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Biologically-validated AI is how scientists are realising the full potential of single-cell RNA sequencing

The promise of single-cell gene expression data

Genomic data are an excellent source of novel disease biomarkers and targets. In fact, genetically validated targets are twice as likely to achieve FDA approval (King et al. 2019). The next-generation sequencing (NGS) technology that underlies these discoveries is now commonplace in many research labs. Validation of target expression by RNA sequencing (bulk-RNA-seq) is also common once a gene of interest is identified. However, in a mixed population of cells, biomarkers and targets of interest are expressed at varying levels – bulk-RNA-seq is only capable of reporting on the average gene expression. As a result, important differences in gene expression as a function of cell type or location may be missed by researchers.

One increasingly promising approach to understanding target expression in individual cells is single cell RNA sequencing (scRNA-seq).  This technology provides increased resolution into how alterations in individual cell types contribute to disease pathology.  As opposed to bulk gene expression methods, scRNA-seq identifies the molecular signatures of many individual cells – revealing tissue heterogeneity, enabling identification of subpopulations that were not previously detectable, and generating novel insights into complex disease biology.

With the launch of 10x Genomics applications coupled with widely available NGS technology, scRNA-seq has grown in popularity over the last five years.  It’s being used to not only characterize the individual expression profiles of the milieu of cells that make up normal tissue (Han et al., 2020), but also to identify how that balance and mixture of cells, when altered, can lead to disease states.

In the field of oncology, scRNA-seq has enabled better characterization of cell-based therapies, such as chimeric antigen receptor (CAR) T-cells. In a recent publication, Sheih and colleagues used scRNA-seq of peripheral blood mononuclear cells (PBMCs) to gain insights into clonal kinetics of CAR-T cells in patients treated with cell immunotherapy (2020).  These analyses also resolved what types of CAR-T cells have the best therapeutic potential in solid tumors by dissecting the cells of the tumor microenvironment.

In the field of immunology, scRNA-seq has provided insight into immune cell responses to acute and chronic viral infections (Yao et al., 2019); as well as enabling investigation into how viruses like HSV-1 impact some host cells differently than others (Drayman et al., 2019).  Most recently, in cardiometabolic diseases, scRNA-seq analysis of aortic tissue has identified altered pathways that can lead to disease states including atherosclerosis and aneurysms (Chen et al., 2019; Li et al., 2020; Chen et al., 2020).  Through scRNA-seq experiments, atypical cell populations are identified and some of these populations are being implicated and subsequently validated as drivers of disease initiation and progression. By better elucidating the biological mechanisms underlying disease at the individual cell level, new targets for therapeutic development can be discovered.

Single-cell RNA-seq experiments produce vast quantities of data; however, unlocking accurate and valuable biological insights is difficult without the appropriate tools for analyzing these complex datasets.

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