Integrated molecular profiles of single cells will provide mechanistic insights into gene regulation and heterogeneity.
Like a new pair of glasses, single-cell sequencing is being used by more and more scientists to take a fresh look at their research. Examining tissues at cellular resolution can give insight into heterogeneity and allow researchers to directly define cell identity, including novel cell types, by comparing molecular states. Methods for single-cell DNA and RNA sequencing are maturing, and a rash of epigenetic methods have recently reached the single-cell milestone. We look forward to further improvements in epigenetic profiling as well as new approaches for extracting multiple profiles from the same cell.
The sequencing of RNA from individual cells is already robust, routine and possible at large scale; it can be used as a phenotypic readout to infer cellular functions and identity. Methods for examining gene regulation in single cells now include the study of DNA methylation, chromatin accessibility, histone modifications and chromosome structure. Although the achievements are extraordinary, most of these approaches would benefit from better genomic coverage and cleaner signal. Analytic issues also need to be solved; high technical noise, data sparsity due to undersampling, and biological variation (for example, from cell cycle differences, batch effects and biochemical stochasticity) are just some of the challenges.
Gleaning mechanistic or causal insights from bulk cell epigenetic data has proven difficult. Many data types are correlated and have complex relationships with gene expression in cell populations. The ability to profile epigenetic features and RNA in the same cell could provide more direct, mechanistic interpretations of how epigenetic states affect gene expression, or how RNA feeds back on epigenetic changes. In addition, gene expression could in principle be used to assign cells to subpopulations in a sample, and pairing single-cell RNA analysis with additional assays probing epigenetic features—DNA methylation in one experiment and DNA accessibility in another, for example—could identify relationships between the epigenetic features in each subpopulation, as well as their potential effect on gene expression.
Combined single-cell approaches will be important for the study of stem cell biology, development and tissue heterogeneity. With enough sampling, it should be possible to resolve how epigenetic changes affect gene expression at individual loci.
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Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution
Biotechnology Advances (2019)
Analytical Chemistry (2018)