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Applications of Single-Cell Sequencing

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Applications of Single-Cell Sequencing

Over recent years, single-cell sequencing has become a method ubiquitously used across different areas of biological research. Why have so many researchers, both in academia and biotech, so rapidly adopted this relatively novel set of techniques? Single-cell sequencing, or single-cell genomics, addresses key caveats of bulk tissue sequencing approaches to enable cellular resolution molecular readouts without the need to purify or enrich specific cell types. Before the widespread adoption of single-cell genomics, RNA or DNA sequencing was performed by isolating and sequencing nucleic acids from a piece of tissue. Since tissues and organs are composed of multiple molecularly diverse cell types, obtaining information about RNA abundance or DNA sequence variation from an ensemble of cells masks the true heterogeneity of cell types. This is a crucial challenge in answering both basic and translational science questions, since we increasingly understand that normal bodily functions and disease processes rely heavily on an orchestrated interplay of specific cell types with defined roles. These cell type-specific functions, in their order, make use of diverse gene regulatory networks and gene expression profiles in each cell type. Single-cell genomics unravels this molecular diversity one cell at a time in an unbiased fashion.

In this list, we discuss the types of single-cell sequencing approaches, the technology behind them and will talk about how applying these techniques to questions in various fields of biomedical science has led to novel biological insights.

Single-cell RNA sequencing

Single-cell sequencing techniques are constantly improving and have been adopted to analyze a range of molecular characteristics of single cells. RNA sequencing was one of the first techniques to be applied to single cells of a tissue or organ. Single-cell RNA sequencing (scRNA-seq) captures single cells in microwells or microdroplets and utilizes reverse transcription (RT) together with barcoded RT DNA primers to convert RNA molecules within each individual cell into barcoded complementary DNA (cDNA). Reverse transcription is followed by the polymerase chain reaction to amplify the resulting DNA, which is then sequenced using next-generation sequencing. Since all cDNA molecules originating from the same cell share a DNA barcode (also called cell barcode), by analyzing sequencing data from a scRNA-seq experiment, one can estimate the identity and abundance of each RNA transcript at the level of an individual cell. Once abundances of RNA transcripts in each cell are known, these RNA expression profiles can be used to identify cell types in an unbiased manner by grouping cells with similar profiles. This approach allows identification of gene expression profiles for all cell types in the tissue. These include rare cell types or cell types that are difficult to purify from the tissue using other techniques, such as cell sorting. Furthermore, using RNA expression profiles and information about cell type memberships, scRNA-seq can be used to determine gene expression changes that take place in each separate cell type in a specific condition, such as in disease.

scRNA-seq has been successfully applied to a multitude of questions in biological sciences. One of the most fruitful was its application to neuroscience due to the large number of cell types in the mammalian brain, and the challenges of purifying and studying them using conventional techniques. scRNA-seq has been used to create atlases of the developing human brain at the single-cell resolution, creating a powerful resource for understanding how a normal human central nervous system develops and how differentiation and maturation of specific cell types is perturbed in neurodevelopmental diseases.

Moreover, a variation of scRNA-seq, termed single-nucleus RNA sequencing (snRNA-seq), recently allowed researchers to identify the cell types and their molecular signatures in mature human and mouse brains. snRNA-seq can be applied to post-mortem brain tissue or to cell types that are difficult to isolate, such as mature neurons, by sequencing RNA from single cell nuclei.1 Understanding the cell type composition of the adult human brain further paves the way to develop targeted cellular and pharmacological therapies for diseases that affect it, such as brain injury and neurodegenerative diseases.

Additionally, snRNA-seq has been applied to directly study human brain disease by profiling normal brain tissue and brain tissue of patients affected by a neurological disorder. As such, snRNA-seq study of autism spectrum disorder (ASD) discovered that a specific subtype of neurons in the human brain cortex change dramatically in this disease.2 In another study, snRNA-seq has been used to identify how specific neuronal and glia cell types respond and change in multiple sclerosis lesions.

Single-cell sequencing for epigenetic profiling

Epigenetics is a rapidly evolving field that focuses on studying gene regulatory mechanisms that control gene expression through altering the state of chromatin, the DNA-protein complex that serves to package DNA in the cell. Chromatin exists in two main states: euchromatin (also called open chromatin) and heterochromatin. Genes in regions of open chromatin are generally transcriptionally active, while those in heterochromatin regions are silenced. In addition to chromatin state, gene expression is regulated directly by chemical modifications of the DNA, most commonly via methylation of cytosine bases. Methylation of DNA encompassing a gene usually leads to gene silencing. Progress in the field of epigenetics has been increasingly suggesting that DNA methylation and chromatic profiles are highly tissue- and cell type-specific. Thankfully, recent developments in single-cell genomics have provided scientists with ways to analyze epigenetic states of single cells by performing single cell Assay for Transposase Accessible Chromatin followed by sequencing (scATAC-seq), which reveals regions of open chromatin, as well as single-cell DNA methylation analysis.

scATAC-seq has already proven instrumental in understanding how specific cell types in various tissues develop and how these developmental programs go rogue in disease. This technique has been used to understand how epigenetic signatures of the stem cells, neuronal and glial cell types in the developing human brain4 and mouse retina change as the cells differentiate and mature.5 In efforts to understand disease pathogenesis, scATAC-seq identified an epigenetic program maintained by memory T cells in type 1 diabetes and mediate autoimmune reactivity.6 Additionally, scATAC-seq has been utilized to gain insight into the role of epigenetic regulation in the pathogenesis of various cancers, including triple-negative breast cancer7 and acute leukemia.8

Single-cell sequencing for immunology

The adaptive immune system relies on the ability of T and B cell clones to produce T cell receptors (TCRs) and antibodies that can bind to an enormous repertoire of antigens. This incredible diversity is developed through random rearrangement and hypermutation of genes encoding TCRs and antibody chains followed by selection of T and B lymphocyte to eliminate cells reacting to self-antigens in the body. This process generates an estimated diversity of 1015-1020 for TCRs and 3×1011 for antibodies. Each T or B cell clone expresses a unique TCR or antibody, and upon reaction to a foreign antigen (such as from bacteria, virus or a cancer cell), this clone undergoes a massive number of cell divisions. Since the nucleotide sequence of a TCR or an antibody chain can be used to produce more T cells and antibodies for therapeutic purposes, or even to predict the target antigen, understanding this sequence is a crucial challenge in immunology. Due to the clonal nature of TCR and antibody diversity, a single-cell approach is best suited to understand the mechanisms at play.

Single-cell B cell profiling has been recently applied to identify neutralizing antibodies against SARS-CoV-2 by sequencing single clones of B cells producing the antibody in COVID-19 patients.9 This highly encouraging new finding highlights the power of single-cell immune profiling to become a major driver of discovering therapeutic antibodies for infectious diseases. Given the enormous importance of immune system in tumorigenesis and metastasis, another highly promising application of single-cell immune profiling is cancer biology. Recently, this technique has also been used to understand the prognostic significance of specific TCRs in patients with renal cell carcinoma, as well as T cell heterogeneity in
pediatric sarcoma. Nicole Velmeshev, assistant professor at the San Francisco State University, argues that single-cell immune profiling presents a great promise in developing new treatments for various types of cancer. Given that suppression of immune reactivity is one of the key mechanisms by which cancer cells evade hosts defenses and taking into account the immense success of immune therapy in many types of cancer, the ability to understand which exact T cell receptors or antibodies target cancer cells is crucial, says Dr Velmeshev. According to her, once we can obtain this information rapidly for each individual cancer patient and for each tumor, we will be able to develop targeted therapies, such as chimeric antigen receptor T (CAR-T) cell or antibodies, that are highly effective in killing the specific individual’s cancer cells.

Multi-omics and the future of single-cell sequencing

Most recently, single-cell sequencing technologies have enabled researchers to obtain multiple molecular readouts from the same single cell. For instance, joint single-cell RNA sequencing and single-cell ATAC sequencing allows the simultaneous determination of the transcriptional and epigenetic profiles of individual cells. Since RNA expression profiles of marker genes are highly informative in determining the cell type, this combinatorial RNA and open chromatin profiling allows the identification of epigenetic profiles of specific cell types in an unbiased manner. Moreover, by analyzing changes in transcriptional and open chromatin profiles within and across cell types, one can make conclusions about the effects of epigenetic regulation on gene expression in various conditions, such as disease pathology or cellular differentiation.

In addition to nucleic acids, single-cell sequencing now allows quantifying abundance of a pre-determined set of proteins on the single-cell level using barcoded antibodies. Since protein levels are most correlated with cellular functions and are not always in perfect agreement with RNA expression due to multiple levels of post-transcriptional regulation, this readout is instrumental in analyzing such fast-pace cellular processes as immune cell activation. Since protein quantification in single-cells is currently limited to tens of proteins at a time, this approach is combined with genome-wide assessment of RNA expression.


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  2. Velmeshev D, Schirmer L, Jung D, et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science. 2019;364(6441):685-689. doi:10.1126/science.aav8130

  3. Schirmer L, Velmeshev D, Holmqvist S, et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature. 2019;573(7772):75-82. doi:10.1038/s41586-019-1404-z

  4. Ziffra RS, Kim CN, Wilfert A, et al. Single cell epigenomic atlas of the developing human brain and organoids. bioRxiv. Published online January 8, 2020:2019.12.30.891549. doi:10.1101/2019.12.30.891549

  5. Norrie JL, Lupo MS, Xu B, et al. Nucleome Dynamics during Retinal Development. Neuron. 2019;104(3):512-528.e11. doi:10.1016/j.neuron.2019.08.002

  6. Abdelsamed HA, Zebley CC, Nguyen H, et al. Beta cell-specific CD8 + T cells maintain stem cell memory-associated epigenetic programs during type 1 diabetes. Nat Immunol. 2020;21(5):578-587. doi:10.1038/s41590-020-0633-5

  7. Shu S, Wu H-J, Ge JY, et al. Synthetic Lethal and Resistance Interactions with BET Bromodomain Inhibitors in Triple-Negative Breast Cancer. Molecular Cell. 2020;78(6):1096-1113.e8. doi:10.1016/j.molcel.2020.04.027

  8. Granja JM, Klemm S, McGinnis LM, et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat Biotechnol. 2019;37(12):1458-1465. doi:10.1038/s41587-019-0332-7

  9. Cao Y, Su B, Guo X, et al. Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients’ B Cells. Cell. 2020;182(1):73-84.e16. doi:10.1016/j.cell.2020.05.025