Transforming our understanding of disease causal mechanisms using single cell RNA sequencing

By Aditya Pai, Vice President, Corporate and Business Development, MedGenome Inc.

The scientific curiosity to understand the cause of a disease has led to many technological innovations. As the cost of genomic sequencing started to fall a decade ago, it opened up numerous new technologies that could provide unique insights in understanding disease biology even at a molecular level. These include whole genome data (genomics), changes in the structure of chromatin, understanding RNA sequences and their expression (transcriptomics) to proteomics-based approaches to understand protein structure, folding and the measurement of various metabolites (metabolomics). These broad array of technological advancements have helped in deciphering causal factors thus enhancing our ability to study and insight into many diseases through different dimensions and resolutions which were not previously possible.

While whole genome sequencing can provide excellent information on genomic data, a large emphasis has shifted to understanding the transcript and understanding how RNA is expressed. Bulk RNA sequencing approaches provide an advantage of being relatively cost effective, yet provide useful transcriptomic data. Typically, RNA is extracted from a tissue comprised of several cell-types. Thus, the name “bulk” as the analysis of the sequencing is not cell specific but instead is an average expression level for genes across a large population of cells. Such approaches can be very useful for differential gene expression analysis or comparing the transcriptome of a given tissue across different species.

However, in order to maximize one’s understanding of a disease or a particular microenvironment where a disease manifests itself, single cell RNA sequencing approaches have gained prominence. This has been aided by technological innovation in single cell sequencing technology and a reduction in the cost of sequencing. This has allowed for a far greater resolution or “fidelity” with which stochastic changes in cellular state or cellular heterogeneity be understood. For example, single cell RNA sequencing approaches allow for an understanding of heterogenous cell types, including rare cell populations, and molecular differences between healthy and abnormal tissue or clusters of cells that can be grouped to allow for a greater understanding of the microenvironment of the disease. Sheih et al1 demonstrated how single-cell transcriptional profiling of CAR-T cells can be used in patients undergoing CD19 CAR-T immunotherapy. Their use of scRNA-seq allowed for a unique assessment of transcriptional attributes in patients infused with CD19 CAR-T cells and how these could be potentially impacted by tumor burden and the tumor microenvironment. Similarly, for developers of cellular therapies, including CAR-T and NK-based products, scRNA-seq affords a unique opportunity to characterize the therapeutic product (i.e. the transduced cells prior to infusion into the patient) and compare it with the genetically-modified and other host cells later recovered from treated patients.

Depending on the number of cells and cell size, the most commonly used Single cell RNA sequencing approaches are 10X Chromium system and Takara SMARTSeq. In the 10X chromium system, normally used for greater than 100,000 cells, a cell suspension is used and the 10X Chromium system partitions reactions into nanoliter-scale droplets containing uniquely barcoded beads called GEMs (Gel Bead-In Emulsions). The system can be used for single cell partitioning or even single nuclei partitioning. For starting cell numbers below 20,000, approaches like Takara SMARTSeq are used where cells are sorted in a plate.

The combination of the above approaches can be used for various single cell RNA sequencing experiments to understand 5’ gene expression, 3’ gene expression, T and B cell immune repertoire and more specific antibody-based approaches such as CITE and ATAC-Seq. In my next blog, we will review the most commonly used single cell approaches used by MedGenome’s clients.

References

1 Sheih et al: “Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T immunotherapy.” Nat Commun 11, 2019-2020

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