The Power of a Single Cell

By Aarushi Bellani

One of the turning points in molecular biology research emerged with the discovery of Sanger sequencing in the 1970s. This technique enabled scientists to accurately determine the contents of the genome of any organism from a microscopic virus (Sanger et al., 1977) to human beings themselves (Lander et al., 2001). After its emergence, sequencing techniques has seen declining pricing and massive improvements in the accuracy of processes such as hybridization microarrays (Taub et al., 1983) to currently heavily used RNA sequencing (Nagalakshmi et al., 2008). A big inadequacy of these techniques though, is that they produce expression data for a single gene as an average measure across the cell population being sequenced. This fails to account for the presence of different cell types procured from the same tissue. 

Further advancements in the field have made it possible to sequence genomes of individual cells, giving researchers the power to uncover the minutest of differences in heterogenous groups of cells. Single cell RNA sequencing (scRNA-Seq) was first discovered in 2009 (Tang et al., 2009) however it started gaining traction many years later, after cost reduction and enhancements in protocol. scRNA-Seq has found applications in various research areas such as identifying different developmental stages within cell populations (La Manno et al., 2016), differentiating diseased cells from healthy ones (Puram et al., 2017) and also observing the response of individual cells to drug treatments (Marquina-Sanchez et al., 2020). 

A very promising example of single cell transcriptomic data being used in finding distinguishing gene signatures in healthy and pathological cells has been seen in multiple studies conducted on cardiac dysfunction (Nomura et al., 2018). One of the most common causes of cardiac dysfunction is excessive internal and external pressure. The process that transitions cardiomyocytes from responding positively to a certain level of pressure to a failing phenotype was studied in detail using scRNA-Seq. Prominent differences were observed in gene signatures of healthy cardiomyocytes and those leading to heart failure. Another novel experiment (Villani et al., 2017) that looked into the activation of the human immune system, used scRNA-Seq to study the different cells involved in this process. Many subsets within the dendritic cell population were found, including one group that is directly involved in T cell proliferation. These results have many implications when it comes to the immune response in case of inflammation and also in diseases such as cancer. 

Being such a dynamic disease, identifying and targeting heterogeneity in cancer is often thought to be an effective therapeutic method. Single cell RNA sequencing is one such technique that has been widely used to detect different cell populations in the tumour microenvironment (Patel et al., 2014). 

Glioblastoma is one of the most lethal forms of the disease and often has poor prognosis when diagnosed and high recurrence rates. This is largely due to tumour heterogeneity which manifests as cells in different stages of the disease being present in one tumour cell population or cells acquiring different mutations and exhibiting distinct epigenetic states leading to an ineffective response to targeted treatments (Patel et al., 2014). An added challenge in successful removal of tumour cells is the development of drug resistance to chemotherapeutic agents. Efforts have been made to identify factors contributing to this in lung adenocarcinoma cells using scRNA-Seq (Kim et al., 2015). Drug resistant cells from this tumour have been sequenced and the gene signatures compared to sensitive cells in order to identify and possibly silence the expression of genes conferring resistance to make the drug as effective as possible.  

The therapeutic potential of scRNA-Seq is undeniable and its use is seen in dissecting pathological conditions and observing the effects of different treatments on gene expression. In a case study (Kim et al., 2020) of a man diagnosed with drug induced hypersensitivity, scRNA-Seq helped elucidate a key cascade of genes that triggered a pathologic phenotype and induced autoimmunity. These two aspects were then targeted and were seen to help mediate the disease symptoms. 

Utilising public scRNA-Seq data, even before conducting studies is also a very useful tool in designing and testing prospective medicines. Artificial Intelligence functions such as Deep Learning (DL) can be used to identify groups of diseased cells in new datasets based on algorithms that are created using previously analysed scRNA-Seq data. A new DL based method called ‘DigitalDLSorter’ has been developed, with the purpose of simplifying and examining sequencing data. It has been successfully used with data from The Cancer Genome Atlas (TCGA) database and has predicted accurately the type and quantity of a particular cell type in a given population (Torroja, and Sanchez-Cabo, 2015).

As highlighted above, scRNA-Seq has emerged as an increasingly popular, cost-effective and precise method of sequencing genomes and differentiating cell types with great precision. Using data generated from such techniques has also paved the way for better experiment design, highly personalised therapeutics and accurate drug targets. 


Kim, D., Kobayashi, T., Voisin, B., Jo, J., Sakamoto, K., Jin, S., Kelly, M., Pasieka, H. B., Naff, J. L., Meyerle, J. H., Ikpeama, I. D., Fahle, G. A., Davis, F. P., Rosenzweig, S. D., Alejo, J. C., Pittaluga, S., Kong, H. H., Freeman, A. F. & Nagao, K. (2020) Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: a case report. Nature Medicine. 26 (2), 236-243. Available from: doi: 10.1038/s41591-019-0733-7.

Kim, K., Lee, H. W., Lee, H., Kim, S. C., Seo, Y. J., Chung, W., Eum, H. H., Nam, D., Kim, J., Joo, K. M. & Park, W. (2015) Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biology. 16 (1), 127. Available from: doi: 10.1186/s13059-015-0692-3.

La Manno, G., Gyllborg, D., Codeluppi, S., Nishimura, K., Salto, C., Zeisel, A., Borm, L. E., Stott, S. R. W., Toledo, E. M., Villaescusa, J. C., Lönnerberg, P., Ryge, J., Barker, R. A., Arenas, E. & Linnarsson, S. (2016) Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells. Cell. 167 (2), 566-580.e19. Available from: doi: S0092-8674(16)31309-5 [pii].

Lander et al. *Genome Analysis, Scientific management: National Human Genome Research Institute, US National Institutes of Health (2001) Initial sequencing and analysis of the human genome. Nature. 409 (6822), 860-921. Available from: doi: 10.1038/35057062.

Marquina-Sanchez, B., Fortelny, N., Farlik, M., Vieira, A., Collombat, P., Bock, C. & Kubicek, S. (2020) Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets. Genome Biology. 21 (1), 106. Available from: doi: 10.1186/s13059-020-02006-2.

Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M. & Snyder, M. (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science (New York, N.Y.). 320 (5881), 1344-1349. Available from: doi: 10.1126/science.1158441.

Nomura, S., Satoh, M., Fujita, T., Higo, T., Sumida, T., Ko, T., Yamaguchi, T., Tobita, T., Naito, A. T., Ito, M., Fujita, K., Harada, M., Toko, H., Kobayashi, Y., Ito, K., Takimoto, E., Akazawa, H., Morita, H., Aburatani, H. & Komuro, I. (2018) Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure. Nature Communications. 9 (1), 4435-7. Available from: doi: 10.1038/s41467-018-06639-7.

Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., Louis, D. N., Rozenblatt-Rosen, O., Suvà, M. L., Regev, A. & Bernstein, B. E. (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (New York, N.Y.). 344 (6190), 1396-1401. Available from: doi: 10.1126/science.1254257.

Puram, S. V., Tirosh, I., Parikh, A. S., Patel, A. P., Yizhak, K., Gillespie, S., Rodman, C., Luo, C. L., Mroz, E. A., Emerick, K. S., Deschler, D. G., Varvares, M. A., Mylvaganam, R., Rozenblatt-Rosen, O., Rocco, J. W., Faquin, W. C., Lin, D. T., Regev, A. & Bernstein, B. E. (2017) Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 171 (7), 1611-1624.e24. Available from: doi: S0092-8674(17)31270-9 [pii].

Sanger, F., Air, G. M., Barrell, B. G., Brown, N. L., Coulson, A. R., Fiddes, C. A., Hutchison, C. A., Slocombe, P. M. & Smith, M. (1977) Nucleotide sequence of bacteriophage phi X174 DNA. Nature. 265 (5596), 687-695. Available from: doi: 10.1038/265687a0.

Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., Lao, K. & Surani, M. A. (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods. 6 (5), 377-382. Available from: doi: 10.1038/nmeth.1315.

Taub, F. E., DeLeo, J. M. & Thompson, E. B. (1983) Sequential comparative hybridizations analyzed by computerized image processing can identify and quantitate regulated RNAs. DNA (Mary Ann Liebert, Inc.). 2 (4), 309-327. Available from: doi: 10.1089/dna.1983.2.309.

Torroja, C. & Sanchez-Cabo, F. (2019) Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data. Frontiers in Genetics. 10 978. Available from: doi: 10.3389/fgene.2019.00978.

Villani, A. C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., Jardine, L., Dixon, D., Stephenson, E., Nilsson, E., Grundberg, I., McDonald, D., Filby, A., Li, W., De Jager, P. L., Rozenblatt-Rosen, O., Lane, A. A., Haniffa, M., Regev, A. & Hacohen, N. (2017) Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science (New York, N.Y.). 356 (6335), Available from: doi: 10.1126/science.aah4573 [doi].

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