A Brief Introduction to Single Cell Sequencing Analysis

Amit U Sinha, Ph.D
Last Updated: Nov 5, 2019

Single cell sequencing has heralded in a new era in the field of omics at the single cell level [1]. Every cell is unique in function and has a distinct lineage that defines the biology of the system. Single cell analysis is the process of deciphering the genomic and transcriptome level differences between cells and uncovering the heterogeneity in the biological sample. It has been transforming the fields of medicine, developmental biology, and systems biology by revealing the unique characteristics of individual cells isolated from multicellular organisms [2, 3]. Single cell analysis generates profiles of each cell to identify differences within and between sub-populations. It allows for the characterization of the response of individual cells to the same stimuli and enhances our knowledge of individual cell phenotypes, and how they affect the overall function of organisms. 

Biological samples are inherently heterogeneous. For example, while collecting cells for tumor biopsy, there are frequently both healthy and cancerous cells. The conventional methods of NGS bulk sequencing mix the data of all the cell types in the sample. Thus, bulk-cell NGS analysis cannot always easily discern signals coming from normal versus tumor cells, not can it distinguish different tumor cell subpopulations. Single cell analysis solves this by integrating a unique DNA barcode into the DNA/RNA of each cell. Once added, the cells are pooled and sequenced together. After the successful completion of the sequencing, the reads for each cell can be separated easily based on the presence of the barcode.

So far, many methods have been developed for single cell analysis, clustering, and lineage analysis. For example, Basepair’s single cell RNA-seq pipelines use Seurat, a popular tool designed for analyzing single cell RNA-seq data. It helps researchers identify and visualize cell sub-populations, identify unique gene markers for each sub-population, and merge multiple scRNA-seq datasets together.  Clustering methods are helpful in identifying cell populations through cell to cell similarity measures, which capture the relationship between the cells and their gene expression profiles.

Current Challenges and Future Perspectives

Single cell analysis is a rapidly emerging technology, but despite substantial advances, many significant challenges persist. One of the primary challenges for single cell sequencing is reliably detecting diverse genomic, transcriptomic, and epigenetic events from tiny amounts of DNA or RNA. Common problems researchers face are trying to tell the difference between viable versus dead or dying cells, or sequencing ambient/contaminated DNA or RNA instead of a cell.  

Other challenges include the cost of sequencing and development of bioinformatics software for dealing with the huge datasets. Single cell sequencing typically takes longer and costs more compared to traditional bulk NGS sequencing.  There is also an urgent need for automation and the development of bioinformatics techniques to extract meaningful results from single cell datasets. The available algorithms and software packages for single cell analysis are less mature than those for standard bulk NGS sequencing. Data analysis is highly demanding because the data produced by single sequencing, especially single cell RNA-seq, is noisier than bulk RNA-seq data and requires significant computational infrastructure. To overcome these challenges and meet the needs of researchers for a comprehensive and automated single cell analysis tool, Basepair has created a single cell RNA-seq analysis pipeline.

Applications of Single Cell Analysis

Single cell analysis is of great significance in cancer research, stem cell biology, developmental biology, and other areas [4, 5]. Single cell sequencing and analysis has revolutionized the field of cell biology by revealing underlying biological processes with unprecedented resolution [6]. The molecular profiling of cancerous stem cells has helped find underlying causes of metastasis and many therapeutic responses [7]. Single cell analysis has the potential to solve many important questions, from helping to further elucidate the importance of bacterial species in our daily life by sequencing their genomes, to unraveling neuro-developments and dynamics of the central nervous system through analysis of neuronal cells [8]. Additionally, it provides cutting edge applications in the field of medicine by enabling the identification of novel drug targets that are essential for understanding disease processes and relevant therapeutic interventions.


1. Wang, D., & Bodovitz, S. (2010). Single cell analysis: the new frontier in ‘omics’. Trends in biotechnology, 28(6), 281-290.

2. Chi, K. R. (2013). Singled out for sequencing: Nature Publishing Group.

3. Eberwine, J., Sul, J.-Y., Bartfai, T., & Kim, J. (2014). The promise of single-cell sequencing. Nature methods, 11(1), 25.

4. Kato, T. (1980). Biochemical characteristics of individual neurons Advances in cellular neurobiology (Vol. 1, pp. 119-176): Elsevier.

5. Li, X., & Lee, A. P. (2018). High-throughput microfluidic single-cell trapping arrays for biomolecular and imaging analysis. Methods in cell biology, 148, 35-50. 

6. Benitez, J. A., Cheng, S., & Deng, Q. (2017). Revealing allele-specific gene expression by single-cell transcriptomics. The international journal of biochemistry & cell biology, 90, 155-160.

7. Baslan, T., & Hicks, J. (2017). Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nature Reviews Cancer, 17(9), 557.

8. Ofengeim, D., Giagtzoglou, N., Huh, D., Zou, C., & Yuan, J. (2017). Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends in molecular medicine, 23(6), 563-576.

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