Single-Cell Omics: Volume 2: Technological Advances and Applications

By Debmalya Barh

Single-cell Omics, Volume 2: Advances in Applications provides the latest single-cell omics applications in the field of biomedicine. The advent of omics technologies have enabled us to identify the differences between cell types and subpopulations at the level of the genome, proteome, transcriptome, epigenome, and in several other fields of omics. The book is divided into two sections: the first is dedicated to biomedical applications, such as cell diagnostics, non-invasive prenatal testing (NIPT), circulating tumor cells, breast cancer, gliomas, nervous systems and autoimmune disorders, and more. The second focuses on cell omics in plants, discussing micro algal and single cell omics, and more.

This book is a valuable source for bioinformaticians, molecular diagnostic researchers, clinicians and several members of biomedical field interested in understanding more about single-cell omics and its potential for research and diagnosis.

• Covers the diverse single cell omics applications in the biomedical field
• Summarizes the latest progress in single cell omics and discusses potential future developments for research and diagnosis
• Written by experts across the world, it brings different points-of-view and study cases to fully give a comprehensive overview of the topic

• Language: English
• Publisher: Academic Press
• Release date: Jul 30, 2019
• ISBN: 9780128175330

Book preview

Single-Cell Omics

Technological Advances and Applications, Volume 2

First Edition

(Applications in Biomedicine and Agriculture)

Debmalya Barh

Vasco Azevedo

Table of Contents

Cover image

Title page

Copyright

Contributors

About the Editors

Preface

Section I: Single-Cell Omics: Biomedical Applications

Chapter 1: Single-Cell Diagnostics, Prognosis, and Therapy

Abstract

1.1 Introduction

1.2 Applications of Single-Cell Omics

1.3 Conclusions and Future Prospects

Chapter 2: Single-Cell Omics in Noninvasive Prenatal Testing (NIPT)

Abstract

2.1 Noninvasive Prenatal Testing in Detection of Abnormalities

2.2 Sample Collection for NIPT and DNA Isolation

2.3 Genomic and Epigenome Screening

2.4 Underlying Algorithm and Bioinformatic Approaches

2.5 Clinical and Ethical Issue

2.6 Commercialization: Global and Indian Scenario of Prenatal Testing

2.7 Applications of Single-Cell Omics in Cancer Biology

2.8 Applications of Single-Cell Omics in Neurobiology

2.9 Conclusions and Future Prospects

Chapter 3: Single-Cell Omics: Circulating Tumor Cells

Abstract

3.1 Introduction

3.2 CTC Enrichment and Detection

3.3 Single-Cell CTC Analysis

3.4 Conclusions and Future Prospects

Chapter 4: Single-Cell Technology for Human Gliomas

Abstract

4.1 Introduction

4.2 Human Gliomas

4.3 Single-Cell Technology

4.4 Application of Single-Cell Analysis in Glioma

4.5 Conclusions and Future Prospects

Chapter 5: Application of Single-Cell Omics in Breast Cancer

Abstract

5.1 Introduction and Significance of Single-Cell Omics

5.2 Methods for Isolating and Analyzing Multiple Types of Molecules From a Single-Cell

5.3 Whole Genome Amplification Methods From Single-Cells

5.4 The Omics Application Based on Single-Cell

5.5 Breast Cancer Subtyping and Molecular Characterization

5.6 Molecular Biomarkers Expression in Various Subtypes of Breast Cancer

5.7 Special Aspects of Single-Cell Omics

5.8 Conclusions and Future Prospects

Chapter 6: Single-Cell Omics: Strategies Towards Theranostic Biomarker Discovery Along the Continuum of Premalignant to Invasive Disease in Oncology

Abstract

6.1 Premalignant to Invasive Disease and Single-Cell

6.2 Omics Technology

6.3 Omics-Based Theranostic Biomarker Discovery

6.4 Conclusions and Future Prospects

Chapter 7: Single-Cell Omics in CVDs

Abstract

7.1 Introduction

7.2 Techniques for Single-Cell Analysis

7.3 Transcriptome and Functional Analysis

7.4 Applications of Single-Cell Technology in CVDs

7.5 Conclusions and Future Prospects

Chapter 8: Single-Cell Omics in Metabolic Disorders

Abstract

8.1 Introduction

8.2 What Is Single-Cell Omics?

8.3 Methodologies in Single-Cell Analysis

8.4 Single-Cell Omics in Metabolic Disorders

8.5 Conclusions and Future Prospects

Chapter 9: Single-Cell Omics in Autoimmune Disorders

Abstract

9.1 Introduction to Single-Cell Omics

9.2 Single-Cell Analysis in Immunology

9.3 Autoimmunity

9.4 Single-Cell Analysis and Autoimmune Disorders

9.5 Conclusions and Future Prospects

Chapter 10: Single-Cell Omics in Human Reproductive Medicine—Our Clinical Experiences in Single-Cell Therapy

Abstract

10.1 Overview

10.2 Intercellular Heterogeneity in Human Sperm and Single-Cell Therapy

10.3 Why Is Testicular Dysfunction a Weakness in Human Reproduction?

10.4 DNA Integrity Is Critical for Human Sperm

10.5 The Comet Assay

10.6 The Procedures of Single-Cell Pulsed-Field Gel Electrophoresis

10.7 In-Gel Trypsin Digestion Is Essential for Exposing DNA Fibers Prior to Electrophoresis

10.8 Macro Pulsed-Field Gel Electrophoresis and SCPFGE

10.9 Positive and Negative Standards for SCPFGE Accuracy Control

10.10 Sensitivity Calibration of SCPFGE by Chemically Induced Dose-Dependent Fragmentation

10.11 DNA-Strand Breakage by Reactive Oxygen Species

10.12 Alkaline-Based Single-Strand Break Assay

10.13 Subcellular Aberrations—The Vacuole in the Human Sperm Head

10.14 Extended Naked DNA Fibers Are a New Tool for Gene Mapping

10.15 Monitoring DNA Integrity During Subculture

10.16 Unknown or Unseen Aberrations Do Not Feel Fear—A Pitfall of Single-Cell Therapy

10.17 Conclusions and Future Prospects

Chapter 11: Single-Cell Omics for Drug Discovery and Development

Abstract

11.1 Need to Profile Single-Cell for Drug Discovery and Development

11.2 Omics for Single-Cell

11.3 Profile Single-Cell for Lineage Tracing of Cellular Phenotypes

11.4 Single-Cell Sequencing for Drug Discovery and Development

11.5 Single-Cell Genomics for Drug Discovery

11.6 Single-Cell Transcriptomics for Drug Discovery

11.7 Single-Cell Proteomics for Drug Discovery

11.8 Single-Cell Metabolomics for Drug Discovery

11.9 From Systems Biology to Single-Cell Omics for Drug Discovery and Drug Development

11.10 Single-Cell Analysis: From Innovative Omics to Target Identification and Therapy

11.11 Microfluidic Devices for Single-Cell Omics in Drug Discovery and Development

11.12 Single-Cell Omics for Drug Discovery in Oncology

11.13 Single-Cell Omics for Drug Discovery in Neurology

11.14 Single-Cell CRISPR Screening in Drug Resistance

11.15 From Bench to Bedside

11.16 Conclusions and Future Prospects

Chapter 12: Single-Cell Omics in Personalized Medicine

Abstract

12.1 Omics for Personalized Medicine

12.2 Single-Cell Omics Allows a Live Systems Biology View

12.3 Personalized Medicine and Single-Cell Omics

12.4 If Single-Cell Multiomics Meets Integrative Personal Omics Profiles

12.5 Conclusions and Future Prospects

Chapter 13: Cell-Based Medicine and Therapy

Abstract

13.1 Introduction

13.2 Regenerative Medicine

13.3 Hematonosis

13.4 Cardiovascular Disease

13.5 Liver Disease

13.6 Other Diseases

13.7 Cellular Immunotherapy

13.8 Other Fields of Cell-Based Medicine and Therapy

13.9 Conclusions and Future Prospects

Section II: Single-Cell Omics in Plants

Chapter 14: Single-Cell Omics Approaches in Plants

Abstract

14.1 Introduction

14.2 Single-Cell Isolation Methods in Plants

14.3 Single-Cell Genomics in Plants

14.4 Single-Cell Transcriptomic in Plants

14.5 Single-Cell Proteomics in Plants

14.6 Single-Cell Metabolomics in Plants

14.7 Applications of Single-Cell Omics in Plants

14.8 Conclusions and Future Prospects

Chapter 15: Bulk to Individuality: Specifying Plants’ Cellular Functions Through Single-Cell Omics

Abstract

15.1 Introduction

15.2 Multiple in One: Single-Cell Omics in Plant Developmental Programs

15.3 Targeted Measurements: Profiling Cellular Products in Plants

15.4 Defense by a Single-Cell: Deciphering Plants’ Stress Response Through Single-Cell Omics

15.5 Single-Cell Omics and Plant-Microbe Interaction

15.6 Forward Genetics Again Rolled by Single-Cell: The Mutation

15.7 Power of Single: Designing Plant Productivity Through Single-Cell Omics

15.8 Biology of Single-to-Systems Biology: Augmenting Omics to Biocomputation Tools

15.9 Conclusions and Future Prospects

Chapter 16: Single-Cell-Type Metabolomics for Crop Improvement

Abstract

Acknowledgments

16.1 Metabolomics

16.2 Single-Cell-Type Metabolomics

16.3 Single-Cell-Type Metabolomics: Applications in Crop Improvement

16.4 Single-Cell-Type Metabolomics: Limitations

16.5 Conclusions and Future Prospects

Chapter 17: Single-Cell Omics in Crop Plants: Opportunities and Challenges

Abstract

Acknowledgments

17.1 Introduction

17.2 Single-Cell Omics Technologies: Steps and Techniques

17.3 Elucidation of the Complexity of Plant Responses: Applications of SCOTs for Crop Improvement

17.4 Conclusions and Future Prospects

Index

Copyright

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Contributors

Shah Rukh Abbas     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Ayesha Aftab     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Shanzay Ahmed     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Hakima Amri     Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, United States

Syeda Marriam Bakhtiar     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Iqra Bashir     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Attya Bhatti     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Alessandro Buriani

Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Synlab Limited

Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy

Hina Aslam Butt     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Maria Carrara     Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy

Kamaljyoti Chakravorty     Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Yong Chen     Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

Colin M. Court

Department of Surgery, University of California Los Angeles

Department of Surgery, Greater Los Angeles Veteran's Affairs Administration, Los Angeles, CA, United States

Dipali Dhawan     PanGenomics International Pvt Ltd, Ahmedabad, India

Benjamin DiPardo

Department of Surgery, University of California Los Angeles

Department of Surgery, Greater Los Angeles Veteran's Affairs Administration, Los Angeles, CA, United States

Shailendra Dwivedi     Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

Syeda Maham Fayyaz     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Stefano Fortinguerra

Maria Paola Belloni Center for Personalized Medicine, Data Medica Group, Synlab Limited

Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy

Li-Wu Fu     State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

Daniela Gabbia     Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy

Muhammad Uzair Hashmi     School of Electrical Engineering and Computer Sciences, National University of Sciences and Technology, Islamabad, Pakistan

Yusuf Izci     Department of Neurosurgery, Gulhane School of Medicine, University of Health Sciences, Ankara, Turkey

Peter John     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Anu Kalia     Electron Microscopy and Nanoscience Laboratory, Department of Soil Science, Punjab Agricultural University, Ludhiana, India

Rohit Kambale     Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, India

Satoru Kaneko     Department of Obstetrics and Gynecology, Ichikawa General Hospital, Tokyo Dental College, Ichikawa, Japan

Rajaretinam Rajesh Kannan     Molecular and Nanomedicine Research Unit, Centre for Nanoscience and Nanotechnology, School of Bio and Chemical Engineering, Sathyabama Institute of Science and Technology, Chennai, India

Raman Preet Kaur     Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Muhammad Qasim Khan     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Sobia Khurshid     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Shao-Bo Liang

State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou

Department of Radiation Oncology, Cancer Center, The First People's Hospital of Foshan Affiliated to Sun Yat-Sen University, Foshan, China

Malavika Lingeswaran     Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

Abhilash Ludhiadch     Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Radhieka Misra     Era's Lucknow Medical College and Hospital, Lucknow, India

Sanjeev Misra     Department of Surgical Oncology, All India Institute of Medical Sciences, Jodhpur, India

Anum Munir     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Anjana Munshi     Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, India

Raveendran Muthurajan     Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, India

Mohammad Nadeem     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Sinem Nalbantoglu

Molecular Oncology Laboratory, TUBITAK Marmara Research Center, Gene Engineering and Biotechnology Institute, Gebze, Turkey

Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, United States

Puneet Pareek     Department of Radio-Therapy, All India Institute of Medical Sciences, Jodhpur, India

Purvi Purohit     Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

Hajra Qayyum     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Hifzur Rahman     Department of Biosciences, Integral University, Lucknow, India

Yumna Saghir     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Praveen Sharma     Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

Sat Pal Sharma     Department of Vegetable Science, College of Agriculture, Punjab Agricultural University, Ludhiana, India

Shonan Sho

Department of Surgery, University of California Los Angeles

Department of Surgery, Greater Los Angeles Veteran's Affairs Administration, Los Angeles, CA, United States

Mohammed Haris Siddiqui     Department of Bioengineering, Integral University, Lucknow, India

Naveed Iqbal Soomro     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Saeed Iqbal Soomro     Genetics and Molecular Epidemiology Research Group, Department of Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

Vincenzo Sorrenti     Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy

Thanga Suja Srinivasan     Centre for Climate Change Studies, School of Bio and Chemical Engineering, Sathyabama Institute of Science and Technology, Chennai, India

Nida Ali Syed     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Huma Syed     Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan

Kiyoshi Takamatsu     Department of Obstetrics and Gynecology, Ichikawa General Hospital, Tokyo Dental College, Ichikawa, Japan

Dibyendu Talukdar     Department of Botany, R.P.M. College, Hooghly, India

James S. Tomlinson

Department of Surgery, University of California Los Angeles

Department of Surgery, Greater Los Angeles Veteran's Affairs Administration

Center for Pancreatic Diseases, University of California Los Angeles, Los Angeles, CA, United States

Jeewan Ram Vishnoi     Department of Surgical Oncology, All India Institute of Medical Sciences, Jodhpur, India

Paul Winograd

Department of Surgery, University of California Los Angeles

Department of Surgery, Greater Los Angeles Veteran's Affairs Administration, Los Angeles, CA, United States

About the Editors

Debmalya Barh holds an MSc in Applied Genetics, an MTech in Biotechnology, an MPhil in Biotechnology, a PhD in Biotechnology, a PhD in Bioinformatics, a Post-Doc in Bioinformatics, and a PGDM in Management. He is honorary scientist at the Institute of Integrative Omics and Applied Biotechnology (IIOAB), India. Dr. Barh is experienced in both academic and industrial research for decades and is an expert in integrative omics-based biomarker discovery, molecular diagnosis, and precision medicine in various complex human diseases and traits. He works with 400 + scientists from more than 100 organizations across 40 + countries. Dr. Barh has published over 150 research publications, 32 + book chapters, and has edited 20 + cutting-edge, omics-related reference books published by Taylor & Francis, Elsevier, and Springer. He frequently reviews articles for Nature publications, Elsevier, AACR journals, NAR, BMC journals, PLOS ONE, and Frontiers, to name a few. He has been recognized by Who’s Who in the World and Limca Book of Records for his significant contributions in managing advanced scientific research.

Vasco Azevedo is a senior professor of genetics and deputy head of the Department of Genetics, Ecology and Evolution at the Universidade Federal de Minas Gerais, Brazil. He is a member of the Brazilian Academy of Sciences and is a Knight of the National Order of Scientific Merit of the Brazilian Ministry of Science, Technology and Innovation. He is also a Researcher 1A of the National Council for Scientific and Technological Development (CNPq), which is the highest position. Professor Azevedo is a molecular geneticist who graduated from veterinary school at the Federal University of Bahia in 1986. He obtained his master’s (1989) and PhD (1993) degrees in molecular genetics from the Institut National Agronomique Paris-Grignon (INAPG) and Institut National de la Recherche Agronomique (INRA), France, respectively. He did his postdoctoral in Microbiology at the Department of Medicine School in 1994 from University of Pennsylvania, USA. In 2017, he did another PhD in the field of bioinformatics. He has published more than 400+ research articles, 3 books, and 30+ book chapters. Professor Azevedo is a pioneer in the genetics of Lactic Acid Bacteria and Corynebacterium pseudotuberculosis in Brazil. He is specialized and currently researching on bacterial genetics, genomics, transcriptomics, proteomics, and development of new vaccines and diagnostics against infectious diseases.

Preface

Debmalya Barh, PhD

Vasco Azevedo, PhD

Higher organisms are composed of heterogeneous groups of cells forming complex and distinct tissues having specific functions. Identification of precise molecular differences among various cell types in an organism, specifically in human, is essential for understanding of normal and disease biology and thereby developing diagnostics and therapeutics. The advent of omics technologies has enabled us to identify the differences between cell types and subpopulations at the level of the genome, proteome, transcriptome, epigenome, and in several other types of omics. In our current era of precision medicine, single-cell omics technology is very promising due to its potential in diagnosis, prognosis, and therapeutics.

Omics-based single-cell technologies have been applied to study diverse fields in biology, where the majority of studies are conducted on microbial populations and cancers. However, these technologies can be equally applicable to other biological areas. Although research publications are appearing regularly in this field, no book has been published in the last 8 years to summarize all the recent progress in this area. Therefore, to fill the gap and to provide up-to-date information in this field, we have introduced Single-Cell Omics: Technological Advances and Applications, covering the latest omics-based technological developments for single cell. Volume 2 of this work is titled Applications of Single-cell Technologies in Biomedicine and Agriculture. It consists of 17 chapters and is divided into two sections.

Part I (Single-Cell Omics: Biomedical Applications) is dedicated to the applications of single-cell omics in biomedicine and consists of 13 chapters. Chapter 1, by Dr. Dhawn, provides a brief overview of applications of single-cell technologies in human disease diagnostics, prognosis, and therapy. Dr. Munshi and colleagues in Chapter 2 discuss the applications of single-cell technologies in noninvasive prenatal testing (NIPT). In Chapter 3, Dr. Winograd’s team has provided a detailed account of single-cell technologies and applications of circulating tumor cells (CTCs). The application of single-cell omics in diagnosis and prognosis of human gliomas is covered in Chapter 4 by Dr. Izci. In Chapter 5, the team of Dr. Dwivedi provides an account of various applications of single-cell omics in breast cancer. In Chapter 6, Drs. Nalbantoglu and Amri discuss single-cell omics-based strategies toward theranostic biomarker discovery in oncology. In the next two chapters, Chapters 7 and 8, Dr. Bakhtiar and Dr. Bhatti’s colleagues review the applications of single-cell omics in cardiovascular diseases and metabolic disorders, respectively. Single-cell omics in autoimmune disorders is discussed in Chapter 9 by Dr. Ahmed and team. In Chapter 10, Drs. Kaneko and Takamatsu share their experiences of single-cell applications in human reproductive medicine. In Chapter 11, applications of single-cell omics in drug discovery and development are highlighted by Dr. Abbas and colleagues. Dr. Buriani’s group, in Chapter 12, summarizes the application of single-cell omics technologies to personalized medicine. The last chapter in this section (Chapter 13) by Dr. Fu and colleagues provides a brief on cell-based medicine and therapy.

Four chapters associated with single-cell applications in plants are included in Part II (Single-Cell Omics in Plants). In Chapter 14, Dr. Rahman’s group provides an overview of single-cell applications in plants. In Chapter 15, single-cell omics applications to elucidate cellular functions in plants are discussed by Dr. Talukdar. In the next chapter (Chapter 16) Dr. Srinivasan’s team focuses on the application of single-cell-type metabolomics for crop improvement. The last chapter of this book, Chapter 17 by Drs. Kalia and Sharma, gives a detailed account of the opportunities and challenges associated with single-cell omics applications in crop plants.

In this book, a global effort was made to accommodate the applications of single-cell omics in plants and the most important of human diseases. We believe that academic researchers, clinicians, molecular diagnostic and personalized medicine professionals, and plant biologists will all benefit from this work.

(Editors)

Section I

Single-Cell Omics: Biomedical Applications

Chapter 1

Single-Cell Diagnostics, Prognosis, and Therapy

Dipali Dhawan    PanGenomics International Pvt Ltd, Ahmedabad, India

Abstract

Single-Cell omics technologies have multiple applications in various fields. The applications of single-cell technologies in diagnostics, prognosis, and therapeutics play important roles in clinical practice. This chapter briefly highlights the role of single-cell omics in various areas, including oncology and gynecology, enabling better patient management.

Keywords

Diagnosis; Prognosis; Therapy; Cancer; Preimplantation genetic screening

1.1 Introduction

Disease biomarkers have gained importance in a number of applications over the past few years, including diagnosis and response to treatment (Wu and Wang, 2015; Tiberti et al., 2013; Fang et al., 2012), intermolecular interactions and the role of molecules in their pathways (Wu et al., 2014; Liu et al., 2014; Villar et al., 2014), prediction of treatment outcomes as prognostic biomarkers (Chen and Ware, 2015; Graves et al., 2014; Frantzi et al., 2014), identification of the function of genetic variants (Oh et al., 2015; Carper and Claudio, 2015), and pharmacodynamics and toxicity prediction (Kiseleva et al., 2015; Cruz et al., 2015; Stansfield and Ingram, 2015). Single-cell omics technologies have various applications in the clinic in terms of diagnostics, prognosis, and therapeutics. However, the field is growing gradually with advances in technologies. More progress is needed in methods for high-resolution image capture (in terms of both time and scale), single-cell molecule analysis on-site, and mathematical algorithms, in addition to the fields of genomics, proteomics, transcriptomics, and epigenomics (Battich et al., 2013; Itzkovitz and van Oudenaarden, 2011; Passarelli and Ewing, 2013; Brazda et al., 2014). These are pertinent for obtaining satisfactory coverage and high measurement accuracy. Single-cell analysis has many advantages in comparison with the traditional methods, especially related to accuracy after sample collection, amplification, and library construction.

Single-cell analysis can be performed in cells of various origins, including fetal cells (Hahn et al., 2009; Lo and Chiu, 2008; Lun et al., 2008), white blood cells (WBCs) (Honda et al., 2010; Lewis and Pollard, 2006), nucleated red blood cells (NRBCs) (Lo et al., 2007), circulating tumor cells (Solmi et al., 2004; Li et al., 2005; Smith et al., 1991), induced pluripotent stem cells (iPSCs) (Narsinh et al., 2011), embryonic stem (ES) cells (Tang et al., 2008, 2010a; Tang, 2006), and oocytes (Tang et al., 2009, 2010b, 2011). These samples are heterogeneous and some have a stochastic nature (Marinov et al., 2014), leading to single-cell analysis as the best option to study such sample types. A classic example to explain the importance of single-cell analysis is the identification of a rare event such as a somatic mutation affecting gene expression or a functional protein; single-cell analysis also enables the classification of a small subpopulation of cells like cancer stem cells, which play an important role in progression of disease. Also, availability of a large quantity of cells for disease diagnosis is a major constraint that can be overcome by single-cell analysis technologies (Speicher, 2013; Sandberg, 2014). This chapter discusses some applications of single-cell analysis in diagnosis, prognosis, and therapeutics (Fig. 1.1).

Fig. 1.1 Applications of single-cell analysis.

1.2 Applications of Single-Cell Omics

1.2.1 Diagnostics

One of the major diagnostic applications of single-cell omics is in oncology. A number of reports highlight the role of this technology in different cancer types when using DNA sequencing, RNA sequencing, or both. Researchers have used this technology in single-cell sequencing of primary human cancer cells and also sequencing of circulating tumor cells (CTCs). Some studies also elucidate the interactions between the tumor microenvironment and tumor cells (Tirosh et al., 2016a). Single-cell omics technology has enabled identification of intratumor heterogeneity and classification of cancer cells into different groups based on their expression profiles (Tirosh et al., 2016b). With the advancement of liquid biopsy testing, it is possible to collect biopsies from cancer patients in a minimally invasive way and process the samples for sequencing of CTCs. It has become possible to characterize tumors on the basis of molecular phenotype with better resolution. Studies have shown that more than half of the mutations responsible for primary and metastatic tumors can be identified in CTCs of lung cancer patients (Ni et al., 2013), colorectal cancer patients (Heitzer et al., 2013), and prostate cancer patients (Lohr et al., 2014).

Preimplantation genetic screening and diagnosis (PGS/PGD) has progressed remarkably due to advances in single-cell genomics technologies. Fig. 1.2 gives a brief overview of PGS using single-cell sequencing. Array comparative genomic hybridization (array CGH) and single nucleotide polymorphism (SNP) arrays help in the rapid identification of inherited or de novo copy number variations across all chromosomes in single cells. Previous methods like fluorescence in situ hybridization (FISH) will be replaced by these newer methodologies, as they offer better resolution and more information. One of the major advantages of single-cell SNP genotyping is the genome-wide identification of inheritance patterns of disease-causing haplotypes (Handyside et al., 2010; Altarescu et al., 2013). Genome-wide haplotyping of single cells is a newer method that is not currently being offered commercially. Single blastomere biopsies from cleavage-stage embryos or trophectoderms from human blastocysts are currently offered in clinical practice for preimplantation genetic screening and diagnosis (PGS/PGD) (Yin et al., 2013; Treff et al., 2013).

Fig. 1.2 Overview of PGS by single-cell sequencing.

Fiorentino and colleagues screened single blastomeres using a next-generation sequencing (NGS)-based method for single-cell analysis (Fiorentino et al., 2014a). The accuracy of this method was compared to array CGH-based methods in further studies by the group Fiorentino et al. (2014b). Better resolution and accuracy are the main advantages of single-cell genome sequencing over microarrays. Further, sequencing of single cells allows detection of mitochondrial DNA variations. Another study was aimed at observing segmental aneuploidies in trophectoderm biopsies using a single-cell NGS method (Vera-Rodriguez et al., 2016). NGS-based methods are also used for noninvasive prenatal screening to identify aneuploid fetuses before birth. One of the studies successfully detected copy number variations (CNVs) in four cells by low-coverage massively parallel sequencing from blood, with a sensitivity of 99.63% and specificity of 97.71%, respectively (Zhang et al., 2013).

RNA-seq has been used to sequence single neurons from different regions of the human cerebral cortex and has enabled identification of neuronal subtypes from the transcriptome profiles (Lake et al., 2016). Single-cell DNA sequencing has been used for identification of CNVs in brain diseases. Numerous mosaic CNVs have been reported in human neurons (McConnell et al., 2013). Somatic CNVs have been identified in hemimegalencephaly (HMG) (Cai et al., 2014).

1.2.2 Prognosis

In order to plan an effective treatment, it is crucial to have a precise prognosis. Single-cell omics methodologies have enabled characterization of many cancer types and identified new prognostic factors. Lindholm et al. (1990) have identified a nuclear area as a prognostic factor for Stage I malignant melanomas using single-cell DNA cytophotometry. Single-cell sequencing of PTEN in prostate cancer can predict prognosis (Heselmeyer-Haddad et al., 2014). Another study has shown that clustered-cell micrometastases of lymph nodes predict poor survival in pN0 early gastric cancer patients as compared to single-cell micrometastases (Cao et al., 2011). Plakoglobin, as per studies, has been observed to potentially cause clustering of circulating tumor cells (CTCs) and is also linked to prognosis in breast cancer patients (Lu et al., 2015).

It has been observed that, for understanding cardiometabolic phenotypes, intravenous injection of low-dose lipopolysaccharide (LPS) enables the inducing of experimental endotoxemia. The observed phenotypes include adipose inflammation and insulin resistance (Mehta et al., 2012). An increase in the mRNA levels of inflammatory cytokines like TNF-alpha and interleukin-6 have been observed in reverse transcription polymerase chain reaction (RT-PCR) analysis of adipose biopsies (Shalek et al., 2013). These experiments elucidate cellular interactions and further lead to better understanding of disease prognosis.

Single-cell sequencing is being used to identify intratumor heterogeneity, which enables better understanding of genomic diversity, classified by a diversity index. Applications of these diversity indexes include prognosis: they help in predicting cancer patient response in the range of poor response to therapy, poor overall survival, good response to therapy, or a higher metastasis probability (Burrell et al., 2013; Murugaesu et al., 2013; Almendro et al., 2014).

1.2.3 Therapeutics

There has been significant success in targeted therapy toward single cells. Molecular research has shown promising results in tumor cells, elucidating the fact that the tumor mass has a pool of cells that are heterogeneous in all aspects including gene expression, protein levels, and their functionality (Kalisky et al., 2011; Cohen et al., 2008). It has been observed that the antidiabetic drug metformin reduces the risk of cancer development. Metformin targets breast cancer stem cells (CSCs) in mouse models, which might show a likelihood for better therapy response in humans as well (Song et al., 2012). Studies have shown that stem cell self-renewal pathways are inhibited by curcumin (Kakarala et al., 2010) and sulforaphane (Li et al., 2010), which are compounds found in dietary products like turmeric and broccoli, respectively. Genomic analysis helps in identification of CSCs that have shown a potential successful response to treatments like cetuximab and bevacizumab for tumors with a particular genetic makeup (Kim et al., 2011). Single-cell genomics has enabled the identification of two novel drugs, crizotinib and PLX4032, that have progressed well in clinical trials with good response toward EML4-ALK translocated NSCLCs (Hallberg and Palmer, 2010; Butrynski et al., 2010) and toward V600E BRAF positive metastatic melanomas (Smalley, 2010). Sequenom OncoCarta and RNASeq have been used to identify expression of fusion genes in micropapillary carcinoma (MPC) (Natrajan et al., 2014).

The therapeutic benefits of PARP inhibitors have also been studied in HER2 amplified breast CSCs (Amorim et al., 2014). Current clinical practice involves anti-HER2 therapy for HER2 positive primary tumors. However, recent advances show that increased HER2 expression, but not HER2 gene amplification, in breast CSCs is through the receptor activator of the NF-κB ligand. This means that HER2 negative luminal breast cancer patients may benefit from adjuvant antibody treatment trastuzumab that targets the breast CSCs (Ithimakin et al., 2013; Korkaya and Wicha, 2013). Single-cell analysis has shown great promise in hormonal therapy as well. Studies conducted on MCF7 (Casale et al., 2014) and others conducted for studying the efficacy of antiestrogen drugs like Faslodex (fulvestrant) have shown potential for responses of breast cancer cells on the basis of single-cell genomics analysis (Ochsner et al., 2009). Many studies have been conducted on mass cytometry to understand the heterogeneity of cancer cells and further elucidate the function of these cells to enable individualized molecular targeted diagnosis and therapies (Giesen et al., 2014). Single-cell analysis will help in transforming the field of cancer biology and enable advanced clinical cancer practice (Li and Teng, 2014; Chen et al., 2013; Livak et al., 2013).

Kim et al. (2016) have observed that single-cell RNA sequencing has enabled optimization of treatment for metastatic renal cell carcinoma. Moreover, drug sensitivity can be predicted in multiple myeloma by single-cell analysis of targeted transcriptome (Mitra et al., 2016). It is also possible to reveal the mechanism of drug resistance by single-cell pharmacokinetic imaging in tumors (Laughney et al., 2014).

It is speculated that intratumor heterogeneity might play an important role in therapeutic resistance (Navin, 2014). The ability of epithelial cells to convert to mesenchymal cells in response to chemotherapy is a mechanism called epithelial to mesenchymal transition (EMT) (Almendro et al., 2014). The first study of this mechanism in CTCs was done using single nucleus sequencing (SNS) over four different time points in patients with metastatic prostate cancer on Aberatone (abiraterone) and chemotherapy treatment (Dago et al., 2014). RNA single-cell sequencing (SCS) has also been used to elucidate various signaling pathways in lung adenocarcinoma cell lines by studying transcriptomes in response to tyrosine kinase inhibitors (TKIs) (Suzuki et al., 2015). The ideal therapeutic targets can be found by identifying founder mutations in single cancer cells by using single-cell omics technologies. This can also help in understanding the effect of combination therapies on the different tumor cell types, thus enabling the targeting of the heterogenous tumor cell population with the correct therapy for maximum response.

1.3 Conclusions and Future Prospects

A sufficient number of single cells are required for appropriate representation of the cell population with sufficient stratification and the stringency of the protocol. Fluidigm offers a high-throughput automatic microfluidics platform for isolation of single cells for further processing. This advancement is a great step in the field of single-cell omics. However, the technology comes with a cost and also needs individual library generation for sequencing (Brouilette et al., 2012). Single-cell omics technologies are used to classify cell types, in normal physiological states as well as pathologic states (Tang et al., 2010a,b), such as cancer variant analysis (Navin et al., 2011; Xu et al., 2012; Hogart et al., 2012; Kreso et al., 2013; Heike and Nakahata, 2002; Swennenhuis et al., 2013). Single-cell omics has a bright future for contributing to medicine and other biological applications, with increasing technological advances.

The number of cells that can be isolated and sequenced has increased reasonably, with the advanced methodologies being used today (Zheng et al., 2017). However, it has been observed that in some studies with more cells, fewer sequencing reads were collected from each cell, which limits the sensitivity of the molecular phenotype data. Hence, there needs to be an improvement in the resolution of data acquired from a large number of cells. There is a need for advances in methods for single-cell isolation to help in wider utility of single-cell technologies. Single-cell multiomics profiling has the potential to provide more data from a single-cell and a better characterization of the cells analyzed. Along with the advanced single-cell omics technologies, it is also important to adopt these methods in routine clinical practice for better patient diagnosis, treatment, and further management.

Two major challenges need to be overcome before single-cell omics can be used effectively in routine clinical use: the time taken for analysis and the high cost incurred. The cost needs to be reduced to make the technology cost effective and affordable to patients. Numerous studies are being conducted to reduce costs by multiplexing strategies (Baslan et al., 2015; Fan et al., 2015; Macosko et al., 2015). Further, better technology is required for testing paraffin-embedded tissue samples (Simmons et al., 2016). Some studies have tried to overcome the problems faced with tissue samples by preparation of nuclear suspensions from these samples (Navin et al., 2011; Baslan et al., 2012).

The currently available methods have reached a coverage of > 90%; however, there is a need to further improve the coverage of single cells and reduce the technical errors observed during single-cell processing (Hou et al., 2012; Zong et al., 2012; Wang et al., 2014).

References

Almendro V., Cheng Y.K., Randles A., Itzkovitz S., Marusyk A., Ametller E., Gonzalez-Farre X., Muñoz M., Russnes H.G., Helland A., et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 2014;6:514–527.

Altarescu G., Zeevi D.A., Zeligson S., Perlberg S., Eldar-Geva T., Margalioth E.J., Levy-Lahad E., Renbaum P. Familial haplotyping and embryo analysis for preimplantation genetic diagnosis (PGD) using DNA microarrays: a proof of principle study. J. Assist. Reprod. Genet. 2013;30(12):1595–1603.

Amorim M., Fernandes G., Oliveira P., Martins-de-Souza D., Dias-Neto E., et al. The overexpression of a single oncogene (ERBB2/HER2) alters the proteomic landscape of extracellular vesicles. Proteomics. 2014;14:1472–1479.

Baslan T., Kendall J., Rodgers L., Cox H., Riggs M., Stepansky A., Troge J., Ravi K., Esposito D., Lakshmi B., et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 2012;7:1024–1041.

Baslan T., Kendall J., Ward B., Cox H., Leotta A., Rodgers L., Riggs M., D’Italia S., Sun G., Yong M., et al. Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Res. 2015;25:714–724.

Battich N., Stoeger T., Pelkmans L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods. 2013;10:1127–1133.

Brazda P., Krieger J., Daniel B., Jonas D., Szekeres T. Ligand binding shifts highly mobile RXR to chromatin-bound state in a coactivator-dependent manner as revealed by single cell imaging. Mol. Cell. Biol. 2014;34:1234–1245.

Brouilette S., Kuersten S., Mein C., Bozek M., Terry A., et al. A simple and novel method for RNA-seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase. Dev. Dyn. 2012;241:1584–1590.

Burrell R.A., McGranahan N., Bartek J., Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501:338–345.

Butrynski J.E., D'Adamo D.R., Hornick J.L., Dal Cin P., Antonescu C.R., et al. Crizotinib in ALK-rearranged inflammatory myofibroblastic tumor. N. Engl. J. Med. 2010;363:1727–1733.

Cai X., Evrony G.D., Lehmann H.S., et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 2014;8(5):1280–1289.

Cao L., Hu X., Zhang Y., Huang G. Adverse prognosis of clustered-cell versus single-cell micrometastases in pN0 early gastric cancer. J. Surg. Oncol. 2011;103(1):53–56.

Carper M.B., Claudio P.P. Clinical potential of gene mutations in lung cancer. Clin. Transl. Med. 2015;4:33.

Casale F.P., Giurato G., Nassa G., Armond J.W., Oates C.J., et al. Single-cell states in the estrogen response of breast cancer cell lines below. PLoS One. 2014;9:88485.

Chen W., Ware L.B. Prognostic factors in the acute respiratory distress syndrome. Clin. Transl. Med. 2015;4:65.

Chen C.L., Mahalingam D., Osmulski P., Jadhav R.R., Wang C.M., et al. Single-cell analysis of circulating tumor cells identifies cumulative expression patterns of EMT-related genes in metastatic prostate cancer. Prostate. 2013;73:813–826.

Cohen A.A., Geva-Zatorsky N., Eden E., Frenkel-Morgenstern M., Issaeva I., et al. Dynamic proteomics of individual cancer cells in response to a drug. Science. 2008;322:1511–1516.

Cruz F.M., Munhoz B.A., Alves B.C., et al. Biomarkers of fatigue related to adjuvant chemotherapy for breast cancer: evaluation of plasma and lymphocyte expression. Clin. Transl. Med. 2015;14:4.

Dago A.E., Stepansky A., Carlsson A., Luttgen M., Kendall J., Baslan T., Kolatkar A., Wigler M., Bethel K., Gross M.E., et al. Rapid phenotypic and genomic change in response to therapeutic pressure in prostate cancer inferred by high content analysis of single circulating tumor cells. PLoS One. 2014;9:e101777.

Fan H.C., Fu G.K., Fodor S.P. Expression profiling. Combinatorial labelling of single cells for gene expression cytometry. Science. 2015;347:1258367.

Fang X., Bai C., Wang X.D. Bioinformatics insights into acute lung injury/acute respiratory distress syndrome. Clin. Transl. Med. 2012;1:9.

Fiorentino F., Biricik A., Bono S., et al. Development and validation of a next-generation sequencing-based protocol for 24-chromosome aneuploidy screening of embryos. Fertil. Steril. 2014a;101(5):1375–1382.

Fiorentino F., Bono S., Biricik A., et al. Application of next-generation sequencing technology for comprehensive aneuploidy screening of blastocysts in clinical preimplantation genetic screening cycles. Hum. Reprod. 2014b;29(12):2802–2813.

Frantzi M., Bhat A., Latosinska A. Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin. Transl. Med. 2014;3:7.

Giesen C., Wang H.A., Schapiro D., Zivanovic N., Jacobs A., et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods. 2014;11:417–422.

Graves C.A., Abboodi F.F., Tomar S., et al. The translational significance of epithelialmesenchymal transition in head and neck cancer. Clin. Transl. Med. 2014;3:60.

Hahn S., Jackson L.G., Kolla V., Mahyuddin A.P., Choolani M. Noninvasive prenatal diagnosis of fetal aneuploidies and Mendelian disorders: new innovative strategies. Expert Rev. Mol. Diagn. 2009;9(6):613–621.

Hallberg B., Palmer R.H. Crizotinib—latest champion in the cancer wars?. N. Engl. J. Med. 2010;363:1760–1762.

Handyside A.H., Harton G.L., Mariani B., Thornhill A.R., Affara N., Shaw M.A., Griffin D.K. Karyomapping: a universal method for genome wide analysis of genetic disease based on mapping crossovers between parental haplotypes. J. Med. Genet. 2010;47:651–658.

Heike T., Nakahata T. Ex vivo expansion of hematopoietic stem cells by cytokines. Biochim. Biophys. Acta. 2002;1592:313–321.

Heitzer E., Auer M., Gasch C., Pichler M., Ulz P., Hoffmann E.M., Lax S., Waldispuehl-Geigl J., Mauermann O., Lackner C., et al. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing. Cancer Res. 2013;73:2965–2975.

Heselmeyer-Haddad K.M., Berroa Garcia L.Y., Bradley A., Hernandez L., Hu Y., Habermann J.K., Dumke C., Thorns C., Perner S., Pestova E., Burke C., Chowdhury S.A., Schwartz R., Schaffer A.A., Paris P.L., Ried T. Single-cell genetic analysis reveals insights into clonal development of prostate cancers and indicates loss of PTEN as a marker of poor prognosis. Am. J. Pathol. 2014;184(10):2671–2686.

Hogart A., Lichtenberg J., Ajay S.S., Anderson S., NIH Intramural Sequencing Center, Margulies E.H., et al. Genome-wide DNA methylation profiles in hematopoietic stem and progenitor cells reveal overrepresentation of ETS transcription factor binding sites. Genome Res. 2012;22:1407–1418.

Honda M., Sakai Y., Yamashita