A dynamical perspective: moving towards mechanism in single-cell transcriptomics

Abstract

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.

Stochastic processes in development and disease

“…Single-cell RNAseq is perhaps, in isolation, not especially good at measuring stochastic gene expression—the data are too noisy, and highly top-sliced, capturing accurate estimates of transcript abundance for only the most strongly expressed genes, although simulations indicate scRNAseq can certainly reflect the outputs of noisy transcription [ 16 ]. Rory Maizels [ 17 ] writes a thoughtful piece on how these technologies have been further developed and exploited to superimpose temporal information onto the data. Although data on specific genes in single cells may suffer from technical noise, aggregated information on similar cells is providing the potential to predict how differences (random or otherwise) between cells at one time point can map onto phenotypic differences later on in development (or cancer, infection, etc.).…”