Spatiotemporally resolved transcriptomics reveals subcellular RNA kinetic landscape

Abstract

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA synthesis, decay, export, and translocation remain obscured due to the limitations of existing transcriptomics methods. Here, we report a spatiotemporally resolved RNA mapping method (TEMPOmap) to uncover subcellular RNA profiles across time and space at the single-cell level in heterogeneous cell populations. TEMPOmap integrates pulse-chase metabolic labeling of the transcriptome with highly multiplexed three-dimensional (3D) in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape of 991 genes in human HeLa cells from upstream transcription to downstream subcellular translocation. Clustering analysis of critical RNA kinetic parameters across single cells revealed kinetic gene clusters whose expression patterns were shaped by multi-step kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated to serve molecular and cellular functions in cell-cycle dependent manner. Together, these single-cell spatiotemporally resolved transcriptomics measurements provide us the gateway to uncover new gene regulation principles and understand how kinetic strategies enable precise RNA expression in time and space.

Mapping the topography of spatial gene expression with interpretable deep learning

“…Second, it would be desirable to extend GASTON to identify 3-D spatial gradients, e.g. by utilizing spatial alignment tools [159, 77, 67, 64], as well as spatiotemporal gradients [114]. A third direction is to extend GASTON to other molecular modalities such as chromatin accessibility [164, 119] or protein/metabolite abundance [141, 82], e.g.…”

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

“…Spatial information can serve to constrain the picture of expression dynamics, helping to distinguish possible from impossible dynamics in systems that would be otherwise underdetermined from single-cell data alone. Work in this direction is ongoing: for example, TEMPOmap [88] provides a method for parallel metabolic labelling and spatial transcriptomics, while Spatio [89] provides tools to model the ‘morphometric’ vector field of time-coursed spatial data. In parallel, the continued improvement of organoid [90,91] and in vitro embryo [92] models may provide three-dimensional structured tissues that are far more experimentally tractable for the level of high-throughput data collection that would be required for spatial dynamical modelling.…”

Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting

“…It has been previously shown that groups of mRNAs encoding proteins with specific molecular functions display distinct kinetics rates along the mRNA life cycle (Chen & van Steensel, 2017;Ren et al, 2023). To this end, we performed gene set enrichment analyses (GSEA) considering the pre-mRNA processing, nuclear retention, cytosolic and membrane stability, respectively, to identify groups of transcripts from the gene ontology (GO) that have with the transcriptional elongation rate, as noted above, and presented a rank correlation of more than 0.5 with gene length (see.…”

“…However, metabolic labeling studies on mammalian cells have mostly considered the total cellular mRNA to determine kinetic rates, overlooking subcellular aspects (Herzog et al , 2017; Rabani et al , 2011; Rutkowski & Dölken, 2017). A recent study based on single-cell in-situ sequencing of 5-ethynyl uridine-labeled RNA measured transcription, translocation and degradation of individual transcript molecules, uncovering subcellular mRNA profiles across time and space at the single-cell level for a collection of almost 1000 genes (Ren et al , 2023). Furthermore, two independent works combined 4-thiouridine, cellular fractionation and RNA sequencing to measure the rates at which RNAs are exported from the nucleus in mammalian cells (preprint: Müller et al , 2023; preprint: Smalec et al , 2022), with one study additionally providing evidence for mRNA degradation in the nucleus (preprint: Müller et al , 2023).…”