Single-cell transcriptomics (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 microscopy^2,3, 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 (LPS) 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 time-lapse imaging 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 Nfkbia 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.

单细胞转录组学 (scRNA-seq) 极大地提高了我们表征细胞异质性的能力¹。然而，scRNA-seq 需要裂解细胞，这阻碍了对同一细胞的进一步分子或功能分析。在这里，我们建立了 Live-seq，一种单细胞转录组分析方法，可在使用流体力显微镜^2,3提取 RNA 期间保留细胞活力，从而将细胞的基态转录组与其下游分子或表型行为耦合。为了对 Live-seq 进行基准测试，我们使用细胞生长、功能反应和全细胞转录组读数来证明 Live-seq 可以准确地对不同的细胞类型和状态进行分层，而不会引起主要的细胞扰动。作为概念证明，我们证明 Live-seq 可用于通过顺序分析脂多糖（LPS）刺激前后单个巨噬细胞以及分化前和分化后脂肪基质细胞的转录组来直接绘制细胞轨迹。此外，我们证明 Live-seq 可以通过预先注册单个巨噬细胞的转录组来充当转录组记录器，随后在 LPS 暴露后通过延时成像来监测这些转录组。这使得能够根据基因影响巨噬细胞LPS反应异质性的能力对基因进行无监督的全基因组排序，揭示基础Nfkbia表达水平和细胞周期状态作为重要的表型决定因素，我们通过实验验证了这一点。因此，Live-seq 可以通过将 scRNA-seq 从终点方法转变为时间分析方法来解决广泛的生物学问题。