PubReading [199] - Live-seq enables temporal transcriptomic recording of single cells - W. Chen, B. Deplanke et al

<p><strong>Single-cell transcriptomics</strong> (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell’s ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell’s trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (<strong>LPS</strong>) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by <strong>time-lapse imaging</strong> after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal <em>Nfkbia </em>expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.</p><p><em><a href="https://doi.org/10.1038/s41586-022-05046-9">https://doi.org/10.1038/s41586-022-05046-9</a> - 2022</em></p>