrifuge settings to normal acceleration and break speed and centrifuge tubes at 400  g for 10 min at 4  C. 9. Carefully aspirate the supernatant and gently resuspend the cell pellet in 5 mL of cold 1 PBS (see Note 9). 10. Collect all the cells from the same UCB into a single new tube. 11. Add cold 1 PBS until volume reaches 50 mL in each tube. 12. Mix the content of the tubes by inverting them a few times. 13. Remove 20 μL of the cell suspension and stain with 20 μL acridine orange/propidium iodide (AO/PI) dye. 14. Incubate the mixture for 30 s at room temperature. 15. Load the “Nexcelom Cellometer 2000” slide with 20 μL of stained cells. Insert the slide in the “Cellometer 2000” and use

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Fig. 1 Purification of CD34+ cells from UCB using SepMate tubes. (a) SepMate tube filled with Ficoll-Paque. (b) SepMate tube after addition of a diluted UCB on top of the Ficoll-Paque layer. (c) SepMate tube postcentrifugation containing the three indicated layers. (d) SepMate tube after removal of ~70% of the plasma layer. (e) SepMate tube after removal of the plasma and buffy coat layer. (f) Pelleted MNCs after centrifugation. (c) Flow cytometry plot of purified CD34+ cells from a UCB unit; Fluorescence minus one (FMO) control in red, and purified CD34+ cells in blue

“Immune cells, high RBC” program for counting. Make sure that the dilution factor in the Cellometer setting is at “2.” Adjust the focus and start counting (see Notes 10 and 11). 16. Calculate the number of viable MNCs by multiplying the cell concentration (cells/mL) by 50, which corresponds to the total volume of cell suspension. The cell concentration is calculated based on the dilution factor of cells with AO/PI solution, and thus excludes all dead as well as the red blood cells. 17. Centrifuge the 50 mL tubes at 400  g for 15 min at 4  C (see Note 12), (Fig. 1f). 3.2 CD34+ Cell Purification from UCBs (See Note 13)

1. To purify CD34+ cells from MNCs, prepare purification buffer by gently mixing 300 μL of PBE buffer with 100 μL of human FcR blocking reagent and 100 μL of CD34-labeled magnetic beads. Such mixture composition is sufficient for isolation of CD34+ cells from 108 MNCs (see Notes 14 and 15).

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2. Carefully aspirate the supernatant from the tube centrifuged at step 17 of Subheading 3.1. 3. Resuspend the cell pellet in purification buffer prepared in step 1 of Subheading 3.2 (use 500 μL of purification buffer per each 108 MNCs). 4. Mix gently by pipetting and incubate at 4  C for 30 min. 5. During the incubation period, turn on the autoMACS Pro Separator. Replace the storage solution with the working buffer as indicated by the manufacturer’s instructions and run the washing and rinsing programs. 6. Fill the tube containing the mixture of MNCs and purification buffer up to the 50 mL mark with cold PBE buffer. 7. Centrifuge the tube at 400  g for 15 min at 4  C. 8. Aspirate the supernatant and resuspend the cell pellet in cold PBE buffer (1 mL/108 MNCs). Transfer the cell suspension into 5 mL round-bottom polystyrene tubes. 9. Place three sets of tubes into the cell separator rack, which is precooled overnight at 4  C. One 5 mL tube contain the labeled cells, a second 5 mL tube is intended for collection of the negative fraction and a third 5 mL tube will be used for collection of the purified CD34+ cells. 10. Load the rack into the autoMACS Pro Separator device and run the “posseld2” preset program (see Notes 16 and 17). 11. Once sorting is completed, centrifuge the tube containing the purified CD34+ cells at 400  g for 15 min at 4  C. Purified CD34+ cells can be cryopreserved for later use (see Note 18). 12. Aspirate the supernatant and resuspend purified CD34+ cells in 1 mL of serum-free StemSpan SFEM II media. 13. To count viable CD34+ cells, mix and stain 20 μL of the cell suspension with 20 μL AO/PI dye. 14. Incubate the mixture for 30 s at room temp. 15. Load the “Nexcelom Cellometer 2000” slide with 20 μL of stained cells. Insert the slide in the “Cellometer 2000” and use “Stem Cell” program for counting. Ensure that the dilution factor in the Cellometer setting is at “2.” Adjust the focus and start counting. 16. The concentration (cells/mL) and number of viable CD34+ cells is the number provided by the Cellometer. 17. Set aside 15,000 purified CD34+ cells, to assess the degree of purity and cell composition by flow cytometry analysis as described below in Subheading 3.4 (see Note 19) (Fig. 1g).

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Fig. 2 Ex vivo expansion of purified UCB-CD34+ cells. Purified UCB-CD34+ cells were primed with the indicated cytokine cocktail for 16 h followed by treatment with VPA for an additional 7 days. Graphs represent the percentage (a) and absolute numbers (b) of CD34+CD90+CD49f+ cells in the ex vivo-expanded cell product analyzed by flow cytometry [2] 3.3 Ex Vivo Expansion of Purified UCB-CD34+ Cells with VPA

1. Prepare a sufficient volume of culture medium to set up the expansion cultures of purified CD34+ cells. The culture medium is composed of: StemSpan SFEM II media supplemented with 100 units/mL Penicillin and 100 μg/mL Streptomycin, 150 ng/mL SCF, 100 ng/mL FLT3L, 100 ng/mL TPO, and 50 ng/mL IL-3. 2. Seed purified CD34+ cells at a density of 3.3  104 cells/mL (5  104 CD34+ cells/1.5 mL of medium/well of a 12-well plate) and incubate for 16 h at 37  C in a 5% CO2 humidified incubator. 3. Add 1 mM VPA to cultured cells as indicated in Subheading 2.2, item 1 (see Note 20). 4. Incubate culture for an additional 7 days at 37  C in a 5% CO2 humidified incubator to ex vivo expand HSPCs including HSCs with LT-repopulating potential [2, 6], which are phenotypically defined as CD34+CD90+CD49f+ (Fig. 2).

3.4 Antibody Staining for Flow Cytometry Analysis

1. Resuspend one well (1.5 mL) of expanded cells by pipetting up and down multiple times. 2. To count the number of viable cells present in cultures treated with VPA, mix and stain 20 μL of the cell suspension with 20 μL AO/PI dye. 3. Incubate the mixture for 30 s at room temperature.

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4. Load the “Nexcelom Cellometer 2000” slide with 20 μL of stained cells. Insert the slide in the “Cellometer 2000” and use the “Stem Cell” program for counting. Ensure that the dilution factor in the Cellometer setting is at “2.” Adjust the focus and start counting. 5. Calculate the number of viable cells by multiplying the cell concentration (cell/mL) by 1.5, which corresponds to the total volume of cell suspension. This concentration is calculated based on the dilution factor of cells with AO/PI solution, and thus excludes all dead as well as the red blood cells. 6. Transfer 2  105 cells into a 15 mL tube. 7. Fill the tube up with cold PBE buffer. 8. Centrifuge tube at 400  g for 15 min at 4  C. 9. To prepare antibody-staining solution that is sufficient to stain 1  104 cells, label a 1.5 mL tube “sample” and put it on ice. 10. Pipet 30 μL of PBE buffer into the tube. 11. Add each of the fluorescently conjugated antibodies at the dilution previously determined by titration and optimization (see Notes 21 and 22). 12. To prepare antibody-staining “fluorescence minus one (FMO)” control tubes, label a 1.5 mL tube for each fluorescently conjugated antibody and put them on ice (see Note 23). 13. Prepare “FMO” solution sufficient to stain 1  104 cells. Pipet 30 μL of PBE buffer into each tube. 14. Add all the fluorescently conjugated antibodies at the required concentration to each of the tubes excluding only one antibody (for instance, in the tube labeled “CD34,” add each of the antibodies required to assess all the other phenotypic markers but not the antibody that recognizes CD34). The precise concentration of each antibody needs to be optimized and determined by titration prior to execution of the experiment. 15. To prepare single color compensation controls, first resuspend the AbC™ Total Compensation capture beads (Component A) and negative beads (Component B) by vortexing for 10 s before use. 16. Label a sample tube for each fluorescently conjugated antibody and add 1 drop of AbC™ Total Compensation capture beads (Component A) and 1 drop of AbC™ Total Compensation capture beads (Component B) into each tube. 17. Add a precise amount of each fluorescently conjugated antibody to the AbC™ Total Compensation capture bead suspension in the designated tube and mix well (for instance, in the tube labeled “CD34,” add only the CD34 antibody) (see Note 24).

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18. To prepare “unstained” controls, pipet 30 μL of PBE buffer into a 1.5 mL tube and keep it on ice. 19. Aspirate the supernatant from the tube containing purified CD34+ cells after completion of centrifugation in step 8. 20. Resuspend the cell pellet in 500 μL cold PBE buffer to obtain a concentration of 4  105 cells/mL. 21. Pipet 50 μL of cell suspension into the “sample” tube and mix gently by pipetting 2–3 times. 22. Pipet 50 μL of cell suspension into each “FMO” tube and mix gently by pipetting 2–3 times. Transfer 1  105 cells into the 1.5 mL tube labeled “unstained” prepared in step 18. 23. Incubate the “compensation” tubes, the “sample” tube, and the “FMO” tubes for 30 min at 4  C in the dark. 24. Add 1 mL of PBE buffer and centrifuge at 400  g for 15 min at 4  C. 25. Carefully aspirate supernatants. 26. Resuspend pellets of “sample” and “FMO” tubes in 300 μL of PBE buffer. 27. Resuspend pellets of “compensation” tubes in 400 μL of PBE buffer. 28. Add 200 μL of PBE buffer into the “unstained” tube. 29. Transfer content of “sample” and “FMO” tubes into a roundbottom 96-well plate. 30. Transfer content of “compensation” and “unstained” tubes into 5 mL round-bottom polystyrene tubes. 3.5 Flow Cytometry Acquisition and Data Analysis

1. Prime the flow cytometer and run performance test according to the manufacturer instructions. 2. Create a new experiment in the cytometer software. 3. Set up the lasers and filters required for acquisition of the fluorochromes that are conjugated to the antibodies used. 4. Acquire unstained cells first to set PMT voltages. 5. Perform automatic compensation using negative gates for background fluorescence mode. 6. Once the compensations are calculated, acquire and record the 96-well plate. 7. Set up gating strategy by using acquired “FMO” stained cells. 8. Apply the “FMO” gating strategy and analyze the cellular composition of your sample. 1. Centrifuge cells at 400  g for 10 min with acceleration and deceleration set to 9.

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3.6 Cryopreservation of Purified UCB-CD34+ Cells or Ex Vivo-Expanded Cells with VPA

2. Carefully remove supernatant from tubes centrifuged in step 2 and resuspend cells in 1 PBS supplemented with 2% FBS to a final volume of 50 mL. 3. Count purified CD34+ cells from UCBs or ex vivo-expanded cells with VPA using the Nexcelom Cellometer as described in Subheadings 3.4, steps 2–5. 4. Centrifuge cells at 400  g for 10 min with normal acceleration and break. 5. During centrifugation, add a cryotube vial filled with 1 mL of ice-cold CryoStor® CS10 cryopreservation medium into the chamber of the CryoMed™ device (controlled rate freezer), place the temperate probe into the vial, and turn CryoMed™ device on. Select “Profile 1” and press “Run” to reach 4  C (see Note 25). 6. During centrifugation, label a sufficient number of Cryotube vials to aliquot 1x107 cells per vial. 7. Carefully remove supernatant without disturbing cell pellet and resuspend cells in ice-cold CryoStor® CS10 at a concentration of 1  107 cells/mL using the cell number calculated in step 1. 8. Aliquot 1 mL of resuspended cells into prelabeled sterile Cryotube vial. 9. Once CryoMed™ chamber, i.e., probe has reached 4  C, place cryovials inside the chamber and press “Run.” 10. Once the program has been completed (~45 min), transfer the vials into a liquid nitrogen freezer for long-term cryopreservation and turn the CryoMed™ device off.

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Notes 1. Efficiency of purification and recovery of viable CD34+ cells is dependent on the time elapsed between collecting fresh UCBs and the purification of the cells of interest. The yield of purified CD34+ cells is dramatically reduced when UCBs are harvested for more than 24 h prior to purification. Such decrease is also correlated with a greater degree of UCB clotting. 2. When using SepMate tubes for density gradient separation, it is important to mix 1 part of 1 PBS with 2 parts of UCB. If the UCB is too highly diluted, the buffy coat layer will appear under the insert placed within the SepMate tube, thus preventing efficient recovery of MNCs. By contrast, if the UCB is not adequately diluted, the red blood cells will appear over the insert and contaminate the buffy coat layer, thus impeding the purity of recovered MNCs.

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Fig. 3 SepMate tube with the insert indicating the central hole (zoomed, insert with the hole)

3. Ficoll-Paque needs to be maintained at room temperature. If maintained at lower or higher temperatures than 20–25  C, efficiency of cell separation with Ficoll-Paque will be severely affected. 4. When loading the Ficoll-Paque into the SepMate tubes, carefully pipet it through the central hole of the SepMate insert. The top of the density gradient medium will appear over the insert (Fig. 3). 5. Upon careful addition of UCB on top of the Ficoll-Paque, the density gradient medium might appear to be mixed with the blood over the insert. This, however, will not affect the efficiency of separation (Fig. 4). 6. SepMate tubes can be replaced by 50 mL conventional conical tubes. However, when using these tubes, pipetting of diluted UCB on top of Ficoll-Paque must be performed very slowly. During this process, the tube must be kept at a 45 angle to prevent mixing of the Ficoll-Paque layer with the UCB. Buffy coat layers will have to be then pipetted with a 10 mL pipette instead of pouring into a new tube (Fig. 5). 7. Do not pool the buffy coat layers from different tubes even though they are used to separate MNCs from the same UCB. Combining buffy coat layers together will influence the efficient washing of cells. 8. Do not hold the SepMate™ tube in the inverted position for longer than 2 s when pouring the buffy coat layer into a new

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Fig. 4 SepMate tube containing mixed blood with Ficoll-Paque layer

Fig. 5 Falcon tube containing three indicated layers following pipetting of blood on top of Ficoll-Paque layer and blood sedimentation

tube as some blood might go through the insert and contaminate the buffy coat. 9. Working with cold solutions following Ficoll-Paque separation is critical to avoid capping of CD34 antigens. 10. When counting cell numbers with the Cellometer 2000, the camera focus needs to be adjusted properly as it can impact the

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accurate counting and calculation of viable total nucleated cell (TNC) numbers (consult the Cellometer’s instruction manual). 11. Cell counting can also be performed with a hemocytometer or any other cell counting device. If a hemocytometer is used, cells can be stained with Trypan blue to exclude dead cells. 12. Cells can be frozen after completion of Subheading 3.1, step 17. 13. This protocol is optimized for purification of a single UCB unit. Despite the prolonged manipulation time, up to 6 UCB units can be purified in parallel without affecting the purification efficiency. 14. To purify CD34+ cells from a greater number than 108 MNCs, reagents and mixture volumes need to be scaled up, accordingly. When working with fewer than 108 MNCs, use the same mixture volume required for purification of CD34+ cells from 108 MNCs. 15. To purify CD34+ cells from a greater number than 2  108 MNCs, 5 mL tubes need to be replaced by 15 mL tubes since the volume of the negative fraction sorted by the “autoMACS Pro Separator” will exceed the capacity of 5 mL tubes. 16. Usage of the “Posseld2s” instead of the “Posseld2” in the autoMACS Pro Separator results in a significant increase in the number of purified CD34+ cells but it reduces the degree of purity. 17. Different manual columns can be used to replace the autoMACS Pro Separator device. However, the use of manual columns limits the number of UCBs that can be processed. The MidiMACS™ system coupled with LS columns (Miltenyi Biotec) can be used and does not require any change in the protocol detailed here. The Mojosort™ system from Biolegend and the Dynabead™ system from ThermoFisher Scientific may be used but the cell labeling will require optimization. 18. Purified CD34+ cells can be pelleted and cryopreserved following aspiration of the supernatant. Cryopreservation can be performed as described in Subheading 3.6. 19. To ensure accurate analyses and great purification efficiencies, it is important to use a CD34 antibody that recognizes an epitope different from that recognized by the CD34 antibody (QBEND/10 clone) used during the purification process (see CD34 antibodies in Subheadings 2.2, items 10–12). 20. Each ex vivo expansion of UCB-CD34+ cells requires freshly prepared VPA.

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21. Flow cytometry devices vary among manufacturers. As such, fluorescently conjugated antibodies need to be selected accordingly and comply with the characteristics of each device. 22. The addition of super-bright staining buffer (see Subheading 2.2, item 20) is recommended if two or more fluorescent antibodies coupled with super-bright molecules will be used for flow cytometry analysis. Addition of super-bright staining buffer avoids nonspecific polymer interaction leading to false positive detection. 23. Fluorescence minus one “FMO” control is a negative control required to accurately discriminate positive versus negative signals. It is designed to allow for proper gating of cells when a multiple fluorochrome panel is used by taking into account any potential fluorochrome(s) leaks into the unlabeled channel. 24. Pipet the fluorescently conjugated antibody directly to the AbC™ Total Compensation capture bead suspension. 25. It may take up to 30 min for the CryoMed™ machine chamber to reach 4  C. To speed up the process, the probe vial can be maintained at 4  C until the start of the program. References 1. Papa L, Djedaini M, Hoffman R (2019) Ex vivo HSC expansion challenges the paradigm of unidirectional human hematopoiesis. Ann N Y Acad Sci 1466:39. https://doi.org/10.1111/nyas. 14133 2. Papa L, Zimran E, Djedaini M, Ge Y, Ozbek U, Sebra R, Sealfon SC, Hoffman R (2018) Ex vivo human HSC expansion requires coordination of cellular reprogramming with mitochondrial remodeling and p53 activation. Blood Adv 2 (20):2766–2779. https://doi.org/10.1182/ bloodadvances.2018024273 3. Chaurasia P, Gajzer DC, Schaniel C, D’Souza S, Hoffman R (2014) Epigenetic reprogramming induces the expansion of cord blood stem cells. J Clin Invest 124(6):2378–2395. https://doi. org/10.1172/JCI70313

4. Papa L, Djedaini M, Hoffman R (2019) Mitochondrial role in stemness and differentiation of hematopoietic stem cells. Stem Cells Int 2019:4067162. https://doi.org/10.1155/ 2019/4067162 5. Moussy A, Papili Gao N, Corre G, Poletti V, Majdoul S, Fenard D, Gunawan R, Stockholm D, Paldi A (2019) Constraints on human CD34+ cell fate due to Lentiviral vectors can be relieved by Valproic acid. Hum Gene Ther 30(8):1023–1034. https://doi.org/10. 1089/hum.2019.009 6. Notta F, Doulatov S, Laurenti E, Poeppl A, Jurisica I, Dick JE (2011) Isolation of single human hematopoietic stem cells capable of long-term multilineage engraftment. Science 333(6039):218–221. https://doi.org/10. 1126/science.1201219

Chapter 17 Isolation, Culture, and Manipulation of Human Cord Blood Progenitors Cristina Prieto, Damia Romero-Moya, and Rosa Montes Abstract Umbilical Cord Blood (CB) is a rich source of hematopoietic stem/progenitor cells (HSPCs) with high proliferative capacity and a naı¨ve immune status. These characteristics, among others, make CB a good source of HSPCs not only for transplantation, but also for biomedical research purposes. Here we describe the methods for human CB-HSPCs isolation, as well as their culture and cryopreservation, viral transduction and sorting, and in vivo and in vitro assays in order to study leukemic processes. Key words Hematopoietic progenitor cells, Sorting, Transduction, Clonogenic potential, Proliferation assay, Xenotransplantation, Engraftment

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Introduction Human hematopoietic stem/progenitor cells (HSPCs) are characterized by the expression of the cell surface glycoprotein CD34. Umbilical cord blood (CB), collected at the moment of birth, represents a rich source of HSPCs for repopulating the hematopoietic system in patients [1]. Although human CD34+ HSPCs represent a rare subset (~0.5%) in hematopoietic tissues, their frequency in CB is higher than in adult bone marrow and mobilized peripheral blood [2]. The easy procurement and the availability of the samples, together with the possibility of shipment, support the use of CB as the source of HSPCs. There are different reasons why a sample can be discarded for transplantation, such as low cellularity. These CBs excluded from banking can still be highly useful in the laboratory with no risk or discomfort inflicted to donors. Moreover, CB-derived HSPCs can easily be frozen and stored indefinitely with no significant effects on cell viability. In addition, CB-derived HSPCs present enriched proliferative capacity, generation of progeny and also replating capacity in vitro [3–7]. Besides being a “rich” source of HSPCs, cord blood contains fewer T cells than bone marrow, permitting a greater degree of

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4_17, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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mismatch without increased graft-versus-host disease. These properties render CB a very suitable source of HSPCs in stem cell transplantation for leukemia patients. Regarding biomedical research, CB has proven to be an even more powerful weapon. The immaturity of CB cells suggests a high degree of plasticity. Also, these cells present lower incidence of viral contamination compared to bone marrow samples [8]. For in vivo studies, CB-HSPCs are able to engraft in mice bone marrow and they can be transduced to express the transgene of interest [3–5, 9, 10]. These properties make HSPCs a powerful cellular tool to study leukemic processes. Regarding to pediatric leukemia, there are evidences of the prenatal origin of chromosomal damages [11]. In this sense, CB-derived HSPCs can mimic the ontogenyrelated cellular context in which leukemia initiating hits take place. In this chapter we include the protocols for isolating human CD34+ HSPCs from Cord Blood, as well as their cryopreservation and culture conditions. Also, transduction of CD34+ HSPCs is described, as it is a useful tool to enforce the expression of genes of interest. In addition, the performance of in vitro and in vivo assays is described.

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Materials

2.1 Human Mononuclear Cells Isolation from Cord Blood

1. Centrifuge. 2. Ficoll-Paque PLUS, GE Healthcare. 3. Red cell lysis buffer: 0.8% NH4Cl and 0.1 mM EDTA in water, buffered with KHCO3 to achieve a final pH of 7.2–7.6. 4. 50 mL Conical tubes. 5. 3 mL Plastic pipettes. 6. Phosphate-buffered saline (PBS,137 mM NaCl, 10 mM phosphate, 2.7 mM KCl, pH 7.4) at room temperature.

2.2 CD34+ HSPCs Cell Isolation

1. Instrument for magnetic separation with appropriate columns and buffers (in this chapter, the protocol is focused on AutoMACSPro Separator system, but manual separation can also be performed). 2. Running buffer: PBS with 0.5% bovine serum albumin (BSA), 2 mM ethylenediaminetetraacetic acid (EDTA), and 0.09% azide, pH 7.2. 3. Flow cytometer. 4. 15 mL Conical tubes. 5. 5 mL Round-bottom polystyrene test tubes. 6. CD34 MicroBead kit, human (includes microbeads suspension and blocking reagent).

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7. Anti-human CD34 antibody conjugated with fluorochrome, suitable for flow cytometry. 8. Pre-separation filters, 30 μm. 2.3 CD34+ HSPCs Cells Cryopreservation

1. Centrifuge. 2. Cryopreservation medium: FBS, 10% DMSO. 3. 2 mL Criovials. 4. Freezing container. 5. Ultrafreezer.

2.4 CD34+ HSPCs Culture

1. Culture medium: Serum-Free Expansion Medium (Iscove’s MDM, Bovine serum albumin, recombinant human insulin, iron-saturated human transferrin, 2-Mercaptoethanol, best purchase from an specialized supplier) supplemented with early hematopoietic cytokines: 100 ng/mL stem cell factor (SCF), 100 ng/mL fms-like tyrosine kinase 3 (FLT3), and 10 ng/mL interleukin-3 (IL-3), as well as antibiotics (Pen/Strep 1). 2. 6-Well plates.

2.5 CD34+ HSPCs Transduction and Sorting

1. Transducing medium: Serum-Free Expansion Medium (as above) supplemented with 1 μg/mL Polybrene,100 ng/ mL SCF, 100 ng/mL FLT3, and 10 ng/mL IL3. 2. 24-Well plate with Fibronectin coating. 3. Cell sorter. 4. FACS buffer: PBS with 2% heat-inactivated fetal bovine serum (FBS), 2 mM EDTA, and 2 mM NaN3.

2.6 Colony-Forming Unit Assay

1. Methylcellulose-based medium with recombinant cytokines for human cells: Methylcellulose-based medium [Methylcellulose in Iscove’s MDM, FBS, BSA, 2-Mercaptoethanol with recombinant human SCF, recombinant human interleukin 3 (IL-3), recombinant human erythropoietin (EPO), and recombinant human granulocyte-macrophage colony-stimulating factor (GM-CSF), best purchase from a specialized supplier]. 2. Antibiotics (Pen/Strep 1). 3. 6-Well plates. 4. Vortex. 5. 2 mL Syringe. 6. Blunt-end 16G Needle.

2.7 Cell Proliferation Assay

1. Culture medium: Serum-Free Expansion Medium (as above) supplemented with early hematopoietic cytokines: 100 ng/mL stem cell factor, 100 ng/mL FLT3, and 10 ng/mL interleukin3 (IL-3), as well as antibiotics (Pen/Strep 1).

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2. Cell counter (manual or automatic counters can be used). 3. 12-Well plates. 2.8 Mice Transplantations and Follow-Up

1. 27G Insulin syringe. 2. 25G Needle. 3. Culture medium: Serum-Free Expansion Medium supplemented with early hematopoietic cytokines: 100 ng/mL stem cell factor, 100 ng/mL FLT3, and 10 ng/mL interleukin-3 (IL-3), as well as antibiotics (Pen/Strep 1). 4. Analgesia: 0.1 mg/kg buprenorphine and 5 mg/kg carprofen in PBS. 5. Isoflurane vaporizing system including inducing chamber and maintenance tube. 6. Anti-human CD34 antibody conjugated with fluorochrome, suitable for flow cytometry. 7. Hemocytometer.

2.9 Analysis of Human Engraftment in Mice

1. Sterile surgical material. 2. Syringes and needles. 3. 5 cm Petri dishes. 4. PBS. 5. Lysing solution: 1% formaldehyde, 3% diethylene glycol, 0.3% methanol, 5.6% water. 6. FACS tubes: 5 mL round-bottom polystyrene test tubes. 7. Antibodies conjugated with fluorochromes, suitable for flow cytometry. 8. Hemocytometer. 9. 50 mL Conical tubes. 10. 70 μm Cell strainers.

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Methods For the following protocols, all reagents should be sterile and all procedures must be performed under sterile conditions using an appropriate flow cabinet.

3.1 Cord Blood Samples Collection and Mononuclear Cells Isolation

1. Depending on the national legislation, the proceedings for obtaining the approval from the corresponding local Ethics and Biohazard Board Committee will vary. The laboratory should start, as soon as possible, the procedure to obtain the agreement for collecting fresh umbilical cord blood units from healthy neonates from local hospitals.

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2. In case the laboratory is not physically close to the CB collecting area, a shipping system should be arranged with a reliable currier. This service should be preferably an urgent overnight delivery system that guarantees a fast shipment, so that the samples arrive as fresh as possible to the processing laboratory. 3. It is highly advisable to get a direct contact person in the collecting area. This way, it will be easier to predict any problem with the collection or a delay in the currier pick up. Also, if possible, the lab can account for the number of cord blood units that are needed. Consider the number of CB units that your hospital can obtain each day. You can estimate ~5  105 CD34+ cells per 100 mL of CB unit. 4. After the collection, the CB unit should be preserved (and shipped if necessary) at room temperature until processing. Wipe the blood pouch with ethanol 70% and transfer the blood to a sterile flask. Pool cord blood units in the flask to reduce the variability among individual samples. Dilute the blood 1:1 in PBS and carefully mix by inversion. 5. The density gradient centrifugation will be held in 50 mL tubes (see Note 1). According to the volume of diluted blood, account for one 50 mL tube for each 20 mL of blood. 6. For proceeding to density gradient centrifugation, invert the Ficoll-Paque bottle several times before use. Place 15 mL of room-temperature Ficoll-Paque (see Note 2) in each tube and add 20 mL of diluted blood carefully on top. Tilt the tube to add slowly the diluted blood to the wall without perturbing the density gradient (see Fig. 1a and Notes 3 and 4). A

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Fig. 1 Schematic representation of MNCs isolation by density gradient centrifugation. (A) Pre-centrifugation step on Ficoll-Paque: diluted blood (A.1) and Ficoll-Paque (A.2) represent two well differentiated phases. (B) Post-centrifugation step with the formation of three phases: MNCs fraction (B.2) is located in the interphase between aqueous phase (B.1) and Ficoll-Paque (B.3). (C) After centrifugation for platelet separation, the supernatant (C.1) contains the platelet fraction, while the pellet (C.2) contains the MNCs fraction. (D) Postcentrifugation step after erythrocytes lysis. The pellet (D.1) contains the MNCs fraction

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7. Centrifuge the tubes at 350  g for 30 min without brake or acceleration of the rotor (see Note 5). After centrifugation, erythrocytes, as well as granulocytes and dead cells, will pass through the Ficoll-Paque layer as they present high density. Alive mononuclear cells such as lymphocytes and monocytes present lower density, forming a ring at the blood-Ficoll-Paque boundary. Using a plastic pipette or a p1000, aspirate carefully the mononuclear cells layer in the interphase and transfer to a new 50 mL tube containing 20 mL of sterile PBS (see Fig. 1b and Notes 6 and 7). 8. Mix carefully and centrifuge at 110  g, 10 min (with acceleration and brake). This step is sufficient for discarding the platelets that remain in the supernatant after centrifugation (see Fig. 1c and Note 8). 9. Wash the pellet with 10–20 mL of PBS. In this step, different pellets can be grouped in a single 50 mL tube. Centrifuge at 300  g for 5 min and discard the supernatant. 10. Resuspend the pellet in 1 mL of PBS, flick carefully, and add 7 mL of red cell lysis buffer (ammonium chloride). Incubate for 7 min at 4  C, or follow manufacturer’s instructions in case another cell lysis buffer is used (see Note 9). 11. After incubation, neutralize the lysis filling the tube with 40 mL of PBS and centrifuge for 5 min, 453  g (see Fig. 1d). Discard the supernatant and resuspend the pellet in 50 mL of PBS. 3.2 CD34+ Cell Isolation

The isolation of CD34+ cells will be performed using human CD34 microBeads and AutoMACS separation system, following manufacturer’s instructions. 1. Count the cells using the preferred method in the lab. 2. Place 108 cells on each 15 mL tube and centrifuge at 453  g, 5 min. 3. Discard the supernatant and resuspend in 300 μL of Running buffer. 4. Add 100 μL of Blocking Reagent. 5. Add 100 μL of CD34 MicroBeads suspension to each tube (see Note 10). 6. Mix pipetting up and down and incubate for 30 min at 4  C. 7. After incubation, fill the tube with Running buffer and centrifuge at 300  g, 5 min. 8. Discard supernatant and resuspend in 500 μL of Running buffer.

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9. Add 200 μL of Running buffer to the pre-separation filter to humidify it. Filter cell suspension through filters to a FACS tube. Add 300 μL of Running buffer to wash down the filter. 10. For the magnetic separation on AutoMACSPro Separator (see Note 11), use Posseld2 program and follow manufacturer’s instructions (see Note 12). Briefly, after priming the instrument, place the tube(s) containing the mononuclear cells in the A row of the rack and provide the tube(s) for collecting the negative (Lineage+ cells) and positive (CD34+ cells) fractions to the B and C rows, respectively (see Fig. 2). 11. After the separation, pool the positive fractions (row C, CD34+ cells) in a 15 mL tube, centrifuge at 350  g for 5 min, and resuspend all the isolated cells in 2–5 mL of PBS. Count the cells. 12. Purity of isolated CD34+ cells can be evaluated by flow cytometry. Transfer 20 μL of cell suspension to a new 5 mL FACS tube to stain them with anti-CD34 antibody (follow manufacturer’s guidelines for the staining). CD34+ purity over 90% is considered optimal for further experiments. The negative fraction from the isolation (Linage+, raw B) can be used as a negative control for the CD34 staining. Keep the Linage+ fraction and cryopreserve it (see Note 13). 3.3 CD34+ Cells Cryopreservation

Isolated CD34+ HSPCs can be frozen without important alteration on their viability. 1. Centrifuge the cell suspension for 5 min, 300  g. 2. Resuspend in 1–2 mL of prechilled cryopreservation medium. 3. Freeze the cells in a cryotube using a freezing container or a programmable freezer (see Note 14). 4. For thawing the cells, follow steps 5–8. 5. Place 10 mL fresh culture medium in a 15 mL tube and pre-warm to 37  C using a clean water bath. 6. Once the new tube is warm, place the cryotube containing the cells in the bath and wait until only a small volume of the cell suspension is frozen. 7. Use a 1 mL pipet to take the cells to the new tube. Pipet up and down to mix well with the fresh medium and dilute the DMSO present in the freezing medium. 8. Centrifuge for 5 min at 300  g and resuspend in fresh culture medium.

3.4 CD34+ HSPCs Culture

1. Count cells with the laboratory reference method. 2. Seed CB-CD34+ HSPCs at 105 cells/mL, using the culture medium specified in Subheading 3. Keep the cell density between 105 and 106 cells/mL.

Fig. 2 Flow cytometry analysis of different fractions after MACS separation (representative example). MNCs plots (upper row) show MNCs fraction and its CD34+ population before MACS separation. Negative (middle row plots) and positive (lower row plots) fractions show the cells obtained after MACS separation: in the negative fraction the CD34+ population is nearly absent, while in the positive fraction the CD34+ population is highly increased (purity of 95%)

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3. Place them into 37  C incubator with 5% CO2 and 20% O2. 4. Observe the cells under the microscope every day and count them every 2 or 3 days. If they need to be split, transfer the number of cells needed into a new 15 mL tube. 5. Spin cells down at 300  g for 5 min. 6. Remove supernatant and resuspend the cells at 105 cells/mL with fresh Serum-Free Expansion medium (see Notes 15–17). 3.5 CD34+ HSPCs Transduction and Sorting

1. Produce the lentiviral vectors containing your interest transgene and a reporter gene, following your laboratory reference method, and concentrate the viral supernatant, obtaining 200 μL aliquots in transducing medium. 2. Place 2  106 cells in a 15 mL tube, centrifuge for 5 min at 300  g, and discard the supernatant (see Note 18). 3. Resuspend the cells in the viral supernatant aliquot (200 μL), pipetting up and down (see Note 19). Place the cell-virus suspension in a 24-well plate with fibronectin. 4. Incubate overnight at 37  C (see Fig. 3a and Note 20). 5. After 24 h incubation, wash the cells with 2 mL of culture medium, pipet up and down, and take them to a new 15 mL tube. 6. Centrifuge for 5 min at 453  g and discard the supernatant. 7. Resuspend the pellet in 2 mL of fresh culture medium and seed the cells in a new well. 8. Culture the cells at 37  C for another 72 h. 9. After incubation, transduced cells can be isolated by FACS sorting, as they express the reporter gene. Centrifuge the cell suspension for 5 min at 453  g. 10. Discard the supernatant and resuspend the pellet in 500 μL of FACS buffer. Follow the manufacturer’s instructions for sorting the cells in a 15 mL tube with 5 mL of culture medium. 11. Centrifuge for 5 min at 453  g and discard the supernatant. 12. Resuspend the cell pellet in a small volume of culture medium (1–2 mL) and count the cells (see Fig. 3b).

3.6 Colony-Forming Unit Assay

CB-CD34+ cells obtained after sorting can be seeded to assess Colony-Forming Unit (CFU) capacity. 6-well plates will be used, and the calculations are made for each well. For additional wells, scale up the volumes. 1. In a 5 mL tube, place 2 mL of methylcellulose. Add 1% Pen/Strep, vortex, and mix using a syringe with a blunt-end 16G needle.

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Fig. 3 (a) CD34+ cells 24 h after lentiviral transduction under a fluorescence microscope. (b) Representative flow cytometry analysis before and after GFP positive cell sorting

2. Add 1000 cells, vortex again, and mix with the syringe. 3. Wait 10–15 min until bubbles disappear. In the meantime, you can scratch the outer bottom of the wells with a blade or scissors, or use a marker to draw a grid, which will make it easier to count the colonies afterward (see Fig. 4a and Note 21). 4. Load the syringe with the mixture and let it stand inside the empty tube for 2–3 min, placing the syringe with the needle at the bottom of the tube. 5. The residual volume on the walls of the tube will fill the bottom then. Add it to the loaded syringe. 6. Use the syringe to place the cells in the 6-well plate. Save some wells in the plate to be filled with sterile water (see Fig. 4b).

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Fig. 4 (a) 6-well plate with grid marks at the bottom to facilitate the CFU counting process. (b) Example of methylcellulose plating, with cells plated on the corner and water in the central wells to prevent cells from drying up

7. Place the plate in the incubator for 14 days. Avoid manipulating it during the course of this time to prevent contaminations (see Note 22). 8. After 14 days, count and evaluate the CFUs according to morphological criteria, using an inverted microscope. 3.7 Cell Proliferation Assay

Sorted CD34+ HSPCs can be used for in vitro proliferation assays. Calculations are made per condition and replicate and can be scaled up. 1. Resuspend 2,5  104 cells in 1.5 mL of culture medium. 2. Plate the cells in a 12-well plate and place it in the incubator. 3. Count the cells every 4 days, splitting them when necessary, for 90 days.

3.8 Mice Transplantations and Follow-Up

NOD/LtSz-scid IL2Rγ / mice can be used as recipients for in vivo assays. As CD34+ HSPCs are valuable and limited, intrabone marrow transplantation is an interesting option to minimize the number of cells needed for a successful engraftment. NSG mice need to be housed under sterile conditions, and the corresponding Animal Care Committee should approve all the animal protocols needed. The procedure is performed under general anesthesia and pain killers are used to avoid any suffering of the mice. 1. Mice (male or female) at 7–14 weeks of age can be used. Irradiate the animals sublethally (2.5Gy) 6–12 h before transplantation. 2. Prepare the cell suspension, using 2–3  105 cells per mouse in 20–25 μL of medium. This number can be scaled up for the total number of mice for transplantation. Load the cell suspension in a 27G insulin syringe (Fig. 5a). 3. Optionally, the hair from the knee can be removed to facilitate the procedure.

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Fig. 5 (a) Image of an insulin syringe loaded with 100 μL of cell suspension prior transplantation into recipient mouse. (b) Sequential steps to inject cells, from perforation (three images on the left) to injection (right image). (c) Clean bones after removing mouse flesh. (d) Spleen and liver just before and after being smashed and filtered to generate a single cell suspension

4. Place the mice on a warming plate to prevent hypothermia during surgery. Induce anesthesia by 5% isoflurane inhalation using an induction chamber. 5. Maintain a flow of 1–2% isoflurane using a nose cone until the end of the procedure. 6. Spray 70% ethanol on the animal skin and flex the knee about 90 . 7. Use a 25G needle to slowly perforate the joint surface through the patellar tendon, facing the tibia. Drill the needle into the bone marrow cavity (Fig. 5b). 8. Remove the 25G needle and replace it with the 27G insulin syringe, loaded with the cell suspension (Fig. 5b). 9. Push the plunger slowly, introducing 20–25 μL of cell suspension into the tibia. 10. For pain relief, administrate analgesia subcutaneously immediately after transplantation (0.1 mg/kg buprenorphine and 5 mg/kg carprofen). 11. Place the mice back in the cage; they should recover in a few minutes. 12. Repeat the analgesia administration 24 h after surgery.

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13. Follow up the mice during the whole experiment. Mice can be bled every week to analyze the percentage of human engraftment by FACS using a human CD34 antibody and counting the White Blood Cells (WBC) using a hemocytometer. 14. Mice have to be sacrificed if any sign of suffering is observed, even in the absence of leukemia. Always follow the animal welfare instructions of your housing institution. 3.9 Analysis of Human Engraftment in Mice

In the absence of any disease signs, an endpoint should be defined for every in vivo assay depending on the readout of the experiment. A minimum of 5–6 weeks is expected in order to ensure a significant percentage of engraftment at the endpoint. 1. Sacrifice the mice at the defined endpoint. In case blood is needed, it is recommended to anesthetize the mice and perform a cardiac puncture in order to retrieve a high volume of blood. 2. Five hematopoietic tissues are normally analyzed for human chimerism: bone marrow (injected tibia and contralateral tibia), spleen, liver, and peripheral blood. 3. Save a small volume of blood (25–50 μL) for analyzing on hemocytometer. 4. Collect the tissues and keep them in PBS; it is recommended to process them immediately. 5. Clean any muscle debris from the bones and cut the ends with a scissor. Place the bone in a 5 mL Petri dish and add 3–5 mL of PBS (Fig. 5c). 6. Use a syringe with a 25G needle to flush the bone marrow out of the bone with PBS several times. Use the same needle to disaggregate the bone marrow to a single cell suspension. 7. Place spleen and liver in a separate 5 cm petri dishes with 3–5 mL of PBS. Smash the tissues with the end of a plunger (Fig. 5d). 8. Transfer the cell suspension (bone marrow, spleen and liver) to labeled 50 mL tubes, filtering them through a 70 μm cell strainer. 9. Add PBS on the top of the cell strainer to recover as much cells as you can. 10. Centrifuge the tubes at 300  g for 5 min. 11. In the meantime, you can prepare the antibody pre-mix, using the antibodies needed for immunophenotyping the cells by FACS (see Note 23). A total volume of 50 μL of premix per sample can be used, including the corresponding dilution of all the antibodies needed in PBS. 12. Discard the supernatant and resuspend the cells in 500 μL of PBS. Place the cells in a 5 mL FACS tube. In the presence of

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high density of cells, use only a fraction of cell suspension for staining. For example, for liver and spleen it is recommendable to use 100 μL of cell suspension. 13. Add the 50 μL of antibody pre-mix to the cell suspension and vortex. 14. Incubate for 15 min at room temperature in the darkness. 15. After the incubation step, add 3 mL of Lysing Solution (1% formaldehyde, 3% diethylene glycol, 0.3% methanol, 5.6% water) and incubate for 10 min at room temperature avoiding light (see Note 24). 16. Centrifuge for 5 min at 300  g. 17. Discard the supernatant and add 3 mL of PBS. 18. Centrifuge for 5 min at 300  g. 19. Discard the supernatant and wash again with 3 mL of PBS. 20. Centrifuge for 5 min at 300  g and resuspend the cell pellet in 100 μL of PBS. 21. Proceed to the analysis in a flow cytometer (Fig. 6).

Fig. 6 Representation of FACS analysis of human multi-lineage xenograft in immunocompromised mice. In the total human CD45+ population, the flow cytometry analysis shows the immature fraction (hCD34+ cells), the B cell fraction (hCD19+ cells) and the myeloid fraction (hCD33+ cells)

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Notes 1. Self-standing tubes with skirted bottom are not recommended in order to avoid horizontal position after gradient centrifugation. 2. Differences in temperature can change the density ratios of the different fluids and impact the results of the density gradient centrifugation. All the fluids, as well as the centrifuge, should be kept at room temperature in order to avoid these changes. 3. When adding the diluted blood on top of the Ficoll tubes, it is recommended to use the minimum speed of the pipet controller. Also, a slight inclination of the tube can help to pipet the blood on the wall and not directly on the Ficoll. A harsh pipetting can result in a gradient break and the loss of part of the mononuclear cells outside the corresponding layer. The blood should remain on top of the Ficoll constituting two clearly differentiated phases. 4. When the tube containing the two phases (diluted blood and Ficoll) stands for a long period before the centrifuging step, the erythrocyte aggregates start dropping to the Ficoll phase. This can be easily seen and avoided by centrifuging the tubes as soon as possible, although it should not affect the effectiveness of the MNC isolation. 5. Deactivation of the centrifuge acceleration and brake will importantly increase the time of the density gradient centrifugation step. Take this into account when planning your experiments. Do not stop the centrifuge manually, that would cause a gradient break. 6. For obtaining a defined interphase, it is essential to avoid any vibration during the pipetting and centrifugation process. Also, the tubes need to be handled with care when placed inside/ outside the centrifuge. 7. When aspirating the mononuclear cell layer from the interphase, the minimum volume of Ficoll-Paque, which is the translucid phase between the cell interphase and cell pellet, should be transferred to the new tube. It has a certain toxicity which may affect cell survival for long exposure times. 8. Removing the platelets will avoid the formation of aggregates which might clog the isolation system afterward. 9. Erythrocytes lysis is usually performed with hypotonic buffers which maintain cell stability. Time is crucial due to hypotonic condition of the buffer; longer exposure time might result in increased cell death. Removing erythrocytes will help working with lower number of cells and taking away not interesting cells for the procedure.

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10. The volumes are calculated for 108 MNC. In the case of harvesting more cells, volumes can be scaled up in sets of 108 cells. For example, for 5  108 cells, resuspend in 1.5 mL of autoMACS Running Buffer and add 500 μL of CD34 MicroBeads and 500 μL of Blocking Reagent. 11. For the magnetic separation, other systems can be used according to the manufacturer guidelines. Depending on the rack available, 15 mL tubes or FACS tubes can be used. 12. It takes approximately 10 min to run the whole cycle in Posseld2 program of autoMACS for each tube. 13. The lineage+ fraction obtained after CD34+ isolation will be of interest for in vivo assays, as they can help CD34+ engraftment. For this purpose, Linage+ cells will be irradiated (15 Gy) before transplantation in order to avoid graft-versus-host effect. 50  103 irradiated Linage+ cells will be transplanted together with the CD34+ cells. Irradiated Linage+ cells can also be cryopreserved for future in vivo assays. 14. DMSO is used to prevent the formation of ice crystals during the freezing process. Nevertheless, it is a toxic compound that can cause cell death when used in higher concentrations, during longer exposure times or at higher temperature than specified. For this reason, it is important to minimize the time of cell exposure to DMSO. Once the cells are resuspended in cryopreservation medium, the freezing container should be placed inside the ultrafreezer as soon as possible. It is also advisable that the cryopreservation medium is prechilled in order to minimize the damage to the cells. 15. It is important to use antibiotics when culturing CB-HSPCs. Although the isolation is performed under the hood and all the materials used are sterilized, it is possible that not all the samples were collected under the same conditions. For this reason, Pen/Strep or the reference antibiotic used in the lab should be added to the HSPCs culture media in order to avoid contaminations. 16. CB-CD34+ cells lose their stemness overtime. After 10 days growing in vitro, less than 10% of the remaining cells maintain the expression of CD34 marker. 17. Do not use FBS to grow CD34+ HSPCs; it has unknown growth factors which potentially will make your cells differentiate spontaneously. 18. In case of using cryopreserved cells for transduction, it is recommendable to thaw the cells in advance and put them in culture for 3–4 h prior transduction, in order to let them recover from the thawing process.

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19. Polybrene, also known as hexadimethrine bromide, is added to the media for augmenting the efficiency of transduction. It is a small, positively charged molecule that binds to cell surface and neutralizes surface charge. 20. After 24 h of transduction, the cells can be observed under the microscope. Usually, the presence of cell debris is a good indication of a successful transduction. 21. Those marks at the bottom of the plate will not interfere any experiment development. The grid will be useful in the posterior process of counting the colonies, to avoid the problem of counting the same colony more than once by mistake. 22. Methylcellulose is a semisolid medium which needs humidity to remain in this condition and prevent excessive solidification during the long incubation periods required for colonies to grow. Therefore, to prevent the medium from drying, it is recommended to use only the four wells on the 6-well plate corners, and to fill the remaining two wells, and the space between wells, with 3–4 mL of autoclaved water. 23. Design the panel of antibodies for FACS immunophenotyping, considering the readout of the experiment, as well as the equipment available in your institution. Different lasers can detect different fluorochromes, and compensations need to be performed when several colors are included in the same sample. Consult the FACS responsible person in your institution for an optimized design of your antibody panels. A thorough design and optimization of the cytometry analysis can be crucial for your experiments and can save a lot of time and effort in data analysis. 24. Lysing solution is added to lyse red blood cells following direct immunofluorescence staining of human peripheral blood cells with monoclonal antibodies, and prior to flow cytometric analysis. Whole blood cell lysis helps eliminating interfering cells and permits effective detection of lymphocytes. References 1. Bari S, Seah KK, Poon Z, Cheung AM, Fan X, Ong SY, Li S, Koh LP, Hwang WY (2015) Expansion and homing of umbilical cord blood hematopoietic stem and progenitor cells for clinical transplantation. Biol Blood Marrow Transplant 21(6):1008–1019. https://doi.org/10.1016/j.bbmt.2014.12. 022 2. Mene´ndez P, Redondo O, Rodriguez A, Lopez-Berges MC, Ercilla G, Lo´pez A, Dura´n A, Almeida J, Pe´rez-Simo´n JA, San Miguel JF, Gratama JW, Orfao A (1998)

Comparison between a lyse-and-then-wash method and a lyse-non-wash technique for the enumeration of CD34+ hematopoietic progenitor cells. Cytometry 34(6):264–271 3. Montes R, Ayllon V, Prieto C, Bursen A, Prelle C, Romero-Moya D, Real PJ, NavarroMontero O, Chillon C, Marschalek R, Bueno C, Menendez P (2014) Ligandindependent FLT3 activation does not cooperate with MLL-AF4 to immortalize/transform cord blood CD34+ cells. Leukemia 28

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(3):666–674. https://doi.org/10.1038/leu. 2013.346 4. Prieto C, Stam RW, Agraz-Doblas A, Ballerini P, Camos M, Castano J, Marschalek R, Bursen A, Varela I, Bueno C, Menendez P (2016) Activated KRAS cooperates with MLL-AF4 to promote extramedullary engraftment and migration of cord blood CD34+ HSPC but is insufficient to initiate leukemia. Cancer Res 76(8):2478–2489. https://doi.org/10.1158/0008-5472.CAN15-2769 5. Prieto C, Marschalek R, Ku¨hn A, Bursen A, Bueno C, Mene´ndez P (2017) The AF4-MLL fusion transiently augments multilineage hematopoietic engraftment but is not sufficient to initiate leukemia in cord blood CD34+ cells. Oncotarget 8(47):81936–81941. https://doi. org/10.18632/oncotarget.19567 6. Hao QL, Shah AJ, Thiemann FT, Smogorzewska EM, Crooks GM (1995) A functional comparison of CD34 + CD38- cells in cord blood and bone marrow. Blood 86 (10):3745–3753 7. Reddy NP, Vemuri MC, Pallu R (2007) Isolation of stem cells from human umbilical cord blood. Methods Mol Biol 407:149–163.

https://doi.org/10.1007/978-1-59745-5367_12 8. Newcomb JD, Sanberg PR, Klasko SK, Willing AE (2007) Umbilical cord blood research: current and future perspectives. Cell Transplant 16 (2):151–158 9. Buechele C, Breese EH, Schneidawind D, Lin CH, Jeong J, Duque-Afonso J, Wong SH, Smith KS, Negrin RS, Porteus M, Cleary ML (2015) MLL leukemia induction by genome editing of human CD34+ hematopoietic cells. Blood 126(14):1683–1694. https://doi.org/ 10.1182/blood-2015-05-646398 10. Tursky ML, Beck D, Thoms JA, Huang Y, Kumari A, Unnikrishnan A, Knezevic K, Evans K, Richards LA, Lee E, Morris J, Goldberg L, Izraeli S, Wong JW, Olivier J, Lock RB, MacKenzie KL, Pimanda JE (2015) Overexpression of ERG in cord blood progenitors promotes expansion and recapitulates molecular signatures of high ERG leukemias. Leukemia 29(4):819–827. https://doi.org/ 10.1038/leu.2014.299 11. Greaves M (2003) Pre-natal origins of childhood leukemia. Rev Clin Exp Hematol 7 (3):233–245

Chapter 18 Lentiviral Transduction for Optimal LSC/HSC Manipulation Gustavo Mostoslavsky Abstract Historically, efficient transduction of hematopoietic stem cells (HSC) to study the role of specific genes on HSC function, as well as to broaden the potential of gene therapy for hematopoietic related diseases has relied on our ability to design vectors capable of delivering the gene of interest without affecting HSC function. While retroviruses have been used extensively for this purpose, HIV-derived lentiviruses prove superior for transduction of quiescent HSC due to their ability to infect nondividing cells. The design of the vector and the quality of the lentiviral preparation are the key elements to obtain reproducible consistent results that will eventually be translated into the clinic. This chapter describes the preparation of concentrated lentiviruses and the transduction of HSC to obtain long-term engraftment with persistent gene transfer and expression of the desired transgene. Key words Third-generation lentiviruses, Lentiviral transduction, Transfection, Ultracentrifugation, Hematopoietic stem cells

1

Introduction For almost three decades, simple retroviruses were the most commonly used vector for gene transfer into mammalian cells [1–3]. However the use of these vectors was limited mostly due to their inefficient capacity to induce gene transfer into nondividing cells [4 , 5]. The development of HIV-1-based lentiviral vectors bypassed this obstacle, allowing for efficient transduction of a wide range of mammalian cells including their ability to integrate into the genome of non-proliferating cells [6–8]. This became essential especially in regard to gene transfer to quiescent HSC populations, first demonstrated by Uchida et al. in 1998 [9]. Since then, lentiviral gene transfer into purified HSC populations has served as the basis for gaining insights into basic HSC biology as well as developing potential therapies for certain human hematological diseases [10–14]. While the use of gene therapy in humans has been progressing cautiously, due to concerns regarding viral integration, and the development of secondary insertional mutagenesis [15, 16], it is clear that the use of more sophisticated viral vectors, including

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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self-inactivating lentiviral-based vectors such as those mentioned above [10–14], would be beneficial for a safer, more promising gene therapy approach to human disease. Indeed, recent advances in clinical trials using more advanced vectors emphasized the importance of combining design, vector preparation, and methodology of transduction to achieve clinical success, while minimizing the risks of insertional mutagenesis [11, 17–20]. The rationale behind the success of gene therapy in the hematopoietic system lies in the use of viral vectors capable of effectively transducing quiescent HSC while maintaining HSC function and, at the same time, limiting the risk of insertional mutagenesis. The viability and long-term engraftment of transplanted cells is dependent upon maintenance of cellular integrity during transduction protocols. Herein resides a main obstacle with clear practical implications that has hampered progress in this field, i.e., finding the right balance between achieving high efficiency of transduction while maintaining the multipotential capacity of the transduced HSC. This chapter provides a protocol detailing the preparation of a high titer lentiviral vector, followed by a method for the efficient transduction of purified murine HSC that preserve their robust multipotent activities, in vitro and in vivo. This methodology provides a basis for an optimized approach to use gene therapy in the clinical arena. Self-inactivating lentiviral vectors are packaged via transfection of HEK-293T (293T) cells with the lentiviral backbone in conjunction with four helper constructs that provide in trans expression of enzymatic and structural viral proteins. While most commercially available lentiviral vectors are based on Tat-independent constructs, in our experience, the use of a lentiviral backbone that preserves the 50 LTR and Tat responsiveness produces higher titer viral preparations. This appears to be related to a specific interaction between Tat and the Rev accessory protein (Balazs, A.B. and Mostoslavsky, G., unpublished) and therefore all our vector preparations rely on the use of four helper plasmids (Tat, Rev, gag-pol, and Vsv-G) (Fig. 1). Transfected 293T cells allow for the packaging and release of lentiviral particles, which are then collected and concentrated by ultracentrifugation to obtain viral titers that range between 5  108 and 5  109 viral particles per milliliter. Accurate titering of obtained viral particles is key to ensure proper MOI (multiplicity of infection ¼ number of infectious particles per target cell). In order to further optimize levels of gene transfer, we include minimal prestimulation with low levels of SCF and TPO during viral transduction [13], both important to preserve HSC function as well as inducing HSC to become activated from a G0 to G1 state [21].

Optimal Lentiviral Transduction Promoter 5’ LTR

PSI

HIV cpPu RRE

Transgene 1

Promoter dU3 5’ LTR PSI

HIV cpPu RRE

Transgene 1

WPRE IRES

Transgene 2 or reporter

dU3 3’ LTR

loxP

Reverse Transcription + integration WPRE IRES

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dU3 3’ LTR

Chrom

Chrom

loxP

loxP

Fig. 1 Diagram of pHAGE lentiviral vector [14], depicting a bicistronic configuration using an IRES element to express two independent transgenes. In this version a loxP sequence inserted in the deleted U3 region is duplicated during reverse transcription creating a vector that upon integration is flanked by loxP sequences and therefore can be excised by Cre. LTR long terminal repeat, PSI packaging signal, RRE Rev responsible element, cpPU Polypurine tract, WPRE Woodchuck posttranscriptional regulatory element, dU3 deleted U3

2

Materials

2.1 Lentiviral Preparation

1. TransIT® Transfection Reagent (Mirus Bio LLC). 2. Transfection Media: Dulbecco’s modified Eagle’s medium, 10% fetal bovine serum, 100 μg/mL Primocin (InVivoGen). 3. Helper Plasmids (HDM-Tat1b, pRC1-Rev1b, HDM-Hgpm2, HDM-Vsv-G) [14] (Originally developed by the Harvard Gene Therapy Initiative). 4. Swinging bucket ultracentrifuge rotor. 5. Ultra-Clear centrifuge tubes. 6. Ultracentrifuge. 7. 15 cm Tissue culture treated plates. 8. 150 mL Bottle top filter. 9. 5 mL Polypropylene tubes. 10. 293T Cells.

2.2 HSC Transduction

1. HSC Transduction Media: serum-free medium specifically formulated to support the development of human hematopoietic cells in culture, supplemented with L-glutamine and penicillin/ streptomycin, 10 ng/mL mouse stem cell factor (SCF), 100 ng/mL human thrombopoietin (TPO), and 5 μg/mL

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Polybrene (hexadimethrine bromide). Prepare fresh and keep at 4  C. 2. 96-Well round-bottom plates. 3. Concentrated lentiviral preparations.

3

Methods

3.1 Transfection of 293T Cells for Viral Packaging/ Concentration of Viral Supernatants

Carry out all steps in this section in a tissue culture hood using proper aseptic tissue culture practices unless otherwise noted. 1. Prepare a 15 cm tissue culture treated plate of 293T cells to a confluence of 85–90% (see Note 1). 2. Prepare the transfection mix by first pipetting 2 mL of DMEM into a 5 mL polypropylene tube. While vortexing, add 112.5 μL of TransIT® transfection reagent to the DMEM drop by drop. Try to avoid the TransIT® hitting the sides of the tube. For each 15 cm plate of 293T cells, make one tube of transfection mix. Allow the transfection mix to incubate for 10 min at room temperature. 3. During this incubation prepare the DNA mix, containing your lentiviral vector and the four helper plasmids; in a 1.5 mL Eppendorf tube add 30 μg of lentiviral vector DNA. Mix with 1.5 μg of HDM-Tat1b, 1.5 μg of pRC1-Rev1b, 1.5 μg of HDM-Hgpm2, and 3 μg of HDM-Vsv-G helper plasmids. Pipette gently to mix (see Note 2). 4. Add the DNA mix to the transfection mix drop by drop while vortexing. Avoid hitting the sides of the tube. Incubate the mix for 15 min at room temperature. 5. During this incubation, change the media of your 293T to 13 mL of new transfection medium (see Note 3). 6. Add the Transfection/DNA mix to the cells GENTLY drop by drop. Gently push the plate front to back and then left to right several times to evenly distribute the mix to all the cells of the plate to ensure homogeneous efficient transfection. 7. Incubate the plate for 48 h in a 37  C, 5% CO2 incubator. Do not change media during this time. 8. To collect your viral supernatant, pipette up the media and transfer through a 150 mL bottle top filter into a sterile glass bottle (using vacuum). Keep the bottle at 4  C for further collections. Replenish media with 15 mL of fresh transfection media. Repeat viral collection to a total of five times. Pool all collections together and place filtered unconcentrated virus at 4  C until ready for concentration (see Note 4).

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9. To concentrate, make sure ultracentrifuge has been set to 4  C and allow the inner chamber to cool. Once cooled, place an ultracentrifuge ultraclear tube into swinging bucket and weigh on a scale. Pipette viral supernatant into ultracentrifuge tube. The weight of virus plus ultracentrifuge tube plus swinging bucket must be equivalent in order to keep the ultracentrifuge balanced during centrifugation (see Note 5). 10. Once all samples have been loaded into the rotor, carefully place the rotor into the ultracentrifuge. Spin viral supernatants for 90 min at 49,000  g at 4  C. 11. Following centrifugation, carefully remove the ultracentrifuge tubes. Use an empty beaker for waste. In one swift motion, dump out the supernatant from the tube. Hold the tube facing down, until the last two drops fall from the edge of the tube. Turn the tube upright and immediately wrap paraffin over the top of the tube (see Note 6). 12. Place the tubes on ice for 2–3 h, then prepare 10 μL aliquots and store at 80  C (see Note 7). 13. Before using concentrated virus for transduction experiments it is necessary to titer all viruses by FACS or Southern blot (see Note 8). 3.2 Lentiviral Transduction of Purified Hematopoietic Stem Cells

4

1. To each well containing HSC, carefully add the volume of virus that corresponds to 200–300 MOI and mix gently by pipetting slowly to prevent bubbles (see Notes 9 and 10). 2. Place cells at 37  C, 5% CO2 overnight (see Note 11).

Notes 1. When transfecting 293T for viral production, it is imperative that the cells are at the proper confluence. Improper confluence may affect transfection efficiency ultimately leading to inefficient viral production. Although instructions from manufacturers of transfection reagents recommend to transfect cells at relatively low confluence, for viral production we strongly recommend to perform transfection when cells are 85–90% confluent. Alternatively to the use of TransIT, we have also tested the use of 25 kDa Linear Polyethylenimine (PEI, Polysciences, Inc. Cat# 23966) for transfection with similar results. 2. All plasmid DNA used for viral production should be of high quality and purity (normally the DNA obtained from a Midiprep or Maxiprep purification kit works well).

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3. 293T cells can be easily detached from tissue culture treated surfaces; therefore take extreme care when changing media in between viral collections. Slowly pipette media to the side wall of the plate in order to prevent loss of cells. 4. We recommend collecting viral supernatants a total of five times. To make collections easier, we suggest collecting virus twice on days one and two (starting 48 h after transfection) once in the morning and again in the evening (8–10 h apart). On the third day of collection, collect supernatant once in the morning, and proceed with concentration (step 9). If necessary, unconcentrated viral particles can be stored at 4  C for up to 4–5 days without losing any viral activity. 5. To limit chances of contaminating viral supernatants carry out this step next to a gas flame. To make balancing of samples easier, use a glass beaker. Zero the beaker and place the bucket plus ultracentrifuge tube inside the beaker. Then slowly add your viral supernatant to the tube. 6. After centrifugation, you may or may not see a small loose white pellet. This is normal. Continue with aliquoting and titering your virus. 7. This 2–3 h incubation allows for any virus to come down off the sides of the centrifuge tubes and also helps viral particles to come into solution. When aliquoting, we recommend making 10 μL working aliquots, and one to two tubes of larger volumes of virus that can be frozen and thawed to aliquot later on. We recommend not to freeze/thaw viral aliquots more than twice. 8. To titer viruses by FACS, transduce HEK293 cells (6-well plate) (~90% confluent), with 0.01 μL, 0.1 μL, and 1 μL of concentrated virus. For Southern blot, transduce HEK293 with 1 μL, 5 μL, and 10 μL of concentrated virus. Transduction is performed in 1 mL of 10% DMEM media containing 5 μg/ mL Polybrene. Add the appropriate volume of virus. Swirl the plate gently to distribute virus. The next day, change media and leave cells for two more days before analyzing cells by FACS, or for gDNA extraction. 9. If your viral aliquots do not appear clean or you suspect the presence of debris (which can interfere with transduction efficiencies), do a quick spin before adding the virus to your cells. 10. Alternatively additional spinfection of cells with virus for 2 h at 800  g at 37  C may increase efficiency of transduction. For spinfection, spin cells in 100 μL of HSC transduction media and add five times the volume of virus as used in 20 μL. Then leave cells overnight as in step 2. The increased volume of media maintains cell viability during centrifugation.

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11. The next day transduced HSC are ready to be used for in vitro assays such as methylcellulose colony forming unit assays or for in vivo transplant experiments. For lentiviruses containing a fluorescent reporter, allow for up to 3 days to observe reporter expression. References 1. Kohn DB, Sadelain M, Dunbar C, Bodine D, Kiem HP, Candotti F, Tisdale J, Riviere I, Blau CA, Richard RE, Sorrentino B, Nolta J, Malech H, Brenner M, Cornetta K, Cavagnaro J, High K, Glorioso J (2003) American Society of Gene Therapy (ASGT) ad hoc subcommittee on retroviral-mediated gene transfer to hematopoietic stem cells. Mol Ther 8(2):180–187 2. Maetzig T, Galla M, Baum C, Schambach A (2011) Gammaretroviral vectors: biology, technology and application. Viruses 3 (6):677–713. https://doi.org/10.3390/ v3060677. viruses-03-00677 [pii] 3. Somia N, Verma IM (2000) Gene therapy: trials and tribulations. Nat Rev Genet 1 (2):91–99. https://doi.org/10.1038/ 35038533 4. Peters SO, Kittler EL, Ramshaw HS, Quesenberry PJ (1996) Ex vivo expansion of murine marrow cells with interleukin-3 (IL-3), IL-6, IL-11, and stem cell factor leads to impaired engraftment in irradiated hosts. Blood 87 (1):30–37 5. Yonemura Y, Ku H, Hirayama F, Souza LM, Ogawa M (1996) Interleukin 3 or interleukin 1 abrogates the reconstituting ability of hematopoietic stem cells. Proc Natl Acad Sci U S A 93(9):4040–4044 6. Blomer U, Naldini L, Kafri T, Trono D, Verma IM, Gage FH (1997) Highly efficient and sustained gene transfer in adult neurons with a lentivirus vector. J Virol 71(9):6641–6649 7. Kafri T, Blomer U, Peterson DA, Gage FH, Verma IM (1997) Sustained expression of genes delivered directly into liver and muscle by lentiviral vectors. Nat Genet 17 (3):314–317. https://doi.org/10.1038/ ng1197-314 8. Naldini L, Blomer U, Gallay P, Ory D, Mulligan R, Gage FH, Verma IM, Trono D (1996) In vivo gene delivery and stable transduction of nondividing cells by a lentiviral vector. Science 272(5259):263–267 9. Uchida N, Sutton RE, Friera AM, He D, Reitsma MJ, Chang WC, Veres G, Scollay R, Weissman IL (1998) HIV, but not murine leukemia virus, vectors mediate high efficiency

gene transfer into freshly isolated G0/G1 human hematopoietic stem cells. Proc Natl Acad Sci U S A 95(20):11939–11944 10. Heckl D, Wicke DC, Brugman MH, Meyer J, Schambach A, Busche G, Ballmaier M, Baum C, Modlich U (2011) Lentiviral gene transfer regenerates hematopoietic stem cells in a mouse model for Mpl-deficient aplastic anemia. Blood 117(14):3737–3747. https:// doi.org/10.1182/blood-2010-09-308262 11. Cavazzana-Calvo M, Payen E, Negre O, Wang G, Hehir K, Fusil F, Down J, Denaro M, Brady T, Westerman K, Cavallesco R, Gillet-Legrand B, Caccavelli L, Sgarra R, Maouche-Chretien L, Bernaudin F, Girot R, Dorazio R, Mulder GJ, Polack A, Bank A, Soulier J, Larghero J, Kabbara N, Dalle B, Gourmel B, Socie G, Chretien S, Cartier N, Aubourg P, Fischer A, Cornetta K, Galacteros F, Beuzard Y, Gluckman E, Bushman F, Hacein-Bey-Abina S, Leboulch P (2010) Transfusion independence and HMGA2 activation after gene therapy of human beta-thalassaemia. Nature 467 (7313):318–322. https://doi.org/10.1038/ nature09328 12. Sakuma T, Barry MA, Ikeda Y (2012) Lentiviral vectors: basic to translational. Biochem J 443(3):603–618. https://doi.org/10.1042/ BJ20120146 13. Mostoslavsky G, Kotton DN, Fabian AJ, Gray JT, Lee JS, Mulligan RC (2005) Efficiency of transduction of highly purified murine hematopoietic stem cells by lentiviral and oncoretroviral vectors under conditions of minimal in vitro manipulation. Mol Ther 11 (6):932–940. https://doi.org/10.1016/j. ymthe.2005.01.005 14. Mostoslavsky G, Fabian AJ, Rooney S, Alt FW, Mulligan RC (2006) Complete correction of murine Artemis immunodeficiency by lentiviral vector-mediated gene transfer. Proc Natl Acad Sci U S A 103(44):16406–16411. https://doi. org/10.1073/pnas.0608130103 15. Baum C, Dullmann J, Li Z, Fehse B, Meyer J, Williams DA, von Kalle C (2003) Side effects of retroviral gene transfer into hematopoietic stem cells. Blood 101(6):2099–2114

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16. Hacein-Bey-Abina S, von Kalle C, Schmidt M, Le Deist F, Wulffraat N, McIntyre E, Radford I, Villeval JL, Fraser CC, CavazzanaCalvo M, Fischer A (2003) A serious adverse event after successful gene therapy for X-linked severe combined immunodeficiency. N Engl J Med 348(3):255–256 17. Cartier N, Hacein-Bey-Abina S, Bartholomae CC, Veres G, Schmidt M, Kutschera I, Vidaud M, Abel U, Dal-Cortivo L, Caccavelli L, Mahlaoui N, Kiermer V, Mittelstaedt D, Bellesme C, Lahlou N, Lefrere F, Blanche S, Audit M, Payen E, Leboulch P, l’Homme B, Bougneres P, Von Kalle C, Fischer A, Cavazzana-Calvo M, Aubourg P (2009) Hematopoietic stem cell gene therapy with a lentiviral vector in X-linked adrenoleukodystrophy. Science 326 (5954):818–823. https://doi.org/10.1126/ science.1171242 18. Hacein-Bey-Abina S, Pai SY, Gaspar HB, Armant M, Berry CC, Blanche S, Bleesing J, Blondeau J, de Boer H, Buckland KF, Caccavelli L, Cros G, De Oliveira S, Fernandez KS, Guo D, Harris CE, Hopkins G, Lehmann LE, Lim A, London WB, van der Loo JC, Malani N, Male F, Malik P, Marinovic MA, McNicol AM, Moshous D, Neven B, Oleastro M, Picard C, Ritz J, Rivat C, Schambach A, Shaw KL, Sherman EA, Silberstein LE, Six E, Touzot F, Tsytsykova A, Xu-Bayford J, Baum C, Bushman FD, Fischer A, Kohn DB, Filipovich AH, Notarangelo LD, Cavazzana M, Williams DA, Thrasher

AJ (2014) A modified gamma-retrovirus vector for X-linked severe combined immunodeficiency. N Engl J Med 371(15):1407–1417. https://doi.org/10.1056/NEJMoa1404588 19. De Ravin SS, Wu X, Moir S, Anaya-O’Brien S, Kwatemaa N, Littel P, Theobald N, Choi U, Su L, Marquesen M, Hilligoss D, Lee J, Buckner CM, Zarember KA, O’Connor G, McVicar D, Kuhns D, Throm RE, Zhou S, Notarangelo LD, Hanson IC, Cowan MJ, Kang E, Hadigan C, Meagher M, Gray JT, Sorrentino BP, Malech HL, Kardava L (2016) Lentiviral hematopoietic stem cell gene therapy for X-linked severe combined immunodeficiency. Sci Transl Med 8(335):335ra357. https://doi.org/10.1126/scitranslmed. aad8856 20. Ribeil JA, Hacein-Bey-Abina S, Payen E, Magnani A, Semeraro M, Magrin E, Caccavelli L, Neven B, Bourget P, El Nemer W, Bartolucci P, Weber L, Puy H, Meritet JF, Grevent D, Beuzard Y, Chretien S, Lefebvre T, Ross RW, Negre O, Veres G, Sandler L, Soni S, de Montalembert M, Blanche S, Leboulch P, Cavazzana M (2017) Gene therapy in a patient with sickle cell disease. N Engl J Med 376(9):848–855. https:// doi.org/10.1056/NEJMoa1609677 21. Ema H, Takano H, Sudo K, Nakauchi H (2000) In vitro self-renewal division of hematopoietic stem cells. J Exp Med 192 (9):1281–1288

Chapter 19 Characterizing the In Vivo Role of Candidate Leukemia Stem Cell Genes Yu Wei Zhang, Julian Mess, and Nina Cabezas-Wallscheid Abstract Acute myeloid leukemia (AML) is a disease caused by multiple distinct genomic events in the hematopoietic stem cell and progenitor compartment. To gain insight into the link between genetic mutations in AML and their clinical significance, AML mouse models are often employed. However, the breeding of genetically modified mouse models is a resource-intensive and time-consuming endeavor. Here, we describe a viral-based protocol to study the role of candidate leukemia stem cell (LSC) genes. Transplantation of virally transduced oncogenic drivers for AML with virally altered expression of candidate leukemia associated genes in murine primary bone marrow cells, is an effective alternative method to assess the impact of cooperating mutations in AML. Key words Hematopoietic stem cells, Leukemia stem cells, Acute myeloid leukemia, Transplantation, Retroviral transduction, Lentiviral transduction

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Introduction Acute myeloid leukemia (AML) is a phenotypically and genetically heterogeneous disease. Blast cells from AML patients contain hundreds of somatic mutations, but can be traced back to a few driver mutations [1, 2]. Mutations within the hematopoietic stem cell and progenitor cell (HSPC) compartment can transform cells into leukemia stem cells (LSCs) harboring unlimited self-renewal and proliferative capabilities [3–5]. A majority of AML patients who reach full remission after chemotherapy eventually succumb to disease relapse due to the failure to completely eradicate LSCs [6–8]. Therefore, therapeutic strategies designed to eradicate LSCs may improve long-term survival of AML patients. Identification of LSC-specific genes required to initiate/maintain/cooperate with the disease could serve as drug targets to facilitate the development of therapies to fully eradicate AML. Our knowledge of the mutational landscape of AML has widely expanded due to efforts in sequencing large patient cohorts. Using

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next-generation sequencing (NGS), several genes have been identified as indicators of diagnosis, treatment, and survival. This highlights that AML, unlike, for example, chronic myeloid leukemia (CML), is not a disease characterized by one specific genetic event but by multiple genetic events resulting in a similar phenotype [1, 9, 10]. Numerous deregulated genes have been studied extensively, but large efforts are still required to establish therapy treatments targeting LSCs through understanding their unique biological properties in specific mutational backgrounds. While there are many mutations within the AML genome, it is suggested that in most cases, only a few cooperating mutations are sufficient to transform HSPCs into LSCs [5, 11]. This highlights the need to functionally validate the role of deregulated/mutated genes identified by NGS, which experimentally often relies on genetic mouse models. The survival of AML mice is an indicator of disease aggressiveness, and can be directly correlated with the frequency of LSCs in the tumor [12, 13]. Often, genetically modified mouse models targeting the candidate genes are crossed with a leukemiapredisposed mouse model [14]. This is a widely accepted method, but it is time-consuming and costly. To circumvent the laborintensive breeding of mice, we propose in this chapter a viral overexpression (OE) and knockdown (KD)-based approach to investigate the role of candidate LSC genes in vivo [15]. Commercially available retroviral vectors expressing oncogenic drivers of AML mimic the progression of the disease. In combination with retroviral transduction of AML oncogenic driver mutations, we will use overexpression or knockdown viral particles to target candidate AML genes in murine bone marrow cells followed by transplantation into irradiated recipient mice. The aim of this dual transduction combined with in vivo transplantation approach is to determine whether a gene of interest plays an active role in AML (see Fig. 1 for overview of the method). Transfection of OE/KD Plasmid

Transfection of AML Driver Mutation

Plat-E/HEK293T 24h-Change Media 24h-Collect Supernatant

Plat-E 24h-Change Media 24h-Collect Supernatant

Transduction

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Fig. 1 Experimental overview to investigate the role of leukaemia associated genes

Irradiation 600 rads

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Materials Virus Production

1. Plat-E cells. 2. HEK293T cells. 3. Blasticidin. 4. Puromycin. 5. Dulbecco’s modified eagle medium (DMEM). 6. Fetal bovine serum (FBS). 7. 15 cm Cell culture dish. 8. jetPRIME –DNA/siRNA transfection reagent. 9. 0.45 μm Filter unit. 10. 30 mL Syringe. 11. Plasmids.

2.2 Dissection and Isolation of Bone Marrow

1. CO2 Chamber (optional). 2. 70% Ethanol. 3. Dissection tools (dissecting scissors, dissecting tray, iris forceps, steel dissecting pins, and scalpel). 4. Phosphate-buffered saline (PBS). 5. 6-Well plate. 6. Mortar and pestle. 7. 40 μm Cell strainer. 8. 50 mL Falcon tube. 9. Erythrocytes lysing buffer (150 mM NH4Cl, 10 mM KHCO3, 0.1 mM Na2EDTA). 10. Dynabeads untouched mouse CD4 cells kit. 11. Magnet. 12. Rotating wheel/shaker.

2.3 Viral Transduction

1. Protamine sulfate. 2. Interleukin-3 (IL3). 3. Interleukin-6 (IL-6). 4. Stem Cell Factor (SCF) 5. 2-Mercaptoethanol 5. 2-Mercaptoethanol. 6. Iscove’s modified Dulbecco’s medium (IMDM). 7. FBS. 8. Cell counter (for example: Hemocytometer).

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Irradiation

1. Antibiotic water: 5 mL Trimethosel in 1 L of tap water. 2. Gamma-irradiator.

2.5 Intravenous Injections

1. 25-gauge Needles.

2.6 Peripheral Blood Analysis

1. Blood lancet.

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2. 1.5 mL EDTA polypropylene microcentrifuge tube. 3. Erythrocytes lysing buffer. 4. PBS. 5. FBS. 6. Antibodies: l

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Anti-CD45.1-AlexaFluor-700 (clone A20) to identify CD45.1+ mouse cells. Anti-CD45.2-APC-Cy7 (clone 104) to identify CD45.2+ mouse cells.

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7. FACS tubes conical 4.5 mL. 8. Flow cytometry system. 9. Hemocytometer system. 2.7 Postmortem Exam

1. Dissection tools (dissecting scissors, dissecting tray, iris forceps, steel dissecting pins, and scalpel). 2. Ruler. 3. Weight scale. 4. Plunger from 5 mL syringe. 5. Mortar and pestle. 6. 40 μm Cell strainer 7. 50 mL Falcon tube 8. Erythrocytes lysing buffer (150 mM NH4Cl, 10 mM KHCO3, 0.1 mM Na2EDTA). 9. PBS. 10. Antibodies (as described above). 11. FACS tubes conical 4.5 mL. 12. Flow cytometry system.

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3.1 Production of Retroviral Particles for AML Oncogenic Driver

Plasmids for several oncogenes driving AML such as MLL-AF9 are commercially available and contain fluorescent or antibiotic selection markers. To produce retroviral particles, we recommend using Plat-E cells, which contain the gag, pol, and env genes allowing retroviral packaging with a single plasmid transfection (see Note 1). 1. Plat-E cells are grown in DMEM with 10% FBS, 1 μg/mL puromycin, and 10 μg/mL blasticidin. 2. One day before transfection seed approximately 18–20 million Plat-E cells into a 15 cm dish. 3. To prepare the transfection mix, add 15 μg of transfection plasmid into 750 μL of jetPRIME buffer followed by 30 μL of jetPRIME. Immediately vortex for 10 s. Incubate the transfection mix at room temperature for 10 min. 4. In the meantime, replace cell culture medium with 20 mL of DMEM with 10% FBS without puromycin and blasticidin. 5. Add the transfection mix prepared in step 3, dropwise into the medium. Mix the solution by gently moving the plate back and forth and from side to side. 6. Approximately 16–24 h later discard medium and add 15–20 mL of fresh DMEM with 10% FBS without puromycin and blasticidin. 7. Collect viral supernatant 48 and 72 h after transfection. 8. Centrifuge the viral supernatant at 400  g for 5 min and filter through a 0.45 μm filter unit using a 30 mL syringe. This step ensures the removal of any unwanted debris. 9. Short-term storage of the viral supernatant at 4 Note 2).

3.2 Production of Lentiviral Particles to Manipulate Expression of Candidate LSC Genes



C (see

Using retroviral particles to target leukemia cells is an acceptable approach. Alternatively, we offer a protocol to produce lentiviral particles. Lentivirus may be advantageous to ensure targeting of quiesence LSCs in the heterogenous AML engineered cell line. While the principles of transfection remain the same, HEK293T cells are used and require the additional psPAX2 (Addgene: 12250) and pMD2.G (Addgene: 12259) plasmids. Of note, some plasmids do contain fluorescence markers that are useful to follow disease development. 1. HEK293T cells are grown in DMEM with 10% FBS. 2. One day before transfection seed approximately 15 million HEK293T cells into a 15 cm dish.

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3. To prepare the transfection mix, add 15 μg of transfection plasmid, with 9 μg of psPAX2 and 6 μg of pMD2.G into 1.5 mL of jetPRIME buffer, followed by 60 μL of jetPRIME. Immediately vortex for 10 s. The transfection mix is incubated at room temperature for 10 min (see Note 3). 4. Follow steps 4–9 from point Subheading 3.1. 5. The viral supernatant can be concentrated using ultracentrifugation (2 h at 100,000  g at 4  C with a 20% sucrose cushion) or through precipitation using PEG-it (30 min at 1500  g at 4  C). Concentrating lentiviral particles may be helpful to reduce the volume of media used during transduction. 3.3 Preparation of Bone Marrow Samples for Transduction

1. Euthanize mice by either cervical dislocation or CO2 asphyxiation. 2. Isolate tibia, femur, and hipbones and place into a 6-well plate filled with cold PBS. 3. Under sterile conditions, transfer bones to a mortar filled with 5 mL of PBS and grind until bones become pale white/pink. 4. Filter the bone marrow cell suspension through a 40 μm cell strainer attached to a 50 mL Falcon tube. 5. Ensure maximum recovery by repeating steps 3 and 4. 6. Spin down cells at 400  g for 5 min and aspirate supernatant. 7. Add 2 mL of erythrocytes lysis buffer to lyse red blood cells and incubate at room temperature for 5 min. Neutralize the reaction by adding 8 mL of PBS and spin down at 400  g for 5 min. Discard supernatant. 8. Lineage-negative bone marrow cells are enriched using the Dynabeads untouched mouse CD4 kit or using a cocktail of biotinylated CD4, CD8, Ter119, Gr-1, B220, and CD11b. Add 100 μL of antibody mix to the bone marrow cells and incubate at 4  C for 30 min on a rotating wheel. 9. While the cells are incubating with the antibody mix, wash 500 μL of Dynabeads (or any Streptavidin beads) two times with PBS to remove any preservatives that may be harmful to the cells. 10. Wash the bone marrow cells with 4 mL PBS, centrifuge at 400  g for 5 min, and bone marrow cells are mixed with Dynabeads. The mixture is incubated at 4  C on a rotating wheel for 15–20 min. This incubation should not exceed 30 min. 11. Place cells on the magnet. Lineage-positive cells will adhere to the magnet, and the lineage-negative cells will remain in suspension. Collect the supernatant with the lineage-negative bone marrow cells.

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12. To ensure maximum recovery, resuspend beads in 1 mL of PBS and repeat magnetic depletion. The recovered cells are now ready to proceed to viral transduction. 3.4 Viral Transduction of Lineage-Depleted Bone Marrow Cells

1. Before preparing cells for transduction, pre-warm the centrifuge to 32  C (see Note 4). 2. To facilitate optimal transduction, add 8-10 μg/mL of protamine sulfate into the viral supernatant. 3. Spin bone marrow cells at 400  g for 5 min. Resuspend in viral supernatant and split into a 6-well plate. We recommend between 5 and 10 million cells per well. 4. Spin lineage-depleted cells with viral supernatant at 1000  g for 2 h at 32  C. 5. Without disturbing cells, remove as much of the viral supernatant as possible and replace with IMDM containing 10% heatinactivated FBS, 100 ng/mL mSCF, 10 ng/mL mIL3, 10 ng/mL hIL6, 10 4 M 2-mercaptoethanol and 1 μg/mL polybrene. 6. 24 h later the medium is replaced with IMDM containing 10% heat-inactivated FBS, 100 ng/mL mSCF, 10 ng/mL mIL3, 10 ng/mL hIL6 and 10 4 M 2-mercaptoethanol but without protamine sulfate. 7. Expression of the plasmid will reach maximum efficiency between 48 and 72 h post-transduction (see Notes 5 and 6). 8. The transduction of the retrovirus/lentivirus with the candidate LSC gene can occur simultaneously or serially after the expression of the AML oncogenic driver. This depends highly on the question that is being addressed. For instance, to address initiation of AML both viral particle are spinoculated simultaneously. Studying the progression of AML requires serial transduction at least 72h post primary transduction.

3.5 Irradiation and Intravenous Injection into the Lateral Tail Vein of Recipient Mice

1. Place recipient mice on antibiotic water prior to irradiation. Antibiotic water should be given for at least 14–28 days postirradiation (see Note 7). 2. One day before injection, recipient mice are sublethally irradiated with a dose of 600 rads. 3. Resuspend desired cells for injection in 100 μL of PBS per recipient mouse. 4. To perform lateral tail vein injection, warm up the tail of the recipient mouse for approximately 10 min and inject into dilated lateral tail vein.

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3.6 Collection of Peripheral Blood

1. To monitor disease progression, collect peripheral blood from mice every 4 weeks from either the superficial temporal vein or the tail vein (see Note 7). 2. The mice are bled with a blood lancet and blood is collected in a tube coated with EDTA.

3.7 Analysis of Peripheral Blood by Hemocytometer and FACS

1. A hemocytometer is used to quantify different parameters in the blood of mice. Approximately 30 μL of blood is required for measurement. Do not perform erythrocyte lysis for this analysis. Elevated white blood cell count and anemia are indicators of blast cells and disease development. 2. The myeloid populations will be monitored using flow cytometry. To prepare cells, add 2 mL of erythrocytes lysing buffer to the blood sample, transfer into a FACS tube, and incubate at room temperature for 5–10 min. 3. Add 2 mL of PBS with 2% FBS to neutralize the solution. Centrifuge at 400  g for 5 min and aspirate the supernatant. 4. Prepare 50 μL of antibody cocktail mix (refer to Antibodies in Subheading 2.6) per sample; vortex; incubate at 4  C for 20 min. 5. Add 2 mL of PBS and centrifuge at 400  g for 5 min. Remove supernatant, but leave approximately 200 μL at the bottom and proceed to acquisition on flow cytometer.

3.8 Monitoring Disease Development

1. As the percentage of donor diseased cells begin to overtake the healthy cells, it becomes critical to visually monitor the mice for disease. This can be determined by FACS either quantifying transplanted CD45-positive cells (CD45.1, CD45.2) or using a fluorescence marker that is expressed from your plasmid. Mice should be sacrificed when reduced mobility, hunched back, rough hair coat, and eye squinting are observed. Consult the animal welfare officer from your institution before starting any experiment and for specific criteria’s to monitor disease development.

3.9 Postmortem Examination of Spleen, Liver, and Bone Marrow of Diseased Mice

1. Postmortem examination of the mice should show splenomegaly and a pale bone marrow that is associated with hepatosplenomegaly. Splenomegaly is assessed by (a) measuring the length of the spleen and (b) determining the ratio of the weight of spleen to the weight of the mouse. 2. A portion of the spleen and liver is taken for fixation and subsequent histological analysis. Histological slides stained with hematoxylin and eosin from the liver and spleen should show an infiltration of blast cells. 3. Crush bones to isolate the bone marrow cells as described in Subheading 3.3.

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4. Mash the spleen into a single cell suspension with a plunger from a 5 mL syringe into a 40 μm cell strainer placed over a 50 mL Falcon. Use PBS to rinse the cell strainer, this will help to dissociate the cells. 5. The bone marrow and spleen cells are stained using the same panel of antibodies as previously described (Subheading 2.6) and analyzed on FACS. 6. In flow cytometry analysis infiltration of the AML cells derived from the donor, accumulation of hematopoietic immature cells (c-Kit positive) and increased myeloid populations (Gr-1 and Mac-1 positive) should be observed. While many leukemias exhibit these attributes, please note that not all leukemias will progress in this manner.

4

Notes 1. Do not freeze retrovirus generated from Plat-E cells, they cannot withstand freeze/thaw cycles. Similarly, you cannot ultracentrifuge retroviral supernatant generated from Plat-E cells. If you wish to ultracentrifuge the retroviral supernatant, please follow the protocol for lentiviral particle generation using the VSV-G pseudotyped plasmid [16]. 2. We recommend collecting media 1–2 times at 24-h intervals. The highest viral titer will be observed in the viral supernatant collected at 48 h post transfection. Using freshly generated virus collected on the same day will give the highest transduction efficiency. 3. In case your plasmid is large and generates low titers, you can alter the volume of the transfection reagent or the amount of DNA. For plasmids larger than 10 kb, we recommend 22.5 μg of plasmid, 13.5 μg psPAX2, and 9 μg pMD2.G into 2.5 mL of jetPRIME buffer, followed by 100 μL of JetPRIME. 4. The origin of LSCs can determine the phenotype, aggressiveness, and prognosis of the disease. Transplantation of irradiated mice with specific early hematopoietic stem/progenitor cells such as Lin cKit+Sca1+ cells can alter the disease progression and help to determine specifically which subpopulation is driving the disease in your mice. 5. 24–48 h post transduction, sort or select for positively infected cells. For antibiotic selection markers, we recommend puromycin at 1–1.5 μg/mL, and neomycin at 100 μg/mL. 6. Dead cells from antibiotic treatment can impair cell growth in culture. We suggest removing dead cells using a dead cells depletion kit.

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7. A more detailed description of lateral tail vein injection and peripheral blood collection can be found in “Assessment of Young and Aged Hematopoietic Stem Cell Activity by Competitive Serial Transplantation Assays” [17]. References 1. Shlush LI, Zandi S, Mitchell A et al (2014) Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506:328 2. Bonnet D, Dick JE (1997) Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 3:730–737. https://doi.org/10. 1038/nm0797-730 3. Eppert K, Takenaka K, Lechman ER et al (2011) Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med 17:1086 4. Ng SWK, Mitchell A, Kennedy JA et al (2016) A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540:433–437. https://doi.org/10.1038/ nature20598 5. Costello RT, Mallet F, Gaugler B et al (2000) Human acute myeloid leukemia CD34+/ CD38- progenitor cells have decreased sensitivity to chemotherapy and Fas-induced apoptosis, reduced immunogenicity, and impaired dendritic cell transformation capacities. Cancer Res 60:4403–4411 6. Ishikawa F, Yoshida S, Saito Y et al (2007) Chemotherapy-resistant human AML stem cells home to and engraft within the bonemarrow endosteal region. Nat Biotechnol 25:1315–1321. https://doi.org/10.1038/ nbt1350 7. Boyd AL, Aslostovar L, Reid J et al (2018) Identification of chemotherapy-induced leukemic-regenerating cells reveals a transient vulnerability of human AML recurrence. Cancer Cell 34:483–498.e5. https://doi.org/10. 1016/j.ccell.2018.08.007 8. Gregory TK, Wald D, Chen Y et al (2009) Molecular prognostic markers for adult acute myeloid leukemia with normal cytogenetics. J Hematol Oncol 2:23. https://doi.org/10. 1186/1756-8722-2-23 9. Ley TJ, Miller C, Ding L et al (2013) Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368:2059–2074. https://doi.org/10.1056/ NEJMoa1301689

10. White BS, DiPersio JF (2014) Genomic tools in acute myeloid leukemia: from the bench to the bedside. Cancer 120:1134–1144. https:// doi.org/10.1002/cncr.28552 11. Welch JS, Ley TJ, Link DC et al (2012) The origin and evolution of mutations in acute myeloid leukemia. Cell 150:264–278. https://doi.org/10.1016/j.cell.2012.06.023 12. Woiterski J, Ebinger M, Witte KE et al (2013) Engraftment of low numbers of pediatric acute lymphoid and myeloid leukemias into NOD/SCID/IL2Rcgammanull mice reflects individual leukemogenecity and highly correlates with clinical outcome. Int J Cancer 133:1547–1556. https://doi.org/10.1002/ ijc.28170 13. Pabst C, Bergeron A, Lavallee V-P et al (2016) GPR56 identifies primary human acute myeloid leukemia cells with high repopulating potential in vivo. Blood 127:2018–2027. https://doi.org/10.1182/blood-2015-11683649 14. Cabezas-Wallscheid N, Eichwald V, de Graaf J et al (2013) Instruction of haematopoietic lineage choices, evolution of transcriptional landscapes and cancer stem cell hierarchies derived from an AML1-ETO mouse model. EMBO Mol Med 5:1804–1820. https://doi.org/10. 1002/emmm.201302661 15. Lehnertz B, Zhang YW, Boivin I et al (2017) H3(K27M/I) mutations promote contextdependent transformation in acute myeloid leukemia with RUNX1 alterations. Blood 130:2204–2214. https://doi.org/10.1182/ blood-2017-03-774653 16. Whitt MA (2010) Generation of VSV pseudotypes using recombinant DeltaG-VSV for studies on virus entry, identification of entry inhibitors, and immune responses to vaccines. J Virol Methods 169:365–374. https://doi. org/10.1016/j.jviromet.2010.08.006 17. Zhang YW, Cabezas-Wallscheid N (2019) Assessment of young and aged hematopoietic stem cell activity by competitive serial transplantation assays. Methods Mol Biol 2017:193–203. https://doi.org/10.1007/ 978-1-4939-9574-5_15

Chapter 20 Clonal Analysis of Patient-Derived Samples Using Cellular Barcodes Sabrina Jacobs, Leonid V. Bystrykh, and Mirjam E. Belderbos Abstract Cellular barcoding is a relatively simple method that allows quantitative assessment of the clonal dynamics of normal, nonmalignant hematopoietic stem cells and of leukemia. Cellular barcodes are (semi-)random synthetic DNA sequences of a fixed length, which are used to uniquely mark and track cells over time. A successful barcoding experiment consists of several essential steps, including library production, transfection, transduction, barcode retrieval, and barcode data analysis. Key challenges are to obtain sufficient number of barcoded cells to conduct experiments and reliable barcode data analysis. This is especially relevant for experiments using primary leukemia cells (which are of limited availability and difficult to transduce), when studying low levels of chimerism, or when the barcoded cell population is sorted in different smaller subpopulations (e.g., lineage contribution of normal hematopoietic stem cells in murine xenografts). In these settings, retrieving accurate barcode data from low input material using standard PCR amplification techniques might be challenging and more sophisticated approaches are required. In this chapter we describe the procedures to transfect and transduce patient-derived leukemia cells, to retrieve barcoded data from both high and low input material, and to filter barcode data from sequencing noise prior to quantitative clonal analysis. Key words Barcode, Clone, Sequencing, Clonal analysis, Leukemia

1

Introduction The progression, chemotherapeutic resistance, and relapse of leukemia are thought to develop through a process of clonal selection and evolution [1]. Hematopoietic stem or progenitor cells can acquire genomic aberrations, which might alter essential cell functions, and develop into a wide variety of genetically and phenotypically distinct clones with different growth properties and chemotherapeutic sensitivity [2, 3]. The relapsing clone is often already present as a minor clone at diagnosis, with additional mutations upon relapse, suggestive of clonal evolution [4–7]. Most studies on the clonal evolution of leukemia rely on sequencing of naturally occurring genomic aberrations in bulk diagnostic, remission, and relapsed patient-derived samples, and use complex

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mathematical models to retrospectively reconstruct the evolutionary trajectories of the retrieved clones [8, 9]. However, due to ongoing clonal evolution, it is challenging to define minor clones in leukemia using this approach [10]. In addition, this approach is barely applicable to normal, nonmalignant hematopoietic stem cells (HSCs), in which genomic aberrations hardly occur. Viral transduction, i.e., the integration of genetic material into the host cell genome, allows to study clonal dynamics of both leukemia cells and HSCs in a prospective manner [11]. One of the first developed techniques of this type used viral integration sites as unique clonal marks. Although this technique allows to track an unlimited number of clones, it requires fragmentation of genomic DNA (gDNA) and as a consequence it has a biased PCR amplification toward shorter DNA fragments, which hampers accurate quantitative clonal analysis [12]. The integration of (semi-) random synthetic DNA sequences of fixed length (i.e., barcodes) into these vectors resulted in more accurate quantification of clones [13–15]. Cellular barcoding has been shown to be of value in the study of both normal, nonmalignant HSCs and leukemia cells [14, 16–21]. However, as long as barcode libraries are generated in a probabilistic manner (e.g., random barcode design and mixed library pool of unknown composition and size), cellular barcoding is prone to problems of correct identification of library size and content [11]. In addition, the identification of barcodes is obscured by accompanying PCR and sequencing errors. As a result, the actual number and identity of barcodes in a library can differ substantially from what is approximated. Therefore, barcode libraries should be thoughtfully designed and validated, with accurate discrimination between “true” barcodes and noise [11, 22]. In the future, synthetic, high-throughput barcode library production using robotics may provide a better strategy for barcode library production, as it allows for the production of large numbers of individual barcodes which can be pooled into libraries of certain size, composition, and complexity (i.e., total number of barcodes). The continuously expanding genome editing toolbox [23] allows for novel possibilities to optimize the barcoding technique and to address some of its disadvantages. For instance, currently, the barcode composition of a given sample can only be determined retrospectively by PCR and sequencing, and sorting of individual barcode clones is not (yet) possible. Furthermore, cellular barcoding requires in vitro culture of target cells, which is an extra bottleneck that may result in the loss of clonogenic cells. In addition, the potential risks of lentiviral transduction and integration of (nonfunctional) barcode DNA into the host cell hamper the use of barcoding in humans. A well-established, alternative method for low-resolution in vivo lineage tracking is Cre-LoxP switching color cassettes, which uses combinations of fluorescent proteins as clonal markers

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[24, 25]. This method has the advantage that it can be directly applied in transgenic models using (tissue-specific) inducible Cre recombinase, and allows for sorting of individual clones [25]. However, viral transduction is still required in patient-derived material, and analysis of quantitative clonal dynamics is limited due to recombination bias and limited number of distinguishable color combinations [24–26]. A second, largely similar method replaced these color cassettes by DNA sequences [27]. Although individual clones can no longer be sorted, it increases the number of traceable clones. However, as by definition the library composition remains unknown and frequently redundant, it is still difficult to discriminate between noise and “true” barcodes. In addition, different clones can be potentially marked with identical barcode combinations [26]. A third approach is based on CRISPR-Cas9 technology, which uniquely barcodes individual cells by introducing indels (“scars”) in targeted regions of the genome or in synthetic DNA sequences (e.g., GFP-repeat) via single-guided RNA [28–30]. “Scars” are first amplified by PCR followed by deep sequencing, and unique scars are then defined by the introduced insertions and deletions (indels; substitutions are a result of PCR or sequencing errors). Applying this method to study the development of zebrafish shows that— although limited—thousands of scars can be generated, with only a few clusters of scars (i.e., sharing specific ancestral indels) that contribute to the development of specific organs [29, 30]. The number of scars can be increased using a system that introduces scars into the guide RNA [31]. However, bias in the introduction of specific scars and reconstruction of the clonal signature faces a problem of low complexity and redundancy of scars. Altogether, although these recently suggested methods definitely look promising, considerable improvements are still needed to increase their efficiency and practicality. In the future, the abovementioned methods may allow modification of the barcoding method, to overcome some of its disadvantages, and to perform even more robust, quantitative clonal tracking. Considerations for barcode design, barcode library preparation, and barcode data analysis have been discussed extensively elsewhere [11, 32]. Therefore, their essential features are only briefly mentioned here. In this chapter, we will focus on the technical procedures to introduce barcodes into target cells, specifically into patient-derived leukemia cells, and to retrieve barcode data, which can be especially challenging from low input material. Figure 1 represents a flowchart of the steps described in this chapter.

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Optional:

Library production

Primograft

Transfection

Patient-derived target cells

Confirm transduction efficiency Optional:

FACS gDNA isolation High input (≥106 cells)

Low input (i.e. blood)

Xenograft

Standard PCR

Nested PCR

No/faint band Clear band

Clear band Pool samples + clean-up Bioanalyzer Illumina HiSeq2500

Fig. 1 Flowchart of a cellular barcoding experiment. After producing and validating the barcode library, the first step is to produce virus. Prior to transducing patient-derived leukemia cells, it is recommended to first confirm the transduction efficiency of the virus using a cell line. When this is confirmed, patient-derived leukemia cells are thawed and directly transduced. Since patient-derived leukemia cells are limited in number and difficult to transduce, they are often first transplanted and expanded in mice (“primograft”). When these mice develop leukemia, increased numbers of patient-derived cells can be harvested and transduced. The resulting barcoded cells—sorted or unsorted—can be used for in vitro or in vivo clone-tracking experiments. The

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Materials Transfection

1. 70% Ethanol in water. 2. HEK293FT cell line. 3. HEK293FT culture medium: DMEM supplemented with 10% heat-inactivated FCS and 1% penicillin and streptomycin. 4. Serum-free expansion medium (SFEM: Iscove’s MDM, bovine serum albumin, recombinant human insulin, iron-saturated human transferrin, 2-mercaptoethanol, best purchased from a specialized supplier), supplemented with 10% heat-inactivated FCS and 1% penicillin and streptomycin. 5. 0.4% Trypan blue solution. 6. 0.1% Gelatin: Dissolve 0.5 g of gelatin type A in 500 mL MilliQ water and autoclave. Cool down before use. 7. Dulbecco’s PBS. 8. 0.05% Trypsin-EDTA. 9. Opti-MEM® I reduced serum medium. 10. Packaging plasmid: pCMV Δ8.91. 11. Envelope plasmid: VSV-G. 12. Vector construct (pEGZ2 B322 barcode library). 13. FuGENE® HD transfection reagent. 14. Disposables: T75 culture flasks, 15 mL collection tubes, 50 mL collection tubes, siliconized Eppendorf tubes, 20 mL syringes, 0.45 μm Millex HV low-protein-binding filters and cryovials. 15. Instruments: Hemocytometer, centrifuge, autoclave, vortex, and ML2-level cell culture facility.

2.2

Transduction

1. 70% Ethanol in water. 2. SupB15 cell line (ATCC®). 3. Patient-derived progenitor B-cell acute lymphoblastic leukemia cells (B-ALL). 4. SupB15 culture medium: RPMI 1640 medium supplemented with 10% heat-inactivated FCS and 1% penicillin and streptomycin.

ä Fig. 1 (continued) first step towards assessing clonal complexity is the isolation of gDNA. Depending on the input material (high vs. low), different gDNA isolation kits can be used. Barcode sequences are amplified by standard PCR, which is confirmed on an agarose gel. Samples that show no band or a faint band can be repeated, or subjected to nested PCR. Samples that show a clear band can be cleaned up and pooled together in batches of 200–300 samples. Quality of the sample is confirmed on the BioAnalyzer, after which the sample is sent for deep sequencing

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5. B-ALL culture medium: SFEM supplemented with 10% heatinactivated FCS, 1% penicillin and streptomycin, 100 ng/mL human recombinant thrombopoietin (TPO), 10 ng/mL human recombinant IL-7, 20 ng/mL human recombinant Fms-related tyrosine kinase 3 ligand (FLT-3 L), and 50 ng/ mL human recombinant stem cell factor (SCF). 6. 0.4% Trypan blue solution. 7. RetroNectin® (Takara) in PBS at a final concentration of 0.025 mg/mL. Stock of 2.5 mg is dissolved in a total volume of 100 mL PBS. Prepare aliquots and store at
20  C. 8. Propidium iodide (PI). 9. SFEM supplemented with 1% penicillin and streptomycin. 10. SFEM supplemented with 10% heat-inactivated FCS and 1% penicillin and streptomycin. 11. Dulbecco’s PBS. 12. Dulbecco’s PBS supplemented with 0.2% or 2.0% bovine albumin fraction V (7.5% stock solution). 13. Dulbecco’s PBS supplemented with 20%, 10%, and 5% heatinactivated FCS. 14. Disposables: T75 culture flasks, 6-well culture plates, 12-well culture plates, 15 mL collection tubes, 50 mL collection tubes, FACS tubes, parafilm, and cell scrapers. 15. Instruments: Hemocytometer, centrifuge, flow cytometer with blue (488 nm, eGFP) and yellow (561–568 nm, PI) laser, and ML2-level cell culture facility. 2.3

DNA Isolation

1. 70% Ethanol in water. 2. Molecular BioProducts™ decontaminant.

RNase

away™

surface

3. Dulbecco’s PBS. 4. Ethanol absolute (96–100%). 5. DNA isolation kit for high input material: DNeasy Blood and Tissue (Qiagen): (a) Add the appropriate amount of ethanol absolute (96–100%) to buffer AW1 and AW2 to obtain the working solution. 6. DNA isolation kit for low input material: QIAamp DNA micro kit (Qiagen): (a) Add the appropriate amount of ethanol absolute (96–100%) to buffer AW1 and AW2 to obtain the working solution.

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7. Disposables: Sterile 1.5 mL Eppendorf collection tubes for elution. 8. Instruments: Heat block, microcentrifuge, and Nanodrop 2000 spectrophotometer. 2.4

PCR

1. Ethanol 70% in water. 2. RNase away™ surface decontaminant. 3. Oligonucleotide primers: Primers are diluted to a stock concentration of 100 μM in nuclease-free H2O. Primers are stored at
20  C: (a) Outer primer set flanking the barcode sequence and the priming region of the second set of primers. (b) Indexed forward primer and the universal reverse primer flanking the barcode sequence. 4. DreamTaq Green PCR Master Mix (2), including nucleasefree water. 5. Disposables: Sterile 1.5 mL Eppendorf tubes and sterile PCR strips. 6. Instruments: UV3 HEPA PCR workstation and thermal cycler with heated lid.

2.5

Agarose Gel

1. TAE buffer (50): Dissolve 242 g of Tris base in 700 mL of Milli-Q water. Add 57.1 mL of acetic acid and 100 mL of EDTA (0.5 μM). Add up to 1 L with Milli-Q water. 2. DNA Ladder Mix, ready to use (100–10,000 bp). 3. Instruments: Gel tray, well combs, electrophoresis tank including power supply, and UV transilluminator.

2.6 PCR Product Purification

1. QiaQuick PCR purification kit (Qiagen): (a) Add the appropriate amount of ethanol absolute (96–100%) to buffer PE to obtain the working solution. 2. Disposables: Sterile 1.5 mL Eppendorf collection tubes for elution. 3. Instruments: Microcentrifuge.

2.7 PCR Product Quality Control

1. Qubit® dsDNA HS assay kit (Thermo Fisher Scientific). 2. Agilent High Sensitivity DNA kit (Agilent Technologies). 3. Disposables: Qubit™ Assay Tubes (Thermo Fisher Scientific). 4. Instruments: Qubit® 2.0 Fluorometer, Agilent Chip Priming Station, IKA model MS3 vortex mixer, and Agilent 2100 Bioanalyzer System.

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Methods

3.1 Considerations for Barcode Library Production

Although the concept of barcode design and library production was discussed extensively in previous publications, here we would like to emphasize a few of it’s essential features [11, 32]. First, any barcoding method relies on some combinatorial principles. For instance, any semi-random barcode is a stretch of DNA that consists of variable and fixed nucleotides. Accordingly, the size of the barcode library (i.e., its maximum complexity) is limited by the theoretical number of combinations, which is proportional to the number of variable nucleotide positions in the barcode. It is important to realize that the size of any experimental barcode library is smaller than its theoretical maximal size, as it is always a subset of the combinations. Accordingly, the subset defines the true library size and determines the distance between barcodes, which can be validated experimentally [14]. In addition, every barcode in the library should be of equal probability of occurrence. Most approaches for lineage tracking, like cellular barcoding, rely on random processes. We assume random synthesis and transduction of barcode DNA sequences, random introduction of scars, and random generation of color combinations in alternative lineage tracking approaches. Although randomness was confirmed for cellular barcoding, in reality we often face the fact of nonrandomness (e.g., Cre-LoxP recombination bias and nonrandom introduction of scars) [14, 16, 26, 30]. Such nonrandomness severely decreases the complexity of the library, and should be taken into account when reporting the final results.

3.2

Note that all steps are performed in a ML2-level laboratory and that a GMO permit is required. We previously reported protocols for making retro- and lentiviral barcode libraries based on semi-random barcode tags integrated into the viral vector backbone [11, 32]. Because of the pitfalls mentioned above, we advocate to use a library of known size, content, and complexity. To generate such a library, we subcloned individual barcode combinations as separate E. coli preps, and collected approximately 800 barcoded vectors in the freezer. Depending on the experimental aim, these barcodes can be pooled in equimolar ratios to libraries of the desired complexity. After validating the barcode library, the library needs to be incorporated into viral particles, which are used to transduce patient-derived (leukemia) cells. The transduction efficiency of these viral particles depends on multiple factors. Obviously, the produced virus should have a suitable envelope protein and a sufficient viral titer. The use of healthy, low-passage HEK293FT cells improves the viral titer. The transduction efficiency can be further facilitated by improving cell-virus contact (e.g.,

Transfection

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RetroNectin®, polybrene, and/or spinfection) and by removing viral inhibitors (e.g., pre-coating of culture plates with virus or use of purification kits). 3.2.1 Day-7: Thaw HEK293FT Cells

1. Pre-warm culture medium. 2. Thaw cells rapidly, and resuspend in 10 mL of culture medium. 3. Centrifuge cells at 450  g for 5 min at 5  C and discard the supernatant. 4. Resuspend cells in culture medium and count the number of living cells using trypan blue. 5. Grow cells at a concentration of 0.25  106 cells/mL in 10 mL of culture medium in a T75 culture flask. 6. Incubate at 37  C and 5% CO2. 7. Passage cells 2–3 times a week. 8. To this aim, remove the culture medium from each T75 culture flask and gently rinse with 10 mL of PBS. 9. Remove PBS and add 2 mL of trypsin-EDTA (0.05%) to the bottom of the standing T75 culture flask. Gently swirl to cover the attached HEK293FT cells and directly remove trypsin. 10. Incubate the T75 culture flask at 37  C and 5% CO2 for 5 min to allow the cells to detach. 11. Resuspend the cells in 10 mL of culture medium and transfer to a 50 mL tube. Note that multiple T75 cell culture flasks can be combined. 12. Centrifuge cells at 450  g for 5 min at 5  C and discard supernatant. 13. Resuspend the cells in 10 mL of culture medium and count the number of living cells using trypan blue. 14. Grow cells at a concentration of 0.25  106 cells/mL in 10 mL of culture medium in a T75 culture flask. 15. Incubate at 37  C and 5% CO2.

3.2.2 Day 0: Plate HEK293FT Cells

1. Pre-coat the required number of T75 culture flasks with 10 mL of 0.1% gelatin for 2 h at 37  C (see Note 1). Every T75 culture flask will yield approximately 5 mL of virus. 2. In the last ~60 min before starting transfection, collect HEK293FT cells. 3. To this aim, first remove the culture medium from each T75 culture flask and gently rinse with 10 mL of PBS. 4. Remove PBS and add 2 mL of trypsin-EDTA (0.05%) to the bottom of the standing T75 culture flask. Gently swirl to cover the attached HEK293FT cells and directly remove trypsin.

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5. Incubate the T75 culture flask at 37  C and 5% CO2 for 5 min to allow the cells to detach. 6. Resuspend the cells in 10 mL of culture medium and transfer to a 50 mL tube. Multiple T75 culture flasks can be combined. 7. Centrifuge cells at 450  g for 5 min at 5  C and discard supernatant. 8. Resuspend the cells in 10 mL of culture medium and count the number of living cells using trypan blue. 9. Dilute the cells to a concentration of 0.15  106 cells/mL in culture medium. 10. Next, remove the gelatin from the T75 culture flasks. 11. Rinse the T75 culture flasks with 10 mL of PBS. 12. Grow the cells at a concentration of 0.15  106 cells/mL in 10 mL of culture medium in the gelatin-coated T75 culture flasks. 13. Incubate for 2 days at 37  C and 5% CO2 to allow the cells to attach. 3.2.3 Day 2: Transfection

1. Prior to starting the transfection, let Opti-MEM™ and FuGENE® reach room temperature. 2. For n  T75 culture flasks, label n x siliconized Eppendorf tubes with “tube 1.” Label one siliconized Eppendorf tube with “tube 2” (see Note 2). 3. Add 400 μL of Opti-MEM™ to each “tube 1.” 4. Prepare “tube 2” for n  T75 culture flasks according to Table 1. 5. Gently vortex “tube 2” and transfer the corresponding volume for one T75 culture flask of “tube 2” to “tube 1.” 6. Vortex FuGENE® and add 21 μL directly into the medium of each “tube 1”. 7. Mix gently by ticking (not vortexing) and incubate at room temperature for 15 min to allow the transfection precipitates to be formed. Table 1 Preparations of “tube 2.” Required amounts to transfect one T75 culture flask Opti-MEM™

100 μL

Packaging plasmid (pCMV Δ8.91)

3 μg

Envelope plasmid (VSV-G)

0.7 μg

Vector construct (pEGZ B322 barcode library)

3 μg

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8. Dropwise and gently transfer the content of one “tube 1” to each T75 culture flask. 9. Gently swirl and incubate overnight at 37  C and 5% CO2. 3.2.4 Day 3: Medium Change

1. Carefully replace the medium in the T75 culture flask by 7.5 mL of serum-free medium (e.g., SFEM) for the target cells supplemented with 1% penicillin and streptomycin. 2. Incubate overnight at 37  C and 5% CO2.

3.2.5 Day 4: Harvest Virus

1. Carefully collect a maximum of 15 mL virus supernatant from every T75 culture flask into a 50 mL tube. 2. To this, collect 15 mL of virus in a 20 mL syringe and filter virus through a 0.45 micron Millex-HV low-protein-binding filter into a new, clean 50 mL tube. Optional: In case the HEK293FT cells detach, it is recommended to centrifuge at 450  g for 5 min at room temperature to prevent clogging of the filter. 3. Aliquot filtered virus into cryovials and store at
80  C.

3.3 Transduction of a Cell Line: Quality Control of Produced Virus

3.3.1 Day-7: Thaw SupB15 Cells

Note that all steps are performed in a ML2-level laboratory and that a GMO permit is required. It is recommended to confirm the quality of the virus using a representative cell line (e.g., SupB15 B-acute lymphoblastic leukemia cell line in case of experiments with primary B-ALL cells) prior to transducing patient-derived target cells. Irrespective of the cell line used, the cells should be low in passage and recovered from thawing before transduction. 1. Pre-warm culture medium. 2. Thaw cells rapidly and collect in 10 mL of culture medium. 3. Centrifuge at 450  g for 5 min at 5  C and discard the supernatant. 4. Resuspend the cells in culture medium and count the number of living cells using trypan blue. 5. Grow cells at a concentration of 0.25  106 cells/mL in 15 mL of culture medium in a T75 culture flask. 6. Incubate overnight at 37  C and 5% CO2.

3.3.2 Day-6: Refresh Culture Medium

1. Collect the SupB15 cells in a 50 mL tube. 2. Rinse the T75 culture flask with 5 mL culture medium to collect the remaining cells and repeat if required. 3. Centrifuge at 450  g for 5 min at 5  C and discard the medium.

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4. Resuspend the cells in culture medium and count the number of living cells using trypan blue. 5. Grow cells at a concentration of 0.25  106 cells/mL in 15 mL of culture medium in a T75 culture flask. 6. Passage cells 2–3 times a week at a concentration of 0.25  106 cells/mL. 3.3.3 Day 0: Pre-coat Wells with RetroNectin®

1. Add 1 mL of RetroNectin® per well to 6 wells of a 12-well culture plate. The other 6 wells will not be used. 2. Seal the plate with parafilm and incubate overnight at 4  C.

3.3.4 Day 1: Transduction

1. Thaw the required volume of virus (see below). 2. Remove RetroNectin® from the 12-well culture plate and add 1 mL of PBS supplemented with 2% bovine albumin to each of the 6 wells to prevent nonspecific binding (see Note 3). 3. Incubate for 30 min at room temperature. 4. Remove PBS supplemented with 2% bovine albumin and rinse wells with PBS. 5. Add 500 μL, 250 μL, 125 μL, 62.5 μL, 31.3 μL, or 15.6 μL of virus to the wells and add up to 500 μL with viral collection medium (i.e., SFEM supplemented with 0.1% penicillin and streptomycin). 6. To promote binding of the virus to the RetroNectin® coating, centrifuge at 1000  g for 45 min at room temperature (acceleration set at 1, brake set at 0). 7. Incubate for another 4 h at 37  C and 5% CO2. 8. In the last ~60 min, before continuing with the transduction protocol, collect SupB15 cells: 9. To this aim, first collect cells into 15 mL falcon tubes, and centrifuge at 450  g for 5 min at 5  C. 10. Discard supernatant and resuspend cells in 10 mL culture medium. Reduce the volume if low cell numbers are expected. 11. Count the number of living cells using trypan blue and dilute the cells to a concentration of 2.5  106 cells/mL in culture medium. 12. Remove the viral supernatant from the 12-well culture plate. 13. Gently rinse each of the 6 wells with 1 mL PBS supplemented with 2% bovine albumin. 14. Replace the PBS supplemented with 2% bovine albumin in the 12-well culture plate by 800 μL of SupB15 cells (2  106 cells/ well). Prevent the wells from drying out during this step.

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15. Perform a spinfection by centrifuging the plate at 900  g for 45 min at room temperature (acceleration set at 1, brake set at 0). 16. Incubate overnight at 37  C and 5% CO2. 3.3.5 Day 2: Remove Virus

1. Gently collect the transduced SupB15 cells from each of the 6 wells of the 12-well culture plate and transfer to individual 50 mL tubes. 2. Add 1 mL of culture medium to each of the 6 wells of the 12-well culture plate and gently detach cells from RetroNectin® by scraping. Transfer the cells to the corresponding 50 mL tube and check whether all SupB15 cells are collected. Repeat if required. 3. Centrifuge the cells at 450  g for 5 min at room temperature and discard supernatant. 4. Resuspend the cells in 50 mL of PBS supplemented with 20% FCS, centrifuge at 450  g for 5 min at room temperature, and discard supernatant. Repeat this for PBS supplemented with 10% FCS and 5% FCS. 5. Resuspend the cells in culture medium and count the number of living cells using trypan blue. 6. Grow the cells at a concentration of ~1  106 cells/mL in a suitable flask or culture plate. 7. Incubate overnight at 37  C and 5% CO2.

3.3.6 Day 3: Determine Transduction Efficiency

1. Resuspend the transduced cells (keep cells transduced with different viral volumes separate) and transfer 10 μL to Eppendorf tube to count the number of living cells using trypan blue. 2. Transfer 0.2–0.5  106 SupB15 cells to a FACS tube, centrifuge at 450  g for 5 min at room temperature, and discard supernatant. 3. Wash the cells with 3 mL of PBS supplemented with 0.2% bovine albumin, centrifuge at 450  g for 5 min at room temperature, and discard supernatant. 4. Resuspend the cells in 200 μL PBS supplemented with 0.2% bovine albumin and add 100 μL PI to discriminate between cells that are alive or dead. 5. Measure samples at the flow cytometer and determine the percentage of GFP+ PI
cells. Use GFP
and GFP+ cells to set the gates.

3.4 Transduction of Patient-Derived B-ALL Cells

When the quality of the produced virus is validated, and a sufficient transduction efficiency of at least 10% on a cell line is reached, the virus can be used to transduce patient-derived B-ALL cells. Here, it

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is important to optimize the transduction efficiency, by titrating the amount of virus added to the target cells. On the one hand, the transduction efficiency should be sufficiently high to produce enough barcoded cells for experimental purposes. On the other hand, if the transduction efficiency is too high, this will increase the probability of having multiple barcode integrations in a single cell. This probability should be assessed both theoretically and experimentally. Theoretically, viral transduction follows a Poisson distribution, allowing to assess the probability of multiple integrations for any given transduction efficiency [33]. In our experiments, we generally aim for a transduction efficiency of ~10%, resulting in 0.5% chance of integrating multiple barcode vectors into one cell. However, in practice, the transduction efficiency of patient-derived cells may be substantially lower, as it is difficult to maintain the viability of patient-derived B-ALL cells in vitro. As a consequence, it is challenging to obtain a sufficient yield of barcoded patientderived B-ALL cells. Therefore, the time of in vitro culture should be limited and it might be necessary to expand the number of patient-derived B-ALL cells via transplantation into sublethally irradiated (1.0 Gy) Nod/SCID/IL2Rγ
/
mice, either before or after barcoding [16, 21]. 3.4.1 Day 0: Pre-coat Wells with RetroNectin®

1. Add 2 mL of RetroNectin® to each well of a 6-well culture plate (see Note 3). Prepare three plates in total. 2. Seal plate with parafilm and incubate overnight at 4  C.

3.4.2 Day 1: Thaw and Transduce Patient-Derived B-ALL Cells

1. Thaw the required volume of virus. 2. Remove RetroNectin® from the 6-well culture plate and add 2 mL of PBS supplemented with 2% bovine albumin to each well to prevent a specific binding. 3. Incubate for 30 min at room temperature. 4. Remove PBS supplemented with 2% bovine albumin and rinse each well with 2 mL PBS. 5. Add 1.5 mL of virus to each well of one 6-well culture plate, add 1.0 mL or 0.5 mL of virus to each well of the other 6-well cell culture plates, and add up to 1.5 mL with SFEM supplemented with 0.1% penicillin and streptomycin (see Note 4). 6. To promote binding of the virus to the RetroNectin® coating, centrifuge at 900  g for 45 min at room temperature (acceleration set at 1, brake set at 0). 7. Incubate for another 4 h at 37  C and 5% CO2. 8. In the last ~60 min, before continuing with the transduction protocol, thaw patient-derived B-ALL cells. 9. To this aim, first pre-warm B-ALL culture medium.

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10. Rapidly thaw patient-derived B-ALL cells and collect in 10 mL of B-ALL culture medium without added cytokines. 11. Centrifuge cells at 500  g for 10 min at 5  C and discard supernatant. 12. Resuspend cells in 10 mL B-ALL medium without added cytokines and count cells using trypan blue. 13. Centrifuge the cells at 500  g for 10 min at 5  C and discard supernatant. 14. Resuspend cells in B-ALL cell culture medium (concentration: 1.0  106–2.0  106 cells/mL). 15. Remove viral supernatant from the 6-well culture plates. 16. Gently rinse each well of the 6-well culture plate with 2 mL PBS supplemented with 2% bovine albumin. 17. Replace the PBS supplemented with 2% bovine albumin by 2 mL (¼2.0–4.0  106 cells/well) of patient-derived B-ALL cells. Prevent the wells from drying out during this step. 18. Perform a spinfection by centrifuging the cells at 900  g for 45 min at room temperature (acceleration set at 1, brake set at 0). 19. Incubate overnight at 37  C and 5% CO2. 3.4.3 Day 2: Remove Virus

Remove the virus as described in Subheading 3.3.5. As patientderived B-ALL cells are difficult to maintain in vitro, the number of viable cells will decrease substantially during the transduction procedure. Cells can be cultured at a concentration between 0.5  106 and 1.0  106 cells/mL, in a suitable culture flask or dish.

3.4.4 Day 3: Determine Transduction Efficiency

Determine the transduction efficiency as described in Subheading 3.3.6. After confirming the transduction efficiency by flow cytometry, barcoded, live cells can be sorted for GFP+ PI
. Depending on the experimental question, sorted/unsorted barcoded cells can be transplanted into sublethally irradiated Nod/SCID/IL2Rγ
/
mice.

3.5 Barcode Retrieval by Next-Generation Sequencing

The clonal dynamics of the transplanted barcoded cells in vivo can be assessed by barcode analysis on longitudinally acquired blood samples. However, several factors may limit the number of GFP+barcoded cells available for analysis. First, nonterminal blood collection from mice is limited to approximately 200 μL once every 3–4 weeks [34]. Furthermore, especially at early time points, the levels of human GFP+ chimerism may be low. Finally, in certain experiments, one may want to sort different hematopoietic cell populations from either blood or bone marrow, which might result in limited cell numbers as well. Low cell numbers, and as a consequence low copy numbers of the barcode sequence, might hamper

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successful barcode retrieval by high-throughput sequencing. Therefore, efficient strategies for DNA isolation and PCR amplification are needed, as described below. 3.5.1 Isolation of Genomic DNA

Successful barcode retrieval depends on the copy number of the barcode sequence and on the quality of the isolated gDNA. There are a wide variety of kits to isolate high-quality gDNA, which are constantly improving. The selected kit should be applicable to the number of cells from which gDNA can be isolated. In our experimental design, we use the Qiagen DNeasy Blood and Tissue for high input material (1  106 cells). To prevent dilution of the barcode sequence, we use the Qiagen QIAamp DNA micro kit with a smaller elution volume for low input material (i.e., blood samples and samples with T, C > G, T > A, T > C, and T > G (mutations referred using the pyrimidine base of the Watson-Crick base pair). Single base substitutions are commonly subclassified into 96 categories by considering the bases that are immediately adjacent 50 and 30 to each somatic mutation. In the past seven years, additional and extended classifications have been proposed for substitutions (SBSs), doublet base substitutions (DBSs), and insertions and deletions (IDs) [18]. These classifications have been used for constructing the matrix V and decomposing this matrix to identify mutational signatures and the activities of each signature [2]. Decomposition of the matrix V results into two nonnegative matrices W and H. The matrix W contains the

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mutational signatures observed in the analyzed cancers, while the matrix H reflects the number of mutations attributed to each signature in each cancer genome. The first analysis of mutational signatures focused on 21 whole-genome sequenced breast cancers and the analysis revealed five SBS signatures in these cancers. In this chapter, we will use the data from these 21 whole-genome sequenced breast cancers to demonstrate how to extract mutational signatures. Note that the presented approach is applicable to any standard whole-genome or whole-exome sequenced samples that have undergone identification of somatic mutations. Our original computational framework for analysis of mutational signatures [2] was developed in MATLAB and required substantial subject matter and computational expertise. Subsequently, additional software packages were developed to meet the needs of bioinformaticians more familiar with other programming languages (Table 1). Some examples include: Emu [19] which uses expectation maximization algorithm mathematically analogous to NMF; SigneR [20] and SignatureAnalyzer (https://software. broadinstitute.org/cancer/cga/msp) written in R and using Table 1 List of computational tools for de novo extraction of mutational signatures Tool

Platform

SigProfilerExtractor Python PyPI R wrapper

Engine

Links

NMF

Python Source: https://github.com/ AlexandrovLab/SigProfilerExtractor R Source: https://github.com/AlexandrovLab/ SigProfilerExtractorR Documentation: https://osf.io/t6j7u/wiki/home/

EMu

Command Line EM

https://www.sanger.ac.uk/science/tools/emu

SomaticSignatures

R-Bioconductor NMF

http://bioconductor.org/packages/release/bioc/ html/SomaticSignatures.html

Moftools

R-Bioconductor NMF

https://bioconductor.org/packages/release/bioc/ html/maftools.html

SigneR

R-Bioconductor Bayesian https://bioconductor.org/packages/release/bioc/ NMF vignettes/signeR/inst/doc/signeR-vignette.html

MutSpec

Galaxy

NMF

https://github.com/IARCbioinfo/mutspec

Sigminer

R

NMF

https://cran.r-project.org/web/packages/ sigminer/index.html

MutationPatterns

R-Bioconductor NMF

SignatureAnalyzer

R

https://bioconductor.org/packages/release/bioc/ html/MutationalPatterns.html

Bayesian https://software.broadinstitute.org/cancer/cga/ NMF msp

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Bayesian NMF; MutSpec [21] implemented on the Galaxy platform with a standard NMF algorithm; and SomaticSignatures [22], Sigminer [23], Matools [24], and MutationalPatterns [25] developed in R and based on a standard NMF algorithm. Further, we recently developed a next-generation Python tool (with an available R wrapper), SigProfilerExtractor, for deciphering mutational signatures that automates the majority of the analysis processes. Here, we provide a detailed set of instructions for using this Python tool and demonstrate its ability to analyze the somatic mutational data from 21 breast cancer genomes [26]. Specifically, in this chapter, we demonstrate the signature extraction procedure using both the Python and R implementation of SigProfilerExtractor. We provide both a quick start and a detailed protocol for extracting mutational signatures. The quick start guide is suitable for users who are more interested in examining the biological results, while the detailed protocol describes all possible types of analysis one can perform.

2

Downloading the Input Data All examples in this chapter rely on somatic mutational data from 21 breast cancer genomes [26]. These data can be freely downloaded from our FTP site. We provide two types of data for the examples included in the chapter: (1) variant call format (VCF) files containing the set of somatic mutations found in the 21 breast cancer genomes; (2) mutational matrix of the single base substitutions identified in the 21 breast cancer genomes. Note that SigProfilerExtractor allows analyzing both formats. The VCF format is standardly produced by most mutation calling algorithms. A mutational matrix describes the mutational patterns of a set of cancer genomes for a predefined mutational classification [18]. In a mutational matrix, a column indicates a sample and a row reflects a specific mutation type. Each cell reflects the number of somatic mutations of a specific type found in a sample. The number of rows is determined by the mutational classification. For example, for SBS96, there are 96 mutation types of single base substitutions. A mutational matrix’s rows refer to the different mutation types and, thus, data using SBS96 has 96 rows. In contrast, for SBS1536, there are 1536 mutation types of single base substitutions and, as such, 1536 rows. The matrix must be stored as a tab delimited text file in order to be recognized by SigProfilerExtractor. Note that SigProfilerExtractor internally transforms a set of VCF files into a mutational matrix prior to analyzing them. To download these data, one can either go to ftp://alexandrovlab-ftp.ucsd.edu/pub/tools/ SigProfilerExtractor/Example_data/ and download the “21BRCA.zip” file or type the command below in bash on OS X or Unix systems:

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$ wget ftp://alexandrovlab-ftp.ucsd.edu/pub/tools/ SigProfilerExtractor/Example_data/21BRCA.zip

After the 21BRCA.zip file is downloaded, it has to be unzipped. The unzipped 21BRCA folder contains the 21BRCA. txt file (i.e., a mutational matrix defined using SBS96 classification) and 21BRCA_vcf subfolder which contains 21 VCF files (one per each breast cancer sample). Here, the 21BRCA.txt file serves as an example matrix format and the 21BRCA_vcf folder serves as the example VCF format for the later part of this book chapter.

3

Quick Start Guide for Mutational Signatures Analysis with SigProfilerExtractor This quick start guide provides a short but essential description of running SigProfilerExtractor. This is suitable for users who want to directly analyze their data without knowing much about SigProfilerExtractor’s functionality or advanced features. Extensive explanation of these advanced features is included later in the chapter. The provided quick start guide demonstrates analysis of mutational signatures for 21 breast cancer genomes [26].

3.1

Prerequisites

3.2 Installing SigProfilerExtractor

l

Mac OS, Linux, or Windows operating system.

l

Python 3.5 or later.

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Anaconda Python 3.7 (required for Windows; recommended for Mac OS and Linux (see Note 1).

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Open the anaconda prompt (Windows) or terminal (Mac/Unix/Linux).

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Inside terminal type the following command: $ pip install SigProfilerExtractor¼¼1.0.3

3.3 Installing a Reference Genome

Mutational signatures analysis of cancer genomes from standard VCF files requires installing the reference genome that was used for identifying somatic mutations in these cancer genomes. In the case of 21 breast cancers, we need to install the human reference genome build GRCh37. After opening a Python interpreter, type the following commands. This process may take a few minutes depending on your Internet connection. >>> from SigProfilerMatrixGenerator import install as genInstall

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>>> genInstall.install(’GRCh37’, ftp¼True)

3.4 Performing Signatures Extraction

First, place the downloaded 21BRCA_vcf folder (see Subheading 2, “Downloading the Input Data”) containing 21 VCF files in the working directory. In a Python interpreter, type the following commands to perform extraction of mutational signatures using between 1 and 10 signatures. Depending on your computer, this step may take a few minutes or a few hours. >>> from SigProfilerExtractor import sigpro as sig >>> sig.sigProfilerExtractor(’vcf’, ’results’, ’21BRCA_vcf’, startProcess¼1, endProcess¼10, totalIterations¼100)

3.5 Interpreting Signatures Extraction Results

4

After the analysis completes successfully, the results will be created in the folder named “results” (specified using the second parameter). The algorithm will first convert the VCF files into a mutational matrix and then decompose this matrix using different number of processes as specified. Specifically, SigProfilerExtractor will decompose the matrix between 1 signature (specify using the startProcess parameter) and 10 signatures (specify using the endProcess parameter). Each decomposition will be repeated 100 times as specified by the totalIterations parameter. SigProfilerExtractor will automatically select the optimal solution between 1 and 10 signatures and, subsequently, match the de novo extracted mutational signatures to the set of reference COSMIC signatures. There results for the SBS96 mutational classification can be found in the SBS96 directory (Fig. 1a). Statistics about each decomposition using between 1 and 10 signatures can be examined in the “All_solution_statistics. csv” file (Fig. 1a). The activities of the identified signatures (Fig. 1b) as well as the set of COSMIC mutational signatures found in these samples (Fig. 1c) can be found in the “Sugested_solution/Decomposed_solution” subdirectory.

Detailed Protocol of Mutational Signature Extraction Using SigProfilerExtractor

4.1 Software Implementation

SigProfilerExtractor is developed in Python by Alexandrov Lab at the University of California San Diego (UCSD). Python is a programming language offering multiple computational paradigms such as object-oriented, functional, and procedural programming. Python is also freely usable and distributable for personal, organizational, and commercial use. To allow maximum usability of the tool, an R wrapper has been provided for users who prefer working in an R environment. R is an open-source programming language widely adopted by computational biologists for analysis of

Fig. 1 Results generated by SigProfilerExtractor as a Decomposed Solution. (a) Files and folders generated in the results folder. The “SBS96” and “DBS78” folders contain results for the SBS96 and DBS78 mutational classifications, respectively. Inside the “SBS96” folder, the “Suggested_Solution” subfolder contains the information on the signatures detected in the samples. The COSMIC signatures and their activities on the sample are characterized in the files generated inside the “Decomposed_Solution” directory. (b) Activities of the COSMIC signatures in the examined samples (blue box) as shown in the file Decomposed_Solution_Activities_plot_SBS96.pdf (blue box in panel a). The numeric values of the activities of the COSMIC signature can be found in the file “Decomposed_Solution_Activities_SBS96.txt” (blue asterisk in panel a). (c) The set of COSMIC signatures found to be operative in the examined samples (red box) as shown in the file Decomposed_Solution_Signatures_plot_SBS96.pdf (red box in panel a). The numeric representations corresponding to the detected COSMIC signatures can be found in “Decomposed_Solution_Signatures_SBS96.txt” (red asterisk in panel a)

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biological data. SigProfilerExtractor is distributed under the permissive BSD-2 clause license allowing users to adopt and modify the tool with minimal restrictions. 4.2 Methods and Materials to Extract Signatures Using Python Platform

Mac/Unix/Linux/Windows.

4.2.1 Required Operating System 4.2.2 Required Tools

4.2.3 Software Installation

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Python: version 3.5 or later.

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Wget: version 1.9 or later.

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Anaconda Python 3.7 (required for Windows; recommended for Mac OS and Linux).

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SigProfilerExtractor: version 1.0.3.

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Reference Genomes: Installed through SigProfilerExtractor.

l

Additional dependencies will be downloaded and installed during the installation process of SigProfilerExtractor. 1. Open the anaconda prompt (Windows) or terminal (Mac/Unix/Linux). 2. Inside terminal type the following command: $ pip install SigProfilerExtractor¼¼1.0.3

The previous commands will automatically install all required packages. After SigProfilerExtractor’s installation is successfully completed, the reference genomes (for example: GRCh37) have to be added using the following commands: $ python >> > from SigProfilerMatrixGenerator import install as genInstall >> > genInstall.install("GRCh37", ftp ¼ True). For more details about installing other reference genomes, please refer to: https://osf.io/s93d5/wiki/1.%20Installation%20%20Python/

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Once all necessary tools are successfully installed, the user can perform signatures analysis. In principle, mutational signatures can be extracted using two types of input format: (i) a mutational matrix and (ii) a set of mutational catalogs supported by SigProfilerExtractor (e.g., a set of VCF files). Figure 2 illustrates the two input formats: a mutational matrix and a set of VCF files. Our detailed protocol demonstrates using both options. The focus of the analysis is on mutational signatures based on single base substitutions and their immediately 50 and 30 adjacent base (i.e., the SBS96 mutational classification [2]). The general function for extracting signatures using SigProfilerExtractor is provided below.

Fig. 2 Example input files supported by SigProfilerExtractor. (a) Twenty-one VCF files used in the provided examples provided. (b) A file reflecting the conversion of the twenty-one VCF into a mutational matrix based on the SBS96 classification of single base substitutions. (c) An example of the minimum information required for a VCF file to be processed by SigProfilerExtractor. Additional columns, consistent with the VCF version 4.2 specification, are also permitted

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sigProfilerExtractor (input_type, out_put, project, refgen¼"GRCh37", genome_build¼"GRCh37", startProcess¼1, endProcess¼10, totalIterations¼100, cpu¼1, mtype¼"default", exome¼False, gpu¼False).

Parameters are described in Box 1. Examples are provided below with specific arguments being passed to each parameter. Box 1 Description of SigProfilerExtractor’s Parameters in a Python Environment Arguments of sigProfilerExtractor function. These are the acceptable parameters that can be passed into the function call. Required Parameters: l

l

l

input_type: The type of input.

Type: string The type of input should be one of the following: – “vcf”: used for vcf format inputs. – “matrix”: used for .txt format inputs (example: tab separated file containing mutational catalog). out_put: The path of the output folder where the results will be generated. If the folder is not present, the folder will be created automatically. Type: string input_data: Name of the input folder (in case of “vcf” type input) or the input file (in case of “matrix” type input). The project file or folder should be inside the current working directory. For the “vcf” type input, the project has to be a folder which will contain the vcf files in vcf format or text formats. The “matrix” type projects have to be a file. Type: string Optional Parameters:

l

l

refgen: The name of the reference genome. This parameter is applicable only if the input_type is “vcf.” Available optional are “GRCh37,” “GRCh38,”“mm9,” and “mm10.” The default reference genome is “GRCh37.”

Type: string genome_build: The build or version of the reference signatures for the refgen. The default genome build is GRCh37. If the input_type is “vcf,” the genome_build automatically matches the input refgen value. Type: string (continued)

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Box 1 (continued) l startProcess: The minimum number of signatures to be extracted.

l

l

l

l

l

4.2.5 Extracting Mutational Signatures from a Matrix

Default: 1. Type: A positive integer endProcess: The maximum number of signatures to be extracted. Default: 10. Type: A positive integer totalIterations: The number of iterations to be performed to extract each number signature. Default: 8. Note: 8 iterations is used to save time for the initial test runs of the application however we recommend at least 100 iterations for reliable results. Type: A positive integer cpu: The number of processors to be used to extract the signatures. Default: 1 which will use all available processors. Type: An integer mtype: Defines the mutational contexts to be considered to extract the signatures. Current possible options are “SBS96,” “SBS192,” “SBS6144,” “SBS1536,” “DBS78,” and “ID83.” However, more options could be added in future. Multiple mutational contexts can be passed as the parameter separated by comma in a string. Default: "SBS96, DBS78, ID83” Type: A string exome: Defines if the exomes will be extracted. Default: False. Type: Boolean

From a Python session, signatures can be extracted using the following commands: >>> from SigProfilerExtractor import sigpro as sig >>> sig.sigProfilerExtractor ("matrix", "results", "path/to/ 21BRCA.txt" , genome_build¼"GRCh37", startProcess¼1, endProcess¼10, totalIterations¼100, cpu¼-1)

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l

4.2.6 Extraction of Signatures from Mutational Catalogs

According to the parameters, the input type used in this analysis is a “matrix”; the output folder name will be “results”; the input data file is 21BRCA.txt; mutations in the matrix have been originally identified using genome build “GRCh37”; minimum number of signatures is 1; maximum number of signatures is 10; number of iteration is 100; all available processors are used. To guess the minimum and maximum number of signatures, see Note 2; to determine the number of iterations, see Note 3. The results will be generated in a “results” directory. See Note 4 to get an idea on the run time.

Signatures can be extracted directly from mutational catalogs (e.g., VCF) files using SigProfilerExtractor without pre-generation of a mutational matrix. In fact, SigProfilerExtractor uses SigProfilerMatrixGenerator [18] in the background to convert mutational catalogs into a mutational matrix. Here, we will extract mutational signatures using “SBS96” and “DBS78” (or doublet base substitutions) contexts using the following commands. After starting a Python session, signatures can be extracted from the VCF files using the following script: >>> from SigProfilerExtractor import sigpro as sig >>> sig.sigProfilerExtractor("vcf", "results", "path/to/ 21BRCA _vcf", refgen¼"GRCh37", genome_build¼"GRCh37", startProcess¼1, endProcess¼10, totalIterations¼100, mtype¼"SBS96, DBS78", cpu¼-1)

4.2.7 Detailed Explanation of Results

l

According to the arguments, the input type used in this analysis is in a VCF format; the output folder name will be “results”; the name of the input data folder is 21BRCA_vcf; matrix will be generated from the VCF files using reference genome GRCh37; the genome_build is “GRCh37” which will be used as the reference of COSMIC in the decomposition step; minimum number of signatures is 1; maximum number of signatures is 10; number of iteration is 100; analyzed mutation contexts are SBS96 and DBS78; all available processors are used.

l

The results will be generated in a “results” directory.

Upon the successful completion of the analysis, an output folder containing the analysis results will be generated. In this example, the output folder will be named “results” and it can be found as a subfolder of the directory in which Python was started. Figure 3a illustrated the structure of the results directory. The “results” directory will contain a file named “JOB_METADATA.txt” and one subdirectory per each type of signatures analysis. In the current example, there will be two subdirectories corresponding to mutational signatures using DBS78 and SBS96 classifications.

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Fig. 3 Selecting optimal number of signatures. (a) Structure of the subfolder SBS96, which contains the results for mutational signatures analysis using the SBS96 classification. (b) Selection plot as found in SBS96_selection_plot.pdf (red box in panel a) illustrating the selection of the optimal number of signatures for decomposing the data. The X-axis shows the number of signatures. This figure has two shared Y axes. The left Y-axis shows the mean L2 norm % between the original and reconstructed samples. The right Y-axis shows the mean silhouette coefficient of the consensus signatures extracted from different NMF replicates. (c) The “All_solutions_stat.csv” file (red box in panel a) providing statics for the decompositions using different number of signatures

A. “JOB_METADATA.txt”: The file contains metadata about the system on which the job was executed and the runtime progress of the analysis. The main sections of the file include the following: l System info. l

Python and package versions.

l

Vital parameters used for the execution.

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Date and time information.

l

Job status.

B. Mutational signatures analysis subfolder. In this example, there will be two folders: DBS78 and SBS96. Each of these subfolders contains the following: l [MutationalContext]_selection_plot.pdf file (e.g., SBS96_selection_plot.pdf in the SBS96 folder). l

All_solutions_stat.csv file.

l

All solutions subdirectory.

l

Suggested solution subdirectory.

l

Samples.txt (A tab separated .txt file of the input Matrix).

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(a) Selection_plot.pdf: This file contains a plot illustrating the selection of the optimum number of signatures for decomposing the original matrix V. The x-axis of the plot reflects the number of signatures used for decomposing V. The plot displays the mean L2 norm % measuring how well the matrix V is being described by the set of de novo signatures. The plot also displays the average stability of the de novo signatures quantified using mean silhouette coefficients. The selected optimal number of signatures is highlighted in light blue. An example selection plot can be found in Fig. 3b. (b) All_solutions_stat.csv: This file provides summary statistics for decomposing the original matrix V using different number of signatures. The first column of the file indicates the number of de novo mutational signatures (see Fig. 3c). The selected number of mutational signatures is indicated using an asterisk sign. The columns provided in the file include the following information: l The average stability of the signatures measured using an average silhouette coefficient. Column Name: Stability (Avg Silhouette). l

The minimum stability of the signatures measured using a minimum silhouette coefficient. Column Name: Minimum Stability.

l

The Frobenius norm of the difference between the original and reconstructed matrices measured in percentages. Column Name: Matrix Frobenius%.

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The mean of the L1 percentage norms calculated between each original and reconstructed sample. Column Name: Mean Sample L1%.

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The maximum of the L1 percentage norms calculated between each original and reconstructed sample. Column Name: Maximum Sample L1%.

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The mean of the L2 percentage norms calculated between each original and reconstructed sample. Column Name: Mean Sample L2%.

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The maximum of the L2 percentage norms calculated between each original and reconstructed sample. Column Name: Maximum Sample L2%.

l

The results from a Wilcoxon rank-sum test. The test is performed for each number of stable de novo signatures to determine whether the change of the average L2 percentage norm is statistically significant. Column Name: Significant decrease of L2.

l

The mean of the Kullback–Leibler (KL) divergences calculated between each original and reconstructed sample. Column Name: Mean Sample KL.

Mutational Signatures in Cancer l

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The maximum of the Kullback–Leibler (KL) divergences calculated between each original and reconstructed sample. Column Name: Maximum Sample KL.

(c) All solutions subdirectory: Contains detailed results when decomposing the matrix V using total number of signatures between startProcess (in this case 1) and endProcess (in this case 10). A folder is generated for each decomposition containing the files listed below. Each file is prefixed by “[Mutation Context]_[Signature Numbers]_”. For example, decomposing SBS96 for 2 mutational signatures will result in a prefix “SBS96_2_”. l Activities.txt. l

Activities_plot.pdf.

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Activities_SEM_Error.txt.

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Samples_stats.txt.

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Signatures.txt.

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Signatures_SEM_Error.txt.

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Signatures_stats.txt.

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Signature_plots.pdf.

l

NMF_Convergence_Information.txt. (i) [Mutation Context]_[Signature Numbers]_Activities.txt: This file contains the activity matrix H for all identified signatures. The first column lists the names of all samples, while subsequent columns list the activities of the respective signatures. The number of columns is one more than the number of extracted signatures with each signature being alphabetically named. For example, decomposing the matrix V for two mutational signatures will result in three columns: (i) sample names; (ii) activities of signature A; and (iii) activities of signature B. The value in each cell reflects the number of somatic mutations attributed to a signature in a sample. (ii) [Mutation Context]_[Signature Numbers]_Activities_plot.pdf: This PDF file contains a figure visualizing the activities of each signature in each sample using a stacked bar plot. (iii) [Mutation Context]_[Signature Numbers]_Activities_SEM_Error.txt: The Activities_SEM_Error.txt contains the standard error for the activity of each signature in each sample. The first column lists the names of all samples, while subsequent columns list the standard errors of the activities of the respective signature. The number of columns is one more than

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the number of extracted signatures with each signature being alphabetically named. For example, decomposing the matrix V for two mutational signatures will result in three columns: (i) sample names; (ii) standard error for the activities of signature A; and (iii) standard error for the activities of signature B. (iv) [Mutation Context]_[Signature Numbers]_Samples_stats.txt: The file contains information indicating how well the extracted mutational signatures describe each individual cancer genome. For each sample, the information includes: (i) total number of somatic mutations in the sample; (ii) cosine similarity between the original and reconstructed sample; (iii) L1 norm and L1 norm in the percentage between the original and reconstructed sample; (iv) L2 norm and L2 norm in percentage between the original and reconstructed sample; and (v) Kullback–Leibler (KL) divergence between the original and reconstructed the samples. (v) [Mutation Context]_[Signature Numbers]_Signatures.txt: This file contains the pattern of each signature across the used classification of somatic mutations. The first column lists each possible mutation types. For example, there are 96 distinct mutation types considered in SBS96 and 78 distinct mutation types considered in DBS78 [18]. The subsequent columns list the proportion of mutation types in each signature. The total values across all the mutation types adds to 1 since a mutational signature is a probability mass function. The number of rows reflects the number of mutation types, while the number of columns reflects the number of extracted mutational signatures. (vi) [Mutation Context]_[Signature Numbers]_Signatures_SEM_Error.txt: The file contains the standard error for the estimating each mutation type in each signature. The first column lists all of the different mutations and the subsequent columns lists the standard errors for each mutation in the respective signature. (vii) [Mutation Context]_[Signature Numbers]_Signature_stats.txt: This file contains the statistics for each of the identified signatures. This information includes signature stability, measured using average silhouette coefficient [27], and total number of mutations attributed to each signature.

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(viii) [Mutation Context]_[Signature Numbers]_Signature_plot_[Mutational Context]_plots_[Signature Numbers].pdf: This file exists when the specified mutational classification has a dedicated plot implemented in SigProfilerPlotting (supported plots are described in https://osf.io/2aj6t/wiki/home/). The file contains one page per signature with information about the stability of the signature and the total number of mutations attributed to the signature. (ix) [Mutation Context]_[Signature Numbers] _NMF_Convergence_Information.txt: This file contains the L1 norm, L2 norm, and KL divergence between the original and reconstructed matrix for each NMF iteration. This file can be perused to evaluate whether the analysis has converged. In principle, if the solution has not converged, different iterations will have different values for either L1 norm, L2 norm, or KL divergence. (d) Suggested solutions subdirectory: Contains two sub-subdirectories: “De Novo Solution” and “Decomposed Solution.” The De Novo Solution directory reports the results and statistics from the best solution (optimum number of extracted signatures). This directory contains the following files. Figure 4a also illustrates the structure of this directory. l

De_Novo_Solution_Activities.txt.

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De_Novo_Solution_Samples_stats.txt.

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De_Novo_Solution_Signatures.txt.

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De_Novo_Solution_Signatures_SEM_Error.txt.

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De_Novo_Solution_Signatures_stats.txt.

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Mutation_Probabilities.txt.

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Signature_plot_[Mutation Context]_plots_De_Novo_Solution.pdf. (i) De_Novo_Solution_Activities.txt: This file (inside the blue box in Fig. 4a) has the same format as the format of the file described in the section [Mutation Context]_[Signature Numbers]_Activities.txt in All solutions subdirectory. (ii) De_Novo_Solution_Activities_Plot_SBS96.pdf: The plot has the same format as the format of the plot described in the section [Mutation Context]_[Signature Numbers] _Activities_plot.pdf in All solutions subdirectory.

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Fig. 4 Results generated by SigProfilerExtractor as a De Novo Solution. (a) Files and folders generated in the results folder. The “SBS96” and “DBS78” folders contain results for the SBS96 and DBS78 mutational classifications, respectively. Inside the “SBS96” folder, the “Suggested_Solution” subfolder contains the information on the signatures detected in the samples. The exact set of de novo extracted mutational signatures can be found in the “De_Novo_Solution” directory. (b) The set of de novo extracted signatures found to be operative in the examined samples (red box) as shown in the file Signature_plotSBS_96_plots_De_Novo_Solution (red box in panel a). The numeric representations corresponding to extracted signatures can be found in “De_Novo_Solution_Signatures_SBS96.txt” (red asterisk in panel a). (c) Activities of the de novo extracted signatures in the examined samples (blue box) as shown in the file De_Novo_Solution_Activities_Plot_SBS96.pdf (blue box in panel a). The numeric values of the activities of the extracted signature can be found in the file “De_Novo_Solution_Activities_SBS96.txt” (blue asterisk in panel a)

Figure 4b illustrates the activities of the De Novo Signatures on the samples. (iii) De_Novo_Solution_Samples_stats.txt: This file has the same format as the format of the file described in the section [Mutation Context]_[Signature Numbers]_Samples_stats.txt in All solutions subdirectory. (iv) De_Novo_Solution_Signature.txt: This file (inside the red box in Fig. 4a) has the same format as the format of the file described in the section [Mutation Context]_[Signature Numbers]_Signatures.txt in All solutions

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subdirectory. Figure 4c illustrates the De Novo Signatures detected in the samples. (v) De_Novo_Solution_Signatures_SEM_Error.txt: This file has the same format as the format of the file described in the section [Mutation Context]_[Signature Numbers] _Signatures_SEM_Error.txt in All solutions subdirectory. (vi) De_Novo_Solution_Signature_stats.txt: This file has the same format as the format of the file described in the section [Mutation Context]_[Signature Numbers]_Signature_stats.txt in All solutions subdirectory. (vii) Mutation_Probabilities.txt: This file contains the probability of each mutation type to be generated by the extracted set of de novo mutational signatures. For example, in SBS96 classification when two signatures are extracted, an A[C > A]A mutation in the first sample may have 50% probability to be generated by Signature A and 50% probability to be generated by Signature B; in contrast, in another sample, an A[C > A]A mutation may be generated with 97% probability by Signature A and 3% probability by Signature B. In all cases, summation of the probabilities over all mutational signatures for a given mutation type in a given sample will be equal to one. (viii) Signature_plot_[Mutation_Context]_plots_De_Novo_Solution.pdf: This file existing when the specified mutational classification has a dedicated plot implemented in SigProfilerPlotting (supported plots are described in https://osf.io/2aj6t/wiki/home/). The file contains one page per de novo signature with information about the stability of the signature and the total number of mutations attributed to the signature. After extracting and selecting the optimum number of de novo signatures, SigProfilerExtractor also matches the de novo signatures to the COSMIC signatures [7] by decomposing them with a modified nonnegative least square (NNLS) algorithm [28]. The Decomposed solution directory reports the results and statistics of the COSMIC signatures assigned to the De Novo Signatures and Samples. This folder contains the following files. l

Comparison_with_global_ID_signatures.csv.

l

Decomposed_Solution_Activities.txt.

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Decomposed_Solution_Samples_stats.txt.

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Decomposed_Solution_Signatures.txt.

l

Decomposition_logfile.txt.

l

Mutation_Probabilities.txt.

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Signature_assaignment_logfile.txt.

l

Signature plot [MutatutionContext]_plots_Decomposed_Solution.pdf. (i) Comparison_with_global_ID_signatures.csv: Each of the de novo extracted mutational signatures are matched with a combination of COSMIC signatures. The first column of the file reflects the contributions (in percentages) of the identified COSMIC signatures to each of the de novo extracted signatures. The next three columns list the L1 norm, L2 norm, and cosine similarity reflecting the accuracy of the selected combination of COSMIC signatures in explaining the de novo extracted signature. (ii) Decomposed_Solution_Activities.txt: The file has the same format as the format of the file described in section [Mutation Context]_[Signature Numbers]_Activities.txt in All solutions subdirectory. However, the activities listed in this file are not for de novo signatures. Rather, the file provides activities for the selected COSMIC signatures. (iii) Decomposed_Solution_Activities_Plot_SBS96.pdf: The file has the same format as the format of the file described in section [Mutation Context]_[Signature Numbers]_Activities_plot.pdf in All solutions subdirectory. However, instead of displaying the de novo signatures, it plots the activities of the selected COSMIC signatures. (iv) Decomposed_Solution_Samples_stats.txt: The file has the same format as the format of the file described in section [Mutation Context]_[Signature Numbers]_Samples_stats.txt in All solutions subdirectory. (v) Decomposed_Solution_Signatures.txt: The file has the same format as the format of the file described in section [Mutation Context]_[Signature Numbers]_Signatures. txt in All solutions subdirectory. However, instead of providing the probabilities of the de novo signatures, it provides the probabilities of the selected COSMIC signatures. (vi) Decomposition_logfile.txt: This file provides information of the decomposition steps used to match the selected de novo signatures to the reference COSMIC signatures. (vii) Mutation_Probabilities.txt: The file has the same format as the format of the file described in section Mutation_Probabilities.txt in De Novo Solution folder. However, instead of providing mutation type probabilities for de novo signatures, it provides mutation type probabilities for the identified COSMIC signatures.

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(viii) Signature_assaignment_logfile.txt: This file records the process of the assignment of COSMIC signatures to the samples. (ix) Signature_plot_[MutatutionContext]_plots_Decomposed_Solution.pdf: This file has a plot for each of the selected COSMIC signatures. The file has the same format as the format of the file described in section [Mutation Context]_[Signature Numbers]_Signature_plot_[Mutational Context]_plots_[Signature Numbers].pdf in All Solutions subdirectory.

5

Methods and Materials to Extract Signatures Using R Platform As described earlier in the chapter, SigProfilerExtractor can be also used in an R environment. Please note that the R version is actually a wrapper of the Python version of the tool. Nevertheless, it is straightforward for an R user to use the tool to extract mutational signature and to perform exactly the same analysis as the one described in the Python platform. Here, stepwise instructions are provided to demonstrate the signature extraction process with R.

5.1 Required Operating System

Mac/Unix/Linux/Windows.

5.2

l

Python: version 3.5 or later.

l

Anaconda 3.3.

l

SigProfilerExtractor: python version 1.0.3.

l

Wget: version 1.9.

l

R: version 3.60 or later.

l

Devtools (R package).

l

Reticulate (R package).

l

SigProfilerExtractorR.

l

On Mac/Unix/Linux, open the terminal, or on Windows open the anaconda prompt/terminal.

l

Since the R platform is dependent on the Python platform, one needs to first install the Python version 1.0.3 of SigProfilerExtractor. To do that, inside terminal write the following command:

Required Tools

5.3 Software Installation

$ pip install SigProfilerExtractor¼¼1.0.3

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5.4 Installation of Packages

1. In an R interpreter, type the following commands to install “devtools” and “reticulate.” >>> install.packages("devtools") >>> install.packages("reticulate") 2. Configure the Python path (see Note 5). >>> library("reticulate") >>> use_python("path to your python") #example"/home/anaconda3/bin/python" >>> py_config() 3. Install SigProfilerExtractorR package from GitHub. >>> library("devtools") >>> install_github("AlexandrovLab/ SigProfilerExtractorR") 4. Load the package in the same R session and install your desired reference genome as follows (available reference genomes are: GRCh37, GRCh38, mm9, and mm10). >>> library("SigProfilerExtractorR") >>> install("GRCh37", ftp¼TRUE)

5.5 Data Preparation for Extracting Mutational Signatures

rsync¼FALSE,

The procedure to extract signatures using the R version of SigProfilerExtractor is similar to the one described in the Python version. Both “matrix” and “vcf” type of input can be used in the R version. The general function to extract the mutational signature is provided below: sigprofilerextractor (input_type, output, inputdata, refgen ¼ “GRCh37,” genome_build ¼ “GRCh37,” minsigs ¼ 1, maxsigs ¼ 3, replicates ¼ 5, mtype ¼ “96, DINUC, ID”, exome ¼ F, cpu ¼ 1).

The description of the arguments and parameters are explained in Box 2.

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Box 2 Description of SigProfilerExtractor’s Parameters in an R Environment Arguments of sigProfilerExtractor function of the R version. These are the acceptable parameters that can be passed into the function call. Required Parameters: l

l

l

input_type: The type of input.

Type: string The type of input should be one of the following: – “vcf”: used for vcf format inputs. – “matrix”: used for .txt format inputs (example: tab separated file containing mutational catalog). output: The path of the output folder where the results will be generated. If the folder is not present, the folder will be created automatically. Type: string inputdata: Name of the input folder (in case of “vcf” type input) or the input file (in case of “matrix” type input). The project file or folder should be inside the current working directory. For the “vcf” type input, the project has to be a folder which will contain the vcf files in vcf format or text formats. The “matrix” type projects have to be a file. Type: string Optional Parameters:

l

l

l

l

l

refgen: The name of the reference genome. This parameter is applicable only if the input_type is “vcf”. Available optional are “GRCh37,” “GRCh38,” “mm9,” and “mm10.” The default reference genome is “GRCh37”.

Type: string genome_build: The build or version of the reference signatures for the refgen. The default genome build is GRCh37. If the input_type is “vcf,” the genome_build automatically matches the input refgen value. Type: string minsigs: The minimum number of signatures to be extracted. Default: 1. Type: A positive integer maxsigs: The maximum number of signatures to be extracted. Default: 10. Type: A positive integer replicates: The number of iterations to be performed to extract each number signature. Default: 8. (continued)

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Box 2 (continued) Note: 8 iterations is used to save time for the initial test runs of the application however we recommend at least 100 iterations for reliable results. Type: A positive integer l cpu: The number of processors to be used to extract the signatures.

l

l

5.5.1 Extraction of Signatures from Mutational Catalogs

Default: 1 which will use all available processors. Type: An integer mtype: Defines the mutational contexts to be considered to extract the signatures. Current possible options are “SBS96,” “SBS192,” “SBS6144,” “SBS1536,” “DBS78,” and “ID83.” However, more options could be added in future. Multiple mutational contexts can be passed as the parameter separated by comma in a string. Default: "SBS96, DBS78, ID83” Type: A string exome: Defines if the exomes will be extracted. Default: False. Type: Boolean

Since extracting mutational signature is very similar in the R and Python versions of SigProfilerExtractor, we will provide an example for deciphering signatures only using VCF files. Similarly, our example will make use of data from 21 breast cancer genomes. Signatures can be extracted directly from VCF files using SigProfilerExtractor without pre-generation of a mutational matrix: 1. The VCF files from the 21 whole-genome sequenced breast cancers need to be places in a folder. In this example, the folder is named 21BRCA_vcf. Each VCF file should contain mutation information from only one sample. If there are n samples, then there should be n VCF files inside the project folder. In this particular case, there are 21 VCF files. >>> library("SigProfilerExtractorR") >>> sigprofilerextractor("vcf", "results", "path/ to/21BRCA _vcf", refgen¼ "GRCh37", genome_build¼ "GRCh37", minsigs¼1, maxsigs¼10, replicates¼100, mtype¼ "SBS96, DBS78", cpu¼ -1)

2. From an R session, signatures can be extracted from VCF files using the following command:

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3. According to the parameters, the input type used in this analysis is a VCF format; the output folder name will be “results”; the name of the input data folder is 21BRCA_vcf; matrix will be generated from the VCF files using reference genome GRCh37; the genome build is “GRCh37” which will be used for the reference set of COSMIC signatures in the decomposition step; minimum number of signature is 1; maximum number of signature is 10; number of iterations is 100; analyzed mutation contexts are SBS96 and DBS78; all available processors are used. 4. The results will be generated in “results” directory. The structure and description of the results are identical to the results outputted by the Python version of SigProfilerExtractor. We are continuously improving the tool with regular major updates released every few months. Please check for regular updates of the software in PyPI (https://pypi.org/project/ SigProfilerExtractor/) for the Python version and our GitHub page (https://github.com/AlexandrovLab/Sig ProfilerExtractorR) for the R version of the software. Finally, please check our Wiki page https://osf.io/t6j7u/wiki/ home/ for more and latest description of the user manual of SigProfilerExtractor.

6

Notes 1. It is recommended to run the Python version of SigProfilerExtractor using a conda environment. Conda allows the user to install and/or update packages independently of system libraries without administrative privileges. The instructions provided in this chapter are for SigProfilerExtractor version 1.0.3. Updated instructions can be found for later versions on the Wiki page mentioned above. 2. Selecting the range of signatures is a delicate task. The maximum number of signatures should be less than the number of samples. One trick is to start with 1–20 signatures by default (or less than the maxim number of samples). If most of the upper-rank signatures (for example signature 15–20) shows good stabilities such as 0.5–1, it is recommended to do a second run extending the range of signatures such as 1–30. This process should be continued until the higher rank signatures decompositions consistently shows low stabilities. 3. It is recommended to run at least 500 iterations to obtain reproducible results. The number of iterations can be set using the parameter of “totalIterations” argument in the Python version or “replicates” argument in the R version.

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4. The runtime differs depending on the sample size, mutational context, number of iterations, and the range of signatures. For example, extracting signature using SBS96 classification with parameters: 1–10 signatures from 25 samples using 100 iterations will take approximately 30 min on a PC with an 8-core processor. In contrast, extracting 1–20 signatures from 500 samples with 500 iterations will likely take more than a day. Hence, it is recommended to use high performance or cloud computing when examining large numbers of samples. 5. Note that for the R version of SigProfilerExtractor, the user will still need to install the Python version since the R version is a wrapper of the Python version. If there are multiple python platforms in the system, the user has to set the path of the python version that has the SigProfilerExtractor installed. References 1. Martincorena I, Campbell PJ (2015) Somatic mutation in cancer and normal cells. Science 349:1483–1489. https://doi.org/10.1126/ science.aab4082 2. Alexandrov LB, Nik-Zainal S, Wedge DC et al (2013) Deciphering signatures of mutational processes operative in human cancer. Cell Rep 3:246–259. https://doi.org/10.1016/j.cel rep.2012.12.008 3. Pon JR, Marra MA (2015) Driver and Passenger Mutations in Cancer. Annu Rev Pathol Mech Dis 10:25–50. https://doi.org/10. 1146/annurev-pathol-012414-040312 4. Futreal PA, Coin L, Marshall M et al (2004) A census of human cancer genes. Nat Rev Cancer 4:177–183. https://doi.org/10.1038/ nrc1299 5. Gao J, Aksoy BA, Dogrusoz U et al (2013) Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci Signal 6:pl1–pl1. https://doi.org/10. 1126/scisignal.2004088 6. Forbes SA, Beare D, Gunasekaran P et al (2015) COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 43:D805–D811. https://doi.org/10.1093/nar/gku1075 7. Alexandrov LB, Kim J, Haradhvala NJ et al (2018) The Repertoire of Mutational Signatures in Human Cancer. In: Cancer Biology 8. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791. https://doi.org/ 10.1038/44565 9. Choo J, Lee C, Reddy CK, Park H (2013) UTOPIAN: User-Driven Topic Modeling

Based on Interactive Nonnegative Matrix Factorization. IEEE Trans Vis Comput Graph 19:1992–2001. https://doi.org/10.1109/ TVCG.2013.212 10. Neher RA, Mitkovski M, Kirchhoff F et al (2009) Blind source separation techniques for the decomposition of multiply labeled fluorescence images. Biophys J 96:3791–3800. https://doi.org/10.1016/j.bpj.2008.10.068 11. Innami S, Kasai H (2012) NMF-based environmental sound source separation using time-variant gain features. Comput Math Appl 64:1333–1342. https://doi.org/10. 1016/j.camwa.2012.03.077 12. Hoyer PO (2003) Modeling receptive fields with non-negative sparse coding. Neurocomputing 52–54:547–552. https://doi.org/10. 1016/S0925-2312(02)00782-8 13. Behnke S (2003) Discovering hierarchical speech features using convolutional non-negative matrix factorization. In: Proceedings of the International Joint Conference on Neural Networks, 2003. IEEE, Portland, Oregon USA, pp 2758–2763 14. Cooper M, Foote J (2002) Summarizing video using non-negative similarity matrix factorization. In: In, vol 2002. IEEE Workshop on Multimedia Signal Processing. IEEE, St. Thomas, VI, USA, pp 25–28 15. Lu J, Xu B, Yang H (2003) Matrix dimensionality reduction for mining Web logs. In: Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003). IEEE Comput. Soc, Halifax, NS, Canada, pp 405–408 16. Berry MW, Browne M, Langville AN et al (2007) Algorithms and applications for

Mutational Signatures in Cancer approximate nonnegative matrix factorization. Comput Stat Data Anal 52:155–173. https:// doi.org/10.1016/j.csda.2006.11.006 17. Devarajan K (2008) Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol 4: e1000029. https://doi.org/10.1371/journal. pcbi.1000029 18. Bergstrom EN, Huang MN, Mahto U et al (2019) SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20:685. https://doi.org/10.1186/s12864-019-60412 19. Fischer A, Illingworth CJR, Campbell PJ, Mustonen V (2013) EMu: probabilistic inference of mutational processes and their localization in the cancer genome. Genome Biol 14:R39. https://doi.org/10.1186/gb-2013-14-4-r39 20. Rosales RA, Drummond RD, Valieris R et al (2017) signeR: an empirical Bayesian approach to mutational signature discovery. Bioinformatics 33:8–16. https://doi.org/10.1093/ bioinformatics/btw572 21. Ardin M, Cahais V, Castells X et al (2016) MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes. BMC Bioinformatics 17:170. https://doi.org/10.1186/ s12859-016-1011-z 22. Gehring JS, Fischer B, Lawrence M, Huber W (2015) SomaticSignatures: inferring

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mutational signatures from single-nucleotide variants: Fig. 1. Bioinformatics:btv408. https://doi.org/10.1093/bioinformatics/ btv408 23. Macintyre G, Goranova TE, De Silva D et al (2018) Copy number signatures and mutational processes in ovarian carcinoma. Nat Genet 50:1262–1270. https://doi.org/10. 1038/s41588-018-0179-8 24. Mayakonda A, Lin D-C, Assenov Y et al (2018) Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28:1747–1756. https://doi.org/10.1101/gr. 239244.118 25. Blokzijl F, Janssen R, van Boxtel R, Cuppen E (2018) MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med 10:33. https://doi.org/10. 1186/s13073-018-0539-0 26. Nik-Zainal S, Alexandrov LB, Wedge DC et al (2012) Mutational Processes Molding the Genomes of 21 Breast Cancers. Cell 149:979–993. https://doi.org/10.1016/j. cell.2012.04.024 27. Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87) 90125-7 28. Bro R, De Jong S (1997) A fast non-negativityconstrained least squares algorithm. J Chemom J Chemom Soc 11:393–401

INDEX A Animal models................... 29, 30, 32, 33, 42, 44, 46, 52 Antibody monoclonal antibody ............. 53, 227, 244, 245, 269

B Barcode sample barcoding ............................... 71, 72, 74, 148, 318–320, 324, 330, 346, 352–357 B cell (¼B lymphocyte) ............................ 33, 52, 60, 294 Bioinformatics ...............32, 96, 109, 147, 403, 447–472 Bioluminescence imaging ............................................. 232 Blasts ................................... 18, 19, 27, 85, 92, 117, 223, 244, 247, 249, 252, 256, 259–261, 307, 314, 424, 430 Blood .......................................3–5, 28, 51, 67, 115, 118, 157, 181, 182, 189, 195, 201, 203, 206, 209, 229, 230, 234, 236, 247, 267, 270, 277, 278, 285, 292, 295, 310, 314, 322, 331, 332, 337, 342, 348, 353, 380, 387, 391, 392, 397, 399, 405, 411, 424, 430 Blood cells ......................................... 4, 5, 16, 30, 51, 52, 62, 65, 135, 157, 181, 182, 184, 195, 196, 198, 203, 209, 244, 247, 256, 271, 274, 276, 297, 312, 314, 350, 353, 373, 381, 387, 400, 401, 424, 431, 432 Bone marrow (BM) bone marrow aspiration ................ 216, 221, 229–231 bone marrow reconstitution................ 196, 197, 199, 203–205 long-term reconstitution ......................... 196, 197 short-term reconstitution ................................... 13 bone marrow transplant (BMT)................... 195–212, 235, 291 competitive bone marrow transplant ............ 195–212 primary BMT........................ 196, 197, 205, 211, 212 secondary BMT .............................197, 205, 211, 212 tertiary BMT ........................................................... 205 Buffy coat ....................98, 101, 270, 271, 276–278, 405

C Cancers ..................................6, 9, 15, 16, 25–33, 39–46, 67, 242, 342, 363, 399, 421, 424–427, 441, 447–472

Cancer stem cells (CSCs)........................... 15, 16, 25, 27, 31, 41, 46, 361 CD34 ...................................... 12, 13, 17, 52, 53, 56, 58, 60–62, 72, 181, 186, 188, 192, 193, 204, 252, 260–265, 267–269, 271–276, 278, 279, 281–285, 287–290, 293, 296, 388–390, 393, 397, 402, 405, 420 CD45 CD45.1.........................................184, 190, 197, 199, 201–205, 310, 314, 402, 408, 409 CD45.2.........................................184, 190, 197, 199, 201–206, 209, 310, 314, 402, 408, 409 Cell competitor cells .............................................. 188, 212 donor cells ...................................................... 196, 268 Cell culture ......................... 68, 137, 138, 155, 183–185, 218–220, 222, 234, 259–265, 309, 311, 321, 322, 330, 331, 348–350, 357, 384, 428 Cell cycle.................................................. 40, 51, 242, 434 Cell division rate ........................................................... 242 Cell therapy .......................................................... 195, 424 Chemotherapy.......................................... 4, 9, 30, 40, 41, 196, 216, 242, 307 Chimerism .................................189, 190, 196, 210, 292, 331, 342, 393, 397, 405, 408, 409 Chromatography ........................................................... 162 Chromosomal translocation .............................. 7, 16, 113 Clonal clonal analysis .........................................181–193, 318 single-cell clonal analysis................................ 181–193 Clonality ....................................................................90, 91 Clustered regularly interspaced short palindromic repeat-associated nuclease Cas9 (CRISPR-Cas9) .................................. 319, 385 Cluster of differentiation (CD) proteins ..................... 135 Colony-forming unit (CFU) ............................... 289, 291 Colony-forming unit assay ......................... 283, 289, 291 Competitive Repopulation Assay ........................ 401, 406 Competitive repopulating unit............................ 195, 210 Computational analysis .............. 243, 245–246, 250, 253 Cord blood (  Umbilical cord blood) ............. 117, 196, 281–297 Cryopreservation............................... 218, 224, 268, 276, 282, 283, 287, 296, 417 Cytokine cytokine-free medium .................................... 259–265

Ce´sar Cobaleda and Isidro Sa´nchez-Garcı´a (eds.), Leukemia Stem Cells: Methods and Protocols, Methods in Molecular Biology, vol. 2185, https://doi.org/10.1007/978-1-0716-0810-4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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LEUKEMIA STEM CELLS: METHODS

476 Index

AND

PROTOCOLS

Cytometry by time-of-flight (CyTOF) .................. 66, 67, 69, 72, 75, 76, 160

D Data modelling..................................................... 440, 441 Demultiplexing.............................................................. 115 Differentiation............................... 4, 5, 7, 10, 15, 16, 19, 30, 41, 65, 135, 182, 195, 210, 256, 259–262, 357, 363, 384, 391–393, 397, 400, 412, 416, 419–421, 424, 427, 428, 434 Dissection ..................................... 45, 182, 309, 310, 375 Drug discovery ..................................30, 39–47, 423, 442

E Electroporation .......................... 385, 386, 388–390, 396 Endothelium......................................................... 415, 420 Engraftment ..............................188, 193, 196, 206, 227, 229, 235, 242, 247, 248, 252, 256, 260, 268, 284, 290, 292, 293, 296, 300, 353, 357, 358, 384, 386, 390–393, 397, 405, 408 Enrichment..................................9, 53, 54, 62, 114, 117, 118, 137, 138, 142, 222, 223 Epigenetics ....................18, 20, 30, 31, 33, 45, 216, 364 Erythrocyte (¼red blood cell) red blood cell lysis ........................198, 203, 244, 247, 297, 312, 350, 353, 387 ETV6-RUNX1 translocation .............113–115, 118, 119 Extracellular matrix ................................................ 51, 155

F Feeders.................................................................. 217, 234 Femur................................ 185, 191, 199, 200, 206, 230, 312, 350, 391, 404 Ficoll ...............................................................92, 282, 295 Flow cytometry ................................8, 25, 28, 51–53, 55, 58, 67, 137, 181, 188–190, 193, 196–198, 200–203, 205–208, 211, 212, 217, 223, 229, 230, 233, 234, 243, 245, 247–251, 260–264, 271–275, 280, 283, 284, 287, 288, 290, 294, 310, 314, 315, 331, 348, 351, 353, 390–393, 407, 408, 420 Fluorescence-activated cell sorting (FACS)............65–67, 69, 71, 72, 136, 138, 143, 167, 176, 184, 196, 198, 200, 203, 209, 218, 234, 243, 261, 263, 283, 284, 287, 289, 292–294, 296, 297, 303, 304, 310, 314, 315, 322, 329, 348, 351, 402, 407, 409 FMS-like tyrosine kinase 3 ligand (FLT3L) ...............268, 273, 416 Fusion gene ............................................................ 96, 113

G Genetically engineered mouse model (GEMM) ........... 45 Genome editing .......................................... 318, 383, 396

Genomic DNA .....................................98, 114, 318, 332, 348, 353, 354, 369, 387, 392 Genomic inverse PCR for exploration of ligated breakpoints (GIPFEL technology).... 113–134 Genotyping..........................................362, 368, 369, 372 Gradient.................................92, 98, 172–175, 234, 268, 270, 271, 276, 277, 285, 295, 397 Grafts ..........................................196, 205, 210, 267, 268 Graft-versus-host disease (GvHD)...................... 196, 252 Granulocytes.................................. 6, 7, 12–14, 196, 204, 209, 211, 310, 432 Green fluorescent protein (GFP) ...................55–62, 224, 243, 244, 248–251, 253, 256, 290, 329, 331, 349, 351, 356

H Hematopoiesis malignant hematopoiesis ................................. 66, 136 normal hematopoiesis ...................................... 17, 318 Hematopoietic differentiation............412, 416, 419, 420 Hematopoietic progenitor cells (HPCs).......... 10, 12–14, 17, 52, 193, 412, 415, 416, 420 Hematopoietic stem cells (HSCs) induced- hematopoietic stem cells (iHSCs) ............................................... 399–409 Hematopoietic stem/progenitor cells (HSPCs) .......... 30, 56, 58, 281–283, 287, 289, 290, 296, 307, 308, 315, 384, 386, 388, 390, 395–397, 412, 416, 420 High-content screening................................................ 431 Histology ...................................... 5, 32, 44, 45, 242, 314

I Iliac crests ............................................53, 55, 58, 62, 206 Imaging 3D imaging.............................................................. 373 high-content imaging (HCI) ....... 423–425, 427, 435 image analysis ................................................. 424, 426 second harmonic generation (SHG) imaging ....... 374 whole-mount tissue imaging .................................. 373 Immunodeficient mice.........................28, 196, 208, 216, 217, 219, 225, 232, 384, 390, 412, 420 Immunoglobulin (IG) ........... 77–85, 88–92, 95–97, 103 Immunophenotyping.................................. 264, 292, 297 Immunostaining......................... 375–377, 424, 428, 441 Indexing................................................................ 346, 355 Induced pluripotent stem cells (iPSCs) patient-derived iPSCs.............................................. 416 Injection intrafemoral (if) injection ....................................... 208 intraperitoneal (ip) injection ........221, 228, 247, 390 intravenous (iv) injection ........................................ 208 retroorbital (ro) injection ..................... 220, 226, 227 subcutaneous injection ............................44, 220, 227

LEUKEMIA STEM CELLS: METHODS Interleukin-3 (IL-3).......................... 269, 273, 283, 284, 309, 402, 414 Intra-tumoral heterogeneity ......................................... 241 Irradiation γ-irradiation ............................................................. 217 γ rays ........................................................................ 205 lethal irradiation .............................55, 193, 197, 206, 211, 406, 408 sublethal irradiation ............................. 217, 232, 291, 313, 330, 331, 346–348, 350, 353 X rays ....................................................................... 405

L Lentiviral vector lentiviral packaging ................................................. 219 lentiviral transduction .................. 216–220, 222–226, 256, 290, 299–305, 318, 346–348, 407 transduction efficiency ...................................... 226 self-inactivating lentiviral vector............................. 300 Leukemia acute lymphoblastic leukemia (ALL) ................4, 5, 7, 9, 17, 18, 52, 77, 95, 215 B acute lymphoblastic leukemia (B-ALL).....5, 52, 321, 327 T acute lymphoblastic leukemia (T-ALL)........... 5, 18, 52, 56, 215, 216, 236 acute myeloid leukemia (AML).................. 4, 7, 9, 15, 17, 30, 52, 55, 96, 241–259, 307, 346, 411, 412, 425 childhood leukemia................................................. 113 chronic myeloid leukemia (CML).................. 4, 7, 16, 17, 20, 30, 33, 308 leukemia therapy ..........................3, 8, 10, 20, 26–31, 33, 39–42, 44–46, 77, 95, 216, 259, 307, 308, 362, 425 leukemia treatment ................................................... 30 Leukemia initiating cells (LICs)................................... 216 Leukemia stem cells (LSCs) .............................16, 17, 20, 25–33, 51, 52, 55–62, 216, 242, 259–261, 264, 307, 308, 315, 345, 346, 348, 351, 356, 357, 362, 364, 411–421 Lineage depletion......................................................53, 54 Lineage-negative cells ..................................................... 53 Lineage-tracing ...................................................... 32, 182 Linux...........................................246, 257, 451, 454, 459 Liver ............................................233, 292, 294, 314, 315 Lymph nodes.............................................................4, 233 Lymphocyte B lymphocyte............... 6, 8, 9, 15–19, 182, 189, 190 T lymphocyte.......................... 4–9, 18, 182, 189, 190 Lymphoma ............................ 3, 4, 7, 9, 10, 33, 196, 421 Lymphopoiesis ................................................................ 65

AND

PROTOCOLS Index 477

M Macrophages ............................................. 6, 7, 10, 12–14 Magnetic-activated cell sorting (MACS) ............... 53, 54, 114, 117, 183, 186, 189, 191, 192, 244, 247, 288, 347, 397 Magnetic beads ...................................114, 271, 427, 428 Mass cytometry ............................................5, 65–76, 160 Mass spectrometry .........................................66, 159–177 Megakaryocyte .................... 6, 13, 14, 52, 374, 378, 379 Microscopy confocal microscopy.............................. 375, 378, 379 fluorescence microscopy ................................ 290, 375 Minimal residual disease (MRD)...............77–92, 95–110 Monoclonal ...........................................5, 51, 83, 91, 297 Monocytes ...................................19, 182, 189, 190, 193, 204, 209, 420 Mononuclear cells (MNC) ........................ 62, 66, 68, 85, 92, 98, 117, 243, 247, 249, 252, 270, 271, 282, 284–287, 295, 296, 402, 405, 412, 413, 424, 426, 427, 431, 432 Mouse/mice C57Bl/6 mice ...................... 197, 205, 347, 375, 376 congenic mice........................................ 197, 205, 401 donor mouse ........................................................... 197 immunocompromised mice................. 243, 244, 255, 260, 264, 294 immunodeficient mice ...................28, 196, 208, 216, 217, 220, 226, 232, 384, 386, 390, 412, 420 NOD SCID g mice .......................291, 330, 331, 386 recipient mouse ............................188, 189, 193, 197, 205, 206, 292, 313 primary recipient ............................................... 197 secondary recipient .................................. 212, 248 reconstituted mouse................................................ 206 transgenic mouse.................... 18, 182, 183, 347, 362 Multiple myeloma ............................ 3, 4, 33, 77, 95, 196 Multiplexing ................................74, 139, 143, 155, 156, 157, 332, 335, 339, 355 Multiplicity of infection (MOI) viral titration ............................................................ 357 Multipotency ............................................... 182, 399, 400 Mutation driver mutation ......................................307–309, 447 passenger mutation ................................................. 447 Mutational landscape .................................................... 307 Mutational signatures .......................................... 447–472 Myeloid cells....................4, 5, 10, 18, 56, 211, 310, 392

N Neutrophil .......................17, 19, 52, 182, 189, 190, 193 Newborn screening .............................................. 113–134

LEUKEMIA STEM CELLS: METHODS

478 Index

AND

PROTOCOLS

Next-generation sequencing (NGS) ................41, 45, 78, 90, 91, 95–110, 308, 345, 346 Niches ........................................... 51, 216, 373, 374, 399 Non-homologous end-joining (NHEJ) repair pathway ............................................... 383, 384 Nonnegative matrix factorization (NMF) ......... 448–450, 459, 461, 463 NOTCH1 ....................................... 52, 56, 424, 428, 433

O Oncogenes.............................. 16, 17, 27, 29, 30, 33, 46, 311, 362, 364, 366–369, 371, 372 Oral gavage........................................................... 221, 228

P Pathology...................................................................45, 46 Peripheral blood (PB) ..........................55, 58, 81, 85, 98, 184, 188, 189, 193, 195–198, 201, 203–206, 209, 211, 217, 222, 227, 229, 243, 245, 247–249, 252, 256, 268, 281, 292, 297, 310, 314, 316, 390, 402, 405, 408, 411–413, 415–418 Pharmacogenomics ............................................ 43, 45, 46 Plasticity (cellular plasticity) ................... 29, 33, 136, 401 Platelets................................. 7, 182, 184, 189, 193, 249, 268, 285, 286, 295 Polymerase chain reaction (PCR) .................... 74, 77–92, 97–99, 101–108, 110, 113–134, 137, 139, 140, 143, 148, 149, 153, 318, 319, 321, 323, 332–340, 342, 346, 348, 354–355, 358, 362, 363, 366, 368, 369, 371, 372, 392, 394, 397, 402, 404, 409 Preclinical animal models ............................................. 216 Preleukemic cells ............................................32, 113–134 Progenitor cells ............................... 6, 10, 12, 14, 19, 30, 44, 46, 157, 207, 210, 260, 262, 267–280, 317, 362, 363, 374, 415 Pronuclear microinjection ............................................ 369 Proteomics single-cell proteomics .................................... 159–177 Python ....................................... 245, 250, 340, 449–452, 454–459, 468, 470–472

R Radiotherapy .......................................................... 40, 196 Real-time quantitative PCR (RQ-PCR) .......... 78, 84–86, 88–90 Rearrangements................................8, 78, 80, 83–85, 87, 88, 90, 92, 97, 109, 110 Red blood cell (¼Erythrocyte) red blood cell lysis ........................184, 198, 203, 244, 247, 297, 312, 350, 353, 387

Relapse .....................................16, 20, 27, 31, 32, 40–42, 96, 216, 259, 307, 317, 362, 424 Remission .................................26, 40, 96, 307, 317, 425 Repopulation repopulating potential ................................... 273, 339 in vivo repopulating assay ..................... 196, 197, 264 competitive in vivo repopulating assay ... 196, 197 non-competitive in vivo repopulating assay .... 197 Reprogramming cellular reprogramming .......................................... 268 epigenetic reprogramming .................................30, 31 reprogramming factors ......................... 408, 412, 413 Retroviral vector retroviral transduction .............................................. 55 Reverse transcription................................... 137, 144, 301 RNA .............................................9, 12, 13, 67, 113, 136, 142, 143, 156, 157, 159, 160, 319, 349, 383–385, 396, 424 RNA-sequencing (RNA-Seq) droplet-based RNA-Seq.......................................... 136 plate-based RNA-Seq.............................................. 136 R programming language ............................................. 452

S Sanger sequencing........................... 84, 90, 91, 117, 126, 132, 335, 368, 392 Self-renewal ........... 6, 30, 181, 195–197, 307, 362, 363, 399–401 Single-cell single-cell analysis...................................................... 67 single-cell clonal analysis................................ 181–193 single-cell sorting ........................................... 184, 187 single-cell transcriptomics.............................. 135–157 single-cell transplantation .............................. 181–194 single-cell proteomics ............................................. 160 Single-cell RNA sequencing (scRNA-Seq)......... 137, 159 Sorting fluorescence-activated cell sorting (FACS).......65–67, 69, 71, 73, 136, 138, 142, 143, 160, 166, 176, 184, 196, 198, 200, 203, 209, 218, 234, 243, 261, 263, 283, 284, 288, 289, 293, 294, 296, 297, 303, 304, 310, 314, 315, 320, 322, 329, 348, 351, 402, 407, 409 magnetic-activated cell sorting (MACS) ..........53, 54, 114, 117, 183, 186, 190–192, 244, 247, 279, 287, 288, 347, 397 Spike-in ...........................................................97, 101, 109 Spleen................................155, 203, 210, 233, 237, 245, 248, 292, 294, 314, 315, 391, 392 Stem cells Stem cell factor (SCF¼c-Kit ligand) .................... 13, 268, 283, 284, 301, 322, 384, 414, 416 Sternum/sternal .........................228, 248, 374, 376, 381

LEUKEMIA STEM CELLS: METHODS Stromal cells ............... 14, 216, 224, 234, 259, 425, 426 Subclonal ......................................................................... 96 Surface markers ................................. 5, 7, 9, 13, 32, 181, 196, 264, 392, 393, 420

T Targeted therapies.....................................................40, 41 T cell (¼T lymphocyte) memory T cell ......................................................... 208 T-cell receptor (TR/TCR) .........................77–80, 83–86, 88–92, 95–97, 103 3D imaging.................................................................... 373 Thrombopoietin (TPO) .............................. 13, 268, 269, 273, 300, 301, 322, 384, 388, 402, 414, 416 Thymus .............................. 203, 210, 215, 233, 391, 392 Tibia ......................... 185, 186, 191, 200, 206, 229, 230, 236, 292, 312, 350, 391, 404 Total body irradiation ................................................... 197 Transcription reverse transcription ................................................ 136 Transcription factors (TFs)...................19, 136, 400, 401 Transcriptomes ............................... 9, 135, 138, 155, 441 Transcriptomics single-cell transcriptomics....................................... 135 Transduction lentiviral transduction ..................224, 290, 299–305, 318, 346, 347, 407 retroviral transduction ............................................ 308

AND

PROTOCOLS Index 479

Transfection............................... 224, 300–304, 309, 311, 312, 315, 321, 324–327, 349, 350, 356, 396, 402, 404 Transgenes ........................243, 248, 282, 289, 301, 362, 364–369, 371, 383, 401, 403, 405, 407 Transplantability.............................................................. 51 Tumor tumor heterogeneity ............................................... 216

U Umbilical cord blood (cord blood) (UCB).............113, 114, 195, 267, 268, 281, 284

V Variant call format (VCF) file .................... 449–452, 455, 456, 458, 469–471

W White blood cells (WBC) .......................... 184, 189, 190, 209, 256, 293

X Xenograft patient-derived xenograft (PDX) .................. 216, 217 Xenotransplantation ..................... 27, 219, 252, 390, 396