Cancer Biomarkers: Methods and Protocols (Methods in Molecular Biology, 2413) 1071618954, 9781071618950

Table of contents :
Preface
Contents
Contributors
Chapter 1: A Method of Bone-Metastatic Tumor Progression Assessment in Mice Using Longitudinal Radiography
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 2: Optical Imaging of Matrix Metalloproteinases Activity in Prostate Tumors in Mice
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 3: Method to Development of PET Radiopharmaceutical for Cancer Imaging
1 Introduction
2 Materials
2.1 Equipments
2.2 Reagents and Supplies
3 Methods
3.1 Prelabeling Setup
3.2 Radiochemistry
3.3 Quality Control Testings
4 Notes
References
Chapter 4: PET Use in Cancer Diagnosis, Treatment, and Prognosis
1 Introduction
2 Tumor-Linked Metabolisms
3 Carbohydrate Metabolism
4 DNA Synthesis and Cell Proliferation
5 Cellular Respiration
6 Protein Synthesis
7 Conclusions
References
Chapter 5: Immunofluorescence-Based Method to Assess Cancer Biomarker in the Hypoxic Region of the Tumor
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 6: Zebrafish Xenograft Model to Study Human Cancer
1 Introduction
2 Materials
2.1 Zebrafish Husbandry Supplies
2.2 Embryo Dissociation Supplies
3 Methods
3.1 Zebrafish Husbandry and Embryo Incubation
3.2 Malignant Cell Preparation for Xenograft
3.3 Embryo Preparation and Tumor Cell Transplantation
4 Notes
References
Chapter 7: Assessing Oligomerization Status of Mitochondrial OXPHOS Complexes Via Blue Native Page
1 Introduction
2 Materials
2.1 Isolation of Mitochondrial-Enriched Fraction
2.2 Preparation of Mitochondrial-Enriched Sample for Blue Native (BN) Page
2.3 BN Page Electrophoresis, Transfer, and Immunoblotting
3 Methods
3.1 Subcellular Fractionation for the Preparation of Mitochondrial-Enriched Fraction
3.2 Preparation of Mitochondrial-Enriched Sample for BN Page
3.3 BN Page Electrophoresis, Transfer, and Immunoblotting
4 Notes
References
Chapter 8: In Vitro Cell Impedance Assay to Examine Antigen-Specific T-Cell-Mediated Melanoma Cell Killing to Support Cancer I...
1 Introduction
2 Materials
2.1 Growing and Maintaining B16.F10 (B16) Murine Melanoma Cells
2.2 Growing and Maintaining Pmel-1 Cytotoxic (CD8+) T Cells
2.3 T-Cell-Mediated Killing of Cancer Cells with xCELLigence Real-Time Cell Analysis (RTCA)
3 Methods
3.1 Growing and Maintaining B16 Murine Melanoma Cells
3.2 Growing and Maintaining Pmel-1 CD8+ T Cells
3.3 T-Cell-Mediated Killing of Cancer Cells with xCELLigence Real-Time Cell Analysis (RTCA) Instrument
3.4 Calculating Target Cell Viability
4 Notes
References
Chapter 9: Methods to Detect Nitric Oxide and Reactive Nitrogen Species in Biological Sample
1 Introduction
2 Materials
2.1 Spectrophotometry-Based Detection of Nitric Oxide (NO): Griess Assay
2.2 DAF-FM Diacetate-Based Detection
3 Methods
3.1 Griess Assay
3.1.1 Reagent Preparation
3.1.2 Procedure
3.2 DAF-FM Diacetate-Based Assay
3.2.1 Fluorimetry Method Using DAF-FM Probe
Procedure
Calculations
3.2.2 Flow Cytometry-Based Detection of Reactive Nitrogen Species
Procedure
Analysis
3.2.3 Confocal Laser Scanning Microscopy-Based Detection Employing DAF-FM
Procedure
4 Notes
References
Chapter 10: Study of Rotary Cell Culture System-Induced Microgravity Effects on Cancer Biomarkers
1 Introduction
2 Materials
2.1 Cell Culture and Maintenance of Cancer Cells in Normal Gravity (1g) Condition
2.2 Rotary Cell Culture System (RCCS) to Simulate Microgravity in Laboratory Condition
2.3 Sample Collection, Whole-Cell Lysate Preparation, and Protein Estimation
2.4 Immunoblotting
2.4.1 SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE)
2.4.2 Wet Transfer of Proteins
2.4.3 Blocking and Antibody Probing
2.4.4 Detection and X-Ray Film Development Using ECL Reagent
3 Methods
3.1 Cell Culture and Maintenance of Cancer Cells in Normal Gravity (1g) Condition
3.2 Rotary Cell Culture System (RCCS) to Simulate Microgravity in Laboratory Condition
3.2.1 RCCS Sterilization and Conditioning
3.2.2 Seeding and Cell Culture Under SMG
3.3 Collection of Cells, Preparation of Whole-Cell Lysate, and Protein Estimation
3.4 Immunoblotting
3.4.1 Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE)
3.4.2 Wet Transfer of Proteins
3.4.3 Blocking and Antibody Probing
3.4.4 Detection and X-Ray Film Development Using ECL Reagent
4 Notes
References
Chapter 11: A Calcium Imaging Approach to Measure Functional Sensitivity of Neurons
1 Introduction
2 Materials
2.1 Primary Cell Culture Supplies
2.2 Calcium Imaging Supplies
3 Methods
3.1 DRG Isolation and Dissociation (Fig. 1)
3.2 Calcium Imaging
4 Notes
References
Chapter 12: Zymography and Reverse Zymography for Testing Proteases and Their Inhibitors
1 Introduction
2 Materials
2.1 SDS-PAGE for Zymography
2.2 Gelatin Zymography
2.3 Casein Zymography
2.4 Collagen Zymography
2.5 Reverse Zymography
2.6 Heparin-Enhanced Substrate Zymography
3 Methods
3.1 SDS-PAGE
3.2 Gelatin Zymography
3.3 Casein Zymography
3.4 Collagenase zymography
3.5 Reverse Zymography
3.6 Heparin-Enhanced Substrate Zymography
4 Notes
References
Chapter 13: Method of Preparation of Cigarette Smoke Extract to Assess Lung Cancer-Associated Changes in Airway Epithelial Cel...
1 Introduction
2 Materials
2.1 Cigarette Smoke Extract Preparation
2.2 Cell Culture
2.3 MTT Assay
2.4 Lysate Collection and Western Blotting
3 Methods
3.1 Preparation of Cigarette Smoke Extract
3.2 Exposure of Lung Adenocarcinoma (A549) Cells with Freshly Prepared CSE
3.3 MTT Assay in CSE Treated A549 cells
3.4 Lysate Collection and Western Blotting
3.4.1 Lysate Collection
3.4.2 Western Blotting
4 Notes
References
Chapter 14: Air-Liquid Interface Culture Model to Study Lung Cancer-Associated Cellular and Molecular Changes
1 Introduction
2 Materials
2.1 Small Airway Epithelial Cells (SAEC) Culture
2.2 Air-Liquid Interface Culture (ALI)
2.3 Hematoxylin and Eosin (H&E) Staining
2.4 Periodic Acid-Schiff (PAS) Staining
3 Methods
3.1 SAEC Culture and ALI Establishment
3.1.1 SAEC Culture
3.1.2 Air-Liquid Interface Culture Establishment
3.2 H&E Staining
3.3 PAS Staining
4 Notes
References
Chapter 15: Co-Immunoprecipitation-Blotting: Analysis of Protein-Protein Interactions
1 Introduction
2 Materials
2.1 Co-IP
2.2 Sodium Dodecyl Sulfate (SDS) Polyacrylamide Gel
2.3 Immunoblotting
2.4 Cell lysates and Antibodies
3 Methods
3.1 Co-IP
3.2 10% SDS Polyacrylamide Gel Electrophoresis
3.3 Immunoblotting
4 Notes
References
Chapter 16: Methods to Assess Oxidative DNA Base Damage Repair of Apurinic/Apyrimidinic (AP) Sites Using Radioactive and Nonra...
1 Introduction
2 Materials
2.1 Radioactive Labeled Probe-Based Detection Assay
2.2 Fluorescence Labeled Probe-Based Detection Assay
3 Methods
3.1 Radioactive Labeled Probe-Based Detection Assay
3.2 Fluorescence-Labeled Probe-Based Detection Assay
4 Notes
References
Chapter 17: Determining the Size Distribution and Integrity of Extracellular Vesicles by Dynamic Light Scattering
1 Introduction
2 Materials
2.1 Sources of Extracellular Vesicles
2.2 Cell Culture
2.3 Centrifuges
2.4 Protease Inhibitor Cocktail
2.5 Commercial Kits for EV Isolation
2.6 Dynamic Light Scattering (DLS)
3 Methods
3.1 Isolation of Extracellular Vesicles from Conditioned Media
3.2 Size Distribution and Integrity of Extracellular Vesicles by DelsaMax PRO
4 Notes
References
Chapter 18: Characterization of Exosomal Surface Proteins by Immunogold Labeling
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 19: Scanning Electron Microscopy of Giant Cells from Giant Cell Tumor of Bone
1 Introduction
2 Materials
3 Methods
3.1 Patient Selection for Obtaining GCT Sample
3.2 Fixation and Processing of Samples for Histopathological Examination
3.2.1 Tissue Fixation and Decalcification
3.2.2 Tissue Processing for Paraffin Infiltration and Embedding
3.2.3 Hematoxylin and Eosin Staining
Sectioning
Deparaffinization of the Section on Slides
Rehydration and Staining
3.3 GCT Sample Collection and Primary Processing for SEM
3.4 Scanning Electron Microscopy (SEM)
4 Notes
References
Chapter 20: Raman Microscopy Techniques to Study Lipid Droplet Composition in Cancer Cells
1 Introduction
2 Materials
2.1 Fatty Acid Methyl Ester (FAME) Standards (Sigma-Aldrich Corp)
2.2 LNCaP Prostate Cancer Cells
2.3 Suppliers, Consumables, etc.
2.4 Instrumentation/Experimental Setup
3 Methods
3.1 FAME Samples Preparations
3.2 Preparation of FAMEs for Tissue Culture
3.3 Treatment of the Cells
3.4 Spectra Recording
3.5 Raman Spectra Processing
3.6 Least Square Fitting
3.7 Raman Vibrational Mode Assignment
3.8 FAME Reference Lipids Biomarkers by Micro-Raman Spectroscopy
3.9 LNCaP Prostate Cancer Cell Lipids Biomarkers by Micro-Raman Spectroscopy
3.10 Lipid Unsaturation/Saturation Analysis
3.11 Lipid Composition Changes Analysis
4 Notes
References
Chapter 21: Surface Plasmon Resonance, a Novel Technique for Sensing Cancer Biomarker: Folate Receptor and Nanoparticles Inter...
1 Introduction
2 Materials
2.1 Materials and Reagents
2.2 Standard Materials Required for Surface Plasmon Resonance
3 Methods
3.1 Nanoparticle Formulation Method
3.2 Method for Synthesis of N-Hydroxysuccinimide Ester-Activated Folate (NHS-FA)
3.3 Poly(ε-Caprolactone) (PCL) Activation
3.4 Preparation of N-Hydroxysuccinimide Ester (NHS)-Activated Folate-Conjugated PEG
3.5 Characterization of PBM Nanoparticles
3.6 COOH Sensor Chip Coupling Procedure
3.7 SPR Analysis of the Protein (FOLR1) and Folic Acid-Conjugated Planetary Ball Milled Nanoparticle (FA-PBM-NP) Binding
3.8 Data Analysis
3.9 Methods for Kinetics Evaluation
4 Notes
References
Chapter 22: Characterization of Tobacco Microbiome by Metagenomics Approach
1 Introduction
2 Materials
2.1 Media for Isolation, Growth, and Identification
2.2 Other Chemicals Required
2.3 Other Materials Required
2.4 PCR Kit Required
3 Methods
3.1 Estimation of Cultivable Microbial Communities of Tobacco
3.1.1 Preparation of Media
3.1.2 Isolation of Cultivable Bacteria from Tobacco
3.2 Estimation of Total Microbial Communities of Tobacco
3.3 DNA Extraction from Microbial Communities of Tobacco
3.4 Estimation of Bacterial Load Through Amplification of 16S rRNA-Based Primers by Real-Time PCR Method
3.4.1 Determination of Bacterial Load
3.4.2 PCR Amplification
Primers
Reaction
Controls
3.5 16S rRNA Sequencing
3.5.1 First Step PCR-16S rRNA Gene Amplification (PCR1)
3.5.2 IInd PCR STEP
3.5.3 IInd Clean up of PCR Product (Using Magnetic Beads)
3.5.4 Sequencing
3.5.5 Analysis
3.6 Analysis of Sequencing Data Via QIIME2
3.6.1 Install QIIME2
3.6.2 Prepare Metadata
3.6.3 Prepare Raw Sample
3.6.4 Demultiplexing
3.6.5 Quality Check, Filtering, and Denoising
3.6.6 Phylogenetic Tree Generation
3.6.7 Visualization of Alpha Rarefaction
3.6.8 Calculation of Diversity Matrix
3.6.9 Assigning Taxonomy
3.6.10 Calculation of Differential Abundance
4 Notes
References
Chapter 23: Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research
1 Introduction
2 Batch Effects Correction and Normalization of scRNA-seq Data
3 Imputation of scRNA-seq Data
4 Single-Cell Clustering and Annotation
5 Single-Cell Trajectory Reconstruction
6 Single-Cell RNA-Seq Application in Identification of Cancer Biomarkers
References
Index

Citation preview

Methods in Molecular Biology 2413

Gagan Deep Editor

Cancer Biomarkers 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.

Cancer Biomarkers Methods and Protocols

Edited by

Gagan Deep Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Urology, Wake Forest School of Medicine, Winston-Salem, NC, USA

Editor Gagan Deep Department of Cancer Biology Wake Forest School of Medicine Winston-Salem, NC, USA Department of Urology Wake Forest School of Medicine Winston-Salem, NC, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1895-0 ISBN 978-1-0716-1896-7 (eBook) https://doi.org/10.1007/978-1-0716-1896-7 © Springer Science+Business Media, LLC, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed 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 Cancer is a serious global health issue, and cancer biomarkers have immensely contributed to better managing cancer burden, including cancer risk assessment, cancer diagnosis, determining cancer progression, and therapeutic response. In the era of precision medicine and targeted therapy, cancer biomarkers have become even more important and could provide valuable molecular information about the tumor and its microenvironment components. The present book consists of several methods used in basic science laboratories to characterize cancer-related biomarkers. Several cancer types metastasize to the bone, causing significant pain and mortality. In the first chapter, Eber et al. have outlined a radiography method to assess bone metastatic tumor progression that could help assess the effect of various therapeutics against bone metastatic disease progression. Matrix metalloproteinases (MMPs) play a key role in extracellular matrix remodeling and cancer growth and metastatic spread. In the second chapter, Susy and Deep have described a method to noninvasively assess MMPs activity in prostate tumors in mice using an optical probe. Positron Emission Tomography (PET) is a powerful tool that is widely used for cancer imaging in the clinic. In the third chapter, Damuka and Sai have elaborated a methodology for the clinical development of PET tracers for cancer imaging. This methodology chapter is followed by an in-depth review article by the same group highlighting the state of PET radiotracers’ development for the molecular imaging of cancer. Hypoxia in tumors indicates an aggressive disease associated with an adverse prognosis. A better understanding of molecular biomarkers in hypoxic regions of the tumor could be valuable in better predicting the disease course and the treatment outcome. In Chapter 5, Rios-Colon et al. have described an immunofluorescence-based method to assess cancer biomarkers’ expression in the hypoxic regions of the tumor. In the sixth chapter, Somasagara et al. have described a methodology for the growth of cancer cells in zebrafish and the application of this model in high-throughput testing of anticancer drugs. In the seventh chapter, Woytash et al. have described a method to assess the oligomeric state of the mitochondrial oxidative phosphorylation-related protein complexes by nondenaturing blue native page electrophoresis. A better understanding of these mitochondrial complexes could be helpful in assessing the metabolic state of cancer cells and could serve as a useful tool to assess the molecular mechanistic details of anticancer agents that target mitochondria. In Chapter 8, Stirling and Soto-Pantoja have described a novel cell impedance assay to examine antigen-specific T-cell-mediated killing of melanoma cells. This assay could be useful in better understanding the interaction between effector immune cells and target cancer cells and in discovering novel drugs for cancer immunotherapy. Higher oxidative stress is an integral component of cancer cells. In the ninth chapter, Kaur et al. have described several spectrophotometric and fluorescence-based assays to assess nitric oxide level, one of the surrogates for oxidative stress. In Chapter 10, Singh and Singh have described a novel rotary cell culture system to grow cancer cells under microgravity conditions and characterize the expression of protein biomarkers under such conditions. Pain associated with chemotherapy and radiation therapy often compromises the treatment schedule and also adversely affects the quality of life in cancer patients. In Chapter 11, Wheeler et al. have described a protocol for the isolation and culture of dorsal root ganglia (DRG) and calcium imaging as a measure of neuron activity and as a potential pain biomarker in cell culture. This approach could be

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useful to determine if any specific biomolecule(s) secreted by tumors is capable of activating the sensory neurons. In Chapter 12, Choudhary et al. have provided in-depth details of the zymography method to assess activities of various MMPs and reverse zymography to access activities of tissue inhibitors of metalloproteinases (TIMPs). The measurement of MMPs and TIMPs in cancer cells, tissue extracts, and biofluids could provide valuable molecular information about cancer, which could be useful in cancer staging, prognosis, and making treatment decisions. Tobacco smoking is a major risk factor for the development of various cancers. To better understand the molecular effects of cigarette smoke on cancer cells, in Chapter 13, Agraval et al. describe a method for the preparation of cigarette smoke extract. In Chapter 14, Agraval et al. describe the 3D culture system called Air-Liquid Interface (ALI) for growing human-derived primary small airway epithelial cells to study the cellular and molecular biomarkers associated with lung cancer. In Chapter 15, Tan and Yammani have elaborated a methodology for immunoprecipitation of protein complexes, a powerful technique to analyze protein-protein interactions in several diseases, including cancer. DNA damage repair is an essential feature in cancer cell survival and progression. In Chapter 16, Gupta et al. have described both radioactive and nonradioactive fluorescence-based methods to assess apurinic/apyrimidinic endonuclease 1 (APE1) activity, a key player in the base excision repair (BER) pathway. Lately, extracellular vesicles (EVs) have emerged as significant players in intercellular communication and play a key role in the remodeling of the local and distant tumor microenvironment. It is important to study the shape, size, and integrity of EVs for their potential usefulness as a biomarker for cancer diagnosis and monitoring treatment decisions. In Chapter 17, Khan et al. have elaborated the dynamic light scattering (DLS) method for determining the size distribution and integrity of EVs. In Chapter 18, Su et al. have elaborated a methodology to study the surface proteins on exosomes by immunogold labeling. This is critical to identify and validate the expression of cancer-specific protein biomarkers on the surface of exosomes for their pulldown from total exosomes and cargo characterization. In Chapter 19, Mridha and Yadav have elaborated scanning electron microscopy, which could be useful in characterizing giant cells and distinguishing an aggressive giant cell tumor from an indolent tumor. In Chapter 20, Potcoava et al. have detailed Raman microscopy techniques to study lipid droplet composition in cancer cells. Cellular lipids play a dynamic role in cellular metabolism, molecular signaling, as well as in developing resistance to therapeutics in cancer cells. Therefore, this novel methodology could be useful in characterizing the unsaturation or saturation degrees and composition changes of the fatty acids in cancer cells toward identifying novel biomarkers. In Chapter 21, Singh and Singh have described a surface plasmon resonance (SPR) based approach to detect and target cancer cells based upon their specific surface biomarker(s). Besides smoking, tobacco chewing is also associated with several cancer types. In Chapter 22, Kumar et al. have proposed that the microbes in tobacco products could affect the biochemicals present in tobacco and thereby influence carcinogenesis. They have described a metagenomics approach using 16S rRNA-based next-generation sequencing methods for the detection and characterization of the microbial community of tobacco. Lastly, in Chapter 23, Song and Liu have compiled a review article describing the state of single-cell RNA-seq and computational analysis tools in cancer research. Together, these methodologies offer multiple ways to study cancer-associated molecular biomarkers. Winston-Salem, NC, USA

Gagan Deep

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

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1 A Method of Bone-Metastatic Tumor Progression Assessment in Mice Using Longitudinal Radiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Matthew R. Eber, Juan Miguel Jime´nez-Andrade, Christopher M. Peters, and Yusuke Shiozawa 2 Optical Imaging of Matrix Metalloproteinases Activity in Prostate Tumors in Mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Susy Kim and Gagan Deep 3 Method to Development of PET Radiopharmaceutical for Cancer Imaging . . . . 13 Naresh Damuka and Kiran Kumar Solingapuram Sai 4 PET Use in Cancer Diagnosis, Treatment, and Prognosis . . . . . . . . . . . . . . . . . . . . 23 Naresh Damuka, Meghana Dodda, and Kiran Kumar Solingapuram Sai 5 Immunofluorescence-Based Method to Assess Cancer Biomarker in the Hypoxic Region of the Tumor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Leslimar Rios-Colon, Susy Kim, Yixin Su, Deepak Kumar, and Gagan Deep 6 Zebrafish Xenograft Model to Study Human Cancer . . . . . . . . . . . . . . . . . . . . . . . . 45 Ranganatha R. Somasagara and TinChung Leung 7 Assessing Oligomerization Status of Mitochondrial OXPHOS Complexes Via Blue Native Page. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Jordan Woytash, Joseph R. Inigo, and Dhyan Chandra 8 In Vitro Cell Impedance Assay to Examine Antigen-Specific T-Cell-Mediated Melanoma Cell Killing to Support Cancer Immunotherapy Drug Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Elizabeth R. Stirling and David R. Soto-Pantoja 9 Methods to Detect Nitric Oxide and Reactive Nitrogen Species in Biological Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Sharanjot Kaur, Kunj Bihari Gupta, Sandeep Kumar, Shishir Upadhyay, Anil Kumar Mantha, and Monisha Dhiman 10 Study of Rotary Cell Culture System-Induced Microgravity Effects on Cancer Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Ragini Singh and Rana P. Singh 11 A Calcium Imaging Approach to Measure Functional Sensitivity of Neurons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Joshua J. Wheeler, John M. Davis, and Santosh K. Mishra 12 Zymography and Reverse Zymography for Testing Proteases and Their Inhibitors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Preety Choudhary, Vineet Kumar Mishra, and Snehasikta Swarnakar

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Method of Preparation of Cigarette Smoke Extract to Assess Lung Cancer-Associated Changes in Airway Epithelial Cells . . . . . . . . . . . . . . . . . . . . . . . Hina Agraval, Jiten R. Sharma, and Umesh C. S. Yadav Air–Liquid Interface Culture Model to Study Lung Cancer-Associated Cellular and Molecular Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hina Agraval, Jiten R. Sharma, Neeraj Dholia, and Umesh C. S. Yadav Co-Immunoprecipitation-Blotting: Analysis of Protein-Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Tan and Raghunatha R. Yammani Methods to Assess Oxidative DNA Base Damage Repair of Apurinic/Apyrimidinic (AP) Sites Using Radioactive and Nonradioactive Oligonucleotide-Based Assays . . . . . . . . . . . . . . . . . . . . . . . . . . Kunj Bihari Gupta, Sharanjot Kaur, Monisha Dhiman, and Anil Kumar Mantha Determining the Size Distribution and Integrity of Extracellular Vesicles by Dynamic Light Scattering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Aslam Khan, Shashi Anand, Sachin Kumar Deshmukh, Seema Singh, and Ajay Pratap Singh Characterization of Exosomal Surface Proteins by Immunogold Labeling. . . . . . Yixin Su, Ashish Kumar, and Gagan Deep Scanning Electron Microscopy of Giant Cells from Giant Cell Tumor of Bone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asit Ranjan Mridha and Subhash Chandra Yadav Raman Microscopy Techniques to Study Lipid Droplet Composition in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mariana C. Potcoava, Gregory L. Futia, Emily A. Gibson, and Isabel R. Schlaepfer Surface Plasmon Resonance, a Novel Technique for Sensing Cancer Biomarker: Folate Receptor and Nanoparticles Interface. . . . . . . . . . . . . . . . . . . . . Santosh Kumar Singh and Rajesh Singh Characterization of Tobacco Microbiome by Metagenomics Approach . . . . . . . . R. Suresh Kumar, Nivedita Mishra, and Amit Kumar Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qianqian Song and Liang Liu

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

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Contributors HINA AGRAVAL • School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India SHASHI ANAND • Department of Pathology, College of Medicine, University of South Alabama, Mobile, AL, USA; Cancer Biology Program, Mitchell Cancer Institute, Mobile, AL, USA DHYAN CHANDRA • Department of Pharmacology and Therapeutics, Centre for Genetics and Pharmacology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA PREETY CHOUDHARY • Infectious Diseases and Immunology, CSIR-Indian Institute of Chemical Biology, Kolkata, West Bengal, India NARESH DAMUKA • Department of Radiology, Wake Forest School of Medicine, WinstonSalem, NC, USA JOHN M. DAVIS • Department of Psychology, University of Chicago, Chicago, IL, USA GAGAN DEEP • Department of Cancer Biology, Wake Forest School of Medicine, WinstonSalem, NC, USA; Department of Urology, Wake Forest School of Medicine, Winston-Salem, NC, USA SACHIN KUMAR DESHMUKH • Department of Pathology, College of Medicine, University of South Alabama, Mobile, AL, USA; Cancer Biology Program, Mitchell Cancer Institute, Mobile, AL, USA MONISHA DHIMAN • Department of Microbiology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India NEERAJ DHOLIA • Faculty of Agriculture and Veterinary Science, Jayoti Vidyapeeth Women’s University, Jaipur, Rajasthan, India MEGHANA DODDA • Department of Radiology, Wake Forest School of Medicine, WinstonSalem, NC, USA MATTHEW R. EBER • Department of Cancer Biology and Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC, USA GREGORY L. FUTIA • Department of Bioengineering, University of Colorado Denver, Aurora, CO, USA EMILY A. GIBSON • Department of Bioengineering, University of Colorado Denver, Aurora, CO, USA KUNJ BIHARI GUPTA • Department of Microbiology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India JOSEPH R. INIGO • Department of Pharmacology and Therapeutics, Centre for Genetics and Pharmacology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA JUAN MIGUEL JIME´NEZ-ANDRADE • Unidad Acade´mica Multidisciplinaria Reynosa Aztla´n, Universidad Autonoma de Tamaulipas, Reynosa, Tamaulipas, Mexico SHARANJOT KAUR • Department of Microbiology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India MOHAMMAD ASLAM KHAN • Department of Pathology, College of Medicine, University of South Alabama, Mobile, AL, USA; Cancer Biology Program, Mitchell Cancer Institute, Mobile, AL, USA SUSY KIM • Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA

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AMIT KUMAR • ICMR-AIIMS Computational Genomics Center, Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India ASHISH KUMAR • Department of Cancer Biology, Wake Forest School of Medicine, WinstonSalem, NC, USA DEEPAK KUMAR • Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, NC, USA R. SURESH KUMAR • Molecular Genetics Lab, Molecular Biology Group, National Institute of Cancer Prevention and Research (ICMR), Noida, Uttar Pradesh, India SANDEEP KUMAR • Department of Microbiology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India TINCHUNG LEUNG • The Julius L. Chambers Biomedical/Biotechnology Research Institute, North Carolina Central University, North Carolina Research Campus, Kannapolis, NC, USA; Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA LIANG LIU • Department of Cancer Biology, Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA; Center for Cancer Genomics and Precision Oncology, Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA ANIL KUMAR MANTHA • Department of Zoology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India NIVEDITA MISHRA • Molecular Genetics Lab, Molecular Biology Group, National Institute of Cancer Prevention and Research (ICMR), Noida, Uttar Pradesh, India SANTOSH K. MISHRA • Department of Molecular Biomedical Sciences, North Carolina State University College of Veterinary Medicine, Raleigh, NC, USA; Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA; Comparative Pain Research and Education Center, North Carolina State University, Raleigh, NC, USA VINEET KUMAR MISHRA • Infectious Diseases and Immunology, CSIR-Indian Institute of Chemical Biology, Kolkata, West Bengal, India ASIT RANJAN MRIDHA • Department of Pathology, All India Institute of Medical Sciences, New Delhi, India CHRISTOPHER M. PETERS • Department of Anesthesiology, Wake Forest University Health Sciences, Winston-Salem, NC, USA MARIANA C. POTCOAVA • Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL, USA LESLIMAR RIOS-COLON • Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA; Julius L. Chambers Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, NC, USA ISABEL R. SCHLAEPFER • Division of Medical Oncology, Genitourinary Cancer Program, University of Colorado Denver School of Medicine, University of Colorado Denver, Aurora, CO, USA JITEN R. SHARMA • School of Life Sciences, Central University of Gujarat, Gandhinagar, Gujarat, India YUSUKE SHIOZAWA • Department of Cancer Biology and Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC, USA AJAY PRATAP SINGH • Department of Pathology, College of Medicine, University of South Alabama, Mobile, AL, USA; Cancer Biology Program, Mitchell Cancer Institute, Mobile, AL, USA; Department of Biochemistry and Molecular Biology, College of Medicine, University of South Alabama, Mobile, AL, USA

Contributors

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RAGINI SINGH • Cancer and Radiation Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, Delhi, India RAJESH SINGH • Department of Microbiology, Biochemistry and Immunology, Cancer Health Equity Institute, Morehouse School of Medicine, Atlanta, GA, USA RANA P. SINGH • Cancer and Radiation Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, Delhi, India; Special Centre for Systems Medicine, Jawaharlal Nehru University, New Delhi, Delhi, India SANTOSH KUMAR SINGH • Department of Microbiology, Biochemistry and Immunology, Cancer Health Equity Institute, Morehouse School of Medicine, Atlanta, GA, USA SEEMA SINGH • Department of Pathology, College of Medicine, University of South Alabama, Mobile, AL, USA; Cancer Biology Program, Mitchell Cancer Institute, Mobile, AL, USA; Department of Biochemistry and Molecular Biology, College of Medicine, University of South Alabama, Mobile, AL, USA KIRAN KUMAR SOLINGAPURAM SAI • Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA RANGANATHA R. SOMASAGARA • The Julius L. Chambers Biomedical/Biotechnology Research Institute, North Carolina Central University, North Carolina Research Campus, Kannapolis, NC, USA QIANQIAN SONG • Department of Cancer Biology, Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA DAVID R. SOTO-PANTOJA • Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA; Department of Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA; Wake Forest Baptist Comprehensive Cancer Center, WinstonSalem, NC, USA; Wake Forest Biotech Place, Winston-Salem, NC, USA ELIZABETH R. STIRLING • Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA YIXIN SU • Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA SNEHASIKTA SWARNAKAR • Infectious Diseases and Immunology, CSIR-Indian Institute of Chemical Biology, Kolkata, West Bengal, India LI TAN • Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA SHISHIR UPADHYAY • Department of Zoology, School of Biological Sciences, Central University of Punjab, Bathinda, Punjab, India JOSHUA J. WHEELER • Department of Molecular Biomedical Sciences, North Carolina State University College of Veterinary Medicine, Raleigh, NC, USA; Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA JORDAN WOYTASH • Department of Pharmacology and Therapeutics, Centre for Genetics and Pharmacology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA SUBHASH CHANDRA YADAV • Department of Anatomy, All India Institute of Medical Sciences, New Delhi, India UMESH C. S. YADAV • Special Centre for Molecular Medicine, Jawaharlal Nehru University, New Delhi, India RAGHUNATHA R. YAMMANI • Section of Molecular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA

Chapter 1 A Method of Bone-Metastatic Tumor Progression Assessment in Mice Using Longitudinal Radiography Matthew R. Eber, Juan Miguel Jime´nez-Andrade, Christopher M. Peters, and Yusuke Shiozawa Abstract Many types of solid tumors metastasize to the bone, where it causes significant morbidity and mortality in patients with advanced disease. Bone metastases are not only incurable but also affect bone health which impairs patients’ quality of life. In order to understand the mechanisms and develop effective treatments for bone-metastatic disease, it is first necessary to develop animal models that permit the assessment of tumor growth in the bone and progressive structural changes of the bone simultaneously. Longitudinal analysis of bone tumor progression is generally performed by bioluminescent imaging; however, this method is not able to assess progressive structural changes of the bone. Here, we describe a simple method for assessment of bone lesions using a scoring system that takes into account disease burden and bone destruction using longitudinal radiographs. Key words Bone metastasis, Radiography, Intrafemoral injection

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Introduction Bone metastases are a common cause of morbidity and mortality in many types of solid tumors, particularly prostate cancer, breast cancer, and lung cancer. Clinically, prostate cancer commonly presents as osteoblastic lesions [1], while breast cancer and lung cancer develop osteolytic lesions [2]. It is also common for bone metastases to be a mix of both osteoblastic and osteolytic phenotypes [2]. These lesions result in skeletal-related events (SREs), such as bone pain, fracture, hypercalcemia, and spinal cord compression, which significantly impair patient quality of life [3, 4]. Therefore, understanding how bone-metastatic cancer cells disrupt normal bone physiology may be as crucial to patient care as revealing the mechanisms of disease progression in bone. In in vivo animal studies, bioluminescent imaging is commonly used to measure tumor growth, especially in nonpalpable tissues, such as bone. However, bioluminescent imaging does not allow the

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_1, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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assessment of tumor growth and structural changes in bone, simultaneously. Additionally, luminescent signals are unable to penetrate deep tissue without a critical mass of cells and high expression of luciferase [5]. In order to achieve high expression of luciferase, lentiviral vectors are commonly used to transduce the luciferase gene into cancer cell lines through a nonspecific mechanism of genome insertion that can cause off-target effects. Additionally, single-cell sorting or cloning methods, which have been used to maximize luciferase expression and prevent in vivo gene silencing, may also exacerbate the off-target effects of lentiviral gene transduction [6]. Alternatively, disease progression and bone density can be assessed simultaneously through the use of magnetic resonance imaging (MRI) or radiography. Although MRI and microcomputed tomography (μCT) analyses can provide very detailed assessments of bone-metastatic lesions, longitudinal follow-up with these technologies is cost-prohibitive. A more accessible tool for monitoring bone-metastatic cancer cells is an ordinary X-ray image. Osteoblastic and osteolytic lesions are clearly visible by X-ray if a baseline radiograph is acquired, and sufficient follow-up imaging is performed. Routine radiograph can provide an easy and costeffective way to observe deep into tissue and track bone tumor progression without genetically modifying the cancer cell line of interest. Here, we describe a method to score and monitor bonemetastatic growth/progression and bone destruction longitudinally with radiographic analysis based on scoring metrics, previously developed [7–9], in mouse models in which cancer cells are inoculated directly into the bone by intrafemoral injection.

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Materials 1. MultiFocus 10
15 Digital Radiography System (Fig. 1a, Faxitron Bioptics; Tuscon, AZ). 2. Isoflurane vaporizer. 3. Inoculum: cancer cell line suspended in small volume (5 μL) of sterile Hank’s Balanced Salt Solution without Calcium and Magnesium (Gibco™ 14170112). 4. 28g internal injector (Plastics One C3131, 11 mm).

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Methods 1. Turn on the X-ray system (Faxitron) and perform the necessary steps for operation (see Note 1).

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Fig. 1 Longitudinal radiograph analysis of tumor-induced bone remodeling in the mouse femur. (a) The image of MultiFocus 10
15 Digital Radiography System. (b) Radiographs were collected prior to inoculation (D0) and weekly (D7, 14, 21, and 28) following intrafemoral injection of 5 μL Hank’s buffered salt solution (Sham, first column) or prostate cancer cells in mice. A blunt 28 gauge injector needle attached to a 25 μL Hamilton syringe with joint connector tubing (Eicom) is inserted between the distal condyles of femur for injecting the cancer cells. Five-week-old athymic nude male mice were injected with 2
104 osteolytic human PC3 cell line (column 2). Five-week-old male C57BL6 mice were injected with 5
103 mixed syngeneic RM-1 mouse prostate cancer cells (column 3). Representative radiographs showing the various progression scores are displayed in the bottom left of each panel and the time point after inoculation in the upper right of the panel. Note the progressive increases in pitted osteolytic lesions (arrows), mid-diaphysis of PC3-inoculated mice that ultimately results in erosion of cortical bone (arrowhead). In RM-1-inoculated mice, the osteolysis is most prominent in the distal metaphysis and results in progressive medullary and cortical bone erosion. The RM-1 cell line also produces extraperiosteal sclerotic lesions evident as slightly radioopaque extracortical regions (outlined by dashed lines) in some mice

2. Anesthetize mouse with isoflurane. 3. Place the mouse prone on the stage with its nose in a secured nosecone. 4. Position the mouse so that the bone to be imaged is centered on the stage (see Note 2).

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5. Place the stage at the level for the desired magnification (see Note 3). 6. Radiograph the mouse using the same settings throughout the entire experiment (see Note 4). 7. Inoculate the animal (see Note 5), and repeat the imaging process at regular intervals, of both the contralateral and ipsilateral bones (see Note 6). 8. Save the images in a de-identified manner. 9. Provide the images to a blinded observer to perform longitudinal scoring using the following scale for assessing the degree of osteolysis (see Note 7). (a) 0 ¼ Bones with no lesions (b) 1 ¼ Bones with one to three small pits of radiolucent lesions (c) 2 ¼ Bones with three to six small pits of radiolucent lesions (d) 3 ¼ Bones with obvious loss of medullary bone and erosion of the cortical bone (e) 4 ¼ Bones with full thickness unicortical bone loss (f) 5 ¼ Bones with full thickness bicortical bone loss and displaced skeletal fracture 10. For cancer cells that produce mixed osteolytic and osteosclerotic lesions, tumor-induced new bone formation is often apparent as intramedullary or extraperiosteal slightly radiopaque regions that have a spongy appearance (Fig. 1b, column 3). The degree of tumor-induced bone formation can be quantified according to previously published methods [10]. 11. Representative radiographs of intrafemorally injected Sham, PC3, and RM-1 prostate cancer cells can be found in Fig. 1b. Over time, sham-injected bones remain without osteolytic or sclerotic lesions (score ¼ 0). PC3-injected femurs start similar to sham at day 0 and 7 (score ¼ 0), but develop visible osteolytic lesions by day 14 (score ¼ 1). These lesions increase in number by day 21 (score ¼ 2), and in size by day 28 (score ¼ 3) when the cortical and medullary bones are clearly eroded. In RM-1-inoculated mice, osteolysis is evident in the distal metaphysis as soon as day 7 (score ¼ 1) and progressively erodes medullary and cortical bone erosion over time [day 14 (score ¼ 2), day 21 (score ¼ 3)] until reaching full thickness unicortical bone loss by day 28 (score ¼ 4). Additionally, an extraperiosteal sclerotic lesion is observed on day 21, which grows in size by day 28.

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Notes 1. The automatic system used in our lab (Faxitron) is turned on with a key and images are captured on an attached desktop computer using Faxitron Vision Software. After both the machine and desktop are turned on, the Faxitron Vision Software is launched, and an automatic initialization and calibration sequence are performed. After these steps are completed, the system is ready to digitally capture images. 2. It may be necessary to secure the animal to the stage with tape if the images are developed blurry due to breathing artifacts. 3. A magnification of 4
or 5
is recommended. It is usually possible to capture the ipsilateral and contralateral limbs in the same image at 4
magnification, which can be a helpful reference for scoring; however, a 5
magnification of the ipsilateral limb is usually preferable. If the time is available, we recommend taking one image with both limbs at 4
magnification, followed by another image of just the ipsilateral limb at 5
magnification. 4. The settings we use for mice on our instrument are 28 kV for 8 s. It may be helpful to first set the machine to fully automated mode to see what the suggested settings are for your conditions. After an automated image is captured, it will be necessary to manually set the machine for subsequent images, as only images captured at the same settings are comparable. 5. For direct intrafemoral injection, we recommend verifying needle placement by radiograph before inoculation to ensure that the inoculum is deposited inside the bone (Fig. 1b, Sham D0). Detailed protocols for intrafemoral injection can be found elsewhere [11]. 6. For slow-growing tumors, perform baseline radiographs before implantation and radiograph animals at least once a week following implantation. For fast-growing tumors, perform baseline radiographs before implantation and radiograph animals at least twice a week following implantation. 7. It may be acceptable to organize the de-identified images in the sequence in which they are taken, from baseline to the end of the experiment. The most quantitative way to perform the analysis would be to completely randomize the images, but this level of de-identification may not be necessary for most experiments.

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Acknowledgments This work is directly supported by the National Cancer Institute (R01-CA238888, Y.S.), Department of Defense (W81XWH-17-10541, Y.S.; W81XWH-19-1-0045, Y.S.; and W81XWH-17-10542, C.M.P.), and the Wake Forest Baptist Comprehensive Cancer Center Internal Pilot Funding (Y.S.). This work is also supported by the National Cancer Institute’s Cancer Center Support Grant award number P30-CA012197 issued to the Wake Forest Baptist Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute. Conflict of Interests Y.S. has received research funding from TEVA Pharmaceuticals but not relevant to this study. References 1. Charhon SA, Chapuy MC, Delvin EE, Valentin-Opran A, Edouard CM, Meunier PJ (1983) Histomorphometric analysis of sclerotic bone metastases from prostatic carcinoma special reference to osteomalacia. Cancer 51(5):918–924. https://doi.org/10.1002/ 1097-0142(19830301)51:53.0.co;2-j 2. Mundy GR (2002) Metastasis to bone: causes, consequences and therapeutic opportunities. Nat Rev Cancer 2(8):584–593. https://doi. org/10.1038/nrc867 3. Coleman R, Body JJ, Aapro M, Hadji P, Herrstedt J, Group EGW (2014) Bone health in cancer patients: ESMO clinical practice guidelines. Ann Oncol 25(suppl 3): iii124–iii137. https://doi.org/10.1093/ annonc/mdu103 4. Tsuzuki S, Park SH, Eber MR, Peters CM, Shiozawa Y (2016) Skeletal complications in cancer patients with bone metastases. Int J Urol 23(10):825–832. https://doi.org/10. 1111/iju.13170 5. O’Neill K, Lyons SK, Gallagher WM, Curran KM, Byrne AT (2010) Bioluminescent imaging: a critical tool in pre-clinical oncology research. J Pathol 220(3):317–327. https:// doi.org/10.1002/path.2656 6. Shearer RF, Saunders DN (2015) Experimental design for stable genetic manipulation in mammalian cell lines: lentivirus and alternatives. Genes Cells 20(1):1–10. https://doi. org/10.1111/gtc.12183

7. Honore P, Luger NM, Sabino MA, Schwei MJ, Rogers SD, Mach DB, O’Keefe PF, Ramnaraine ML, Clohisy DR, Mantyh PW (2000) Osteoprotegerin blocks bone cancer-induced skeletal destruction, skeletal pain and painrelated neurochemical reorganization of the spinal cord. Nat Med 6(5):521–528. https:// doi.org/10.1038/74999 8. Bloom AP, Jimenez-Andrade JM, Taylor RN, Castaneda-Corral G, Kaczmarska MJ, Freeman KT, Coughlin KA, Ghilardi JR, Kuskowski MA, Mantyh PW (2011) Breast cancerinduced bone remodeling, skeletal pain, and sprouting of sensory nerve fibers. J Pain 12(6):698–711. https://doi.org/10.1016/j. jpain.2010.12.016 9. Grenald SA, Doyle TM, Zhang H, Slosky LM, Chen Z, Largent-Milnes TM, Spiegel S, Vanderah TW, Salvemini D (2017) Targeting the S1P/S1PR1 axis mitigates cancer-induced bone pain and neuroinflammation. Pain 158(9):1733–1742. https://doi.org/10. 1097/j.pain.0000000000000965 10. Thompson ML, Jimenez-Andrade JM, Chartier S, Tsai J, Burton EA, Habets G, Lin PS, West BL, Mantyh PW (2015) Targeting cells of the myeloid lineage attenuates pain and disease progression in a prostate model of bone cancer. Pain 156(9):1692–1702. https:// doi.org/10.1097/j.pain.0000000000000228 11. Park SH, Eber MR, Shiozawa Y (2019) Models of prostate cancer bone metastasis. Methods Mol Biol 1914:295–308. https://doi.org/10. 1007/978-1-4939-8997-3_16

Chapter 2 Optical Imaging of Matrix Metalloproteinases Activity in Prostate Tumors in Mice Susy Kim and Gagan Deep Abstract The molecular characterization of cancer could have significant clinical benefits, including early diagnosis, making treatment decisions, and monitoring therapeutic response. In this regard, noninvasive assessment of expression/activity of specific molecules in tumors could be vital in managing cancer. Optical probes have demonstrated promise in the molecular imaging of cancer. Here, we have described a method to noninvasively assess the activity of matrix metalloproteinases (MMPs) in human prostate tumors in mice. We used an activatable probe MMPSense™ 750 FAST (MMPSense750) with fluorescent properties in the nearinfrared (NIR) range with peak excitation at ~749 nm and peak emission ~775 nm. These optical properties offer the advantage of a higher depth of detection. This probe has shown immense potential in imaging MMPs activity in deeper tissue with high target-specific signal and low background autofluorescence. Therefore, this probe could be valuable in assessing MMPs activity in primary tumors and metastasis. Key words Matrix metalloproteinases, Cancer, Optical imaging, Biomarker

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Introduction Matrix metalloproteinases (MMPs) are a group of proteases characterized by a zinc ion at the catalytic site. Their biological activities include extracellular matrix (ECM) degradation, tissue remodeling, and the release of various growth factors, chemokines, and cytokines. MMPs play an important role in normal tissue maintenance, including wound healing and repair, menstruation and reproductive processes, and innate immune defense [1–4]. Several studies have now established the critical role of MMPs in primary tumor growth, epithelial-mesenchymal transition, apoptosis resistance, angiogenesis, lymphangiogenesis, premetastatic niches preparation, and metastasis [5–11]. We have recently reported that exosomes secreted by prostate cancer cells under hypoxia promote MMPs activity at premetastatic niches [12]. Therefore, in vivo visualization of MMPs activity would provide valuable information regarding the spatial and temporal expression of MMPs enzymatic

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_2, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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activity and could be valuable in monitoring tumor growth, progression, and in assessing treatment response. MMPSense 750 is a commercially available fluorescent probe from Perkin Elmer and has been used to measure MMPs activity in vivo in various disease conditions [12–16]. It is an activatable fluorescent imaging agent that is optically silent upon injection and only produces a fluorescent signal after enzyme-mediated activation by MMPs, including MMP 2, 3, 7, 9, 12, and 13. Using this type of imaging agent with the In Vivo Multispectral FX imaging system allows us to image the tumors in deeper tissue and overlay this image on an X-ray for anatomical identification. Here, we describe a method to noninvasively assess MMPs activity in human prostate tumors growing in male nude mice using MMPSense 750 probe and In Vivo Multispectral FX imaging system.

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Materials 1. MMPSense 750 FAST. 2. In Vivo Multispectral FX imaging system. 3. Isoflurane Vaporizer. 4. 1 Dulbecco’s phosphate-buffered saline (DPBS) without Ca2+ and Mg2+. 5. Hsd Athymic Nude-Fox1nu male mice, at 4–6 weeks of age. 6. Alfalfa-free rodent chow. 7. Isoflurane. 8. Matrigel. 9. Cancer cells in serum-free media.

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Methods 1. At least 2 weeks before the imaging study, mice are fed an alfalfa-free diet (see Note 1). 2. Inject cancer cells in the prostate of mice and allow the tumor to grow as done previously [17]. 3. Follow manufacturer’s instruction to prepare dye and then image a small aliquot of dye, 2–5 μl, and PBS with the imaging instrument (see Subheading 7 and see Fig. 1) to determine excitation and emission settings to capture fluorescent images (see Note 2). 4. For all animals, take a baseline fluorescent image as a control (see Fig. 2a) with the same excitation and emission of the dye. Without moving the subject, take an X-ray in order to overlay

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Fig. 1 Imaging the dye alone. Image of MMPSense 750 in an eppendorf tube with (a) auto setting for scale and (b) then the same image with scale set to no background

Fig. 2 Fluorescent and X-ray images of mice following injection with MMPSense750. (a) Nude mice with human prostate cancer cells orthotopic xenografts in their prostate were imaged at base line for both fluorescence and X-ray by In Vivo Multispectral FX instrument. An overlay image is shown. (b) Mice were injected MMPSense750 (IV injection); and 24 h later, fluorescence and X-ray images were captured. An overlay image is shown. Intensity scales for both X-ray (top of the images) and fluorescent (bottom of the images) captures are presented

the fluorescent one on top of the X-ray for the anatomical position of the signal (see Note 3). 5. Inject the recommended dose of MMPSense 750 intravenously (tail vein or retro-orbital). 6. Image mouse at different time points, again following manufacturer’s suggestions. With MMPSense 750, the optimal imaging time point is 12–24 h (see Fig. 2b) (see Note 4).

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7. In Vivo Multispectral FX imaging instrument: (a) Open Carestream molecular imaging software. Click on Capture In Vivo FX to enter exposure time, binning, emission, and excitation filter. These parameters need to be optimized to the dye of interest and intensity of the signal. For MMPSense 750, the optimal setting was determined to be 60–120 s, 2 binning, Ex(730), and Em (790). (b) Anesthetize the animal with isoflurane (O2 flow rate to 1–2% and isoflurane to 2–3%) and place in the nosecone of the chamber in either ventral or dorsal position (see Note 5). (c) Preview the mouse or mice with the visible light setting with the door open to determine that the animal is in the field of view. Change the field of view (FOV) if needed. The default is set to 100. (d) Capture the fluorescent image and an X-ray. Make sure the FOV is the same for both and save the images. (e) Adjust the images using the command image display. The Display changes the color, and the Max and Min will change intensity. (f) To overlay an image on an X-ray, open both files. Click on Window and either tile or cascade the images. Click on the X-ray image, open Image Display, then click on Overlay, then Transparency. (g) Add annotations and intensity scales by clicking on Annotation and Add Intensity Scales which are located in the Navigation tab. Once you have added the scales go to Edit—select all—copy and then copy to powerpoint file.

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Notes 1. This is important to feed mice an alfalfa-free diet to reduce autofluorescence. 2. Image the dye before you inject it into the animal to make sure your dye and machine are working. The peak excitation (749 nm) and emission (775 nm) of the probe are a reference point to capture images, and you may need to adjust these for optimization. For example, we used excitation (730 nm) and emission (790 nm) to capture the best images. 3. Make sure you take baseline control images before you inject the animals with the probe in order to set the scale of the fluorescent image at baseline to have no signal. This helps to make sure that you image the probe and not autofluorescence

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or any artifact. Once the fluorescent scale is set for control, the same scale is used for other images for comparison. For a time course, inject a few animals with the vehicle of the dye, in this case 1 PBS, as a control for each time point. 4. An initial time course imaging study is recommended to determine the optimal imaging time for each experiment. 5. The camera is located at the bottom of the instrument, and therefore mice need to be placed accordingly. For example, if you want to image ectopic tumors on the flank of a mouse, you need to place the animal’s dorsal side down so that the area of interest is closest to the camera.

Acknowledgments We acknowledge the support provided by DOD awards # W81XWH-15-1-0188; # W81XWH-19-1-0427 (to GD). References 1. Cabral-Pacheco GA, Garza-Veloz I, CastruitaDe la Rosa C, Ramirez-Acuna JM, PerezRomero BA, Guerrero-Rodriguez JF, Martinez-Avila N, Martinez-Fierro ML (2020) The roles of matrix metalloproteinases and their inhibitors in human diseases. Int J Mol Sci 21(24):9739. https://doi.org/10. 3390/ijms21249739 2. Caley MP, Martins VL, O’Toole EA (2015) Metalloproteinases and wound healing. Adv Wound Care (New Rochelle) 4(4):225–234. https://doi.org/10.1089/wound.2014.0581 3. Goetzl EJ, Banda MJ, Leppert D (1996) Matrix metalloproteinases in immunity. J Immunol 156(1):1–4 4. Khokha R, Murthy A, Weiss A (2013) Metalloproteinases and their natural inhibitors in inflammation and immunity. Nat Rev Immunol 13(9):649–665. https://doi.org/10.1038/ nri3499 5. Erler JT, Bennewith KL, Cox TR, Lang G, Bird D, Koong A, Le QT, Giaccia AJ (2009) Hypoxia-induced lysyl oxidase is a critical mediator of bone marrow cell recruitment to form the premetastatic niche. Cancer Cell 15(1):35–44. S1535-6108(08)00378-4 [pii]. https://doi.org/10.1016/j.ccr.2008.11.012 6. Psaila B, Lyden D (2009) The metastatic niche: adapting the foreign soil. Nat Rev Cancer 9(4):285–293. https://doi.org/10.1038/ nrc2621

7. Kaplan RN, Riba RD, Zacharoulis S, Bramley AH, Vincent L, Costa C, MacDonald DD, Jin DK, Shido K, Kerns SA, Zhu Z, Hicklin D, Wu Y, Port JL, Altorki N, Port ER, Ruggero D, Shmelkov SV, Jensen KK, Rafii S, Lyden D (2005) VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche. Nature 438(7069):820–827. nature04186 [pii]. https://doi.org/10.1038/nature04186 8. Hiratsuka S, Nakamura K, Iwai S, Murakami M, Itoh T, Kijima H, Shipley JM, Senior RM, Shibuya M (2002) MMP9 induction by vascular endothelial growth factor receptor-1 is involved in lung-specific metastasis. Cancer Cell 2(4):289–300 9. Gonzalez-Avila G, Sommer B, GarciaHernandez AA, Ramos C (2020) Matrix Metalloproteinases’ role in tumor microenvironment. Adv Exp Med Biol 1245:97–131. https://doi.org/10.1007/978-3-03040146-7_5 10. Quintero-Fabian S, Arreola R, BecerrilVillanueva E, Torres-Romero JC, AranaArgaez V, Lara-Riegos J, Ramirez-Camacho MA, Alvarez-Sanchez ME (2019) Role of matrix metalloproteinases in angiogenesis and cancer. Front Oncol 9:1370. https://doi.org/ 10.3389/fonc.2019.01370 11. Gialeli C, Theocharis AD, Karamanos NK (2011) Roles of matrix metalloproteinases in cancer progression and their pharmacological

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targeting. FEBS J 278(1):16–27. https://doi. org/10.1111/j.1742-4658.2010.07919.x 12. Deep G, Jain A, Kumar A, Agarwal C, Kim S, Leevy WM, Agarwal R (2020) Exosomes secreted by prostate cancer cells under hypoxia promote matrix metalloproteinases activity at pre-metastatic niches. Mol Carcinog 59(3):323–332. https://doi.org/10.1002/ mc.23157 13. Barber PA, Rushforth D, Agrawal S, Tuor UI (2012) Infrared optical imaging of matrix metalloproteinases (MMPs) up-regulation following ischemia reperfusion is ameliorated by hypothermia. BMC Neurosci 13:76. https:// doi.org/10.1186/1471-2202-13-76 14. Li L, Du Y, Chen X, Tian J (2018) Fluorescence molecular imaging and tomography of matrix metalloproteinase-activatable nearinfrared fluorescence probe and image-guided orthotopic glioma resection. Mol Imaging Biol 20(6):930–939. https://doi.org/10.1007/ s11307-017-1158-7

15. Cho H, Bhatti FU, Lee S, Brand DD, Yi AK, Hasty KA (2016) In vivo dual fluorescence imaging to detect joint destruction. Artif Organs 40(10):1009–1013. https://doi.org/ 10.1111/aor.12685 16. Waschkau B, Faust A, Schafers M, Bremer C (2013) Performance of a new fluorescencelabeled MMP inhibitor to image tumor MMP activity in vivo in comparison to an MMP-activatable probe. Contrast Media Mol Imaging 8(1):1–11. https://doi.org/10. 1002/cmmi.1486 17. Singh RP, Raina K, Deep G, Chan D, Agarwal R (2009) Silibinin suppresses growth of human prostate carcinoma PC-3 orthotopic xenograft via activation of extracellular signal-regulated kinase 1/2 and inhibition of signal transducers and activators of transcription signaling. Clin Cancer Res 15(2):613–621. https://doi.org/ 10.1158/1078-0432.CCR-08-1846

Chapter 3 Method to Development of PET Radiopharmaceutical for Cancer Imaging Naresh Damuka and Kiran Kumar Solingapuram Sai Abstract The increasing number of different novel positron emission tomography (PET) radiopharmaceuticals poses challenges for their manufacturing procedures at different PET research facilities. Recent commercially available radiochemistry units with disposable cassettes are becoming common stations to produce radiopharmaceuticals with high specifications to understand the critical PET imaging outputs of the study. Therefore, several radiochemists across the PET research centers develop and optimize their own radiochemistry protocols to develop a novel or routine radiopharmaceutical at their lab. In this report, we describe the general procedure and steps followed to develop a (clinical-grade) radiopharmaceutical on a commercially available radiochemistry unit, TRASIS AIO. As an example, we use our routine protocol followed for the production of [11C]acetate, a fatty acid metabolic PET imaging ligand for several cancer imaging studies. Key words PET, Radiopharmaceutical, Hot cell, Acetate, Quality control

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Introduction Positron Emission Tomography (PET) is a non-invasive, sensitive imaging modality used for imaging biological processes in vivo, including blood flow, metabolic pathways, receptor expressions, and multiple cancer mechanisms [1]. It is a powerful tool, particularly in oncology due to its high molar concentration resolution (from 10 11 to 10 12 mol/L), along with relevant functional information and quantitative capabilities [2, 3]. PET imaging requires the injection of a radiopharmaceutical, labeled with a short-lived radioisotope (generated by cyclotron) that emits a positron, which annihilates with an electron in the biological tissue to release gamma rays captured by a PET camera [4]. Advances in nuclear medicine imaging have led to an increased demand for PET radiopharmaceuticals for early and accurate diagnosis of cancer and other diseases [4]. Here we report the common and basic steps involved in the production of radiopharmaceuticals in a typical PET

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research lab and illustrate it with an example of [11C]acetate production. The radiopharmaceutical [11C]acetate is widely used for imaging fatty acid metabolism in several types of cancers, including the brain, prostate, hepatocellular carcinoma, renal adenocarcinoma, urinary bladder, and head and neck cancers [5–9]. Owing to its increasing clinical significance in oncology and cardiology imaging [8], several research groups have reported [11C]acetate radiolabeling procedures using both in-house and commercially available radiochemistry modules, including TRASIS AIO, GE, and Synthera systems [10–13]. TRASIS AIO is an automated commercially available radiochemistry module that produces clinicalgrade PET radiopharmaceuticals using cassette-based protocols [13, 14]. We use TRASIS AIO module for [11C]acetate production [13] at the Wake Forest PET imaging facility.

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Materials PET radiopharmaceutical production needs two key components (a) equipments and (b) reagents and supplies [15].

2.1

Equipments

2.2 Reagents and Supplies

3

Cyclotron, hot cell, radiochemistry module with HPLC and radiation detectors, chemistry hood (optional). Precursor, anhydrous solvents, SepPak/resins, HPLC semipreparative, QC columns, mobile phases, needles, syringes, final production vial, and 0.22 μm filter units. For example, with [11C]acetate production, methylmagnesium chloride (3.0 M, THF), anhydrous THF, acetic acid (1.0 M), and citric acid buffers were purchased from Sigma Aldrich, MO, USA. Ion-exchange column resins were directly purchased from Labnet Inc. USA. All sterile filters were purchased from Thermo Scientific Inc., USA.

Methods To avoid any possible contamination and ensure safety, these radiopharmaceuticals are typically produced using an automated radiochemistry module located in a specialized hood, called “hot cell” [15]. Recently several radiochemistry modules are available to simplify the task of complying with clinical-grade requirements and allowing radiochemists to produce multiple productions by simple cleaning processes [15, 16]. Radiochemistry modules are very efficient and heavily used production units that perform all the key steps of radiochemistry, including receiving PET radioactive isotopes, radiolabeling reaction, purification, separation, and final elution. Typical radiopharmaceutical production can be separated into three phases: (a) Prelabeling setup, (b) Radiochemistry, and (c) QC testings (Fig. 1).

PET Radiopharmaceutical Production Protocol

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Fig. 1 Flow chart of common steps involved in a typical PET radiopharmaceutical production 3.1 Prelabeling Setup

1. Most of the precursors for radiopharmaceutical productions are commercially available and can be purchased directly from the vendor. These precursors need to be refrigerated and thawed at least for 10–15 min at room temperature before starting production. Few radiopharmaceuticals need freshly prepared precursors, including [11C]acetate and [11C]acetoacetate [13, 14]. 2. Turn the cyclotron master computer ON to [11C]-target (see Note 1). 3. Place all the required materials, including starting materials, activated sepPaks, solvents, and final sterile vial with filtration setups. For [11C]acetate production, make the following items available: methylmagnesium chloride (3 M, 0.12 mL) in anhydrous tetrahydrofuran (0.6 mL), acetic acid (1 M, 10 mL), aqueous citrate buffer pH 4.5 (5 mL), activated ion-exchange column resin sepPak (chromofix PS-H+, PS-AG+, PS-OH ), and sterile pyrogen-free 0.22 μm sterile filter. 4. Activate the SepPaks ~15 min before the production. C18 SepPaks are commonly activated with 5 mL of ethanol (or acetonitrile) and 10 mL of water. For [11C]acetate production, activate Chromofix PS-H+ and PS-AG+ ion-exchange resins with 10 mL deionized water, and PS-OH with 1 M NaOH (10 mL) followed by 10 mL deionized water elutions. 5. Turn ON the power switch to the module/hot cell and the necessary gas valves. For [11C]acetate on TRASIS AIO module, turn ON the power switch and valves for compressed air and ultra-pure nitrogen gases (see Note 2). 6. Place the right HPLC semipreparative column with the corresponding freshly-made filtered mobile phase solutions. Also, select the right UV wavelength for the HPLC purification (see Note 3). No HPLC purification is needed for [11C]acetate production.

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Fig. 2 TRASIS AIO reaction program screenshot for [11C]acetate production

7. Open the TRASIS AIO software system and select the right radiopharmaceutical production program. Follow the steps for production and acknowledge each step after completion of the performance of the respective tasks. 8. All productions in TRASIS AIO involve leak checks in gases, vacuum leak checks, and then cassette compatibility checks (see Note 4). Load the cassette after the right prompt. For [11C] acetate, once the system checks for all possible leaks, load the reagents at the right place in the cassette following the schematic representation in the program (Fig. 2). 9. Now the system checks for leaks with the reagents (see Note 4). Make sure all the components of the cassette are leak-tight and ready to be proceeded for next steps of cyclotron bombardment. 10. Make sure all the gases, vacuum, water temperatures, and valves on the cyclotron are turned ON and then login to the cyclotron computer (see Note 1). For our GE PETtrace 800 series cyclotron, we leave all the gases, vacuum, and water valves always ON. 11. Login into the system and select the right PET isotope. For [11C]acetate, select C11 target (see Note 1). 12. Input the right current (μA) and bombardment time (min). For a typical clinical production of [11C]acetate, we use 59 μA of bombardment for 25 min.

PET Radiopharmaceutical Production Protocol

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Fig. 3 Radiolabeling route for [11C]acetate production

13. Make sure that the beam is turned ON and the right delivery path/hot cell is selected for the release of the corresponding PET isotope. For example, [11C]acetate production needs [11C]CO2 to be released in hot cell containing TRASIS AIO module. 14. Make arrangements of precursor setup, ~5–8 min of the radioisotope delivery from cyclotron. [11C]acetate needs the Grignard reagent, methylmagnesium bromide (3 M, 0.12 mL) in anhydrous THF (0.6 mL) at lower temperatures (0 to 10  C) in a dry reaction vial of the TRASIS AIO module. 3.2

Radiochemistry

1. Select “delivery” option from the cyclotron computer after the completion time of target bombardment, for example, at 25 min for [11C]acetate. 2. Visually inspect the delivery of radioactive material into the radiochemistry module. TRAIS AIO system shows an increase in reaction vial radioactivity during the delivery process (see Note 2). For [11C]acetate production, [11C]CO2 from cyclotron will be directly bubbled into the closed reaction vial containing the precursor, methylmagnesium bromide/THF (Fig. 3). 3. Acknowledge the prompt on TRASIS AIO of “receiving all the radioactivity” and record the radioactivity numbers after achieving the maximum load and then “start the radiolabeling reaction”. 4. Acknowledge and terminate the production on cyclotron system accordingly. It usually takes ~2.5 min to completely deliver all the radioactive [11C]CO2 into the TRASIS AIO module (see Note 4). 5. Allow the radiolabeling to proceed and record the radioactivity along with the time (see Note 5). For [11C]acetate radiolabeling, [11C]CO2 carboxylation of methylmagnesium bromide is needed ~2 min at room temperature. 6. Quench the reaction mixture after the radiolabeling step with some aqueous solutions. [11C]acetate is quenched with 1 M aqueous acetic acid solution (10 mL). 7. Load the crude reaction mixture onto the semipreparative HPLC chromatography for purification (see Notes 3 and 6).

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Collect the desired radioactive peak into a flask/vial/syringe with 25  10 mL of deionized water. Pass the collected pure aqueous radioactive solution through the activated sepPak/ resins into the waste. As [11C]acetate production does not involve any HPLC purification, it is purified by ion-exchange Chromofix resins. 8. The pure radioactive product will be trapped into the activated SepPak (see Note 7). For [11C]acetate, the crude acidic reaction mixture will be passed through activated chromofix PS-H+, PS-AG+, and PS-OH ion-exchange resin sepPaks. [11C]acetate will be trapped in the last PS-OH resin. 9. Elute the final radiopharmaceutical from the SepPak/resin using ethanol or acetonitrile or aqueous buffer solutions. For [11C]acetate, the final product will be eluted from PS-OH ion exchange resin using aqueous citrate buffer pH ~ 4.5 solution (6 mL) into the sterile final product vial via a pyrogen-free 0.22 μm sterile filter unit. 10. Degas, if necessary to remove any excess undesired gases, including CO2, CO from the final product vial (see Note 7). For [11C]acetate, the final product vial needs to be degassed for an additional 4 min using nitrogen gas to remove any excess CO2. 3.3 Quality Control Testings

Release of the final radiopharmaceutical dose takes place after a series of QC testings, and the common tests are listed in Table 1.

Table 1 List of common QC testings performed for a typical PET radiopharmaceutical production QC testings

Typical acceptable parameters for [11C]acetate release

Appearance

Clear, colorless, and particle-free

pH

4.0–5.5

Filter bubble point test

>30 psi

Radiochemical purity (HPLC)

>90%

Chemical mass (HPLC)

90%

Specific activity (HPLC)

>300 mCi/μmol at injection

Bacterial endotoxin test

Pass

Radionuclide half-life

20  1 min

GC solvent analysis

One peak, Instruments from the DelsaMax analysis software menus. 8. Select File > New from the software menus.

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Fig. 4 Image profile of extracellular vesicles acquired through DelsaMaxPro

Fig. 5 Dynamic Light Scattering distribution shows peaks characteristic of large, moderate, and small extracellular vesicles

9. In the experiment window, set parameters (select preset size). 10. Next, connect the software with the analyzer and begin the analysis. 11. To collect desired data, select diameter, radius, Pd index, and % intensity from the control panel (Fig. 4). 12. Apply the same procedure to collect data from different subfractions of EVs (Large, moderate, and small). 13. Collect data on the size distribution and polydispersity index (Fig. 5). 14. For the analysis of zeta potential, place the samples in a quartz cuvette. 15. Make sure that the sample amount does not exceed 45 μL while measuring zeta potential.

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16. Select phase analysis light scatter from the software. 17. Collect data on zeta potential.

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Notes 1. The ultracentrifugation method is a time-consuming process and not suitable for small sample amounts [25]. Furthermore, high speeds can affect the integrity of EVs by inducing aggregation, breakage, and coprecipitation of soluble proteins present in the biofluids. Therefore, commercially available kits can be used to address these limitations [25, 26]. 2. Commercially available kits are easy and quick and often do not require large volumes of samples. Moreover, specialized equipment such as ultracentrifuge is also not required. However, these kits cannot be used to isolate different subtypes of EVs [25]. Therefore, isolation methods should be wisely selected based on the downstream requirements of the experimental studies. 3. DLS analysis is easy and quick, does not require additional chemicals, and works great in homogenous samples [27]. However, one of the limitations of DLS is the analysis of heterogeneous mixtures. The results produced by DLS remain skewed toward larger particle sizes in a heterogeneous mixture of widesize-ranged particles in the suspension. It is attributed to the fact that the intensity of scattered light is proportional to the sixth power of particle diameter, making smaller particles harder to detect. 4. Low-speed centrifugation helps to remove dead cell particles and other contaminants, which might interfere in the isolation process and also the integrity of EVs. 5. EV pellets can be stored in PBS containing a cocktail of protease inhibitors for a longer duration without losing their integrity. 6. EVs tend to form aggregates that interfere with DLS analysis. Therefore, to overcome this problem, mild sonication to the samples is helpful that breaks the aggregates.

Acknowledgments The authors would like to acknowledge the funding from NIH/NCI [R01CA224306, U01CA185490 (to APS) and R01CA204801, R01CA231925 (to SS)] and USA MCI (to APS and SS).

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References 1. Patton MC, Zubair H, Khan MA, Singh S, Singh AP (2020) Hypoxia alters the release and size distribution of extracellular vesicles in pancreatic cancer cells to support their adaptive survival. J Cell Biochem 121(1):828–839. https://doi.org/10.1002/jcb.29328 2. Mathieu M, Martin-Jaular L, Lavieu G, Thery C (2019) Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication. Nat Cell Biol 21(1):9–17. https://doi.org/10.1038/ s41556-018-0250-9 3. Patel GK, Patton MC, Singh S, Khushman M, Singh AP (2016) Pancreatic cancer exosomes: shedding off for a meaningful journey. Pancreat Disord Ther 6(2):e148. https://doi. org/10.4172/2165-7092.1000e148 4. Xu R, Rai A, Chen M, Suwakulsiri W, Greening DW, Simpson RJ (2018) Extracellular vesicles in cancer - implications for future improvements in cancer care. Nat Rev Clin Oncol 15(10):617–638. https://doi.org/10.1038/ s41571-018-0036-9 5. Patel GK, Khan MA, Bhardwaj A, Srivastava SK, Zubair H, Patton MC et al (2017) Exosomes confer chemoresistance to pancreatic cancer cells by promoting ROS detoxification and miR-155-mediated suppression of key gemcitabine-metabolising enzyme, DCK. Br J Cancer 116(5):609–619. https://doi.org/10. 1038/bjc.2017.18 6. Menck K, Sonmezer C, Worst TS, Schulz M, Dihazi GH, Streit F et al (2017) Neutral sphingomyelinases control extracellular vesicles budding from the plasma membrane. J Extracell Vesicles 6(1):1378056. https://doi.org/10. 1080/20013078.2017.1378056 7. Greening DW, Simpson RJ (2018) Understanding extracellular vesicle diversity - current status. Exp Rev Proteom 15(11):887–910. https://doi.org/10.1080/14789450.2018. 1537788 8. Hochreiter-Hufford A, Ravichandran KS (2013) Clearing the dead: apoptotic cell sensing, recognition, engulfment, and digestion. Cold Spring Harb Perspect Biol 5(1): a008748. https://doi.org/10.1101/ cshperspect.a008748 9. Siveen KS, Raza A, Ahmed EI, Khan AQ, Prabhu KS, Kuttikrishnan S et al (2019) The role of extracellular vesicles as modulators of the tumor microenvironment, metastasis and drug resistance in colorectal cancer. Cancers (Basel) 11(6):746. https://doi.org/10.3390/ cancers11060746

10. Zhao H, Achreja A, Iessi E, Logozzi M, Mizzoni D, Di Raimo R et al (2018) The key role of extracellular vesicles in the metastatic process. Biochim Biophys Acta Rev Cancer 1869(1):64–77. https://doi.org/10.1016/j. bbcan.2017.11.005 11. Thery C, Ostrowski M, Segura E (2009) Membrane vesicles as conveyors of immune responses. Nat Rev Immunol 9(8):581–593. https://doi.org/10.1038/nri2567 12. Raposo G, Nijman HW, Stoorvogel W, Liejendekker R, Harding CV, Melief CJ et al (1996) B lymphocytes secrete antigenpresenting vesicles. J Exp Med 183(3):1161–1172. https://doi.org/10. 1084/jem.183.3.1161 13. Console L, Scalise M, Indiveri C (2019) Exosomes in inflammation and role as biomarkers. Clin Chim Acta 488:165–171. https://doi. org/10.1016/j.cca.2018.11.009 14. Taverna S, Pucci M, Alessandro R (2017) Extracellular vesicles: small bricks for tissue repair/regeneration. Ann Transl Med 5(4):83. https://doi.org/10.21037/atm. 2017.01.53 15. Hong BS, Cho JH, Kim H, Choi EJ, Rho S, Kim J et al (2009) Colorectal cancer cellderived microvesicles are enriched in cell cycle-related mRNAs that promote proliferation of endothelial cells. BMC Genomics 10: 556. https://doi.org/10.1186/1471-216410-556 16. Pang B, Zhu Y, Ni J, Thompson J, Malouf D, Bucci J et al (2020) Extracellular vesicles: the next generation of biomarkers for liquid biopsy-based prostate cancer diagnosis. Theranostics 10(5):2309–2326. https://doi.org/ 10.7150/thno.39486 17. Whiteside TL (2017) Extracellular vesicles isolation and their biomarker potential: are we ready for testing? Ann Transl Med 5(3):54. https://doi.org/10.21037/atm.2017.01.62 18. Garofalo M, Villa A, Rizzi N, Kuryk L, Rinner B, Cerullo V et al (2019) Extracellular vesicles enhance the targeted delivery of immunogenic oncolytic adenovirus and paclitaxel in immunocompetent mice. J Control Release 294:165–175. https://doi.org/10.1016/j. jconrel.2018.12.022 19. Kanchanapally R, Deshmukh SK, Chavva SR, Tyagi N, Srivastava SK, Patel GK et al (2019) Drug-loaded exosomal preparations from different cell types exhibit distinctive loading capability, yield, and antitumor efficacies: a comparative analysis. Int J Nanomedicine 14:

Dynamic Light Scattering Measurement of Extracellular Vesicles 531–541. https://doi.org/10.2147/IJN. S191313 20. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7(1):1535750. https://doi.org/10.1080/20013078.2018. 1535750 21. Chuo ST, Chien JC, Lai CP (2018) Imaging extracellular vesicles: current and emerging methods. J Biomed Sci 25(1):91. https://doi. org/10.1186/s12929-018-0494-5 22. Lim J, Yeap SP, Che HX, Low SC (2013) Characterization of magnetic nanoparticle by dynamic light scattering. Nanoscale Res Lett 8(1):381. https://doi.org/10.1186/1556276X-8-381 23. Bhattacharjee S (2016) DLS and zeta potential - what they are and what they are not? J Control Release 235:337–351. https://doi.org/ 10.1016/j.jconrel.2016.06.017

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Chapter 18 Characterization of Exosomal Surface Proteins by Immunogold Labeling Yixin Su, Ashish Kumar, and Gagan Deep Abstract Exosomes are an intriguing class of nanosized vesicles (~30–150 nm in diameter) released by all cell types for intercellular communication and also for cellular metabolic waste removal to maintain cellular homeostasis. Exosomes secreted by cancer cells play an important role in supporting tumor growth and metastasis by communicating with other cells in the tumor microenvironment and distant sites. Several studies have reported that the exosomes secreted by cancer cells show distinct characteristics, including size, cargo, and surface proteins from the normal cells, and can be used as important biomarkers for diagnosis and prognosis for various cancer types. Exosomes represent many distinct biochemical and morphological characteristics than other -extracellular vesicles (EVs), including their size and surface proteins. Understanding the functional role of exosomes requires specific methods for their characterization to distinguish them from other EV and non-EV structures. Transmission electron microscopy with the immunogold labeling method allows direct detection of exosomes based on their size and specific surface protein. In this chapter, we outlined the required materials and detailed method for immunogold labeling for exosomal surface proteins and size characterization. Key words Exosomes, Transmission electron microscopy, Cancer, Biomarker

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Introduction Exosomes, nanosized extracellular vesicles (EVs) with a size range of ~30–150 nm in diameter, are released by cells as part of their normal physiology and also during physiological and pathological stress [1, 2]. Biogenesis of exosomes includes sequential invagination of the plasma membrane that eventually leads to the formation of multivesicular bodies, which ultimately fuse with the plasma membrane to release exosomes in the extracellular environment [3]. Depending on the cell of origin, exosomes can contain many constituents of cells as cargo, including nucleic acids, lipids, metabolites, and cytosolic and cell-surface proteins. Exosomes are regarded as “snapshot” of the cells, and their cargos prominently reflect the pathophysiological state of the parental cell. Exosome

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role has been implicated in the removal of cellular metabolic waste and excess biomolecule to maintain cellular homeostasis [4]. Moreover, these vesicles also facilitate effective intercellular communication through the delivery of the cargos to the recipient cells. Many studies have shown that exosomes secreted by cancer cells cross talk and communicate with other cells in the tumor microenvironment and also with the distant cells to facilitate tumor growth and their metastatic spread [5]. Emerging evidence indicates that various tumor cell types secrete more exosomes, with significant changes in composition, which reflects molecular signature distinct from the exosomes secreted by corresponding normal or non-neoplastic cells [6]. Therefore, exosome cargo contents have emerged as a potential diagnostic and prognostic biomarkers to classify tumor types [7]. The wide distribution of exosomes in body fluids like blood, bile, urine, tear, and saliva allows their easy isolation and detection. However, to analyze the functional role of exosomes, it is also important to distinguish them from other EVs like microvesicles and apoptotic bodies and non-EVs (such as viruses or lipid bodies). Ongoing technological and experimental advances in the active exosome field provide valuable information regarding their morphological and biological function. The varying sizes and morphologies of exosomes can be distinguished by microscopy techniques with high resolution, such as Transmission electron microscopy (TEM). In the field of exosomes, TEM with an imaging resolution of ~1 nm has been valued for its capability to detect and characterize single exosome from other non-EV particles [8]. Furthermore, employing immunogold labeling with TEM could give information regarding biochemical properties of exosomes, including expression of specific surface marker proteins. Here, we describe a method for labeling the exosomes with primary antibodies against the specific protein on their membrane followed by gold particleconjugated secondary antibodies, which could be observed and imaged by TEM.

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Materials Prepare all solutions and buffers using deionized water and analytical grade reagents. Store all reagents at 2–8  C (unless indicated otherwise). 1. Vortex mixer. 2. 200-mesh copper grids (Fig. 1). 3. Ethanol: 100%. 4. Prepare a 4% (w/v) paraformaldehyde (PFA) solution in PBS. To make a volume of 50 mL, add 40 mL of ddH2O with

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Fig. 1 200-mesh copper grids

stirring in a fume hood; and dissolve 2 g of PFA (Electron Microscopy Sciences) and filter the solution in a graduated cylinder using a filter paper. Add 5 mL of 10 PBS. Adjust pH to 7.4. Add ddH2O to bring the volume to 50 mL. 5. Washing buffer (PBST): Dulbecco’s PBS without Calcium or Magnesium (DPBS) with 0.1% Tween-20. 6. 50 mM Glycine in PBS: Dissolve 188 mg of glycine in 50 mL of PBS (1). 7. Blocking buffer: Bovine serum albumin (BSA) (0.5%). Dissolve 0.5 g of BSA in 100 mL of PBS (1). 8. 2% glutaraldehyde. 9. Uranyl acetate (1%). 10. Gold nanoparticle-tagged secondary antibodies (anti-mouse gold IgG or anti-rabbit gold IgG) (see Note 1).

3

Methods Carry out all procedures at room temperature unless otherwise specified. 1. Activate grids: carefully transfer grids in 100% ethanol for 20 min at room temperature (RT) (see Note 2). 2. Fix exosomes at RT for 10 min by 4% paraformaldehyde in PBS (PFA) solution (see Note 3). 3. Pipette fixed sample (50 μL) onto a 200-mesh copper grid with carbon-coated formvar film and incubate for 1 h at RT.

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4. Transfer the grids to PBS containing 50 mM glycine for 5 min and repeat this step 3 times (see Note 4). 5. Transfer the grids to the blocking buffer and incubate the grids in the blocking buffer for 30 min at RT (see Note 5). 6. Transfer the grids to primary antibody: add a primary antibody to the blocking buffer and place it on a plate overnight in a cold room (see Note 6). 7. After the primary antibody binding, wash the grids with PBST three times for 5 min each wash. Add a gold nanoparticletagged secondary antibody to PBST and incubate for 2 h in the dark. Wash the grids again with PBST three times for 5 min each wash. The grids are ready to fix and stain (see Notes 7 and 8). 8. Transfer grids to 50 μL/sample in 2.5% glutaraldehyde (GLUT) for 5 min at RT. 9. Wash the grids seven times with PBS, 5 min each time (see Note 9). 10. Next, the grids are placed in 50 μL of 1% uranyl acetate (w/v) for 1 min. 11. Transfer grids to 100 μL of distilled water for 2 min. 12. Finally, the grids are ready to be imaged using TEM (FEI Tecnai Spirit transmission electron microscope system) (Fig. 2). Representative images (with scale bar and magnification) for the exosomal surface expression of L1 cell adhesion molecule (L1CAM) and glutamate aspartate transporter (GLAST) are shown in Fig. 3 (see Note 10).

4

Notes 1. The size of gold particles used for immunogold labeling varies from 1 to 40 nm and can be chosen according to the type of labeling technique employed. 2. Pick up the grid with forceps carefully and use forceps to handle grids at edges. 3. Gloves and safety glasses should be worn and solutions should be prepared inside a fume hood. 4. Use PBS containing 50 nmol/L glycine to saturate free aldehyde. 5. Blocking solution, an essential step in immunogold labeling, is applied before primary antibody incubation. Blocking solution will reduce the nonspecific binding of the primary or secondary antibodies to the exosomes.

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Fig. 2 FEI Tecnai Spirit transmission electron microscope system

Fig. 3 Presence of L1CAM (a) and GLAST (b) on the surface of exosomes was assessed by immunogold labeling. Yellow arrows indicate gold particles bound to exosomes. Magnification and scale bar for each image are shown

6. The optimal amount of antibody should be decided experimentally for individual antibodies. Low-affinity antibodies require extended incubation time, and the dilution for primary antibody may be variable based on the particular antibody.

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7. Gold conjugates tend to aggregate. Generally, the temperature for incubation is kept at ambient room temperature around 16–22  C to avoid aggregation. 8. Include only grid/s labeled with gold nanoparticle-tagged secondary antibody (without primary antibody) as a control. Except for the absence of primary antibody, all other steps will be the same for this control group. 9. Increasing the washing time can help to decrease the background. 10. The higher magnification image appears to have a better background quality, i.e., fewer non-EV particles that may interfere with exosome recognition.

Acknowledgments The authors acknowledge the Department of Defense (DOD) award W81XWH-19-1-0427 (to GD) and R01DA049267 (to GD). The authors also acknowledge Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) Cellular Imaging Shared Resource supported by NCI (P30CA012197, PI: Dr. Boris Pasche). References 1. Vlassov AV, Magdaleno S, Setterquist R, Conrad R (2012) Exosomes: current knowledge of their composition, biological functions, and diagnostic and therapeutic potentials. Biochim Biophys Acta 1820(7):940–948. https://doi.org/10. 1016/j.bbagen.2012.03.017 2. Kalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Science 367(6478):eaau6977. https://doi.org/ 10.1126/science.aau6977 3. Zhang Y, Liu Y, Liu H, Tang WH (2019) Exosomes: biogenesis, biologic function and clinical potential. Cell Biosci 9:19. https://doi.org/10. 1186/s13578-019-0282-2 4. Rashed MH, Bayraktar E, Helal GK, Abd-Ellah MF, Amero P, Chavez-Reyes A, RodriguezAguayo C (2017) Exosomes: from garbage bins to promising therapeutic targets. Int J Mol Sci 18(3):538. https://doi.org/10.3390/ ijms18030538

5. Azmi AS, Bao B, Sarkar FH (2013) Exosomes in cancer development, metastasis, and drug resistance: a comprehensive review. Cancer Metastasis Rev 32(3–4):623–642. https://doi.org/10. 1007/s10555-013-9441-9 6. Whiteside TL (2016) Tumor-derived exosomes and their role in cancer progression. Adv Clin Chem 74:103–141. https://doi.org/10.1016/ bs.acc.2015.12.005 7. Huang T, Deng CX (2019) Current progresses of exosomes as cancer diagnostic and prognostic biomarkers. Int J Biol Sci 15(1):1–11. https:// doi.org/10.7150/ijbs.27796 8. Jung MK, Mun JY (2018) Sample preparation and imaging of exosomes by transmission electron microscopy. J Vis Exp (131):56482. https://doi.org/10.3791/56482

Chapter 19 Scanning Electron Microscopy of Giant Cells from Giant Cell Tumor of Bone Asit Ranjan Mridha and Subhash Chandra Yadav Abstract Surface ultrastructures of giant cells (GCs) may help distinguish an aggressive tumor from an indolent giant cell tumor (GCT). This protocol describes a better way for ultrastructural surface imaging of GC from GCT of bone by scanning electron microscope (SEM). Fresh GCT samples collected in Dulbecco’s modified Eagle medium (DMEM) are thoroughly washed to remove the blood and treated with collagenase to isolate the GCs. The collagenase-treated and critical point dried (CPD) samples yield a greater number of isolated GCs with better surface morphology, including membrane folding and micro-vesicular structures on the surface. Collagenase digestion and CPD should be performed for ultrastructural surface imaging of individual giant cells. Key words Giant cell tumor, Scanning electron microscopy, Bone

1

Introduction GCT of bone is a benign, locally aggressive tumor which usually affects bone around the joints [1, 2]. The tumor has a minor component of neoplastic cells and major population of reactive tissue, including osteoclast-like giant cells [3]. Giant cells have the property to resorb bone-producing lytic bone lesions. The surface ultrastructure morphology of GCs may help in better understanding the role of GCs in the pathophysiology of GCT. Processing and ultrastructural imaging techniques for the study of GCT specimens using transmission electron microscopy (TEM) are well reported [4]. However, the surface ultrastructural imaging of the GCs of bone GCT is sparingly reported. SEM may provide valuable information about the morphological and pathophysiological characteristics of GCs. The primary aim of this work is the imaging of the surface ultrastructures of isolated GC (by collagenase treatment) with the best possible resolution. The ultrastructural characteristics may help in determining the aggressiveness of GCT. After the fixation

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and dehydration, the samples are dried by critical point drying to evaluate the ultrastructural information. Our following protocol yields a greater number of isolated GCs for surface ultrastructural studies by SEM.

2

Materials 1. All glassware must be washed with aqua regia (3 HCl: 1 HNO3), followed by rinsing several times by double distilled water. 2. Collagenase. 3. Dulbecco’s Modified Eagle Medium (DMEM). 4. 1 PBS buffer (25  C, pH 7.4). 5. Karnovsky’s fixative. 6. Double distilled 18.3 mΩ deionized water. 7. 10% neutral phosphate buffered formalin (1.0 L): 100 mL commercial formaldehyde (37–41% w/v LR) + 900 mL distilled water + 4.0 g sodium dihydrogen phosphate monohydrate, 6.5 g di-sodium hydrogen phosphate anhydrous. 8. 10% EDTA (1.0 L): Dissolve 100 g of EDTA powder in 1 L of distilled water over 15–20 min (EDTA disodium salt AR ACS). 9. Xylene (Sulphur free, rectified, EP and LR grade, C8H10). 10. Paraffin wax for Histology (melting point

60  2  C).

11. Alum hematoxylin: Mixture A (dissolve 100 g of alum (Aluminum ammonium sulfate extra-pure) in 1 L of distilled water and boil), Mixture B (mix 5 g of hematoxylin crystals) and 50 mL of 95% alcohol and warm the mixture, add mixture B to mixture A slowly and boil for some time (Mixture C), add slowly 2.5 g of Mercury (II) oxide red to mixture C and let it cool down in room temperature and add 10 mL of acetic acid as a preservative after complete cooling. 12. Acid-alcohol (1%): Add 1 mL HCl (Hydrochloric acid 35–38% AR) in 99 mL of 95% alcohol. 13. Eosin: Add 1 g of Eosin Y (yellowish) + 200 mL of distilled water + 800 mL of 95% alcohol + 10 mL acetic acid (Acetic acid glacial supra pure and P-Test) + 5 mL 10% Certistain Phloxin B (10 g phloxin B + 100 mL of 95% alcohol). 14. D.P.X (Dibutylphthalate Polystyrene Xylene). 15. DMEM media supplemented with heat-inactivated (56  C, 0.5 h) 10% FBS and 1% pen-strep antibiotics. 16. RPMI 1640 supplemented with 10% FBS and 1% penicillinstreptomycin.

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Methods

3.1 Patient Selection for Obtaining GCT Sample

1. The patients with clinical suspicion of GCT are recruited for GC imaging by scanning electron microscope. Patient with GCT radiographically exhibits a lytic, expansile lesion, which usually involves the metaphysis and epiphysis of skeletally mature long bone with narrow zone of transition (see Notes 1–3 and Fig. 1a) [5]. 2. Subsequently magnetic resonance imaging (MRI) is recommended for further corroboration of the GCT findings. The patient with MR image findings of a homogenous hypointense lesion on T1-weighted and hyperintensity on T2-weighted conforming to GCT is included for further processing (see Note 4 and Fig. 1b, c). 3. Finally, the diagnosis of GCT is confirmed by histopathologic examination after taking a core biopsy from the lesion (see Notes 5 and 6 and Fig. 2).

3.2 Fixation and Processing of Samples for Histopathological Examination

The core biopsy sample is optimally fixed and processed for paraffin infiltration and embedding (paraffin-embedded tissue blocks). About 5 μm sections are obtained, deparaffinized, and stained with hematoxylin and eosin.

Fig. 1 Radiologic images of GCT. (a) Anteroposterior radiograph of right knee of a 28-year-old male is showing expansile lytic lesion with narrow zone of transition involving metaphysis and epiphysis of lower end of right femur. No periosteal reaction or specific matrix mineralization is seen. (b) T1-weighted magnetic resonance image of the right knee is showing a solid expansile intramedullary hypointense mass replacing the normal marrow. (c) The mass is hyperintense on T2-weighted magnetic resonance image

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Fig. 2 Histopathology images of GCT on a light microscope (Hematoxylin and eosin stained section). The image is showing many osteoclasts-like giant cells admixed with mononuclear cells. The giant cells exhibit multiple vesicular nuclei with prominent nucleolus 3.2.1 Tissue Fixation and Decalcification

1. The core biopsy sample is fixed in 10% neutral buffered formalin overnight. 2. This bone tissue is subjected for decalcification by using 10% ethylenediaminetetra acetic acid (EDTA) disodium salt. 3. The end-point test for decalcification is confirmed by any of the following (described next). 4. Chemical method: By acidifying the used solutions; this forces EDTA to release calcium for precipitation by ammonium oxalate. 5. Physical test: It is done by probing, needling, slicing, bending, or squeezing tissues. It is an imprecise method and may damage the tissue and produce artifacts. 6. In our laboratory, small bone biopsy usually requires about 3 days for decalcification. EDTA solution is changed every day, and the decalcification end-point test is done by physical method.

3.2.2 Tissue Processing for Paraffin Infiltration and Embedding

1. Decalcified tissue is transferred into a cassette and washed in a running tap water for 4–8 h. 2. The sample is processed for paraffin infiltration in a tissue processor (Thermo Scientific STP 120 Spin Tissue Processor, Walldorf, Germany) overnight in the following sequence. 3. Fixation in 10% neutral buffered formalin with three changes for 1 h each.

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4. Dehydration in 75%, 85%, and 95% alcohol as well as two subsequent changes in acetone for 1 h each. 5. Clearing by two changes in xylene with each change for 1 h. 6. Paraffin infiltration in two changes at 68  C with change for 1 h each. 7. Tissue embedding is done (SLEE MPS/P1, Germany or other similar kind) sequentially as follows by maintaining the temperature of paraffin, surface, and bowl at 70  C. 8. Paraffin wax is dispensed from a nozzle into the suitable size mold. Tissue is placed to the bottom. Cassette is placed on the top and filled with liquid wax. Finally, the tissue block is placed on a cold plate to solidify the wax at temperature 10  C. 3.2.3 Hematoxylin and Eosin Staining

1. Sections are made from the paraffin-embedded tissue block by a rotary microtome (Thermo Scientific HM 325, United States).

Sectioning

2. Paraffin-embedded tissue block is trimmed (15–30 μm) to expose the tissue on cutting surface and finally 4–5 μm sections are made by smooth slow strokes that form a ribbon. 3. 4–5 drops of 40% alcohol is placed over a glass slide (76  25 mm and thickness 1.0–1.2 mm). 4. The ribbon is placed on alcoholic surface of slide and slowly dipped horizontally in to DW in the flotation bath maintained at 60  C (Medite Tissue Flotation Bath TFB 45, Germany). This allows flattening of floating sections. If folds are formed in floating tissue section, it is flattened by teasing needle carefully without breakage of section. 5. Individual section or ribbon floated on the water surface is taken on a clean glass slide coated with albumin (to prevent tissue detachment from the slide). Bath should be cleaned after this process to avoid contamination in next sample.

Deparaffinization of the Section on Slides

1. The slide with tissue section is kept on slide warmer (Wiswo Slide Warmer) at 70  C for 5 min. 2. It is dipped into Xylene (three changes with each change for 3 min) and further two changes of acetone for 3 min each to remove xylene.

Rehydration and Staining

1. The slides are dipped into descending grades of alcohol (95%, 85%, and 75%) for 3 min each and wash in distilled water for 5 min. 2. The sections are stained in Alum hematoxylin for 30 s to 1 min and washed in running tap water for 5 min or unless sections turn ‘blue’.

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3. Differentiation in 1% acid-alcohol for 5–10 s (1–2 quick dips). 4. Wash in running tap water for approximately 5 min until sections turn again ‘blue’ to develop optimum color in nucleus. 5. Stain in 1% eosin for 30 s. 6. Slides are dipped into ascending grades of alcohol (75%, 85%, and 95%) for 1 min each and acetone with two changes 1 min each. 7. Slides are dipped into xylene (two changes for 1 min each). 8. After air drying, mounting is done by D.P.X (Dibutylphthalate Polystyrene Xylene). 3.3 GCT Sample Collection and Primary Processing for SEM

1. Patients with confirmed giant cell tumor (GCT) of bone (osteoclastoma) are selected for the scanning electron microscopy. 2. The samples are obtained by curettage of GCT from the patients in the operation theater (OT) and immediately immersed in DMEM media supplemented with heatinactivated (56  C, 0.5 h) 10% FBS, and 1% pen-strep antibiotics to preserve the native condition. 3. The curettage samples are washed with PBS in sterile conditions to remove blood. 4. Minced in small pieces and the pieces without blood are segregated. This is done to remove RBC contamination that may hamper the imaging of giant cells. 5. Minced tissue pieces without blood are treated with collagenase to digest the collagen fibers (collagenase type 2 (500 U/ mL) for 3 h at room temperature) (see Note 7). 6. These samples are checked under a light microscope without staining to confirm the presence and abundance of isolated giant cells (Fig. 3). The tissue from a patient is washed in a biosafety cabinet to remove blood (RBCs, blood clots) from the surface by gentle washing using medium/sterile PBS and brush cleaning. 7. The sample is centrifuged for 5 min at 500  g at RT and the supernatant is discarded. 8. The sample is washed 1 PBS twice with medium to remove collagenase. 9. The sample is fixed in Karnovsky’s fixative at room temperature for 3 h and kept overnight at 4  C. 10. The fixed sample is centrifuged for 5 min at 500  g to remove fixative and washed twice with PBS. 11. The sample is dehydrated by ethyl alcohol (30%, 50%, 70%, and 100%; two changes each) for 30 min incubation for each

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Fig. 3 Light microscope image of collagenase-treated GCT sample without staining to confirm the isolation process. Mechanically crushed sample showed tissue fragments with clumped GC. The clear single giant cell was observed in collagenase-treated GCT from the same patient

concentration. At each step of alcohol dehydration, the alcohol is removed by centrifugation at 500  g for 5 min. 12. The sample is subjected to critical point drying (CPD) using a critical point drier (K 850, Electron Microscope Sciences) and mounted on a carbon-coated stub (see Note 8). 13. The sample is sputter-coated with a sputter coater (BU015331-T, Baltec Switzerland) using an Au-Pd coating. 3.4 Scanning Electron Microscopy (SEM)

4

1. The dried sample is imaged by SEM (Zeiss, EVO 18) at 20 kV in secondary electron mode (Fig. 4a–c).

Notes 1. X-ray may help in excluding other benign and malignant bone lesions such as chondroblastoma, aneurysmal bone cyst, osteosarcoma, chondrosarcoma, plasmacytoma, metastasis, etc. [5]. 2. Patients with prior chemotherapy such as denosumab, aledonate, zolendronate, etc., should be excluded from this study. The chemotherapeutic agents may cause a significant reduction in the giant cells. 3. Sample collection procedures must be performed according to the institutional ethics on the use of human samples for research. The biopsy should contain multiple cores from different areas of the lesion for optimum sampling because the GCT

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Fig. 4 Scanning Electron Images of collagenase-treated GCT of a patient: (a) Critical Point Dried whole GCT, (b) CPD dried GCT magnified surface for better surface view, and (c) CPD dried GCT at highest magnification to show the external microsomal structure

may show a heterogenous morphology with fibrosis, hemorrhage, and paucity of giant cells at places. 4. Magnetic Resonance Imaging (MRI) excludes the possibilities of aneurismal bone cyst, fibrous cortical defects, chondromyxoid fibroma, osteosarcoma, etc. [5]. 5. Histopathologic examination is recommended for confirmation of GCT because there are clinical and radiologic overlaps between GCT and other benign and malignant bone lesions [6]. 6. Under the light microscope, the hematoxylin and eosin stained sections from GCT show a large number of osteoclasts-like giant cells admixed with round to spindle-shaped mononuclear cells. The giant cells are large in size with the presence of multiple plump vesicular nuclei, prominent nucleoli, and eosinophilic cytoplasm. The mononuclear cells are round to oval to spindle-shaped with fine nuclear chromatin and inconspicuous nucleoli (Fig. 2). 7. Collagenase-treated sample increases the number of isolated giant cells without debris on the cell surface. 8. Collagenase-treated, dehydrated, CPD dried samples are suitable for SEM imaging of human giant cells. Thus, this method is recommended for the surface ultrastructural study of giant cells.

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Acknowledgments The authors acknowledge the individual financial support given by All India Institute of Medical Sciences (AIIMS), New Delhi, as intramural grants to SCY (F.8-419/A-419/2016/RS) and ARM (F.8-594/A-594/2018/RS). References 1. Arbeitsgemeinschaft K, Becker WT, Dohle J et al (2008) Local recurrence of giant cell tumor of bone after intralesional treatment with and without adjuvant therapy. J Bone Joint Surg Am 90(5):1060–1067 2. Balke M, Schremper L, Gebert C et al (2008) Giant cell tumor of bone: treatment and outcome of 214 cases. J Cancer Res Clin Oncol 134(9):969–978 3. Morgan T, Atkins GJ, Trivett MK et al (2005) Molecular profiling of giant cell tumor of bone and the osteoclastic localization of ligand for receptor activator of nuclear factor kappa B. Am J Pathol 167(1):117–128

4. Aparisi T (1978) Giant cell tumor of bone. Electron microscopic and histochemical investigations. Acta Orthop Scand Suppl 173:1–38 5. Chakarun CJ, Forrester DM, Gottsegen CJ et al (2013) Giant cell tumor of bone: review, mimics, and new developments in treatment. RadioGraphics 33(1):197–211 6. Zambo I, Vesely K (2014) WHO classification of tumours of soft tissue and bone 2013: the main changes compared to the 3rd edition. Cesk Patol 50(2):64–70

Chapter 20 Raman Microscopy Techniques to Study Lipid Droplet Composition in Cancer Cells Mariana C. Potcoava, Gregory L. Futia, Emily A. Gibson, and Isabel R. Schlaepfer Abstract Raman spectroscopy using feature selection schemes has considerable advantages over gas chromatography for the analysis of fatty acids’ composition changes. Here, we introduce an educational methodology to demonstrate the potential of micro-Raman spectroscopy to determine with high accuracy the unsaturation or saturation degrees and composition changes of the fatty acids found in the lipid droplets of the LNCaP prostate cancer cells that were treated with various fatty acids. The methodology uses highly discriminatory wavenumbers among fatty acids present in the sample selected by using the Support Vector Machine algorithm. Key words Raman spectroscopy, Support vector machine (SVM), Lipids, Fatty acids, Prostate cancer, Near-infrared, Noninvasive

1

Introduction Cancer cells have very well-defined pathways to facilitate fatty acids’ metabolism. Fatty acids are obtained from endogenous de novo biosynthesis or from dietary sources and can be used for energy storage in the form of cytoplasmatic lipid droplets (LDs). These LDs contain neutral lipids, like triacylglycerides (TAG) and steryl esters (STE), and are surrounded by a monolayer of phospholipids and proteins [1–3]. The fatty acid biosynthesis requires the activation of enzymes that lead to the production of a 16-carbon chain of saturated fatty acid (16:0, palmitate) [4–6], which serves as a precursor for the generation of longer chain and unsaturated fatty acids like oleic acid (18:1). Hormones that bind steroid receptors in cancer cells (like androgen to androgen receptor and progesterone to progesterone receptor) are known to induce the lipid synthesis program inside the cells [7, 8]. Palmitic and oleic acids are abundantly made by the cancer cells in response to hormone treatment, and pharmacological inhibition of their synthesis has been

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_20, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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shown to decrease cancer cell viability and resistance to chemotherapy agents [9]. Thus, the accumulation of LDs inside cancer cells seems to be a hallmark of cancer metabolism and growth that can be exploited for biomarker discovery. Lipid content is usually analyzed by using gas chromatography/mass spectroscopy [9], but the cellular dynamics and the lipids’ distribution are lost during the homogenization process. Moreover, these techniques cannot be used in vivo or for live cell studies. Raman spectral data sets are large, with subtle differences and spectral overlapping, which requires dimension reduction to extract essential information from the original data. The feature selection can be improved using Support Vector Machine (SVM) and kernel functions for feature extraction [10, 11]. Therefore, we have combined Raman spectroscopy with a wavenumber selection multiclass-SVM algorithm to identify those discriminative wavenumbers of fatty acid methyl esters (FAMEs) present in the samples in order to analyze with better accuracy the fatty acids’ composition changes and the unsaturation/saturation degrees of the samples under investigation. In this study, we validate our methodology on prostate cancer cell line LNCaP samples; the control sample, the sample treated individually with specific FAMEs, and a sample treated with a mixture of FAMEs. Although we focus on prostate cancer cell samples in this study, we believe that the study of unsaturation/ saturation of fatty acids in individual LDs will open new lipid-based avenues for cancer cell research in general.

2

Materials

2.1 Fatty Acid Methyl Ester (FAME) Standards (SigmaAldrich Corp)

1. Methyl Oleate 31111
99% (GC) liquid (OA). 2. Methyl Linoleate L1876
99% (GC) liquid (LOA). 3. Methyl Palmitoleate P9667
99% (GC), liquid (POA). 4. Methyl Arachidonate A9298,
99% (GC) liquid (AA). 5. Methyl Stearate S5376 ~99% (GC) solid (SA). 6. Methyl Palmitate P5177
99% (GC) solid (PA).

2.2 LNCaP Prostate Cancer Cells

2.3 Suppliers, Consumables, etc.

The LNCaP prostate cancer cells were purchased from the University of Colorado Cancer Center Cell Technologies Shared Resources. 1. 35 mm glass bottom dishes no. 1, poly-D-lysine coated. 2. 4% formaldehyde. 3. Phosphate buffer saline (PBS). 4. 200 proof Molecular-grade ethanol to dissolve FAMEs.

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5. RPMI cell culture media supplemented with 10% FBS and 1% pen/strep antibiotics. 2.4 Instrumentation/ Experimental Setup

1. Use a confocal Raman microscope to measure the LDs. A custom microscope [12] represented in Fig. 1 was built in backscattering geometry using an Olympus IX70 inverted research microscope. 2. Choose a 300 lines/mm grating to spectrally disperse the signal onto the sensor plane. 3. Use a motorized scanning stage (ASI Inc., MS-2000) to record groups of Raman spectra with five scans per measurement point.

3

Methods Carry out all procedures at room temperature unless otherwise specified.

3.1 FAME Samples Preparations

We recorded the palmitate and stearate samples spectra in solid and liquid form. The liquid samples were obtained by melting the solid samples, and the solid and liquid spectra were averaged. 1. Use individual FAME samples of OA, LOA, POA, AA, PA, and SA. 2. Use mixtures of OA + LOA (50% by volume), OA + LOA + POA (33% by volume), and OA + LOA + POA + AA (25% by volume) samples.

3.2 Preparation of FAMEs for Tissue Culture

1. Dissolve the FAMEs in 200 proof molecular-grade ethanol to a concentration of 10 mM and store at 20  C.

3.3 Treatment of the Cells

1. Grow LNCaP cells in glass bottom dishes to a 70% confluency.

3.4 Spectra Recording

1. Record a few groups of five Raman spectra from a point scan of polystyrene beads in the region between 300 and 1800 cm1, and take the average. The mean values of these spectra would count for the calibration of the Raman shifts.

2. Prepare single or mixture of FAMEs by diluting the FAME stocks to 100 μM in cell culture media containing RPMI supplemented with 10% FBS and 1% pen/strep antibiotics.

2. Keep a few dishes of LNCaP prostate cancer cells without FAMEs treatment; these will be used as the control samples.

2. Record several Raman spectra of each pure fatty acid in the region between 300 and 1800 cm1: OA, LOA, POA, and AA in liquid form, and PA, SA in solid and liquid form, 45 scans per

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Fig. 1 Scheme of the Raman micro-spectroscopy setup; 785 nm laser (Innovative Photonic Solutions), L1, L2, L3, L4 ¼ lenses with focal lengths f1 ¼ 15 mm, f2 ¼ 175 mm, f3 ¼ 75 mm, f4 ¼ 25 mm, M ¼ mirrors, DM ¼ dichroic mirror (Semrock, 785 nm Razor Edge), FM ¼ flipper mirror, Ph ¼ pinhole with diameter d ¼ 150 μm, F ¼ longpass filter (Semrock, 785 nm Razor Edge Ultrastep with cutoff at 786.7 nm and rejection OD of 6), S ¼ slit, imaging spectrograph (Czerny-Turner style, Acton SP2500), CCD (cooled CCD camera, Pixis 100, Princeton Instruments), CMOS camera (for sample visualization), XYZ (motorized scanning stage, ASI Inc., MS-2000), and MO ¼ microscope objective (Olympus, UPlanSApo 60 W IR, NA ¼ 1.2)

samples with 10 s integration time. The PA and SA were melted using a heat gun. 3. Record several Raman spectra of a mixture of fatty acid in the region between 300 and 1800 cm1: OA + LOA, OA + LOA + POA, and OA + LOA + POA + AA, 45 scans per samples with 10 s integration time. Calculate the average spectra. 4. Record several Raman spectra of LNCaP cancer cell LDs in the region between 300 and 1800 cm1. We used in this study 12 groups (12 cells) of 45 spectra each, acquired from nine lipid droplets inside the cell, with five spectra recorded for each point scan. Calculate the average spectra for each group. 5. Record a group of five Raman spectra from a point scan of media outside the cell area in the region between 300 and 1800 cm1. The mean values of these spectra would count for the background subtraction from each of the spectra mentioned above. 3.5 Raman Spectra Processing

1. Process the Raman data with MATLAB (Mathworks Inc.) using custom routines and the bioinformatics toolbox. 2. Remove the background signal containing cosmic rays, and signal from the glass coverslip and PBS solution performing the following data processing steps: (1) cosmic rays removal [13] (see Note 1), (2) data smoothing using a 5-point moving average filter, (3) background removal (see Note 2) by subtracting an average of several-point Raman spectra acquired off of the cell sample (glass and PBS only), (4) baseline correction using bioinformatics tool routines (see Note 3), and (5) data

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Fig. 2 FAME Raman spectra below 1800 cm1; (a) Raman spectra of individual FAME; the pure substances are, from the bottom to the top, oleate OA (Sigma, >99% GC), linoleate LOA (Sigma, >99%), palmitoleate POA (Sigma, >98.5%, GC), arachidonate AA (Sigma, >99%), palmitate PA (Sigma, >99%, GC), and stearate SA (Sigma, >99%, GC); we recorded the palmitate and stearate samples spectra in solid and liquid form (not shown) by melting the solid samples, and the solid and liquid spectra were averaged; (b) Raman spectra of FAME mixtures: 50% OA + LOA, 33% OA + LOA + POA, and 25% OA + LOA + POA + AA. OA oleic acid, POA palmitoleic acid, LOA linoleic acid, AA arachidonic acid, PA palmitic acid, SA stearic acid

normalization by the area under the curve for the FAMEs and by the mean area under the curve of the LNCaP control cells spectra for the other LNCaP cancer cells. 3. Calibrate the Raman shifts (cm1) using polystyrene reference spectra (see Note 4). 4. The processed Raman spectra for the pure fatty acids, mixture of fatty acids, and LNCaP cancer cell lines are shown in Figs. 2a, b and 3, respectively. 5. Use the MATLAB function Error Correction Output Codes (ClassificationECOC) to predict labels (see Note 5) or posterior probabilities for the processed data by using multiple binary learners SVMs (Fig. 4a–h). The SVM template was created by using a Gaussian rbf kernel to standardize the predictors. The results of the ECOC classifier are presented in Subheading 3.10. 6. Choose those peaks with very high classification accuracy, that are >0.92. 7. Do not choose the peaks at 1263 and 1299 cm1 for unsaturation/saturation analysis. The classification accuracy for the two peaks combination is too low 0.7642 (Fig. 4f). These peaks overlap. Details about peaks selection are given in Subheading 3.10.

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Fig. 3 Average low wavenumber Raman spectra of treated (single and mixture), and vehicle (control) LNCaP cancer cells with discriminative wavenumbers found by SVM. The most pronounced changes between spectra of treated and control cell samples occurred for in the 800–1800 cm1 region

Fig. 4 Maximum posterior probabilities of FAMEs; (a) peak 970 cm1 to peak 930 cm1, accuracy 0.9886; (b) peak 930 cm1 to peak 1464 cm1, accuracy 0.9858; (c) peak 970 cm1 to peak 1464 cm1, accuracy 0.9206; (d) peak 1003 cm1 to peak 1464 cm1, accuracy 0.9602; (e) peak 1110 cm1 to peak 1062 cm1, accuracy 0.9858; (f) peak 1263 to peak 1294–1300 cm1, accuracy 0.7642; (g) peak 1655 cm1 to peak 1464 cm1, accuracy 0.9651; (h) peak 1737 to peak 1464 cm1, accuracy 0.7642. OA oleic acid, POA palmitoleic acid, LOA linoleic acid, AA arachidonic acid, PA palmitic acid, SA stearic acid, Chol cholesterol

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To fit the Raman experimental data, we assume that each LD spectra can be described by a linear combination of the spectra of pure components. In this case, P we wish to solve for the coefficients of the linear equation: d ¼ c i si where d is the experimental LD spectrum, ci are coefficients, iand si are the pure substance spectra. To solve for the coefficients, perform the following steps: 1. Perform a least square fit of the processed data using built-in functions in Matlab (Mathworks Inc.) in the Curve Fitting toolbox, constraining the coefficients to positive values (see Note 6). 2. Calculate the standard deviation of the mean of samples (SE) by taking the ratio of the sample standard deviation divided by the square root of the sample size. The sample size would be N ¼ 12, which is the number of the groups or the number of the averaged spectra. 3. Use the MATLAB subroutine barweb to plot five groups of 6 bars with errors, five represents the number of treatments: OA, LOA, POA, PA, and SA, and six represents the number of pure fatty acids being identified: OA, LOA, POA, AA, PA, and SA (see Note 7). 4. The processed Raman spectra for the pure fatty acids and the LNCaP cancer cell lines are shown in Figs. 2a, b and 3, respectively.

3.7 Raman Vibrational Mode Assignment 3.8 FAME Reference Lipids Biomarkers by Micro-Raman Spectroscopy

Various fatty acid vibrational modes responsible for the differences observed in the spectra in the figures above are readily assigned to known lipid/triglycerides vibrational modes (see Table 1). 1. Create a database of reference fatty acid spectra by acquiring Raman spectra of FAMEs: oleate C18:1 (OA, liquid), linoleate C18:2 (LOA, liquid), palmitoleate C16:1 (POA, liquid), arachidonate C20:4 (AA, liquid), palmitate C16:0 (PA, solid), and stearate C18:0 (SA, solid). 2. Normalize FAME spectra by the area under each curve and not by the carbonyl peak, as it is described in reference [22]. The carbonyl peak changes its position in regards to the saturation status of the fatty acids, and it cannot be considered as an internal standard for all the fatty acids under investigation. The normalization is necessary since both forms of unsaturated and saturated fatty acids are utilized. 3. Typical corrected and normalized mean Raman spectra of FAME are shown in Fig. 2a. 4. Record Raman spectra of liquid FAME mixtures in amounts of 50% OA and LOA, 33% of OA, LOA, and POA, and 25% of OA, LOA, POA, and AA (Fig. 2b). These spectra are needed to

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Table 1 Raman Frequencies of FAME and triglycerides [14–21] Peak number

Wavenumber (cm1)

Assignment

1

727

δ (¼C-H) in-plane

2

800–920

ν (C1-C2), CH3,rk, ν (C¼O) Solid: mixture of stretches and rocks at acyl and methyl terminals. Complex broad plateau in liquid state

3

972

δ (¼C-H) out-of-plane

4

1060–1065

ν (C-C)op Out-of-phase: aliphatic C-C stretch all trans

5

1080–1110

ν (C-C)ig Liquid: aliphatic C-C stretch in gauche and ν (C-C), Solid: aliphatic C-C stretch all trans

6

1120–1135

ν (C-C)ip in-phase aliphatic C-C stretch all-trans

7

1171

(CH2) rotation

8

1250–1280

δ (¼CH)ip in-plane cis olefinic hydrogen bend

9

1295–1305

δ (CH2)tw Methylene twisting deformations

10

1400–1500

δ (CH2)sc Methylene scissor deformations, δ (CH2)

11

1640–1680

ν (C¼C) cis double bond stretching mode (Olefinic)

12

1730–1750

ν (C¼O) in -CH2-COOR ester carbonyl stretching mode

Note: ν and δ indicate stretching and deformation vibrations

validate our method to assign the unsaturation/saturation degrees of fatty acid mixtures. 5. The analysis of these spectral features is described below, in Subheading 3.10. 3.9 LNCaP Prostate Cancer Cell Lipids Biomarkers by MicroRaman Spectroscopy

1. Acquire Raman spectra of LNCaP prostate cancer cell line, control, and treated samples with individual FAMEs and mixture of FAMEs for 4 days. 2. Normalize the Raman spectra of the LNCaP cells by the mean area under the curve of the LNCaP control cells spectra after the background removal mentioned in Subheading 3.5. 3. Typical corrected and normalized mean Raman spectra of LNCaP cancer cells are shown in Fig. 3. 4. These spectra have an overall shape of unsaturated fatty acids, and therefore we expect to see more unsaturation in the LNCaP lipid droplets. All of the LNCaP cells did respond to the treatment. The specific Raman peaks responsible for the differences observed in the spectra are also assigned to known lipid vibrational modes (see Table 1) and are sensitive to differences in lipid composition within cytoplasmic lipid droplets. The analysis of these spectral features is also described below in Subheading 3.10.

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Lipid droplets in cells consist of a neutral lipid core (primarily triacylglycerides (TAGs) and cholesteryl esters) enclosed by a phospholipid membrane. TAGs consist of a glycerol molecule joined by an ester bond to three fatty acid molecules. Raman signal from cellular lipid droplets occurs primarily from the chemical bonds C-O, C-C, C¼C, and C-H [14, 22–26]. Unsaturated fatty acids contain more C¼C bonds (represented by the 1653–1655 cm1 band), while saturated fatty acids contain more CH2 groups and therefore have larger Raman peaks for those vibrational modes associated with CH2. 1. Understand the iodine value (IV). By definition, the iodine value (IV) is the measurement of the unsaturation of fats and oils in a sample and is expressed in terms of the grams iodine absorbed or consumed by 100 g of fat under standard conditions. Several methods were employed for determining the iodine value of lipids, but the most used one is “Hanus method” [27]. The method consists of treating the fatty acid under study with iodine monobromide (IBr) in glacial acetic acid and after that treating the excess reagent with a standard solution of sodium thiosulfate. The IV of fatty acids can be correlated with their degree of unsaturation in various ways. 2. It should be possible to determine the degree of saturation by calculating the peak ratios of specific bands or by looking at the relative concentrations between the total amount of saturated fatty acids and the total amount of unsaturated fatty acids (see Note 8). However, taking the ratio of the bands at 1294–1299 and 1263 cm1, this is problematic due to the overlap of these two bands (accuracy in separation ¼ 0.7642, Fig. 4f). The same procedure is possible for the peak ratio of the Raman bands at 1655 and 1448 cm1 (NC¼C/NCH2), the latter one being a particular shoulder of the 1442 cm1 peak. We localized this band from 1448 to 1464 cm1. These two bands represent the ν (C¼C) cis double bond stretching mode (olefinic), which is proportional with the number of C-double-bonds, and the δ (CH2)sc methylene scissor deformations, which is proportional to the number of C-single-bonds. These two bands show very distinctive peaks in Figs. 2a, b and 3, and do not overlap with other Raman peaks (accuracy 0.9651, Fig. 4f). The greater the number of C¼C bonds, the higher the IV, and the more reactive and oxidative the fatty acids are. 3. Take the peak ratio of the Raman bands at 1655 and 1448 cm1 (NC¼C/NCH2). Repeat this step for all Raman spectra, pure fatty acids, and LNCaP cancer cells, treated with fatty acids and control. 4. Collect all peak ratios in a table (see Table 2). 5. Explore the linear relationship between the two peaks, 1655 and 1448 cm1, of various fatty acids with different degrees of unsaturation (Fig. 5a).

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Table 2 Summary of Raman characteristics of pure methyl ester fatty acidsa Pure unsaturated FAME PA OA LOA POA AA EPA OA + LOA OA + LOA + POA OA + LOA + POA + AA LNCaPControl LNCaP-OA LNCaPLOA LNCaPPOA LNCaP-PA LNCaP-SA LNCaPMixture

NC=C, # of double bonds per molecule 0 1 2 1 4 5

NCH2, # of CH2 groups per molecule 14 14 12 12 10 8

NC=C/ NCH2

1655/1448 ν (C=C)/ δ(CH2)sc

Carbonyl position C=O

0 0.0714 0.1666 0.0833 0.4 0.625 0.1078 0.1031

IV Iodine value [28] 0 89.85 180.99 99.76 333.43 419.56 122.7758 118.2045

0 0.6478 1. 4158 0. 7449 3.8419 5.61 0.9854 0.9424

1743 1745 1745 1745 1738

0.151

163.0853

1.3797

1743

0.5232 ± 0.015

1744 ± 3.2017

0.0573 0.0567

71.8048 72.0338

0.5251 ± 0.0128 0.7684 ± 0.0448

1748 ± 0.3684 1744 ± 0.7838

0.0828

99.3046 0.4632 ± 0.0142

1742 ± 1.0733

0.0505 0.0554 0.0511

64.8995 71.0026 66.4014

0.5162 ± 0.014 0.4765 ±0.0103 0.5853 ± 0.0077

1743 ± 0.3855 1744 ± 0.2133 1746 ± 0.4996

0.0622

78.8697

1745 1745

a

OA oleic acid methyl ester, POA palmitoleic acid methyl ester, LOA linoleic acid methyl ester, AA arachidonic acid methyl ester, PA palmitic acid methyl ester, SA stearic acid methyl ester, EPA eicosapentaenoic acid

Fig. 5 Relationship between the ratio 1655 cm1/1448 cm1 and the average ratio of double-to-single carbon–carbon bonds NC¼C/NCH2; (a) Individual FAME and mixture of FAME; (b) Individual FAME and LNCaP treated with FAME. We kept the POA and OA on the calibration curve to be able to expand the region between the PA and POA

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Fig. 6 Iodine values calibration; (a) Individual FAME and mixture of FAME; (b) Individual FAME and LNCaP treated with FAME. We kept the POA and OA on the calibration curve to be able to expand the region between the PA and POA

Fig. 7 Relative concentrations of fatty acids from least squares fit of experimental Raman measurements of lipid droplets for individual FAMEs-treated LNCaP without SVM (a) and with SVM (b). Standard deviations are shown. OA oleic acid, POA palmitoleic acid, LOA linoleic acid, AA arachidonic acid, PA palmitic acid, SA stearic acid. All FAMEs sample were liquids, except the PA and SA samples. PA and SA samples spectra were recorded in liquid form and solid form as well by melting the solid samples with a heat gun. The final spectra for PA and SA samples were an average spectra between the liquid and solid form spectra

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6. Repeat step 5 for the LNCaP cancer cells LDs, without knowing their fatty acid composition, Fig. 5b. 7. We imported here some notations from reference [29]. For a better linear fit, we introduced the eicosapentaenoic acid (EPA) with five of C-double-bonds. The best fit-line is a linear equation that allows us to predict the unknown NC¼C/NCH2 ratios and has the expression y ¼ 9.1272x  0.0012, with RMSE of about 0.99. 8. Using this expression, predict the degree of unsaturation for the mixture samples of 50% OA + LOA, 33% OA + LOA + POA, and 25% OA + LOA + POA + AA, from the spectra in Fig. 2b. The results are shown in Table 2, column NC¼C/NCH2. The unsaturation ratios of these samples lie between the unsaturation values of LOA and POA (Fig. 5a). In a similar manner, we can predict the unsaturation/saturation degrees for the LNCaP cancer cell samples treated or untreated with FAME. These samples are more saturated than the FAME mixture samples, and therefore the unsaturation/saturation ratios of these samples lie between the OA and PA (Fig. 5b). The experimental values are colored in black, and the predicted values are colored in red in Table 2. 9. The iodine values do not follow a linear relationship either with the ratio NC¼C/NCH2 nor the peak ratio of the Raman bands at 1655 and 1448 cm1. The best fit-line of the FAME’s (Fig. 6a) iodine values versus the ratio 1655 cm1/1448 cm1 is a quadratic equation that allows us to predict the unknown iodine values and has the expression 2 y ¼ 9.3228x + 124.28x + 9.3564, with RMSE of about 0.99 (Fig. 6a). 10. Using this expression, predict the iodine values for the mixture samples of 50% OA + LOA, 33% OA + LOA + POA, 25% OA + LOA + POA + AA (Fig. 6a), and for the LNCaP cancer cell samples, treated or untreated with FAME (Fig. 6b). 11. Notice from Fig. 7b and from the values in Table 2, column IV Iodine value, that the iodine values of the LNCaP samples has the same trend as the unsaturation ratios and they lay on the quadratic curve between the iodine values of the pure fatty acids OA and PA. 3.11 Lipid Composition Changes Analysis

In order to further quantify the changes in the composition of the fatty acids-treated LNCaP cancer cells’ lipid droplets compared to those of the control cancer cells, the least square fit was performed utilizing the full experimental Raman spectrum and discriminative Raman peaks selected by the SVM software, as opposed to ratios of values at particular bands. The experimental spectra were assumed as being a linear combination of spectra of pure fatty acid

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Fig. 8 Relative concentrations of fatty acids from least squares fit of experimental Raman measurements of lipid droplets for control and FAMEs mixture-treated LNCaP without SVM (a) and with SVM (b). Standard deviations are shown. OA oleic acid, POA palmitoleic acid, LOA linoleic acid, AA arachidonic acid, PA palmitic acid, SA stearic acid. PA and SA samples spectra were recorded in liquid form and solid form as well by melting the solid samples with a heat gun. The final spectra for PA and SA samples were an average spectra between the liquid and solid form spectra

components in the fitting process. A cytoplasm component was not included in the fit because the lipid droplet size was bigger than the detection volume of the Raman microscope, and the cytoplasm signal was not recorded. The goal of this study was to perform an analysis of the FAMEs stored by the LNCaP cancer cells, and therefore the cholesterol spectra were also not included in the analysis. The cholesterol investigation would be the subject of another study in the near future. 1. Use the Raman spectra of individual pure samples and mixtures of the pure samples in the low wavenumber regions used for the fitting routine, which are shown in Fig. 2a, b, respectively, and the Raman spectra of the LNCaP cells with the discriminative wavenumbers (dotted lines) found by the SVM routine, which are shown in Fig. 3. 2. Notice the discriminative wavenumbers values (dotted lines in Fig. 3) being as follows: 930, 970, 1003, 1062 cm1, a larger band around 1110 cm1, a band around 1417 cm1, a band around 1456 cm1, a band around 1655 cm1, and a band around 1737 cm1. Use a larger band around the peak at

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1110 cm1 because the ECOC model predicts very high accuracies all over that band (e.g., 0.9858, Fig. 4e) in that region and a very narrow band in the region below 1080 cm1 due to peaks’ overlapping. 3. Perform the fits for low wavenumber regions for FAMEsresponsive LNCaP cells to compare changes in intracellular lipid composition upon FAMEs treatment to the lipid composition of the control LNCaP cancer cells. Perform fits without the SVM peak selection (Figs. 7a and 8a) and with the SVM peaks selection (Figs. 7b and 8b). 4. Notice the trend of the coefficients for each of the LNCaP cancer cell lines: control, treated by different fatty acids, and a mixture of fatty acids. Notice the changes in intracellular lipids in the LNCaP cancer cells upon exposure to individual fatty acids (Fig. 7b) or control and a mixture of fatty acids (Fig. 8b). This is due to fatty acids-mediated lipogenesis and lipid droplet formation. We noticed that the treatment with individual fatty acids produced more of that fatty acid, for example, OA treatment produced more OA. However, the treatment with SA and PA produced more OA. The likely explanation is that after absorption, the saturated SA molecules are converted rapidly to the monounsaturated OA by the formation of a double carbon bond, mediated by the enzyme SCD1 [30]. Another interesting result is that the OA and SA treatments similarly affect the changes in the lipid droplet composition, with very little differences. This has been seen in [31], where SA has a beneficial effect on lowering the low-density lipoprotein (LDL). We also noticed that the PA is not readily converted to the POA. However, PA is converted to OA by a two-reaction pathway. It starts with a conversion reaction from PA to SA (16:0 to 18:0), by elongation followed by desaturation, resulting in the generation of the monounsaturated acid OA. The treatment with LOA produces some LOA and also more AA, likely due to the elongation and desaturation steps. The treatment with POA produces more POA but also PA. The control LNCaP cancer cell line is more saturated than the one treated with a mixture of fatty acids. Thus, the unsaturated fatty acids (OA, LOA, and POA) are canceling or neutralizing the effect of the saturated fatty acids (SA and PA), making the LDs of these samples less saturated. Overall, OA is a predominant fatty acid in the LD of LNCaP cells. The addition of a mixture of fatty acids changes the composition and saturation of the LD, but OA remains the main constituent (Figs. 7b and 8b). 5. Notice the low wavenumber data resulted in fits with lower standard deviations. This is likely due to the fact that the low wavenumber spectra have more discrete peaks that can be better separated in the fitting routine.

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4

207

Notes 1. There are some sharp spikes in the spectra, which are typically narrower than the Raman peaks. These spikes are the cause of cosmic rays hitting the detector, and they need to be removed before going deeper into data analysis. You can use the Savitzky–Golay (SG) filter, sgolayfilt(). Sometimes, this filter needs an extra processing step to remove the spikes, and you can use a thresholding method based on data’s histogram after using the SG filter. 2. Recording a group of five Raman spectra of media outside the cell area, from a point scan, in the region between 300 and 1800 cm1 is mandatory due to a very intense signal from the media and coverslips. 3. Baseline correction is needed due to imperfect removal of the background. Use the function msbackadj() regression method with various combinations of parameters: quantilevalue, windowsize, stepsize, and regressionmethod to get a better fit of the data. 4. Find the most important Raman peaks of the polystyrene beads in the recorded spectra by using the function findpeaks() and find the corresponding peaks in the polystyrene beads calibration charts of the Mccreery’s group [32]. Interpolate the measured peaks versus the actual peaks to obtain the calibrated Raman shifts. 5. Find the best accuracy (>0.92) by combining two by two Raman peaks. Example: choose the peak at 1448 cm1 fixed and run the ECOC classifier by selecting this peak and a different one from 800 to 1800 cm1. Keep the combination of peaks with accuracy >0.92. 6. Use the function lsqnonneg() to compare the sample spectra with the standard pure fatty acid spectra, constraining the coefficients to positive values. 7. Use the function barweb() to produce bar graphs with error bars. 8. As an additional metric, there is another way to look at the relative concentrations between the saturated fatty acids (PA-palmitic and SA-stearic) and the unsaturated fatty acids (OA-oleic, LOA-linoleic, POA-palmitoleic, and AA-arachidonic). This can be represented as percent saturation (namely, saturated FA concentration divided by total FA concentration). This approach could be followed to assess the unsaturation/saturation degrees only when there is more information on all fatty acid compositions and cholesterol. Using this metric for predicting the unsaturation or saturation

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degrees using only five or six fatty acids as a reference will underestimate the results. It will take further investigation to state accurately the relative concentrations of fatty acids and cholesterol inside LDs.

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25. Schie IW, Wu J, Weeks T, Zern MA, Rutledge JC, Huser T (2011) Label-free imaging and analysis of the effects of lipolysis products on primary hepatocytes. J Biophotonics 4 (6):425–434. https://doi.org/10.1002/jbio. 201000086 26. Frank CJ, McCreery RL, Redd DCB (1995) Raman spectroscopy of normal and diseased human breast tissues. Anal Chem 67 (5):777–783. https://doi.org/10.1021/ ac00101a001 27. Hanusˇ J (1901) Die Anwendung von Jodmonobromid bei der Analyse von Fetten und Oelen. Zeitschrift fu¨r Untersuchung der Nahrungs- und Genußmittel, sowie der Gebrauchsgegenst€ande 4:8. https://doi.org/10.1007/ BF02431226 28. Ham B, Shelton R, Butler B, Thionville P (1998) Calculating the iodine value for marine oils from fatty acid profiles. J Am Oil Chem Soc 75(10):1445–1446. https://doi.org/10. 1007/s11746-998-0197-2 29. Samek O, Jona´sˇ A, Pila´t Z, Zema´nek P, Nedbal L, Trˇ´ıska J, Kotas P, Trtı´lek M (2010) Raman microspectroscopy of individual algal cells: sensing unsaturation of storage lipids in vivo. Sensors 10(9):8635 30. Mason P, Liang B, Li L, Fremgen T, Murphy E, Quinn A, Madden SL, Biemann HP, Wang B, Cohen A, Komarnitsky S, Jancsics K, Hirth B, Cooper CG, Lee E, Wilson S, Krumbholz R, Schmid S, Xiang Y, Booker M, Lillie J, Carter K (2012) SCD1 inhibition causes cancer cell death by depleting mono-unsaturated fatty acids. PLoS One 7(3):e33823. https://doi. org/10.1371/journal.pone.0033823 31. Hunter JE, Zhang J, Kris-Etherton PM (2009) Cardiovascular disease risk of dietary stearic acid compared with trans, other saturated, and unsaturated fatty acids: a systematic review. Am J Clin Nutr 91(1):46–63. https://doi.org/ 10.3945/ajcn.2009.27661 32. McCreery RL (n.d.) Raman materials. https:// www.chem.ualberta.ca/~mccreery/raman. html

Chapter 21 Surface Plasmon Resonance, a Novel Technique for Sensing Cancer Biomarker: Folate Receptor and Nanoparticles Interface Santosh Kumar Singh and Rajesh Singh Abstract Despite advances in healthcare technology, the early detection biomarker and treatment remain one of the biggest challenges in humanity. Thus, developing a biosensor for timely diagnosis is well-justified to improve the prospect of remission of cancer patients. Surface plasmon resonance (SPR), a biosensor, is of interest that monitors many cancer biomarkers with high sensitivity and rapidity. For various cancers, nanoparticle (NP)-based targeted drug/gene delivery has been widely employed as it directs mainly the receptors expressed specifically on the cell membrane of the cancer cell. Folate Receptor 1 (FOLR1) or FOLRα, predominantly expressed on epithelial cells, remains a principal target for drug discovery in several cancers, including prostate or ovarian. Therefore, conjugation of folic acid to the NPs precisely targeting the biomarkers on the tumor cells allows the detection and helps in the treatment of various cancers. In the present study, we discuss the folate receptor as of diagnostic interest and focus on the use of the targeted planetary ball milled nanoparticles (PBM-NPs) and its formulation, emphasizing the approach using sensor chips in the Open SPR system for cancer biomarker detection. Key words Surface plasmon resonance, Folate Receptor 1, Planetary ball milled Nanoparticles, Sensor chip

1

Introduction The cell membrane receptor mediates signal recognition and transduction from external stimuli. Many biological processes, transport of ions, molecules, chemical reactions, energy transduction, are the main functions; any mutation in membrane protein can generate severe disease, including cancer [1]. Therefore, membrane protein remains a principal target for drug discovery [2, 3]. In biomedical research, the drug’s effectiveness depends on its specific binding to the ailment receptor. Folate Receptor 1 (FOLR1) or FOLRα is a 37–42 kDa membrane-bound protein predominantly expressed on epithelial cells of the tumor and mediates the high affinity for binding and cellular uptake of folic acid (FA) into cells [4]. We

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_21, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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used the folate receptor, highly expressed in several cancers, including prostate, to test the ligand–receptor binding-based cancer therapy. Determining the binding affinity and kinetics of a ligand to its receptor has been a major task in drug design and screening [5, 6]. Given the importance of ligand (drug)–receptor interaction, we used a technique called Surface Plasmon Resonance (SPR) for the screening of interaction between folic acid (FA)-conjugated planetary milled nanoparticles (PBM-NP) to the folate receptor 1. SPR-based detection has been used as a powerful tool in biomolecules’ interactions, including ligand–receptor kinetics, antibody– antigen interactions, enzyme–substrate reaction, and epitope mapping [7]. Although several systems for ligand immobilization are available [8], we employed OpenSPR (Nicoya Life Science, ON, Canada) as it works on the surface of its sensors and can monitor the interactions between the molecules in a real-time fashion. OpenSPR allows detection by monitoring the changes in the refractive index on coupling ligands. Based on surface chemistry, two types of ligand coupling methods are most in use, i.e., covalent and capture coupling. For covalent immobilization, COOH and Amine sensor chips are used, which bind to the amine and activate carboxyl group of the ligand, respectively. Additionally, Gold sensor chips are used where no surface chemistry is found. However, capture coupling sensors, Streptavidin, Biotin, and NTA (Ni2 +
nitrilotriacetic acid) chips, are used to determine the coupling to biotinylated, streptavidin, and his-tagged ligands (Nicoya Life Science). To demonstrate the prostate cancer biomarker, we used the COOH sensor chip activated with the EDC:1-(3-dimethyl aminopropyl)-3-ethyl carbodiimide hydrochloride; NHS: N-hydroxy succinimide (EDC/NHS) to couple the ligand, FOLR1, and calculated the binding affinity between FOLR1 and analyte (FA- conjugated PBM-NP) (Fig. 1). Multidisciplinary research has been explored to treat an early diagnosis of cancer. The mechanism by which FOLR1 mediates

Fig. 1 Schematic presentation of capture coupling; ligand (FOLR1 recombinant protein) bound to the analyte (FA-PBM-NPs)

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tumorigenesis in prostate cancer is still not well studied. As most chemotherapy drugs applied in clinics are associated with multidrug resistance (MDR), severe side effects, and relapse, researchers are turning on to chemotherapy mediated by nanocarriers. Targeted nanotherapy that has the potential to suppress tumor cell progression selectively by increasing therapeutic efficacy is now in clinical research [9]. In the present chapter, we used the FOLR1 as target ligand and focused on state-of-art advances in targeted nanoparticle (analyte) formulation, emphasized the approach using a carboxyl sensor chip in Open SPR system, and measured binding kinetics of analyte to the ligand.

2

Materials

2.1 Materials and Reagents

2.2 Standard Materials Required for Surface Plasmon Resonance

Soluble starch, Phosphate buffer saline (PBS), Dimethyl Sulphoxide (DMSO), Diethyl Ether, Acetone, 4-(dimethyl amino) pyridine, Dicyclohexylcarbodiimide (DCC), and Isopropanol were purchased from Fisher Scientific (Fisher Scientific, Pittsburgh, PA). Folic acid, N-Hydroxysuccinimide ester, Triethyl amine, Polyethylene Glycol (PEG), Dioxane, N, N0 -disuccinimidyl carbonate (DSC), and Polycaprolactone (PCL) were purchased from Sigma (St. Louis, MO). 1. Open SPR instrument (Nicoya Lifesciences, ON, Canada). 2. COOH Sensor chip: stored at 4  C (Nicoya Lifesciences, ON, Canada). 3. Amine coupling kit: EDC:1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride; NHS: N-hydroxysuccinimide (EDC/NHS)—stored at 4  C (Nicoya Lifesciences, ON, Canada) (see Note 1). 4. Activation buffer: stored at 4  C (Nicoya Lifesciences, ON, Canada). 5. Blocking solution: stored at 4  C (Nicoya Lifesciences, ON, Canada). 6. Recombinant human FOLR1 protein with 6x-His-tag (R&D Biosystem, MN, US). 7. Analyte (Folic acid-conjugated PBM-Nanoparticle). 8. Running buffer: PBS-P (10.1 mM Na2PO4, 1.8 mM KH2PO4, 137 mM NaCl, pH 7.4, 0.005% Tween20). Other common running buffer can also be used, i.e., HBS-PE (10 mM HEPES pH 7.4, 150 mM NaCl, 3.4 mM EDTA, 0.005% Tween20), or TBS-P (50 mM Tris–HCl pH 7.4, 150 mM NaCl, 0.005% Tween20) (see Note 2). 9. 80% Isopropanol (IPA) solution (v/v in distilled water).

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10. Syringes with blunt-ended injection tips. 11. Tweezers.

3

Methods

3.1 Nanoparticle Formulation Method

3.2 Method for Synthesis of NHydroxysuccinimide Ester-Activated Folate (NHS-FA)

The detailed formulation of PBM-Nanoparticle presented here is according to our previous publication [10] and has the patent US 8,231,907. In brief, 4% starch in PBS (1) was heated with continuous stirring to dissolve. The heat absorbent zirconium oxide planetary milling balls were used to mill the nanoparticle in a milling jar. To generate the particles from the microparticles containing starch, and the drug (any hydrophobic or hydrophilic drugs can be encapsulated), the milling jar containing balls is rotated about its axis and in the opposite direction as well, around a common axis of the chamber wheel. The particle size was controlled by applying the centrifugal force by varying the revolution/sec (Ω), duration and number of cycles, and a number of zirconium oxide balls. A schematic presentation of the PBM nanoparticle formulation method is shown in Fig. 2. 1. To synthesize NHS-FA, first, the carboxylic group of folic acid is activated by dicyclohexylcarbodiimide (DCC) and N-hydroxysuccinimide (NHS). 2. The N-hydroxysuccinimide (NHS) ester of folic acid (NHS-folate) is synthesized by dissolving FA (5 g) in dry

Fig. 2 A schematic presentation of PBM–nanoparticle formulation and biomarker detection through Surface Plasmon Resonance (SPR)

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DMSO (100 mL) and 2.5 mL of triethylamine and allowed to react with N-hydroxysuccinimide (2.6 g) in the presence of dicyclohexylcarbodiimide (DCC) (4.7 g) overnight at room temperature (ratio: folic acid: NHS: DCC molar ratio ¼ 1:2:2). 3. After the reaction, a byproduct, Dicyclohexylurea, formed is removed by filtration through a 0.22 μm size filter. 4. Next, under reduced pressure and heating, concentrate the DMSO solution. 5. Precipitate the formulated product NHS-folate in diethyl ether and wash the resulting product NHS-folate several times with anhydrous ether, dry under vacuum, lyophilize, and store as powder. 3.3 Poly(ε-Caprolactone) (PCL) Activation

1. Mix PCL (2gm) and dry dioxane (6 mL) and heat in a water bath until complete solubilization of the polymer and cool to room temperature. 2. Next, add N N0 -disuccinimidyl carbonate (DSC) 307 mg to 2 mL of dry acetone and mix with continuous stirring, and finally adjust the pH to 6.4 using Calcium Carbonate. 3. Add 9.4 mg of pyridine in dry acetone (2 mL), and place in a shaker to react for 6 h. 4. Subsequently, filter the precipitate with diethyl ether and redissolve in acetone and allow it to become a dry powder.

3.4 Preparation of NHydroxysuccinimide Ester (NHS)-Activated Folate-Conjugated PEG

1. For PEG activation: Dissolve PEG (5 g) in dry dioxane (25 mL) and heat in a water bath to solubilize the polymer fully and react with 6 mmol of N,N0 -disuccinimidyl carbonate (DSC) in the presence of 6 mmol of 4-(dimethylamino) pyridine in 10 mL of dry acetone with continuous stirring for 6 h at room temperature. 2. Precipitate the succinimidyl carbonate (SC)-PEG by adding diethyl ether until no further precipitation is observed (typically 3–4 volume of solvent use). 3. Redissolve the precipitated product in acetone and repeat the precipitation step twice using diethyl ether to remove the excess reactants and stored as powder. 4. Next,dissolve the resulting substrate in 2.6 mL (148 mM) of the NHS-FA stock solution and add to the activated Polyethylene Glycol (PEG) 500 mg (147 mM) in DMSO (5.0 mL), in the presence of triethylamine (4.0 mL). Keep the mixture overnight under shaking condition until it is dried to powder. 5. To remove unconjugated folic acid, purify the final product on a Sephadex G25 column equilibrated with 0.1 M NaHCO3 and then lyophilized.

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6. Finally, the formulated PBM nanoparticle (4% starch particle) (see Subheading 3.1) cores coat with activated PEG (2%), and PCL (2%) and dissolve in methylene chloride with continuous stirring overnight. Keep the mixture under vacuum until it is dried, and finally, lyophilize. Now the particle is ready for the experiment. 3.5 Characterization of PBM Nanoparticles

3.6 COOH Sensor Chip Coupling Procedure

Measure the size and zeta potential of folate-PCL-PEG-coated PBM nanoparticles at pH 6.8 using a Malvern Zetasizer ZS instrument at a concentration of 0.1 mg/mL (5% mass, assuming a density of 1 g/cm3) of nanoparticles. 1. Before loading the sensor chip in OpenSPR, follow the manufacturer’s protocol (Nicoya Lifesciences, ON, Canada) for instrument priming and obtain references and ensure the flow cell is clean and dry. 2. Once the references are complete, load the COOH sensor chip into the instrument by following standard procedure (see Note 3). 3. Pump a running buffer (1 PBS pH 7.4) at flow speed (150 μL/min) for 10 min. 4. Ensure there is no air bubble in the sample loop and flow cell. 5. To perform bubble removal, first ensure the pump is set at a maximum speed of 150 μL/min. 6. Inject 300 μL of the 80% isopropanol solution through a sample port and turn the valve (in the instrument) to load position for bubble removal. 7. After 10 s, quickly turn the valve back to the inject position. 8. For any evidence of leaking or bubbles, inspect the flow cell by using a flashlight (see Note 4). 9. Repeat steps 5–7 if any bubbles are present. 10. To set the valve at load position, rinse the sample or injection loop with 1 mL running buffer and purge with air. 11. Once all bubbles are removed, clear the points by clicking right on the response graph and select clear points. Now the instrument is ready for the ligand injection.

3.7 SPR Analysis of the Protein (FOLR1) and Folic Acid-Conjugated Planetary Ball Milled Nanoparticle (FA-PBM-NP) Binding

1. After cleaning the sensor chip surface, slow the pump speed to 20 μL/min to maximize the ligand interaction time with the surface. 2. Rinse injection port with 1 mL running buffer and purge with air. 3. For surface conditioning, load 200 μL of 10 mM HCl (pH 2.0) to clean the sensor surface with a maximum pump flow rate

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(150 μL/min). Further, wait for the signal to be stable, then slow the pump speed to 20 μL/min. 4. Next, the carboxylic group surface activates with EDC/NHS (1:1); for surface activation, slow the pump speed to 20 μL/ min; thaw and mix 1 aliquot of EDC and NHS and inject immediately. This step should be performed quickly because an EDC/NHS ester has a short half-life. The total time for interaction is 5 min. Subsequently, rinse the sample loop with buffer and purge with air. Now the sensor surface is ready for ligand (FOLR1 protein) immobilization. 5. Dilute a FOLR1 protein to a concentration of 25 μg/mL in an activation buffer (available in an amine coupling kit supplied by Nicoya life sciences). 6. Following dilution, inject 200 μL of FOLR1 through the injection port into the instrument; set up a pump flow rate 20 μL/min and wait for 5 min to complete the interaction time (see Note 5). 7. After the interaction, observe the baseline to be stable for 5 min, then rinse the sample loop thoroughly with a running buffer, and purge with air (see Note 6). 8. Further, load and inject 200 μL of blocking solution with a flow speed of 20 μL/min to deactivate the remaining active COOH group on the sensor chip. Subsequently, rinse the sample loop with a 500 μL running buffer and purge with air. 9. Next, load and inject 200 μL of buffer blank through an injection port at a pump speed of 20 μL/min. Wait for 4 min to complete the interaction and then switch the valve back to load position. Let it run for the next 12 min to acquire data for an accurate stable baseline before proceeding to the next step. To calculate the accurate kinetics, subtract the baseline drift if any found during buffer blank injection from the binding curve in TraceDrawer software. Now, the sensor surface is ready for analyte (FA-conjugated-PBM-NP) injection. 10. Further, perform the kinetic measurement of different concentrations of analytes (FA-PBM-NP) performed on the FOLR1 immobilized sensor chip. 11. Prepare dilutions of 4 different concentrations (10 nM, 20 nM, 50 nM, 100 nM) of analyte (FA- conjugated PBM-NP) in a running buffer and filter through 0.22 μM filter (see Notes 7– 9). 12. Load and inject 200 μL of analyte (10 nM) through the injection port to confirm the activity of FOLR1 protein. Likewise, for FOLR1 injection, the interaction time is 5 min. This ensures the maximum binding capacity of the surface.

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13. Observe the baseline stability for 5 min, then rinse the sample loop thoroughly with a running buffer, and purge with air. 14. Likewise, for the remaining three analytes concentrations, inject a volume of 200 μL of each sample through the injection port and wait for 5 min to complete the interaction time followed by rinsing with running buffer and purge with air. 15. Once the experiment of an analyte injection is complete, export the data in .CSV file. The exported file contains the response of SPR versus time. 3.8

Data Analysis

1. For kinetic analysis, import data into TraceDrawer evaluation software and follow the setup for data analysis steps. 2. Click add run to import the saved file (.txt file) (Fig. 3). 3. Following OpenSPR data analysis, select the checkmark for all curves to be included in the analysis. On the left side of the graph, run properties can be modified by adding the run name and description. In addition, below the graph, curve names and concentrations can also be changed by clicking on the corresponding cell. Next, specify the analyte injections in molar (M) on the screen (Fig. 4) (see Note 10). 4. Proceed to click ok to add run, and then click New Overlay. Add run thumbnail displayed at the left corner of the screen. Drag file from add run thumbnail to new overlay by leftclicking (Fig. 5). 5. Further, right-click on the overlay and select duplicate items.

Fig. 3 Represents the Add run menu where one can modify the run properties

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Fig. 4 Start page of TraceDrawer software showing add run button

Fig. 5 Drag file from add run thumbnail to new overlay by left click. At this step, check the analyte and reference curve and uncheck the nonrelevant curve

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Fig. 6 Select the modification tab and then select the reference curve to apply curve subtraction

6. From the display window of the right top corner, check the analyte and reference curve and uncheck the nonrelevant curve (Fig. 5). 7. Next, zoom to extents by right-click on the plot and then leftclick and drag a rectangle to zoom in on a specific region of the plot followed by move curve steps. 8. Highlight the selected curve to be moved by left click and then in the modification tab click move curve and enter the changes in X and Y values to align all the curves and then finally click apply changes. 9. Zoom to extend and follow to curve subtraction of buffer blank injection. In this step, select running buffer blank injection curve, i.e., reference curve from the curve subtraction in Modification tab and subtract from the analyte raw data to obtain corrected response and press apply (see Note 11) (Fig. 6) 10. In the modification tab, select the curve X-axis extents, crop the curves at the boundaries of analytes by entering start and end values and then click apply (Fig. 7). 11. Similarly, set the beginning of the curve to Y ¼ 0 by clicking the curve offset in the modification tab and then click apply (Fig. 7).

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Fig. 7 Represents the steps of curve x-axis extents and curve offset menu in the modification tab

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3.9 Methods for Kinetics Evaluation

1. Click New Evaluation at the left side of the window to open and add an evaluation item. From the drop-down list of Evaluation type, select Kinetics evaluation and the desired overlay from the data box to analyze and press OK (Fig. 8a). 2. Window displays the time point box to enter the time for the start of concentration change or simply right-click on the Yaxis where Y ¼ 0 before all curve starts to rise and press add changes. Following the OpenSPR method for kinetic

Fig. 8 This figure represents all steps of kinetic evaluation screen; started from (a) new evolution to the globally fitted binding model 1:1 to (f) using TraceDrawer software

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Fig. 8 (continued)

evaluation, add the end of concentration change by summing the association time with the start time of concentration change (Fig. 8b, c). 3. Next, in the table, right-click on the row and press use curve property concentration as displayed in the figure. By using curve properties, add sample concentrations at an initial concentration (top) or manually enter the values (see Note 12). Press next (Fig. 8d). 4. Further, select the Fit model (One to One) from the dropdown list, this is the most used model for kinetic data (Fig. 8e).

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Fig. 9 Screen fit setting windows displayed, ka and kd are set to a global fit type

5. Apply the Fit model to calculate the KD value (Fig. 8f). Check the good fit responses, errors, and Chi2 value. To ensure any deviation of actual response from the fit curve, press residuals. 6. Press setting and ensure the scope of fitted argument Bmax, and BI are set to local; ka and kd are set to Global (Fig. 9). 7. The fit model results show the kinetic measurements of several concentrations of FA-PBM-NPs (10 nM: Red; 20 nM: Black; 50 nM: Green; 100 nM: Blue) applied on the ligand, respectively. A Ligand (FOLR1) binds to PBM-NPs with an association rate constant ka ¼ 9.21  105 M
1 S
1 and dissociation rate constant kd ¼ 4.45  10
2 S
1. A typical response curve from an experimental data at different analyte concentrations is shown in Fig. 10; the analyzed binding kinetics represents dissociation constant (KD) value (4.83  10
8 mole) (Table 1). Kinetic analysis is performed using a globally fitted 1:1 binding model (see Notes 13–22). 8. SPR-based detection can be applied in many other biomolecule interactions, including antibody–antigen interactions, enzyme–substrate reaction, etc. An example of measuring recombinant protein FOLR1–antibody (FOLR1) interactions are shown in Fig. 11.

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Fig. 10 One to one Fit model displaying the calculated ka, kd, and KD values Table 1 Data show the FA-conjugated-PBM-NP and FOLR1 interaction; the Kinetic evaluation type Fit (1:1) model applied Curve name

Ka (1/M*s))

Analyte 10 nM

9.21  10

Analyte 20 nM

Kd (1/s)

KD (M)
2

4.83  10
8

9.21  105

4.45  10
2

4.83  10
8

Analyte 50 nM

9.21  105

4.45  10
2

4.83  10
8

Analyte 100 nM

9.21  105

4.45  10
2

4.83  10
8

4

5

4.45  10

Notes 1. Preparation of EDC/ NHS aliquots: (1) Dissolve NHS into activation buffer and make aliquots of 100 μL and store at
20  C. (2) Dissolve EDC into the activation buffer and make aliquots of 100 μL and store at
20  C. 2. All running buffers should be degassed and filtered through a 0.2 μm filter. 3. Rinse sensor chip with distilled water, dry with clean nitrogen or compressed air. 4. During bubble removal, keep the injection time short, otherwise, additional bubbles can be produced. 5. The ligand (recombinant protein) injects in quick succession after the EDC/NHS finishes pumping.

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Fig. 11 FOLR1 protein–antibody binding interactions. The kinetic evaluation overlay displays the calculated ka, kd, and KD values after applying the One to One (1:1) Fit model in Trace drawer software. The graph shows the affinity between ligand (recombinant protein FOLR1) and the analyte (FolR1 antibody) (R& D Biosystem, USA). The FolR1 antibody was tested at three different concentrations, each corresponding to the different color (0.1 μg: green; 1 μg: blue; and 4 μg: red) curve on the graph. The fit model results show an association rate constant ka ¼ 9.47  104 M
1 S
1, and dissociation rate constant kd ¼ 2.34  10
5 S
1. The binding kinetics represents KD value 2.47  10
10 mole

6. To ensure FOLR1 protein binding, compare the signal after the EDC/NHS activation to the surface signal and after the immobilization step. If the signal is not strong, a second and third injection of FOLR1 can be performed. 7. Injecting one concentration of analyte three times is the best practice to ensure the reproducibility of data. During this step, follow the same methods of injection as given the first time of analyte injection. All analyte samples should be filtered through 0.2 μm filters. 8. To confirm the ligand activity, inject a high concentration of analyte on the ligand, which will ensure the maximum binding capacity of the surface.

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9. To optimize the ligand density, the following equation (Max analyte binding level (pm) ¼ Analyte MW/Ligand MW  ligand immobilization level (pm)) can be used that will estimate the analyte response based on the ligand immobilization level. 10. During OpenSPR data analysis, the concentration represents in a specific manner, for example, if the concentration is nM, then input prefix “n” will be used; and if the concentration is in μM, use the input as “u”. 11. Following OpenSPR kinetic handbook, if any spike is present in the middle of the response curve, select the response curve, then go to Modifications tab>Cut spikes, and move start/end cursor to sandwich the spike. Press Apply. This will remove all data points in the selected section of the selected response curve. 12. The start time should have the sample concentrations and the end time should be at zero. 13. In kinetic evaluation, any small errors, Chi2, and residuals are the indications of a good fit curve on the graph. 14. One to One (1:1) Fit binding model yields a maximum level of biding and KD value. 15. Association reaction (ka or kon), a constant used to calculate association rate or “on- rate”, characterizes how quickly analyte or antibody binds to its target ligand. 16. The association constant or reaction (kon) is simply the ratio at the equilibrium of the product and the reactant concentrations. 17. Dissociation reaction (kd or koff) is used to calculate the dissociation rate or “Off rate” that characterizes how quickly analyte or antibody dissociates from its target ligand. 18. Equilibrium reached when. nkoff ½A ˜ n½B˜ nkon ¼ ½AB˜ 19. For binding affinity calculation, the following equation is used. K D ¼ kd =ka to experimentally measure off- and on-rates, where Equilibrium dissociation constant (KD) is the ratio of kd/ka or koff/ kon between the analyte and ligand. The binding affinity and KD are inversely related, which means the lower the KD value, the higher the affinity of the analyte (nanoparticles) to the ligand (recombinant protein). 20. The unit used for ka and kd is 1/(concentration-time) or M
1 s
1 and 1/time or s
1, respectively.

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21. Flatter slope represents slower off-rate/stronger binding; however, steeper downside slope represents faster off-rate/weaker binding. 22. In kinetic evaluation, different concentrations of analyte represent different color curves on the graph.

Acknowledgments This study was supported by the National Cancer Institute of the National Institutes of Health under award numbers SC1CA193758 and U54CA118638, and by the Department of Defense under award number W81XWH1810429. References 1. Patching SG (2014) Surface plasmon resonance spectroscopy for characterisation of membrane protein-ligand interactions and its potential for drug discovery. Biochim Biophys Acta 1838(1 Pt A):43–55. https://doi.org/ 10.1016/j.bbamem.2013.04.028 2. Drews J (2000) Drug discovery: a historical perspective. Science (New York, NY) 287 (5460):1960–1964. https://doi.org/10. 1126/science.287.5460.1960 3. Rask-Andersen M, Almen MS, Schioth HB (2011) Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10 (8):579–590. https://doi.org/10.1038/ nrd3478 4. Kelemen LE (2006) The role of folate receptor alpha in cancer development, progression and treatment: cause, consequence or innocent bystander? Int J Cancer 119(2):243–250. https://doi.org/10.1002/ijc.21712 5. Swinney DC (2009) The role of binding kinetics in therapeutically useful drug action. Curr Opin Drug Discov Devel 12(1):31–39 6. Wang W, Yin L, Gonzalez-Malerva L, Wang S, Yu X, Eaton S, Zhang S, Chen HY, LaBaer J,

Tao N (2014) In situ drug-receptor binding kinetics in single cells: a quantitative label-free study of anti-tumor drug resistance. Sci Rep 4: 6609. https://doi.org/10.1038/srep06609 7. Nguyen HH, Park J, Kang S, Kim M (2015) Surface plasmon resonance: a versatile technique for biosensor applications. Sensors (Basel, Switzerland) 15(5):10481–10510. https://doi.org/10.3390/s150510481 8. Schuck P (1997) Use of surface plasmon resonance to probe the equilibrium and dynamic aspects of interactions between biological macromolecules. Annu Rev Biophys Biomol Struct 26:541–566. https://doi.org/10. 1146/annurev.biophys.26.1.541 9. Zhao CY, Cheng R, Yang Z, Tian ZM (2018) Nanotechnology for cancer therapy based on chemotherapy. Molecules 23(4):826. https:// doi.org/10.3390/molecules23040826 10. Singh SK, Lillard JW Jr, Singh R (2018) Reversal of drug resistance by planetary ball milled (PBM) nanoparticle loaded with resveratrol and docetaxel in prostate cancer. Cancer Lett 427:49–62. https://doi.org/10.1016/j. canlet.2018.04.017

Chapter 22 Characterization of Tobacco Microbiome by Metagenomics Approach R. Suresh Kumar, Nivedita Mishra, and Amit Kumar Abstract Chronic consumption of tobacco in all forms, either smoked/smokeless forms, causes major health hazards to humans that include cancer, cardiovascular, lung diseases, diabetes, fertility issues, etc. Among tobaccomediated cancers, the prominent one being the oral cancers are caused due to chronic tobacco chewing. The biochemicals present in tobacco are involved in carcinogenesis, and their presence is partly mediated by the existence of microbes in tobacco products. The microbial characterization has been evolved from classical microscopical observation to the recent development of 16S rRNA sequencing by next-generation sequencing methods. The metagenomics approach using 16S rRNA-based next-generation sequencing methods enables the detection and characterization of the complete microbial community of tobacco, including both cultivable and non-cultivable microorganisms. Identification of microbes will help in devising strategies to limit the carcinogenic compounds present in tobacco. Key words Microbial community, Metagenomics, Tobacco, Nitrosomines, Oral cancer

1

Introduction Consumption of tobacco products poses a major health concern all over the world. Tobacco is consumed in the form of smokeless and smoked tobacco products and has been used for varied purposes. Smokeless tobacco products have been consumed by approximately 356 million people all over the world [1]. Previous studies on profiling tobacco products have identified around 233 chemical compounds from 82 types of chewing products, of which 69 are classified as carcinogens by the International Agency for Research in Cancer (IARC) [2]. Chewing smokeless tobacco causes cancer, particularly oral cancers that include gingival, lip, tongue, the floor of the mouth, etc. The tobacco constituents’ form DNA adducts and reactive oxygen species (ROS) that indulge in creation of mutations, particularly in tumor suppressor genes and resulting in uncontrolled cell division.

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_22, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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The chemical compounds present in tobacco vary in quantity depending on origin, cultivation conditions, phyllotaxical position of leaf, etc. The different phases like the cultivation of the plant, postharvest processing, and storage of tobacco leaves lead to differences in the microbial content, which greatly influences the chemical compounds to an extent. Several studies have reported that the presence of microorganisms in tobacco products may have some effect on human health through biochemical compounds present in tobacco. For instance, certain microbes like Bacillus pumilus, B. subtilis have a role in converting nitrates to nitrites [3]. These nitrites act as a precursor in the nitrosation of nicotine to make it tobacco-specific N-Nitrosomines (TSNA) [4, 5]. Nicotine as well as the TSNA are carcinogenic and cause health hazards like cancer, heart diseases, etc. In addition, the presence of microbes may alter the chemical constituents even after the packaging of the tobacco. Therefore, identifying and characterizing the microbes associated with tobacco can help in identifying and minimizing the potential health hazards present in a particular make. Identification and classification of microbes have been evolved through classical technologies based on microscopical observations of phenotype, structure, staining pattern, and culturing with certain media. The identification and isolation of microbes got impetus with the discovery of microscopes. Early works in microbes concentrated on isolation, identification of bacteria in pure culture [6–8], or enrichment culture techniques [9]. The characterizations were based on the stains, antibiotic resistance, and other biochemical and structural phenotypic characterization. However, cultivation-based studies of microorganism are tedious, time-consuming, selective, and biased artificial process. These traditional methods were inadequate for discovering novel microorganisms since fewer bacterial species were discovered over the last two decades [10] compared with estimated global bacterial diversity between 107 and 109 species [11]. As most microbes may not grow in the provided media, the uncultivable bacteria present a major challenge to the traditional culturing techniques. Now, it is known that more than 90% of existing microorganisms are uncultivable and cannot be isolated through the classical cultured methods. Carl Woose and his group in 1977 identified 16S rRNA could be used as a marker to characterize the microbes and included several modifications in protocols with refinement in primer sequences [12]. This further made ease through PCR-based approaches, like PCR amplification and cloning the fragments and sequencing them, and characterizing the sequences. RFLP-based, Denaturing Gel electrophoresis, Terminal restriction fragment length polymorphism (T-RFLP), FISH techniques have evolved based on 16S rRNA [13]. The advent of Next-generation sequencing methods ease the work and had broadened the scope of 16S

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Fig. 1 Variable and Conserved sequence arrangement of 16S rRNA. Arrows indicate forward (red) and reverse (green) primers. The 8F and 1492R primers are for full length sequence of the 16S rRNA

RNA sequencing and identification of microbes without the need of culturing and accomplished through sequence characterization. So the Omics approach brought options of identifying microorganism without the need of culturing and characterizing them through noncultural means known as metagenomics. Metagenomics refers to the study of microbial diversity based on the collective genome of microorganisms existing in a particular place and at a particular time. The microbial diversity has been analyzed by various methods, and the field has evolved through various technological advancements to get a comprehensive picture of the existence of microorganisms [14]. The 16S rRNA gene exists universally among bacteria in the small subunit of 30S ribosome and some regions in the sequences are highly conserved. 16S rRNA is ~1500 bp region, comprising of 9 variable regions (V1–V9) and 9 conserved regions (Fig. 1). The conserved regions are used to prime the amplification and variable regions are used to characterize the bacteria. There are nine conserved and nine hypervariable regions present in its genome of ~1500 bp size, of which the conserved regions are used to prime the amplification and microbial classification based on variable regions. The PCR amplified 16S rRNA sequences are sequenced using NGS, and the sequences from the reactions are clustered in the Operational Taxonomic Units (OTU). These are cluster of similar sequence variants of 16S rRNA subregion. Each of these clusters represents a taxonomic unit of bacteria, either genus or species. The clustering of 16S rRNA sequences for genus levels is kept with a threshold of 97% similarity, whereas for species, higher threshold of 98–99% sequence similarity is required. Previously it was assumed that 95% similarity represents the genus and more than 97% identity represents the same species [15]. The below procedure provides a method to characterize a microbial community, both cultivable and non-cultivable microbes, through metagenomics approaches.

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Materials

2.1 Media for Isolation, Growth, and Identification

1. Nutrient Agar (NA). 2. Nutrient Broth (NB). 3. Potato Dextrose Agar (PDA). 4. Luria Bertani Agar (LA). 5. Luria Bertani (LB). 6. Mueller Hinton Agar. 7. MacConkey Agar.

2.2 Other Chemicals Required

1. Phosphate Buffer 0.1 M pH 7 (PB). 2. Disinfectant (70% Ethanol). 3. 0.1 M Na2HPO4. 4. 0.1 M EDTA. 5. 0.1 M Tris-base. 6. 1.5 M NaCl. 7. 20% SDS. 8. β-mercapto-ethanol. 9. Phenol-chloroform-isoamyl alcohol (25:24:1) solution.

2.3 Other Materials Required

1. Bunsen burner/Spirit Lamp. 2. Sterile wooden tooth picks or Inoculation needle. 3. Test tube racks. 4. Sterile Centrifuge Tubes (15 mL). 5. PCR tubes (1.5 mL). 6. Sterile Test Tubes. 7. Sterile Petri Dishes (60–90 mm). 8. Sterile Spreader. 9. Conical flasks with cotton plug (250 and 500 mL). 10. Variable Micro Pipette (20–1000 μL). 11. Pipette Tip Box. 12. Sterile Pipette Tips. 13. Sterile forceps. 14. Sterile Spatula. 15. Paper towels. 16. Marker pencils. 17. Weighing balance.

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18. Incubator cum shaker. 19. Micro Centrifuge (Refrigerated). 20. Refrigerator (
20  C). 21. Normal Domestic Refrigerator (2  C to 8  C). 22. Real TIME PCR machine BioRad –CFX 96 Or ABI-7500. 2.4

3

PCR Kit Required

1. SYbr green Amplitaq green (Qiagen OR Applied Biosystem).

Methods

3.1 Estimation of Cultivable Microbial Communities of Tobacco

1. Label conical flasks with marker pen. Weigh media powder in and pour in the labeled flask (see Note 1).

3.1.1 Preparation of Media

3. Autoclave the medium at 121  C for 15 min. Cool to the temperature of 45  C (see Note 3).

2. Dissolve in approximately 90 mL of distilled water. Allow it to completely dissolve and make up the required volume (see Note 2).

4. Pour the agar media into presterilized petri dishes in the laminar hood. Allow the plate to solidify for 30–40 min (see Notes 4 and 5). 3.1.2 Isolation of Cultivable Bacteria from Tobacco

All the experiments were set in triplicate and at room temperature unless otherwise stated. The protocol followed is based upon standard microbiological practices [16]. 1. Weigh 1 g of tobacco sample in sterile centrifuge tubes (15 mL). Add 10 mL Phosphate Buffer (0.1 M, pH 7) and keep on the shaker for 1 h at room temperature (28–30  C). 2. Vortex at full speed for 30 s and centrifuge at 800 rpm for 10 min to remove the debris. The supernatant needs to be collected, and debris should settle as a loose pellet. If required, centrifuge second round to remove debris. Collect supernatant in sterile test tubes. 3. Take 1 mL of supernatant and dilute with 9 mL of PB in a sterile test tube. Repeat the process 6–7 times to make successive dilutions. 4. Take 100 μL and pour on various media plates for spread plating. 5. Spread using a sterile spreader and keep plates for incubation (see Note 6). 5. Examine the plates after incubation for the different types of colonies. Count the colonies to ascertain colony-forming units.

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6. Pick out visually different colonies based on their color, texture, and shape, and plate them on individual plates repeating earlier described steps. 7. Store plates at 4  C covered with aluminum foil for further microbial identification and studies. 3.2 Estimation of Total Microbial Communities of Tobacco

This procedure to isolate bacterial genomic DNA from the tobacco leaves has been adapted and modified from a previous study [17]. All the experiments were set in triplicate and at room temperature unless otherwise stated. 1. Weigh 5 g of tobacco leaves in a sterile conical flask (250 mL). Add 100 mL sterilized Phosphate Buffer (0.1 M, pH 7) and keep on the shaker, with gentle agitation for 30 min at room temperature (28–30  C). 2. Vortex gently at moderate speed for 30 s to suspend bacteria residing on the leaf surface into the solution (see Note 7). 3. The sample was centrifuged at 101  g for 5 min to sediment the leaf debris. The supernatant was collected in sterile centrifuge tubes in 5–6 aliquots (see Note 8). 4. Centrifuge at 10,062  g for 30 min to get bacterial pellets. Discard the supernatant and keep the pellet (see Note 9).

3.3 DNA Extraction from Microbial Communities of Tobacco

1. The bacterial pellet was resuspended in 2 mL DNA extracting solution (0.1 M Na2HPO4, 0.1M Tris pH8, 0.1M EDTA, 1.5 M NaCl) and 80 μL proteinase K and incubated in a water bath for 60 min at 37  C (see Note 10). 2. Then, add 1 mL SDS (20%) and 60 μL β-mercapto-ethanol and incubate for 120 min at 60  C in a water bath (see Notes 10 and 11). 3. Centrifuge the mixture at 4251  g for 10 min to remove the precipitate (Optional). 4. Collect the supernatant in a fresh tube and mix with an equal volume of phenol-chloroform-isoamyl alcohol (25:24:1) with gentle pipetting and centrifuge at 7270  g for 2 min. 5. To the supernatant, add only chloroform iso amyl alcohol and repeat the step as in step 4. 6. Transfer the supernatant to a new tube and keep in icebox. Add 0.6 times volume of isoamyl alcohol to the supernatant, and mix gently by inverting the tube, and keep at room temperature for 30 to 60 min or 30 min in 4  C (see Note 12). 7. Isolate the DNA by centrifugation at 11,093  g. Discard the supernatant and keep the DNA pellet for next use or store at
20  C (See Note 13).

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3.4 Estimation of Bacterial Load Through Amplification of 16S rRNA-Based Primers by Real-Time PCR Method

The qPCR was performed to quantify the bacterial load, using either SYbr green or probe-based method [18]. Another set of reaction for inhibition control, E. coli DH10B genomic DNA, was set as an independent reaction and rspL primer (0.2 μM) is used. As a standard procedure, 5 different dilution series of DH10B genomic DNA were taken to make a standard curve.

3.4.1 Determination of Bacterial Load

The protocol was set as an independent reaction. In parallel, a standard curve was prepared by using 5 to 10 serial dilutions of DH10B genomic DNA.

3.4.2 PCR Amplification

Inhibition Control rpsL (specific for DH10B rRNA).

Primers

16 S Forward Primer -1406F-50 -GYACWCACCGCCCGT-30 and 16S Reverse Primer 1525R- 50 -AAGGAGGTGWTCCARCC-30 . Rsp L Forward (50 -GTAAAGTATGCCGTGTTCGT-30 ). Rsp L Reverse (50 -AGCCTGCTTACGGTCTTTA-30 ).

Reaction

PCR was set up as follows: DNA template

50–100 ng

Sybr green amplitaq mix

5 μL

Universal primer of 16SRNA 1406/

0.4 μM

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Water to final volume

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0.2 μM primers are used

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Controls

To find out the inhibitory substances that are found in tobacco product, inhibitory to bacterial growth, the tobacco products were autoclaved and resuspended in sterile PBS. Each sample with 3 dilutions in triplicates was used to perform the experiment. In parallel, a standard curve was prepared by having 5 to 10 serial dilutions of DH10B genomic DNA.

3.5 16S rRNA Sequencing

For generating metagenomic data, the isolated genomic DNA (from Bacteria infested in tobacco) is taken and further processed for two-step amplification method that had amplified V3-V4 region

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V3-V4

P5

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Index primer P5 & P7 seq adaptors

Fig. 2 Two-Step PCR approach 16S rRNA Sequencing Approach (Adapted from [20])

of the 16S rRNA [19, 20] followed by purification, sequencing, and analysis of the reads. This experiment consisted of 1) 16S rDNA amplification-first step PCR, PCR purification, Library preparation with indexed PCR-second PCR, PCR purification, Loading in Sequencing, and Data analysis (Fig. 2). 3.5.1 First Step PCR-16S rRNA Gene Amplification (PCR1)

Setting up 16S rRNA amplification: first PCR step 1. Take 12.5 μL of Enzyme mix consisting of dNTPs, Buffer, MgCl2 and High-fidelity TA polymerase was added to 96 well plate. 2. Primers of 1 μM each were taken and mixed in the desired wells where premix is added. 3. 50 ng of DNA was taken in 96 well plate, mixed it in the wells. 4. Double distilled autoclaved water is added to the final volume of 25 μL. 5. Plates are sealed, or striped caps were added. 6. Plates were centrifuged with a plate centrifuge machine. 7. The plate is fixed in PCR machine for the reaction. Forward Primer 50 - TCGTCGGCAGCGTCA G ATGTGTA TAAGAGA CAGCCTAC. GGGNGGCWGCAG-30 .

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Reverse primer 50 - GTCTCGTGGGCTCGGAGATGTGTA TAAGAGACAGGACTACHVGGGTATCTAATC C-30 . The cycle conditions are as follows: PCR cycle Initial Denaturation—95  C for 7 min. 30–35 cycles of Denaturation—95  C for 30 s. Annealing—55 C for 30 s. Extension—72  C for 30 s. Final extension at 72  C for 7 min. The PCR product may be run in agarose gel electrophoresis in 1.5% gel for 550 bp product. Post PCR product is cleaned using PCR purification kit. 3.5.2 IInd PCR STEP

In this step, the illumine-based kit contains Index primer 1(12) and 2(8), these are arranged in the same way as the 96 well plate. For aliquoting primers, to the sides of 12 rows, Index primer 1 is kept and index primer 2 is kept on the sides of 8 row well side. Each first row of Index 1 will have all combinations of index 2 in the well. Ind 2 1

3

4

5

6

7 8

9

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Ind 2

8 Rows of well

1. Take 12.5 μL high fidelity PCR enzyme, buffer, dNTP mix. 2. Add 2.5 μL of each Index 1 and Index 2 primers- Adopter primer (barcoded) (Add the Index 1 primers in column 1–12, and Index primer 2 in A–H rows). 3. DNA template (Amplified product of 16S rRNA) of 2.5 μL is added. 4. Water to the final volume of 20 μL is added. 5. The plate is sealed and centrifuged in plate centrifuge at 101  g, 4  C for 1 min. PCR cycle: Initial Denaturation: 95  C for 4 min. 8 cycles of. Denaturation—95  C for 30 s. Annealing—55  C for 30 s. Extension—72  C for 30 s. Final extension at 72  C for 7 min.

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3.5.3 IInd Clean up of PCR Product (Using Magnetic Beads)

(a) 10 mM Tris pH 8.5 Stored at
15  C. (b) Ethanol (80%)-always prepared fresh. (c) AMPure XP beads (Magnetic beads) 20 μL per samples
2–8  C. (d) 96 well Real time PCR plate. 1. Pool the PCR products in to Autoclaved DNase and RNAasefree Eppendorf tube and process the products for purification or PCR products can be processed in the plates itself. 2. The plate is centrifuged briefly. 3. AMPure bead magnetic beads were kept at room temperature and vortexed. 4. To this, 56 μL of AMPure beads were added to each well. 5. Mix up the product with bead by pipetting up and down multiple times. 6. Let the plate stand at room temperature for 5 min, and keep it on the magnetic stand for 2 min. 7. Once the cleared solution is found, the supernatant can be discarded. 8. Keeping the plate or tubes in magnetic stand. 9. The beads were washed with 200 μL of freshly prepared 80% ethanol to each sample well and incubate for 30 s. 10. The supernatant can be removed. 11. The same procedure is repeated once more and remove the extra ethanol. 12. Let the plate or tube air dry. 13. Add 27.5 μL of 10 mM Tris pH 8.5 to each well or Elution buffer of DNA isolation kits. 14. Gently pipette out multiple times and keep it for 2 min. 15. Keep the plate for 2 min at magnetic stand and let the beads get precipitated. 16. The clear solution is carefully removed and transferred to the fresh plate.

3.5.4 Sequencing

Set up the sequencing reaction as per the sequencing machine manufacturer’s instruction. This procedure needed aliquoting and denaturing, and loading of samples into cartridges. Once the NGS reaction run was completed, the data were retrieved, and the raw reads were analyzed with specific software.

3.5.5 Analysis

Metagenomics pipelines can be categorized into two broad groups based on the methodology. Clustering-first or alignment-based approaches start with clustering of reads on the basis of similarity

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in different into OTUs. From each cluster, a representative sequence is extracted based on defined properties and aligned to the 16S rDNA sequences of a reference database. Thus, the OTU is assigned to a taxonomic group. Examples of this group are Mothur [21], QIIME [22], and BMP [23]. Assignment-first approaches first compare all of the reads with a reference database by k-mer or read mapping and assign the lowest taxonomy to each reads based on the lowest common ancestor and then regrouped into higher taxonomic units, e.g., Kraken [24] and CLARK [25]. The below method represents Alignment-based approach. 3.6 Analysis of Sequencing Data Via QIIME2

The protocol is based on the alignment-based approaches and written to help researchers to explore the analysis steps of 16S rRNA sequencing data via QIIME2.

3.6.1 Install QIIME2

Generally, conda installation is recommended for QIIME2 pipeline. But users can also try other methods to install this program (https://docs.qiime2.org/2020.6/install/). Once installed, a user can activate working environment by using the command: -- conda activate qiime2

3.6.2 Prepare Metadata

In addition to downloading the sequences, a user should also download metadata information, which describes characteristics of the sample of interest, collection date, collection site, method of collection and processing, etc. This information is generally stored in text file (.tsv ot .txt). Detailed information is available at https://docs.qiime2.org/ 2019.10/tutorials/metadata/ QIIME2 compatible metadata files can be checked by using command: -- qiime tools inspect-metadata filename

For metadata file visualization via QIIME2 use command: --qiime metadata tabulate

3.6.3 Prepare Raw Sample

The structure of raw sample can differ depending upon the sequencing platform (e.g., Illumina vs Ion Torrent) and sequencing approach (e.g., single-end vs paired-end). For more information, refer https://docs.qiime2.org/2018.2/tutorials/importing/ Extract barcodes from pair-end reads and convert the pair-end and barcode files in qiime zipped format (.qza). -- extract_barcodes.py -- qiime tools import -- qiime tools peek filename.qza

3.6.4 Demultiplexing

If the data are already demultiplexed, skip this step; otherwise run command: -- qiime demux emp-paired (output is .qza file) -- qiime demux summarize (output is .qzv file)

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3.6.5 Quality Check, Filtering, and Denoising

Methods such as Dada2 [26] and Deblur [27] introduce advanced quality control measures by denoising sequences for removal of sequencing errors. Trimming is done on the basis of the graph obtained in the previous step of summarization (.qzv). The step will produce two .qzv files which can be explored on the QIIME2 viewer to explore noises in samples (decision to drop samples having error). -- qiime dada2 denoise-paired -- qiime feature-table summarize -- qiime feature-table tabulate-seqs

3.6.6 Phylogenetic Tree Generation

The phylogenetic tree is constructed to find the alpha diversity metric, which defines the structure of an ecological community for its richness (number of taxonomic groups) and evenness (distribution of abundances of the groups) [28]. QIIME2 uses more than one method MAFFT [29], FastTree [30] to build a phylogenetic tree. -- qiime alignment mafft -- qiime phylogeny fasttree

3.6.7 Visualization of Alpha Rarefaction

This step adjusts different library sizes of samples for comparing alpha diversity [28]. -- qiime diversity alpha-rarefaction

3.6.8 Calculation of Diversity Matrix

It calculates all diversity matrices at once by the command. For alpha diversity Shannon, Faith’s PD matrices [31] can be calculated by the above commands. For beta diversity, Weighted Unifrac and Unweighted Unifrac [32] are calculated and visualized in a scatter plot. -- qiime diversity core-metrics-phylogenetic -- qiime diversity alpha-group-significance -- qiime diversity beta-group-significance -- qiime emperor plot

3.6.9 Assigning Taxonomy

In this step, denoised and cleaned sequences are used to assign taxonomy such as phylum, class, and genus using as a classifier. User can also define their own classifier or can download available online and visualized in a bar plot. The clustering of 16S RNA sequences for Genus levels is kept with a threshold of 97% similarity, whereas for Species, higher threshold of 98–99% sequence similarity is required. Previously it was assumed that 95% similarity represents the genus and more than 97% of identity represent same species [15]. -- qiime feature-classifier classify-sklearn -- qiime metadata tabulate -- qiime taxa barplot

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The goal of differential abundance is to identify microbiome taxa associated with certain clinical conditions. In this protocol, differential abundance is calculated across the samples. QIIME2 uses ANCOM [33] to detect differential abundance taxa. One can use supervised machine learning techniques such as random forest classifiers and regressors to classify and identify particular phenotypes as biomarkers. -- qiime feature-table filter-samples -- qiime composition add-pseudocount -- qiime composition ancom -- qiime sample-classifier classify-samples -- qiime sample-classifier regress-samples

For more information, please refer links below: https://chmi-sops.github.io/mydoc_qiime2.html https://curr-protoc-bioinformatics.qiime2.org/

4

Notes 1. Calculate the media to be weighed as per instructions of the manufacturer given on the bottle of different media. Generally, NA 2.8 g in 100 mL, PDA 3.9 g in 100 mL, Blood Agar 4 g in 1000 mL, Mueller Hinton Agar 3.8 g in 100 mL, MacConkey Agar 4.95 g in 100 mL, LA 3 g in 1000 mL, or as per manufacturer’s instructions. 2. Dissolve the powder media completely by swirling the beaker, gentle agitation, and mild heating, and then make up the required volume. Never make up the volume before dissolving the powder media completely. Add agar in the required quantity and dissolve by gentle heating and shaking if required before making up the volume. Dispense broth media in tubes if required at this stage before autoclaving. 3. Autoclave all required items like Petri Dishes, Spreader, Micro Pipette, Pipette Tip Boxes with Pipette Tips, Test Tubes, racks, etc., beforehand. Assemble required items in laminar hoods as Disinfectant (70% Ethanol), Bunsen burner/Spirit Lamp, autoclaved items, and media before starting. 4. Pour media at a temperature above 45  C to avoid clumping. If clumps start forming, redissolve them before pouring. Typically, 60 mm dishes take 15–20 mL media, and 90 mm dishes take approximately 25 mL media. Calculate the amount of media required beforehand as per experimental requirements. Prepare few extra plates to cater for unforeseen eventualities. 5. If not using immediately, plates can be stored in 4  C covered with aluminum foil. It is preferable to leave plates under incubation conditions for 12–24 h before using to check for

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contamination. Stored plates should be taken out and left for few hours at room temperature before use. 6. MacConkey and Mueller Hinton plates were incubated at 37  C for 48 h, PDA plates were incubated at 37  C for 7 days, and LA plates were incubated at 37  C for 48 h. In NA and LA plates, the bacteria can appear in 16 to 24 h after incubation at 37  C. 7. Alternatively, grounded tobacco leaves in PBS can be put in a sonicator for 10 min to suspend most of the bacteria on leaf surfaces in the phosphate buffer. 8. If required, centrifuge second round at 101  g to remove debris before collecting supernatant in sterile tubes. 9. The pellet can be stored directly at
20  C for further DNA analysis. The same pellet can be dissolved in a minimal volume of LB/NB and stored as 30% glycerol stock at
20  C for other microbial analysis. 10. Keep mixing the bacterial solution by gently inverting the tube after every 20 min interval. 11. After every 40 min, the sample was frozen with liquid nitrogen or follow the freeze-thaw cycle three times at
80  C. 12. Cut tips should be used once the cells are lysed. 13. Alternatively, Users can opt for DNA isolation kits which are available with various manufacturers.

Acknowledgments We thank Ms. Sanchita, Dr. Archana, and Dr. Anita Kumari (ICMR-PDF, NICPR) for reading and correcting the write-up. References 1. Mehrotra R, Yadav A, Sinha DN, Parascandola M, John RM, Ayo-Yusuf O, Nargis N, Hatsukami DK, Warnakulasuriya S, Straif K, Siddiqi K, Gupta PC (2019) Smokeless tobacco control in 180 countries across the globe: call to action for full implementation of WHO FCTC measures. Lancet Oncol 20(4): E208–E217 2. IARC (2007) Monographs on the evaluation of carcinogenic risks to humans smokeless tobacco and some tobacco-specific N-nitrosamines. IARC Monogr Eval Carcinog Risks Hum 89:1–592 3. Gregory LG, Bond PL, Richardson DJ, Spiro S (2003) Characterization of a nitrate-respiring bacterial community using the nitrate

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Chapter 23 Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research Qianqian Song and Liang Liu Abstract The recent maturation of single-cell RNA sequencing (scRNA-seq) provides unique opportunities for researchers to uncover new and potentially unexpected biological discoveries and to understand the complexity of tissues by transcriptomic profiling in individual cells. This review introduces the latest scRNA-seq techniques and platforms as well as their advantages and disadvantages. Moreover, we review computational tools and pipelines for analyzing scRNA-seq data, and their applications in cancer research, highlighting the important role of scRNA-seq techniques in this area. Key words Single-cell RNA sequencing, Computational analysis, Cancer, Biomarker

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Introduction Single-cell RNA sequencing (scRNA-seq) is at the forefront of phenotyping complex samples with high resolution, which is now widely applied in research. This technique provides unprecedented opportunities for understanding the complexity of tissues [1] by transcriptomic profiling in individual cells that enables to uncover a variety of cell types and subpopulations within the patient tissue [2, 3]. Not only the profound insights into cellular composition, scRNA-seq allows for the interrogation of cellular hierarchies and the identification of cells transitioning between states [4–6], such as development [7] and differentiation [8]. Various scRNA-seq platforms have been developed to improve efficiency and accuracy for transcriptional profiling of individual cells within a sample. These platforms differ in several aspects, including the capture format, cell loading, single-cell indexing, molecule identifier, additives in reverse transcription, cDNA amplification, and fragmentation transcript coverage that is a major difference in the various scRNA-seq protocols. One major protocol uses the unique molecular identifier (UMI)-tag-counting strategy; that is, a single UMI sequence is added to each reverse-transcribed

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7_23, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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mRNA molecule to quantify the number of transcripts expressed in a cell. This strategy is used in protocols that profile short 50 or 30 RNA sequence tags and create cDNA libraries with low complexity. The platforms using this protocol include inDrops (1CellBio) [9], Drop-seq (Dolomite) [10], Seq-Well [11], Chromium V2 (10X Genomics) [12], Quartz-Seq2 [13], SPLiT-seq [14], CEL-seq2 [15], and MARS-Seq [16]. Others, such as STRT-seq [17] and STRT-seq-2i [18], are 50 tag counting techniques, another popular protocol for scRNA-seq. Other than the UMI-tag based protocols, full-length transcript sequencing, e.g., Smart-seq2 [19], is widely used to reverse-transcribe and amplify full-length transcripts. Smart-seq [20], MATQ-seq [21], and Quartz-Seq [22] are also developed based on this sequencing protocol. These platforms differ substantially with respect to their RNA capture efficiency [23], and sequencing library complexity as well as sensitivity for identifying genes and cells [24, 25]. Varying library complexities affect the capability to quantify gene expression levels and, consequently, impact the indentation of specific cell-type markers and the resolution of cell phenotyping. For example, a study demonstrated that Smart-seq2 can detect a larger number of expressed genes than other scRNA-seq protocols such as CEL-seq2, MARS-Seq, Smart-seq, and Drop-seq [26], while MATQ-seq, another full-length transcript sequencing platforms, outperforms Smart-seq2 in detecting low-abundance genes. In addition, scRNA-seq protocols vary in their scale and costs, which remind the consideration of the balance between research goal and sequencing cost for the selection of scRNA-seq protocol and platform.

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Batch Effects Correction and Normalization of scRNA-seq Data The advancement of scRNA-seq technique enables to measure dozens of samples simultaneously. However, research projects often require the compilation of scRNA-seq data from multiple different sources, which introduces variability from many experimental factors, such as experimental design, cell dissociation protocol, library preparation, and sequencing technique, and strongly interfere with the downstream analysis. Therefore, computational correction is critical for eliminating batch-to-batch variation and allowing data integration for valid downstream analysis. Meanwhile, scRNA-seq data suffers from stochastic dropouts and over-dispersion problems [27–29] due to low input RNA or PCR amplification bias; therefore, traditional approaches developed for bulk-seq data such as ComBat [30] and limma [31] are not suitable for single-cell data. Altogether, there is a need to develop a tailored approach to remove batch effects in single-cell data.

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The Seurat [32] and its updated version Seurat 3 [33], one of the most popular scRNA-seq analysis approaches, use the canonical correlation analysis for dimension reduction (Seurat) or subspace projection (Seurat3), and then correct the batch effects by mutual nearest neighbors in the subspace. LIGER [43] uses integrative nonnegative matrix factorization to identify shared and batchspecific factors, and then cells are aligned by constructing a shared factor neighborhood graph whereby cells have similar factor loadings. The ZINB-WaVE [34] method accounts for batch effects by using a zero-inflated negative binomial model, and outputs a normalized expression matrix across genes and samples. MNNCorrect [35] is another approach that corrects discrepancies by the mutual nearest neighbors method, which is used to identify the most similar cells across batches. A similar approach was employed by other methods, e.g., Scanorama [36], BBKNN [37], and BEER [38]. scMerge [39] identifies cell clusters within each batch first, then employs mutual nearest clusters to map cell clusters from different batches. In contrast, Harmony [40] first reduces data dimension with principal component analysis (PCA), and then iteratively removes batch effects and clusters cells in the reduced PCA space.

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Imputation of scRNA-seq Data Though scRNA-seq technique has been widely used, it suffers from dropout events which limit the detection of genes with zero or near zero sequencing reads due to technical biases such as the low reverse transcription efficiency [41]. This issue increases the sparsity of scRNA-seq data and may lead to biases or even errors in cell type identification and downstream analysis. Thus, imputation of missing values to overcome the dropout issue will remedy the sparsity problem of scRNA-seq data and promise the data accuracy for downstream analysis. Probabilistic models are generally used for this purpose. For example, SAVER [42] uses a Bayesian method that gains information across genes and cells to recover the expression of missing genes. It estimates the prior parameters with a Poisson LASSO regression and calculates the posterior distribution of expression of individual genes, the mean of which is used as the recovered value of this gene in the cells with dropout issue. BISCUIT [43] employs a hierarchical Dirichlet process mixture model that can be leveraged for data imputation. This method recovers cell clusters along with technical variations simultaneously in a decoupled manner. scTSSR [44] proposes a two-side sparse self-representation model to recover scRNA-seq data. This method simultaneously considers gene-to-gene and cell-to-cell relations, where a bilinear combination of similar genes and cells are used for imputation.

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ScImpute [45] uses a Gamma-Normal mixture model to impute dropout values by learning from similar cells that are not likely affected by dropouts. Deep learning is also used in the scRNA-seq data imputation. This type of method learns a latent space from the raw data first, and then reconstructs a non-sparse data matrix from this latent space. For example, AutoImpute [46] uses an autoencoder-based approach to capture the inherent data distribution for data imputation. SAUCIE [47] uses a regularized autoencoder that recreates its data input via a low-dimensional hidden layer and learns the representations of the data input. Moreover, SAUCIE is a one-step data analysis pipeline that explicitly performs clustering tasks without using external tools. Other methods, such as MAGIC [48] and DrImpute [49], overcome data sparsity by smoothing the raw counts and adjusting the gene expression values. MAGIC [48] is a data diffusion-based approach, which builds a Markov affinity graph and performs local averaging to update gene expression values. DrImpute [49] is a clustering-based approach that repeatedly identifies cell clusters and averages the expression values from similar cells for final imputation.

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Single-Cell Clustering and Annotation A key task of scRNA-seq data analysis is to identify cell clusters based on transcriptome similarity and annotate cell types. Currently, there are some approaches specially designed for scRNAseq data for unsupervised cell clustering, such as the clustering approach implemented in Seurat [33] and scanpy [50], which identify cell clusters based on the Louvain algorithm. Single-cell consensus clustering (SC3) [51] is another unsupervised approach using a small subset of principal components. CIDR [52] applies hierarchical clustering and considering imputed values into distance calculation. Other methods, such as BackSPIN [53] and pcaReduce [54], apply iterative strategy that improves their capability to identify subtle clusters. After cell clustering, most researchers annotate cell types by manually checking the expression of cell-type markers in the cell clusters. This manual approach relies on the researchers’ knowledge and, however, is time-consuming and labor-intensive and limits reproducibility. Several computational tools have been developed for this purpose. SCSA [55] is proposed as an automatic and accurate tool for cell-type annotation by using a scoring model based on differentially expressed genes and pre-defined cell type markers. scCATCH [56] is a cluster-based automatic annotation toolkit developed for accurate and replicable annotation of cells. It applies the tissue-specific taxonomy reference database and develops the

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evidence-based score to annotate clusters as different cell types. ACTINN [57] employs the neural network to predict unknown cell types, using parameters trained on scRNA-seq datasets with known cell types. SingleR [58] annotates cells by correlating reference bulk RNAseq data from pure cell lines with scRNAseq data. scID [59] uses the Discriminant Analysis to identify transcriptionally related cell groups across reference datasets and target datasets. Specifically, scID obtains cluster-specific genes from reference datasets and then measures their relevance in target datasets by a statistical classifier. Garnett [60] is a popular tool for automatic annotation by training an elastic-net-based classifier from data with known cell type information and then applying the classifier to the undefined scRNA-seq dataset. scPred [61] is proposed as a supervised method that provides an accurate classification of single cells based on a probability-based machine-learning method using unbiased features selected from a low dimension space.

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Single-Cell Trajectory Reconstruction In many biological activities, cells are involved in continuous processes and experience transitions among different cellular states. When analyzing such a dynamic process, a commonly used approach is to reconstruct cell trajectory to order the cells in accordance with their continuous process [62], whereby cells are placed on a developmental continuum connecting different cell states. In single-cell trajectory, pseudotime refer to an ordering of cells along the continuous process, allowing for the identification of the cells at different states of the trajectory. Many tools are available for reconstructing single-cell trajectories. Monocle [62] builds a minimum spanning tree based on independent component analysis. This method is derived from a previous algorithm [63] for ordering bulk microarray samples but meanwhile accounts for single-cell variation. Monocle 2 [64] uses reversed graph embedding to learn the structure of trajectories with multiple branches and delineate cell fate decisions in an unsupervised manner. It also identifies potential key factors that divert cells to different fates. Waterfall [65] is developed as an applicable pipeline to perform unbiased statistical analysis involving preprocessing, pseudotime reconstruction, and gene expression analysis. It reconstructs the trajectory by connecting k-means as minimum spanning trees and assigns pseudotime for each cell based on its relative location on the trajectory. DPT [66] measures cell transitions and estimates the temporal order of cells using efficient diffusion-like random walks. This method enables to identify transient cell states, branching decisions, and endpoints. Different from the prior bifurcation-centered algorithms, CellRouter [67] is a transition-centered single-cell trajectory detection method, which

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is capable of exploring the structure of cell states and reconstructing complex transitions among cell states. Single-cell clustering using bifurcation analysis (SCUBA) [63] is proposed to extract lineage relationships and model cell dynamic changes. It uses stochastic differential equation theories and nonlinear dynamics for modeling complicated biological processes. Wishbone [65] is similar to Wanderlust [68] that applies the nearest neighbor graphs to identify developmental distance and the cell ordering using shortest paths. TSCAN [69] is proposed to facilitate single-cell trajectory reconstruction by using the traveling salesman problem algorithm. This method aims to construct a pseudo-temporal cell ordering so that the total distance of linking all cells is minimized.

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Single-Cell RNA-Seq Application in Identification of Cancer Biomarkers Tumors are highly complex, comprising heterogeneous cell populations indicated by different cellular markers. Characterization of tumor heterogeneity and cellular architecture is critical to the treatment of cancer patients [70–72]. The development of scRNA-seq technology paves new ways for characterizing and exploring tumor heterogeneity precisely, which promotes the understanding of transcriptional programs in cancer development and progression and the identification of molecular biomarkers [70, 71, 73]. Compared with previous bulk RNA-seq data, scRNA-seq enables cell typespecific biomarker identification. In the study of melanoma, the unbiased scRNA-seq analysis showed that the expressions of MITF and AXL in melanoma cells are implicated in resistance to BRAF inhibition [74]. In liver cancer, LAYN is associated with suppressive tumor regulatory T cells (Tregs) and exhausted CD8 T cells, which are important for patient’s response to cancer immunotherapies [75]. TNFRSF9 and IL1R2 are identified by scRNA-seq analysis as the markers of antigen-specific Tregs, which are associated with poor prognosis in lung adenocarcinoma [76]. In hepatocellular carcinoma, the LAMP3+ dendritic cells (DCs) are observed with the potential to migrate from tumor to lymph node. This specific dendritic cell also expresses divergent immune-relevant ligands and potentially regulates lymphocytes [77]. Additionally, a specific state of macrophages in the tumor, i.e., tumor-associated macrophages (TAMs), exhibit inflammatory markers (SLC40A1 and GPNMB) and are associated with poor prognosis in hepatocellular carcinoma [77] and are also identified in non-small cell lung cancer [78]. These TAMs have been studied in breast cancer [79] and renal cancer [80] using scRNA-seq data. In breast [81] and colorectal tumors [82], scRNA-seq analysis reveals different types of cancer-associated fibroblasts differentiated by reliable cell markers, which have been shown with special

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functions in recruiting immune cells and inducing the epithelial– mesenchymal transition activities in tumor cells. ScRNA-seq profiling of heterogeneous circulating tumor cells (CTCs) populations identified both epithelial and mesenchymal markers [83]. The noncanonical Wnt signaling pathway in prostate cancer CTCs shows potential drug resistance [84], while the plakoglobin in breast cancer CTCs associates with tumor metastasis [85]. In triple-negative breast cancer, within tumor-infiltrated CD8+ T cells, tissue-resident memory T cells are shown to express cytotoxic genes, including GZMB and PRF1, as well as immune checkpoint molecules [86]. Such tissue-resident memory T cells are shown as key modulation targets by immune checkpoint inhibition, which is crucial for successful immunotherapeutic treatment. All these studies stress the importance of scRNA-seq for therapeutic decisionmaking and oncological treatment. References 1. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, Bodenmiller B, Campbell P, Carninci P, Clatworthy M, Clevers H, Deplancke B, Dunham I, Eberwine J, Eils R, Enard W, Farmer A, Fugger L, Go¨ttgens B, Hacohen N, Haniffa M, Hemberg M, Kim S, Klenerman P, Kriegstein A, Lein E, Linnarsson S, Lundberg E, Lundeberg J, Majumder P, Marioni JC, Merad M, Mhlanga M, Nawijn M, Netea M, Nolan G, Pe’er D, Phillipakis A, Ponting CP, Quake S, Reik W, Rozenblatt-Rosen O, Sanes J, Satija R, Schumacher TN, Shalek A, Shapiro E, Sharma P, Shin JW, Stegle O, Stratton M, Stubbington MJT, Theis FJ, Uhlen M, van Oudenaarden A, Wagner A, Watt F, Weissman J, Wold B, Xavier R, Yosef N, Human Cell Atlas Meeting P (2017) The Human Cell Atlas. eLife 6:e27041. https:// doi.org/10.7554/eLife.27041 2. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suva ML, Regev A, Bernstein BE (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344 (6190):1396–1401. https://doi.org/10. 1126/science.1254257 3. Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, FallahiSichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb

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INDEX A Air-liquid interface (ALI) ................................... 134–137, 139–143 Airway epithelial cells (AECs) ............................ 121–131, 133–135, 138 Apurinic/Apyrimidinic endonuclease 1....................... 155

B Base excision repair (BER) .................................. 155, 156 Biomarkers................................. 37–42, 77–94, 122, 166, 178, 194, 211–228, 240, 250–251 Biosensors ........................................................................ 64 Bones ........................................ 1–5, 28, 78, 99, 183–191

Exosomes..................................7, 37, 166, 170, 177–182 Extracellular vesicles (EVs)..........................165–172, 177

F Fatty acid methyl esters (FAMEs) ...................... 194, 195, 197, 198, 200, 203, 205, 206 Flow cytometry ............................................................... 67 Fluorescein .................................................................... 159 2-[ 138 18F]fluoro-2-deoxy-D-glu-cose (FDG) ............................................................28, 29 Folate receptor 1 (FOLR1) ....................... 211–213, 216, 217, 224–226 Fura-2AM .......................................................98–100, 102

G

C Calcium imaging .....................................................97–105 Cancers ...................................... 1–4, 6, 8, 13–21, 23–32, 37–42, 45–52, 55, 63–69, 77–94, 97, 98, 146, 155, 166, 178, 182, 193–208, 211–230, 245–251 Carnitine palmitoyltransferase 1 (CPTIA)..................... 38 Caseins ................................. 93, 110, 111, 113–116, 119 Cell impedance ..........................................................63–67 Cigarette smoke extract (CSE)....................121–131, 135 Collagen.....................................107, 111, 113, 115, 118, 119, 135, 137, 142, 143, 188 Collagenases ............................................. 46, 50, 98, 111, 115, 116, 119, 183, 184, 188 Computational analysis ........................................ 245–251 Computed tomography (CT) ........................... 23, 24, 28 Confocal laser scanning microscopy .............................. 74 Cyclotron............................................... 13–17, 19–21, 25

D Dorsal root ganglia (DRG) .................. 98–100, 102, 103 Drug screening..........................................................46, 48 Dynamic light scattering (DLS) .......................... 165–172

E Electrophoresis ........................................... 56–59, 85, 89, 108, 112, 114–116, 129, 147, 149, 156, 157, 160, 230, 237 Epithelial to mesenchymal transition (EMT) ............... 82, 122, 124, 128, 131

Gelatin ........................................ 108, 110–114, 116–119 Giant cell tumor (GCT) ...................................... 183–190 Griess assay ...................................................70–72, 74, 75

H Histopathology ............................................................. 186 Hot cell ..................................................14, 15, 17, 19, 25 HPLC ...........................................................14–16, 18–21 Hypoxia ............................................ 7, 29, 30, 37, 38, 41

I Immunoblotting ........................... 57, 58, 80, 81, 83–86, 89–92, 145–148, 150, 151 Immunofluorescence ...................................................... 38 Immunogold labeling .......................................... 177–182 Immunoprecipitation (IP) ...................... 39, 40, 145, 146 Immunotherapies ............................................. 63–68, 250 Intrafemoral injection ...............................................2, 3, 5

L Lipid droplets (LDs) .................. 193, 196, 200–203, 205 Lung cancers ................................. 1, 27, 28, 38, 81, 121, 122, 134, 250

M Matrix metalloproteinases (MMPs) ................. 7–11, 108, 112, 114, 118 Metabolism........................ 14, 25–29, 31, 155, 193, 194

Gagan Deep (ed.), Cancer Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2413, https://doi.org/10.1007/978-1-0716-1896-7, © Springer Science+Business Media, LLC, part of Springer Nature 2022

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258 Index

Metagenomics ...................................................... 229–242 Microbial communities ............................... 231, 233, 234 Microgravity ..............................................................77–94 Mitochondria..............................30, 55, 56, 59, 156, 169 Mitochondrial DNA (mtDNA)................................55, 56

Reverse zymography (RZ)................................... 107–119 Rotary cell culture system (RCCS) ........................ 78, 79, 81–84, 87–88, 93

S

Operational Taxonomic Units (OTU) ............... 231, 238 Optical imaging........................................................... 7–11 Optical probes ......................................................... v, 8, 10 Oral cancers ................................................................... 229 Osteoclast .................................................... 183, 186, 190 Oxidative phosphorylation (OXPHOS) ............................................. 25, 55–61

Scanning electron microscopy (SEM) ............... 183, 184, 188–190 SDS-polyacrylamide gel electrophoresis (SDS-PAGE)................................... 84, 85, 89, 90, 108–110, 116–119 Sensor chips ................................212, 213, 216, 217, 225 Single-cell RNA sequencing (scRNA-seq) ............................................. 245–251 Skeletal-related events (SREs) .......................................... 1 16S rRNA sequencing ................................ 230, 231, 235 Substrate-gel.................................................................. 107 Support vector machine (SVM) ................ 194, 197, 198, 202, 203, 205, 206 Surface plasmon resonance (SPR)...................... 212, 213, 216, 218

P

T

N Native page ................................................................55–61 Neurons ...................................................................97–105 Nitric oxide (NO) .....................................................69–75 Nitric oxide synthase (NOS) .......................................... 69

O

Pain ..............................................................................1, 97 Particle aggregation ...................................................... 167 Pimonidazole...................................................... 38, 40, 41 Planetary ball milled nanoparticles (PBM-NPs) ......... 224 Positron emission tomography (PET) ....................13–21, 23–28, 30–32, 134, 135, 137, 138, 142, 162 Prostate cancer (PCa) ......................................... 1, 3, 4, 7, 9, 27, 28, 37, 60, 79, 194, 195, 200, 212, 213, 247, 251 Protease-activity .....................7, 107, 108, 112, 114–119

R Radiopharmaceuticals ........................................ 13–21, 27 Radiotracers ...............................................................23–32 Raman spectroscopy...................................................... 194 Reactive nitrogen species (RNS) ..............................69–75 Reactive oxygen species (ROS) .............................. 30, 55, 56, 69, 229 Real-time cell analysis (RTCA).................................64–67

T cells ....................................................... 63–67, 250, 251 3D culture .........................................................................vi Tissue inhibitors of metalloproteinases (TIMPs) ................. 108, 109, 114, 116, 118, 119 Tobacco ........................................................129, 229–242 Transmission electron microscopy (TEM) ......................................166, 178, 180, 183 Transplantation ............................................46–48, 50, 51

U Ultracentrifuge ..................................................... 169, 172

X Xenografts................................................ 9, 38–40, 45–51

Z Zebrafish ....................................................................45–52 Zymography ...................... 107–111, 113–116, 118, 119