Single-Cell Omics: An Overview

CHAPTER 1

Single-Cell Omics: An Overview Zeenath Jehan Department of Genetics and Molecular Medicines, Vasavi Medical and Research Centre, Hyderabad, India

Single-cell omics is the profiling of single cells sampled from heterogeneous population of cells from different cellular states, where the normal development and disease processes can be studied and dissected at a single-cell resolution. The fast-evolving next-generation sequencing technologies have enabled high-throughput multidimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans (Macaulay et al., 2016). The molecular term omics implies a comprehensive, or global, assessment of a set of molecules (http://omics.org/) (Hasin et al., 2017). The majority of functional genomics studies are currently based on the analysis of multiomics layers, which are termed genomics, transcriptomics, epigenomics, and proteomics. A cell’s state is determined by the complex interplay of these multiomics layers (Macaulay et al., 2015). Single-cell multiomics research has revolutionized genomic studies by speeding up the discovery of and simultaneously recording the identity as well as the function of a cell. In the future, the virtual multiomics layers will integrate the study of these systems to yet another level of complexity encompassing every level of biological organization. The intertwined molecular signatures containing genes, proteins, RNA, and miRNAs (micro RNA), but also epigenomic characterizations, will be able to capture the interlayer connections to help researchers understand the complexity of the resultant phenotypes (Nardini et al., 2015). Earlier genomic analysis through multiomics studies has been applied to study variation across individual cell types by bulk assays, which profile ensemble of seemingly homogenous cell populations. These cell populations are apparently anatomically and morphologically identical; however, an increasing number of studies show they differ dramatically. These differences drive important consequences for the health and cellular function of the tissue (Clark et al., 2016, 2018). The heterogeneity in behavior of cells that were hitherto considered to be homogeneous cell types has necessitated the need to characterize the Single-Cell Omics. https://doi.org/10.1016/B978-0-12-814919-5.00001-4 © 2019 Elsevier Inc. All rights reserved.

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diversity and plasticity among these cellular phenotypes. Single-cell genome sequences are being utilized to reconstruct a cell lineage tree, and parallel RNA sequencing of these same cells would reflect the types and functional states of the cells (Mikkelsen et al., 2007). Complementing these studies with highresolution microscopy can deduce the tissue architecture from the heterogeneous population of cells investigated. With the advent of the “Lab-on-a-Chip” era, new technological platforms are available to characterize single cells (Mannello et al., 2012). Manual separation of a single-cell under a microscope, followed by single-cell RNA-seq experiment where only one cell was sequenced in a single run, was first reported by Tang et al. (2009). Technological advances of wet lab skills in molecular biology as well as computational biology have made the multiomics profiling at single-cell resolution possible (Hou, 2016; Ortega et al., 2017; Clark et al., 2016, 2018). In addition, automation and miniaturization of omic technologies have provided a detailed comprehensive spatiotemporal analysis of genes, transcripts, proteins, metabolites, and their interactions in single cells and their subcellular compartments (Ortega et al., 2017). Single-cell isolation through optical detection and such microfluidics protocols as valve-, droplet-, and nanowell-based have facilitated the understanding of these biological interactions (Prakadan et al., 2017). It is now possible to profile multiomics layers at a single-cell resolution and provide snapshots of interactions that occur simultaneously in a single-cell. Macaulay et al. (2016) have described G&T-seq (Genome & Transcriptome sequencing) where paired genome and transcriptome sequencing libraries from eight single cells can be produced in  3 days. With programming and liquid-handling robots, however, paired DNA and RNA libraries from 96 single cells can be produced in the same time frame. Single-cell omics may very soon transform cellular heterogeneity from a source of noise to a source of new discoveries. Omics deals with large-scale study of genes, and the movement of omics to single-cell analysis represents an outstanding shift in the postgenomic era (Mannello et al., 2012). The methodology for different multiomics layers has been separately developed, and these methods are applied to a single-cell simultaneously. The development of next-generation sequencing to study genomic variability and the transcriptome investigates differences in expression between different cells; this focuses on differential expression of messenger RNAs (mRNAs), in addition to studying noncoding RNAs (ncRNAs), including miRNAs (Redensˇek et al., 2018). Gene expression is also regulated by epigenetic modifications of genes acquired through endogenous or exogenous factors, as well as pathological processes that cause differential expression of genes. Gene expression through RNA or epigenetic changes is reflected in differential protein expression. The level of protein expression can be further modulated by ncRNA, whose expression interferes with transcription and translation (Cech and Steitz, 2014).

Single-Cell Omics: An Overview

Proteins expressed from differentially expressed genes can be utilized to construct protein-protein interaction (PPI) networks as well as segregation of proteins to different functional pathways (Wu et al., 2013, 2014). Several PPI computational prediction algorithms can be used to understand protein interactions and to create an interactome that reveals the PPIs that happen in a cell, an organism, or a specific biological context (Schmitt et al., 2014; McDowall et al., 2009; Luo et al., 2014). Online resources like STRING, GeneMANIA (Zuberi et al., 2013), FunCoup, I2D, and ConsensusPathDB (Niu et al., 2010) provide an integration of both known and predicted interactions proteins. The single-cell technologies can also be utilized to investigate variation between single cells to probe regulatory associations within and between molecular layers by established protocols that allow the methylome and the transcriptome or, alternatively, the methylome and chromatin accessibility to be assayed in the same cell (Clark et al., 2018). Omics net is a computational tool developed for the analysis of multi omics data where the input layers correspond to genotype features, the intermediate layers may represent gene sets or biological concepts, which together help in functional interpretation of the data to produce an output layer which corresponds to the phenotype (Akhmedov et al., 2017). Inorder to understand the pathogenesis of a disease, instead of analyzing data types separately, systems biology systematically measures the molecular perturbations within the cell by multiomic analysis and targets them to elucidate the features of the deregulated biological pathways which result in the disease/phenotype (Auffray et al., 2009). Different experiments may be used to measure alterations in DNA, RNA, protein, and metabolites levels between disease and control systems. The omics data so obtained narrates the complementary parts of the same biological process; further analysis is required to enhance our understanding of complex circuitry models of interaction between different layers in a cell. Advanced bioinformatic tools are now available to integrate several sources of data into one entity. In a landmark study Hou (2016) applied scTrio-seq to 25 single cancer cells derived from a human hepatocellular carcinoma tissue sample; this study yielded genomic, transcriptomic, methylomic, and translational information. The researchers were able to identify two subpopulations of cells based on copy number variations (CNVs), DNA methylome, and transcriptome of individual cells. They were further able to dissect the complex contribution of genomic and epigenomic heterogeneities to the transcriptomic heterogeneity within a population of cells. Similar investigations of the molecular characterization of different cell types at the single-cell transcriptomic level have been described for blood (Paul et al., 2015), lung (Treutlein et al., 2014), human brain (Pollen et al., 2014), and mouse brain (Tasic et al., 2016; Marques et al., 2016). Technologies based on single-cell sequencing have revealed that mutations acquired during the

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lifetime of an individual organism result in a mosaic of genomes (Shapiro et al., 2013). There is a spectacular degree of genetic variation in the human brain, ranging from low-frequency aneuploidies to high-frequency copy number variants and single nucleotide variants (SNVs), which may create hurdles in identifying the genetic defect associated with neurodevelopmental disorders (Evrony et al., 2016; Hu et al., 2014). To understand the biology of complex tissues and cell populations, researchers used Drop-seq to perform large-scale single-cell analysis of transcriptomes from 44,808 mouse retinal cells; they identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes (Macosko et al., 2015). Nguyen et al. (2018) have created a comprehensive cell atlas of the human breast epithelium; this atlas will provide the foundation for understanding how multiomic perturbations occur during breast cancer development. This study provides insights into the cellular blueprint of the heterogeneous human breast tissue, and similar studies may extend to other organs. Single-cell analysis is developing into key topics in biology, with different key areas of research in developmental biology, stem cell biology, reproductive medicine, cancer biology, and personalized medicine. Future single-cell omics studies may define the most desirable phenotype required. Most of the applications of single-cell technologies are directed toward understanding the biology of human disease and the therapeutic implications. Animal model studies are performed to help understand the underlying mechanisms. The field of single-cell plant genomics is in its infancy, with single-cell studies in plants being applied to understand the developmental dynamics of plant tissues and to identify nonanatomical markers for important cell populations, as well as to examine plant stress signaling, responses, and adaptation (Yuan et al., 2018).

1.1

SINGLE-CELL ISOLATION AND SEQUENCING

The general protocol of single-cell omics is to dissociate and isolate bulk cell samples into individual cells without inducing unnecessary mechanical or chemical stress on the cell. This is done either by enzymatic digestion or by mechanical perturbation, with the latter requiring optimization at a tissuespecific level (Ortega et al., 2017). Once the cells are separated into a suspension, they are isolated by flow cytometry, laser capture microdissection, serial dilution, the use of antibody-coated magnetic beads, or microfluidic-based techniques (Zheng et al., 2017). More advanced isolation is done by dropletbased technologies such as Drop-seq, inDrop, Chromium, and DDS, which can produce thousands of uniquely barcoded cells (Macosko et al., 2015;

1.1

Single-Cell Isolation and Sequencing

Zilionis et al., 2017; Yu et al., 2018). Macosko et al. (2015) developed a molecular barcoding strategy to remember the cell-of-origin of each mRNA. Next the STAMPs (Single-cell Transcriptomes Attached to Microparticles) are reversetranscribed and amplified. Thousands of STAMPs can be sequenced in one reaction, and the STAMP barcodes are used to infer each transcript’s cell of origin (Macosko et al., 2015). A small variation of this protocol requires that these barcoded cells be further enriched using bead-based surface chemistry for preparation of the molecular sample. The cell is encapsulated in an emulsion or aqueous microfluidic partition. The bead is then broken for further amplification and sequencing steps. In an alternative approach, cells are captured individually through the Integrated Microfluidic Circuit (IMC) chips in small chambers, followed by a quality control step where the doublets are visualized microscopically before the desired application/sequencing step is performed (Unger et al., 2000). Other gentle lysis methods for single-cell genome and transcriptome analysis dismantle the cellular membrane without damaging the nuclear membrane, allowing the intact nucleus to be separated from the cytoplasmic lysate. The nucleus can be used as a substrate for genomic (Han et al., 2014) and epigenomic analysis (Hou, 2016; Hu et al., 2016), while the cytoplasmic lysate will be utilized for mRNA profiling of the single-cell. Other microfluidics platformbased methods physically separate cytoplasmic mRNA from nuclear genomic DNA (gDNA) of the same single-cell for targeted amplicon sequencing using nanoliter reaction chambers (Macaulay et al., 2016). A single-cell yields 6 pg of gDNA, 10–30 pg of total RNA, and roughly 250–300 pg of protein. The DNA and RNA can be further amplified to provide single-cell whole-genome and whole-exome sequencing (scWGS and scWES) data. There are no amplification methods for proteins and metabolites comparable to those for nucleic acids. The scTrio-seq technique, described by Clark et al. (2018), involves the lysis of a cell to release the mRNAs into the solution while keeping the nucleus intact. The lysed product will be further centrifuged, to separate the mRNA-containing supernatant from the nucleus-containing precipitate, and then transferred to different tubes. The supernatant will be subjected to the scRNA-seq method (Tang et al., 2009, 2010); the precipitate will be subjected to DNA methylome sequencing using the scRRBS method as described by Guo et al. (2013). These methods simultaneously provide genomic data in terms of CNVs, DNA methylomic, and transcriptomic information from the same cell. Proteomics is a developing technology. For single-cell measurements the transient nature of functional proteins makes quantification more difficult (Heath et al., 2016). Whereas the single-cell proteomics approaches remain limited to multiplexed measurements of a low number of proteins per cell,

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the technology will be targeted very specifically by systems for which panels of reliable antibodies are available (Macaulay et al., 2017). In addition, it is possible to do single-cell western blots (scWest chips) (Nguyen et al., 2018). In normal and disease states, cellular function is regulated through signaling networks, and mass spectrometry–based proteomics are utilized for decoding these signaling networks. Large-scale proteomics-based signaling research at single-cell resolution will fundamentally change our understanding of signaling networks. These provide large-scale precision proteomics, which allow system-wide characterization of signaling for posttranslational modifications, PPIs, and changes in protein expression. Thus, quantitative changes in protein expression of thousands of genes and their modifications to external perturbations in the cell can be accurately measured (Choudhary and Mann, 2011). Other rapidly developing sequencing technologies can interpret more than one analyte in parallel; for example, Pacific Biosciences and nanopore sequencers such as Oxford Nanopore (Feng, 2015) can detect DNA modifications such as methylation (Laszlo et al., 2013; Schreiber et al., 2013) in addition to sequencing native DNA molecules. Nanopores are also capable of direct RNA sequencing and can detect protein modifications (Garalde et al., 2016).

1.2 1.2.1

APPLICATIONS OF SINGLE-CELL ANALYSIS Reproductive Medicine

Single-cell PCR was one of the first techniques to be used for the purpose of preimplantation genetic diagnosis (PGD) (Spits and Sermon, 2009; Chang et al., 2011) for monogenic disorders such as thalassemia, cystic fibrosis, hemophilia, spinal muscular atrophy, neurofibromatosis, and congenital deafness (Handyside et al., 1990; Chen et al., 2011; Natesan et al., 2014; Wu et al., 2010). Single-cell omics could be an extension of the established protocols of PGD genetic diagnosis. For PGD, generally a biopsy is performed on the first or second polar body, the cleavage-stage embryo, or blastocyst. The limitation with polar body biopsy is that only the female contribution to the embryo is tested. The most common technique adopted for PGD in the diagnosis of monogenic disorders is the embryo biopsy at the six to eight cells cleavage stage. Due to ethical concerns, next-generation sequencing testing for PGD is still not established (Harper et al., 2012). With the emergence of assisted reproductive technologies in the treatment of infertility, the developmental potential of an oocyte or embryo has gained importance. The previous strategies of embryo morphology and cleavage rate for assessing the developmental potential of an embryo are now being replaced by genomic, transcriptomic, proteomic, or metabolomic profiling of oocytes, granulosa, or cumulus cells (CCs) and embryos for assisted reproduction

1.2

Applications of Single-Cell Analysis

technologies. Embryos with the highest developmental potential have to be chosen to result in a successful pregnancy (Seli et al., 2010). In a normal oocyte, CCs surround the oocyte, and the granulosa cells form an inner layer of the follicle that remain associated with it even after ovulation. The oocyte and CCs form the cumulus-oocyte complex, and their communication through gap junctions plays a central role in the regulation of folliculogenesis and oocyte maturation. CCs are also known to prevent polyspermic fertilization ( Jin et al., 2013). From transcriptomic studies, CCs are known to have inside-out gradient in gene expression from the oocyte perspective (Hussein et al., 2005) as well as the corona radiata cells, which are known to be in direct contact with the oocyte through the zona pellucida. Because the oocyte and CC complexes grow and develop in a coordinated and mutually dependent way, techniques that screen gene expression in CCs may provide a genetic profile associated with oocyte and embryo competence. A multitude of studies of microarrays and high-throughput deep sequencing and whole-genome expression profiling have correlated changes in cumulus or granulosa cell gene expression with clinically relevant outcome parameters, including in vitro embryo development and pregnancy (Uyar et al., 2013; Dias et al., 2018; van Montfoort et al., 2008; Assidi et al., 2015). The multiomics study of a stimulated follicle may pose a unique difficulty since there is no clear image of the normal physiology of a nonstimulated follicle. Hence, single-cell omic study may provide an understanding of the non stimulated to the stimulated ovarian follicles and provide an insight into the response of different patients to drugs utilized for ovarian stimulation. Similarly, transcriptomic studies of somatic tissues are easier to analyze than embryos, mainly because they are more abundant and their RNA is a more direct representation of the protein content that drives the phenotype of the cell. These studies can also aid in developing tools for the selection of developmentally competent embryos, which have the best chances of pregnancy, in addition to identifying biomarkers associated with oocyte and embryo viability caused by treatment perturbations on the profiles of metabolites, RNA, or proteins (Seli et al., 2010). Guo et al. (2017) have developed a single-cell multiomics sequencing technology called single-cell COOL-seq (Chromatin Overall Omic-scale Landscape Sequencing) for mouse preimplantation embryonic cells. The researchers were able to simultaneously analyze the chromatin state, nucleosome positioning, DNA methylation, CNVs, and ploidy from the same single-cell. This strategy allowed them to observe chromatin accessibility and DNA methylation on a genome-wide scale at single-cell and single-base resolution in embryonic stem (ES) cells and in a parental allele-specific manner in mouse preimplantation embryos at seven critical developmental stages. They could also detect both the degree of chromatin openness and endogenous DNA methylation levels for the promoter regions of majority of the RefSeq genes in a single ES cell.

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Within less than 12 hours of fertilization, each individual cell was shown to undergo global genome demethylation together with the rapid and global reprogramming of both maternal and paternal genomes to a highly opened chromatin state. After this, the researchers observed a decrease in demethylation from the four-cell stage to the blastomere stage, where DNA methylation remained in intergenic regions of the paternal alleles and intragenic regions of maternal alleles in each individual blastomere. This investigation provided an insight into how a fully differentiated gamete is reprogrammed from a totipotent state to a pluripotent state, where the interplay between the highly ordered epigenetic reprogramming of both DNA methylation and the chromatin state reveals the heterogeneous yet highly ordered features of epigenomic reprogramming during early embryonic development. Multiomic study of single cells of the embryo provides multilayered information to understand cell fate decisions, identity, and function in normal development (Kelsey et al., 2017). Studies to investigate the lineage commitment process in early embryonic development prior to implantation and formation of the epiblast provided clues to the lineage commitment process, which was found to be largely driven by signaling mechanisms (Mohammed et al., 2017). Dramatically different transcriptomes were observed between human epiblast (EPI) cells and primary human embryonic stem cells (hESCs) outgrowth, when single-cell integrated preimplantation data was analyzed from a single preimplantation embryo (Yan et al., 2013). The first in silico model of human blastocyst development was built by combining single-cell geneexpression analysis with computational 3D reconstruction to examine the biology of cell commitment in the blastocyst (Durruthy-Durruthy et al., 2016). However, these studies are still in their infancy, and more research is required to understand the embryonic development process and cell fate decisions (Guo et al., 2017).

1.2.2

Cancer

The genome is generally replicated with high fidelity during cell division. In a normal genome the average rate of mutations is three per cell division. During the lifespan of an individual, trillions of cell divisions occur; this results in the accumulation of mutations over time, which may result in a mosaic genome. These include SNVs and interchromosomal or intrachromosomal rearrangements to gains or losses of whole chromosomes or even entire genomes, which increase genetic variations and the likelihood of a neoplasm developing. These mutations in driver genes can lead to increases in proliferation in a single-cell, resulting in a clonal expansion of that cell into cancer. Acquisition of additional driver mutations will result in the accumulation of more subclones with metastatic unique aggressive phenotype, which may not be amenable to treatment.

1.2

Applications of Single-Cell Analysis

Single-cell genome sequencing can establish lineage trees of the subclones that ultimately result in cancer evolution (Macaulay et al., 2017). Population-level tumor genomics are the standard tools for cancer profiling. Tumor heterogeneity is now recognized as a pervasive feature of cancer biology, due to the presence of multiple tumor clones and the presence of stromal and infiltrating cell populations. Every step of cancer development, progression, metastasis, and mortality is dependent on tumor heterogeneity, which necessitates genomic profiling at the single-cell level (Roman et al., 2017). Tumor heterogeneity has been shown to be predictive of progression and patient outcomes. It has been observed that very often the rare cell population, rather than the dominant clones, determines progression (Heselmeyer-Haddad et al., 2012). Extensive cell-to-cell variability was first observed in studies conducted on single cells by FISH (fluorescent in-situ hybridization), but this method has its own limitations as it can profile thousands of cells at the same time but for only a limited number of markers at a time. Single-cell sequencing can derive genome-wide profiles of hundreds to thousands of cells in single tumors (Shackney et al., 2004). However, rapidly improving single-cell sequencing and other single-cell genomic technologies have shown a variety of mechanisms that were hitherto not envisaged (Navin et al., 2011). Data on breast tumors (a larger number of samples than any other organ cancer type) is available on public domains, and previous studies on breast tumors with different molecular technologies on bulk cells have identified well-documented clinical subtypes HER2 +, ER/PR +, and Triple Negative. Kim et al. (2018) have applied single-cell DNA to 900 cells and RNA sequencing to 6862 cells of 20 triple negative patients who were resistant to neoadjuvant therapy. They were able to show preexisting resistant genotypes, as well as showing that resistance was acquired through reprogramming in response to chemotherapy (Kim et al., 2018). Single-cell RNAseq has been successfully applied to understand the complex subpopulations of various cancers, including melanoma (Tirosh et al., 2016), glioblastoma (Patel et al., 2014), and within circulating tumor cells (CTCs) from patients with pancreatic cancer (Ting et al., 2014). Ramsk€ old et al. (2012), utilizing the Smart sequencing technique, could obtain a detailed analysis of alternative splicing and identification of single nucleotide polymorphisms (SNPs) and mutations from CTCs. By comparing only two primary melanocytes to six melanoma CTCs, the researchers could identify biologically meaningful differences (Ramsk€ old et al., 2012). In many solid tumors, structural variations such as CNVs are the major drivers of progression, and profiling them is more challenging than DNA and RNA sequencing (Heng et al., 2013). Single-cell technologies will have a great impact on our understanding of the biology of cancer and will speed up the process of providing tailored therapies to resistant phenotypes. Single-cell technologies may also resolve the stem cell

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debate about the possible origin of cancer cells: “clonal tumor evolution” versus “stemming tumor evolution” (Gilbertson and Graham, 2012).

1.2.3

Regenerative Medicine

Stem cell therapy is one of the fast-growing areas of research, and it may revolutionize the art of cell therapies and regenerative medicine. Extensive research to understand mechanisms behind human somatic cells to pluripotency and reprogramming remains elusive. Still, it is necessary to understand how pluripotent progenitor cells of the embryo decide their fate through the complex interplay of different omic layers. Single-cell heterogeneity among stem cell populations may underlie important cell fate decisions. Investigations by Narsinh et al. (2011) of gene expression on single hESCs and single hiPSCs revealed that human induced pluripotent stem cells (hiPSCs) were less stable than hESCs. These studies suggest that more caution needs to be exercised when utilizing hiPSCs for regenerative therapy, and the researchers would limit the applicability of iPSCs to regenerative medicine. To achieve successful reprogramming, deciphering the underlying molecular pathways is essential (Zunder et al., 2015; Buganim et al., 2012a, 2012b). Performing single-cell transcript analysis of MRC-5 human lung fibroblasts undergoing reprogramming by single-cell transcript profiling, and coupling that analysis with mathematical modeling, has shown that gene-specific chromatin states in the starting cells control gene activation dynamics during the reprogramming process (Smith et al., 2010; Samavarchi-Tehrani et al., 2010). The key morphological changes and cell cycle–related changes that occur during reprogramming to hiPSC were also investigated, and live imaging studies suggest that an ordered set of phenotypic changes precedes acquisition of the fully pluripotent state (Mikkelsen et al., 2007). Somatic cell reprogramming is known to be associated with dramatic nuclear remodeling, so single-cell epigenomic studies will add an important layer of information to understand the mechanisms of cell fate decisions (Clark et al., 2016, 2018). Animal models have provided an understanding of the key developmental signals that control midbrain dopaminergic neuron development (Arenas et al., 2015). Generation of pluripotent stem cell (hiPSC)– derived dopaminergic neurons was shown to induce behavioral recovery in animal models of Parkinson’s disease (Kirkeby et al., 2012; Kriks et al., 2011). To gain a better understanding of neuron development, La Manno et al. (2016) performed single-cell RNA sequencing to examine ventral midbrain development, which is of major interest for Parkinson’s disease both in humans and in mice. The researchers identified 25 molecularly defined human cell types, including 5 subtypes of radial glia-like cells and 4 progenitors. They observed

1.3

that cell types and gene expression were generally conserved between human and mouse but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Their study could provide a foundation for the development of cell replacement therapies for Parkinson’s disease. Chu et al. (2016) analyzed 1776 single cells by scRNA-seq and reconstructed the differentiation trajectory from the pluripotent state through mesendoderm to endoderm cell differentiation. These are some of the examples where single-cell omic studies can enhance our understanding of stem cell fate and differentiation processes.

1.2.4

Personalized Medicine

Single-cell omics has revealed previously undetectable differences in the biomolecular composition of individual cells—differences that can be employed in clinic to perform an exact prediction of disease susceptibility as well as diagnosis, and to finally develop a personalized therapeutic strategy (Hood and Tian, 2012). In the era of personalized treatment, single-cell omics will be a powerful tool for discovery of innovative and effective drugs that could be used for individualized therapy in the treatment of cancer, brain disorders (Rubakhin and Sweedler, 2008), immune cells, and related disorders (Kim et al., 2012). Multiomic study of a single cancer stem cell, circulating or resident, may provide an opportunity to develop pharmacogenomic and pharmacoproteomic strategies for more efficient personalized medicine options and may provide reproducibility of the response to treatment. Such study may also provide a pharmacological approach targeted to effectively treating the cancer cell, either by killing the cell or reprogramming the pathological feature of the cell, which could be translated into clinic, in order to improve management of patients (Samuel and Hudson, 2013; Hartmaier et al., 2012). The application of multiomic technology has had a profound effect on precision medicine strategies for cancer treatment.

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CONCLUSIONS

Single-cell omics studies can integrate several layers of data and deconvolute the relationship between expression, function, and identity of multicellular subtypes. In the future, single-cell omic studies may replace bulk-level analysis, which can describe only the general trends in a population that can mask cellular subtypes. High-throughput technologies like scRNA-seq and Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution—thus enabling exploration of cellular behavior in health and disease. Technological advances in single-cell omics are moving at a fast pace; however, many experimental and computational challenges have not yet been resolved. Ongoing technological advances in high-throughput

Conclusions

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technologies will further enhance the coverage and sensitivity of multiomics approaches, as well the number of analytes that can be surveyed in parallel. Multiomics investigation of single cells has advanced our understanding of normal developmental processes as well as disease processes. Single-cell RNA sequencing has become an indispensable tool in biological discovery and its downstream applications in medicine to design tailored therapies. A comprehensive cellar atlas utilizing single-cell omics has already been created for breast epithelium, paving the way for similar blueprints of other tissues in the future. This comprehensive atlas will form the foundation for understanding the cellular level perturbations that lead to disease process. Although single-cell omics research is in its infancy, it may also have an impact on animal breeding and improve crop development.

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