Project description:Single-cell RNA sequencing reveals the transcriptional heterogeneity of single cells, but offers static snapshots of gene expression and fails to reveal the temporal dynamics of transcription. Herein, we develop Well-TEMP-seq, a high-throughput, cost-effective, accurate, and efficient method for massively parallel profiling the time-resolved dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with our recently developed microwell-based single-cell RNA sequencing method (Well-Paired-seq) to distinguish newly transcribed RNAs marked by T-to-C substitutions from pre-existing RNAs in each of thousands of single cells. We further apply Well-TEMP-seq to profiling the dynamics of gene expression of colorectal cancer cells exposed to low-dose of 5-AZA-CdR, a clinically used DNA-demethylating anti-tumor drug. Well-TEMP-seq also for the first time reveals the activation of interferon responsive genes by 5-AZA-CdR induced viral mimicry in the first three days after treatment. Well-TEMP-seq will be broadly applicable to unveil the dynamics of single-cell gene expression in diverse biological processes.
Project description:A fundamental objective of genomics is to track variations in gene expression programs that define cell state progression during development, differentiation, and response to stimuli. While metabolic RNA labeling-based single-cell RNA sequencing offers insights into temporal biological processes, its limited applicability to in vitro models challenges the study of in vivo gene expression dynamics. Herein, we introduce Dyna-vivo-seq, a strategy that enables time-resolved dynamic transcription profiling in vivo at the single-cell level by simultaneously examining new and old RNAs. Leveraging Dyna-vivo-seq, we characterized the heterogeneity of RNA dynamics and quantified turnover rates of 21, 231 genes in 24, 237 single cells. The new RNAs can offer an additional dimension to facilitate the discernment of cellular heterogeneity. Based on new RNAs, we discerned two distinct high and low metabolic labeling populations among proximal tubular (PT) cells. Leveraging the enhanced sensitivity of new RNA-based analysis, we identified 90 rapidly responded transcription factors (TFs) and explored the heterogeneous response of PT cells for the AKI, highlighting that PT cells with high RNA metabolic activity exhibit heightened susceptibility to injury. Dyna-vivo-seq has introduced a temporal dimension to traditional otherwise static measurements in vivo, providing a powerful tool to the characterization of dynamic transcriptome changes at single cell leveles in living organisms and holding great promise for a wide range of biomedical applications.
Project description:In this study, we attempted to identify the heterogeneity and temporal dynamics of leukocytes at single-cell level in mouse heart after inducing MI using the longitudinal single-cell RNA sequencing and spatial transcriptome sequencing.
Project description:This SuperSeries is composed of the following subset Series: GSE21353: Gene expression profiles of human immature dendritic cells after 3h, 6h and 12h of co-cultivation with Aspergillus fumigatus GSE28806: The temporal dynamics of differential gene expression in human alveolar epithelial and endothelial cells interacting with the human pathogenic mould Aspergillus fumigatus in vitro Refer to individual Series
Project description:Gene expression is a dynamic process on multiple scales, e.g. the cell cycle, response to stimuli, normal differentiation and development, etc. However, nearly all techniques for profiling gene expression in single cells fail to directly capture these temporal dynamics, which limits the scope of biology that can be effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a proof-of-concept, we applied sci-fate to a model system of cortisol response, and characterized expression dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and of glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for estimating cell state transition probabilities, and to identify factors whose dynamic expression potentially regulates these transitions. The experimental and computational methods described here may be broadly applicable to quantitatively characterize cell state dynamics in in vitro systems.