Single Cell Sequencing for Cancer Research Rebecca Nadler

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Single Cell Sequencing Single Cell Sequencing for Cancer Research for Cancer Research

The fi rst study utilizing scRNA-seq examined the whole transcriptome of single cells of a four-cell stage murine embryo. A study of single cell resolution from the University of Cambridge accelerated the general understanding of gene expression and regulation under this new innovation. They were able to detect the expression of 5,720 more genes than microarray, the previous standard method for transcriptomics analysis.1 This represents a 75% increase in unique transcripts detected by the microarray technique, which clearly provides more in-depth information about the cellular states.

Tang et al. ultimately showed cell types can be clustered by tracking transcriptomic changes through scRNA-seq. While their technique was successful, there were many challenges that slowed development of the technology. The scRNA-seq libraries were generated manually in individual tubes after isolating single cells, which is a time and labor-intensive process.2 This inability to increase the scale of scRNA-seq limited potential applications for several years. Researchers at Harvard Medical School innovated a method to utilize microfl uidics, whereby a single cell and functional bead are contained within a droplet in an oil emulsion in order to compartmentalize cell lysing, barcoding, and reverse transcription. This procedure allowed researchers to build up large-scale sequencing libraries and is regularly used today.

There is a standard methodological protocol generally used to conduct scRNA-seq.3 Arguably, the most important step of scRNA-seq is the primary isolation of viable, single cells from the cell population of interest. Establishing an unbiased, representative sample of cells is a critical step for ensuring validity of downstream data analysis. The cells are then lysed to capture RNA molecules, which are converted to complementary DNA (cDNA) via reverse transcription. In doing so, adaptor sequences are added to the ends of the cDNA molecules to serve as unique molecular barcoding tags for analysis, allowing researchers to analyze multiple samples together without ambiguity. However, reverse transcription only provides a minimal amount of cDNA, so it is amplifi ed exponentially by polymerase chain reactions (PCR). The resulting cDNA is pooled and sequenced using Next Generation Sequencing techniques, and bioinformatic methods are utilized

Written By Rebecca Nadler Designed By Saraswati Sridhar

to interpret the vast amount of scRNA-seq data generated. With the spread of scRNA-seq technology, increasingly accessible and affordable commercial kits and reagents are now available for researchers to carry out each step of the protocol.3

Much information can be obtained from scRNA-seq analysis to bolster our understanding of tumor progression. Cancer develops through the random accumulation of somatic mutations — those in non-germ line cells — creating gene mutation heterogeneity and genomic instability. Both genetic phenomena create tremendous intra-tumor heterogeneity, particularly infl uencing the expression of genes that contribute to cancer development. scRNA-seq technology can be employed in the study of tumor heterogeneity for analysis of the characteristic cell state of malignant cells, while also elucidating the infl uence of genetic factors and epigenetic modifi cations.4 In addition to the assessment of intratumoral heterogeneity, scRNA-seq can be utilized to distinguish between tumoral cells and the tumor microenvironment. Immune cells, fi broblasts, and endothelial cells typically comprise the tumor microenvironment, all of which are implicated in tumor development.5 Supplementary investigation of these cell types may reveal mechanisms of immunosuppression and cancer growth.

More recently, researchers are pairing scRNA-seq technology with other assays in order to understand individual cell transcriptomics with greater depth. Researchers at the University of California San Francisco recently developed the XYZeq workfl ow as a means of incorporating information about the spatial organization of cells with scRNA-seq libraries. This enables scientists to simultaneously cluster cells present in sample tissue by physical location and gene expression. Using this approach, Lee et al. identifi ed spatially variable patterns of gene expression and heterogeneity within lung cancer-associated mesenchymal stem cells, specifi ed by proximity to the tumor core.6 The authors hypothesize that this technology will facilitate the discovery of tissue microenvironment pressures on cellular infi ltration in a tumor setting and may enable profi ling of an entire organ. With a greater understanding of the hallmark cellular phenotypes that dominate certain cancer types across organs, researchers will be better able to design targeted therapies for patients, as they will be able to assess the barriers and vulnerabilities within the tumor.

Today, scRNA-seq has already provided key insights to accelerate the theoretical understanding of tumor development and progression. Gu et al. also demonstrate how it may provide clinical benefi ts in the context of cervical cancer. Across fi ve cervical biopsy samples, they identifi ed differential gene expression and activation of various signaling pathways that differ between chemoresistant and chemosensitive cervical cancer patients.7 In examining the immune infi ltrate, they found that immune checkpoint molecules were highly expressed in a manner that inhibits cytotoxicity, indicating that immunotherapy could be useful in cervical cancer. Furthermore, inhibition of certain components of the pathways upregulated in chemoresistant samples has the potential to overcome resistance. Ultimately, this information provides novel insights into human cervical cancer and has the potential to advance both diagnostic and treatment efforts.

Thus far, the scientifi c journal Nature Methods named spatially resolved transcriptomics the 2020 Method of the Year.8 It has the potential to expose novel biomarkers and therapeutic targets to aid in cancer treatment. scRNA-seq may also guide clinical decision-making throughout treatment, as it can be used to track drug resistance and tumor evolution. As personalized medicine becomes more prevalent in the clinical setting, scRNA-seq will prove incredibly benefi cial for identifying candidate treatment options for patients according to their specifi c disease presentation. REFERENCES