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What Is Single Cell Sequencing?
Single cell sequencing is a collection of methods that researchers use to isolate and analyze sequence information from individual cells. Named the Nature Method of the Year in 2013,1 single cell sequencing techniques allow researchers to understand more than ever before about cells’ inner workings.
Many traditional sequencing methods cannot help researchers analyze material from individual or small numbers of cells—rather, they sequence bulk cell populations where a large number of cells, with their contents of interest, are pooled prior to analysis. Studying cells in bulk masks information about the cell-to-cell variability that exists in a population, presenting instead the population’s average genome. In contrast, single cell sequencing allows DNA or RNA from individual cells to be amplified and sequenced, capturing each cell’s uniqueness.2
Scientists often use single cell sequencing to detect genetic variants by analyzing the genome, understand epigenetic variation by sequencing the methylome, or track gene expression differences by investigating the transcriptome of individual cells in a population. Through these studies, researchers can identify rare yet important cell subtypes within heterogenous cell populations.
Focus On: Single Cell RNA Sequencing (scRNA-seq)
scRNA-seq has emerged as an important technique for identifying differences in cells that otherwise appear homogeneous and understanding cellular responses on the molecular level. In 2009, two years after researchers performed RNA sequencing using next-generation techniques on bulk cell samples, scientists from the Wellcome/Cancer Research UK Gurdon Institute at the University of Cambridge executed the method on a single cell.3 From there, scientists developed many forms of scRNA-seq that increased the number of cells assayed at a time, decreased costs, and enhanced data reliability over time.4
A General scRNA-seq Workflow
While scientists have developed numerous scRNA-seq protocols, many follow the same basic steps and principles.5
- Cell isolation
- Extraction and amplification of genetic material
- Sequencing library preparation
- Next-generation sequencing (NGS)
- Data analysis
Isolating living single cells from a tissue of interest is the most important step in this process. 5 If a sample contains few cells, such as an early-stage embryo, researchers can manually capture individuals through micropipetting. An additional low-throughput method, laser capture microdissection, allows researchers to isolate single cells directly from tissue.6 Higher-throughput methods typically involve first mechanically and/or enzymatically dissociating cells from tissue. Then, cells can be sorted into microwells via flow-activated cell sorting (FACS) or microdroplets through microfluidic technologies. 6,7 Alternatively, particularly if cells are multi-nucleated, researchers may prefer to isolate single nuclei.8
After isolation, cells must be lysed to release their mRNA, which is typically captured by poly(T)-primers that bind to 3’ poly-(A) mRNA tails.5 The mRNA is then reverse transcribed into cDNA. During this step, researchers often add nucleic acid adaptors, barcodes, or other molecular identifiers, depending on the needs of the sequencing method utilized, to the ends of the cDNA.5 The generated cDNA is at this point in minute quantities—the nucleic acid sequence is then amplified either by PCR or in vitro transcription.
For NGS, scientists then prepare cDNAs according to the needs of the sequencing method, often necessitating barcoding, pooling, and quality control at this step. After sequencing is complete, researchers use bioinformatic and/or computational tools to assess data quality and analyze and interpret results. scRNA-seq data is noisy with many confounding factors that affect the read counts.7 These include technical variation, such as amplification bias and dropouts, and biological variation due to a cell’s environmental niche or where they were in the cell cycle during isolation. Bioinformatics methods such as principal component analysis help researchers cluster cell subpopulations based on differential gene expression patterns and define or refine molecular relationships between single cells.5
Single Cell Sequencing in Action
To date, researchers have used various types of single cell sequencing approaches in a range of life science fields. A few examples of single cell sequencing in action include analyzing genomic copy number variation, DNA methylation, and gene expression changes during human colorectal cancer metastasis,9 identifying different brain cell subtypes that are more susceptible to common risk factors for brain diseases,10 and determining molecular characteristics and key regulators of spermatogenesis.11 Additionally, more than 2,600 researchers worldwide have come together to use single cell and spatial techniques to create reference maps of all human cells through the Human Cell Atlas project.12,13
Finally, as technology advances, scientists have combined different single cell sequencing methods with other techniques to better understand the connection between a cell’s genome and ultimate functions. With these multiomic approaches—combining genomics with transcriptomics, transcriptomics with epigenomics, or transcriptomics with proteomics, for example—researchers are gaining crucial insights that transform the understanding of health and disease.14
- “Method of the Year 2013,” Nat Methods, 11:1, 2014. https://doi.org/10.1038/nmeth.2801
2. Y. Kashima et al., “Single-cell sequencing techniques from individual to multiomics analyses,” Exp Mol Med, 52:1419-27, 2020. https://doi.org/10.1038/s12276-020-00499-2
3. F. Tang et al., “mRNA-Seq whole-transcriptome analysis of a single cell,” Nat Methods, 6:377-82, 2009. https://doi.org/10.1038/nmeth.1315
4. V. Svensson et al., “Exponential scaling of single-cell RNA-seq in the past decade,” Nat Protoc, 13:599-604, 2018. https://doi.org/10.1038/nprot.2017.149
5. A. Haque et al., “A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications,” Genome Med, 9:75, 2017. https://doi.org/10.1186/s13073-017-0467-4
6. A.A. Kolodziejczyk et al., “The technology and biology of single-cell RNA sequencing,” Mol Cell, 58:610-20, 2015. https://doi.org/10.1016/j.molcel.2015.04.005
7. B. Hwang et al., “Single-cell RNA sequencing technologies and bioinformatics pipelines,” Exp Mol Med, 50:1-14, 2018. https://doi.org/10.1038/s12276-018-0071-8
8. W. Zeng et al., “Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity,” Nucleic Acids Res, 44:e158 (2016). https://doi.org/10.1093/nar/gkw739
9. S. Bian et al., “Single-cell multiomics sequencing and analyses of human colorectal cancer,” Science, 362:1060-63, 2018. https://doi.org/10.1126/science.aao3791
10. B.B. Lake et al., “Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain,” Science, 352:1586-90, 2016. https://doi.org/10.1126/science.aaf1204
11. Y. Chen et al., “Single-cell RNA-seq uncovers dynamic processes and critical regulators in mouse spermatogenesis,” Cell Res, 28:879-96, 2018. https://doi.org/10.1038/s41422-018-0074-y
12. “More about the Human Cell Atlas,” Human Cell Atlas, https://www.humancellatlas.org/learn-more/, accessed 3-26-2023.
13. C.-C. Hon et al., “The Human Cell Atlas: Technical approaches and challenges,” Brief Funct Genom, 17:283, 2018. https://doi.org/10.1093%2Fbfgp%2Felx029
14. K. Vandereyken et al., “Methods and applications for single-cell and spatial multi-omics,” Nat Rev Genet, 1-22, 2023. https://doi.org/10.1038/s41576-023-00580-2