Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor. Great advances in single-cell RNA sequencing (scRNAseq) enabled scientists to overcome this limitation and allow for in depth interrogation of previously unexplored rare cell types. Due to the high sensitivity of scRNAseq, adequate attention must be put into experimental setup and execution. Careful handling and processing of cells for scRNAseq is critical to preserve the native expression profile that will ensure meaningful analysis and conclusions. Here, we delineate the individual steps of a typical single-cell analysis workflow from tissue procurement, cell preparation, to platform selection and data analysis, and we discuss critical challenges in each of these steps, which will serve as a helpful guide to navigate the complex field of single-cell sequencing.
Keywords: cell isolation; cellular heterogeneity; computational biology; single-cell analysis; single-cell genomics.