Fri. Jul 30th, 2021


Single cell RNA sequencing has been applied in a wide range of fields in the life sciences. Its application in medical research has revealed findings that can inform clinical practices in diagnostics and treatment selection. The level of cell-to-cell variation that can be captured by scRNA-seq analysis makes scRNA-seq the ideal tool for studying rare and diverse cells in organisms (Li et al. 2017). ScRNA-seq has been responsible for novel discoveries in embryology (e.g. Fan et al. 2015), immunology (e.g. Mahata et al. 2014), cancer diagnosis and treatment (e.g. Kumar et al. 2014, Filbin et al. 2018), and responses to drug treatments (e.g. Miyamoto et al. 2015). 

Embryology- genetic screening

Noninvasive genetic screening is preferred over invasive procedures during pregnancy due to their higher accuracy and safety (Fan et al. 2015). scRNA-seq has been demonstrated to be an indispensable tool in typing prior to implantation (Fan et al. 2015, Petropoulos et al. 2016). A single-cell universal poly(A)-independent RNA sequencing (SUPeR-seq) method was developed and used to analyze linear and circular RNA (thought to play an important role in regulatory processes) from mice embryos before implantation (Fan et al. 2015). In addition to identifying known transcripts they also discovered novel circRNAs and linear RNAs present during embryo development. Petropoulos et al. (2016) used scRNA-seq analysis to generate a detailed transcriptional resource from human embryonic cells collected prior to implantation. 


ScRNA-seq analysis is primed to offer in-depth profiling of the complex interactions involved between the immune system and the surrounding microenvironment. For example, scRNA-seq analysis of immune responses involved in allergic reactions have been conducted. Using scRNA-seq analysis, denovo production of pregnenolone by T helper 2 cells was identified and was posited to be involved in immunosuppression (Mahata et al. 2014). In multiple sclerosis, scRNA sequencing revealed that the immune response was antigen-driven (Held et al. 2015). These authors also reported some previously unrecorded CD8+ T cell behavior, which might shed light to some mysteries associated with the pathology of MS. Single cell RNA seq data revealed a new subset of dendritic cells  and led to a new proposed taxonomy of dendritic cells which may have major implications for immune monitoring and disease management (Villani et al. 2017).

Cancer diagnosis and disease progression

Cancer is a heterogeneous disease, a complete and thorough profiling of all the different subtypes involved can hold the key to improved prognosis and appropriate therapy selection and management of future relapses (Nadeu et al. 2016).Chung et al. (2017) conducted an extensive profile of immune and tumor cells in primary breast cancer. These authors demonstrated that the heterogeneity found in breast cancer transcriptome is shaped by the surrounding immune cells (T lymphocytes, B lymphocytes and macrophages) and heterogeneity observed in the surrounding microenvironment. In chronic myeloid leukemia, difficult to isolate rare cancer stem cells were isolated and characterized using scRNA-seq analysis (Giustacchini et al. 2017). This study also identified important gene expression signatures that may be linked with poor chemotherapy response. 

Gliomas, the most lethal human brain cancer, have also been extensively studied using scRNA-seq tools (e.g. Kumar et al. 2014, Filbin et al. 2018). In glial tumors mutation accumulation displayed spatial heterogeneity (Kumar et al. 2014). These authors uncovered a mixture of important copy number variations and point mutations that show potential as therapy targets for the, otherwise, poor prognosis tumor. An integrated approach—bulk and scRNA-seq analysis—revealed that tumor microenvironments and signature tumor events led to gene expression differences in IDH-mutant gliomas (Venteicher et al. 2017). scRNA-seq analysis of childhood cancer (H3K27M-glioma) revealed key players in the phenotype such as oligodendrocyte precursor cells (high proliferation potential) and specific markers that have a potential to serve as targets for therapeutic interventions (Filbin et al. 2018). An open access database has been created that collects glioblastoma transcriptional information, including transcriptomes generated from scRNA-seq (Puchalski et al. 2018).

Treatment responses

Different cancer incidents often show mixed level of response to treatment courses. scRNA-seq technologies are proposed as the ideal tool to study the reasons behind these mixed responses and to guide personalized medicine (Shalek and Benson 2017). In lung cancer, prognosis in intratumor heterogeneity in the clinical setting shows mixed levels of treatment resistance (Kim et al. 2015). ScRNA-Seq analysis of the lung adenocarcinoma that survived anticancer drug treatment found that active KRAS variants in tumor cells may be the key to understand and predict drug response (Kim et al. 2015). In recurrent prostate cancer, the mixed effectiveness of androgen receptor (AR) inhibitors was correlated with high levels of heterogeneity in transcriptomes, including expression patterns of candidates such as AR genes and splicing variants (Miyamoto et al. 2015). The importance of understanding the multicellular ecosystem surrounding melanoma cells was demonstrated by Tirosh et al. (2015). This study showed that the same tumor can display a wide range of phenotypes linked to the cell cycle and affecting drug response. For example, exhaustion markers in T-cells could play a role in treatment outcomes.

Some studies (e.g. Wucherpfennig and Cartwright 2016) have proposed that enhancing the immune system can aid in fighting cancer and thus improve the treatment outcome.  Wucherpfennig and Cartwright (2016) also propose that CRISPR/Cas9 technologies, armed with the precise mechanisms (from scRNA transcriptome data) by which the immune system improves recovery chances from cancer, can be used to edit patient genomes to improve prognosis. 



ScRNA-seq has led to novel discoveries that have the potential to revolutionize clinical practices, drug development and, in turn, improved disease prognosis. Discoveries made thus far promise to prime the next wave of treatment transformations. The information gained from scRNA-seq analysis also has potential to develop minimally invasive treatments and improving recovery especially from cancer treatments. 

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