Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Abstract

The cell cycle is a fundamental process of life, however, a quantitative understanding of gene regulation dynamics in the context of the cell cycle is still far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to its dynamics without externally perturbing the cell. Here, we build a high-resolution map of the cell cycle transcriptome based on scRNA-seq and deep-learning. By generating scRNA-seq libraries with high depth, in mouse embryonic stem cells and human fibroblasts, we are able to observe cycling patterns in the unspliced-spliced RNA space for single genes. Since existing methods in scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach to fit these cycling patterns sorting single cells across the cell cycle. We characterize the cell cycle in asynchronous pluripotent and differentiated cells identifying major waves of transcription during the G1 phase and systematically study the G1-G0 transition where the cells exit the cycle. Our work presents to the scientific community a broader understanding of RNA velocity and cell cycle maps, that we applied to pluripotency and differentiation. Our approach will facilitate the study of the cell cycle in multiple cellular models and different biological contexts, such as cancer and development.

Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

“…2D). We confirmed an increase in expression of two major ABC transporters, including ABCB1, and ABCG2, and a decrease in PGR, another marker previously shown downregulated in the SP + compared to the SPof human myometrium cells (16) (37)(38)(39) were determined by the cell cycle score and the velocity of the scRNA-seq data, respectively. We identified a small group of cells within the vascular myocyte cluster in the G1/G0 phase (Fig 4A) using a computational assignment of cell-cycle stage, as described by Scialdone et al (40).…”

“…Similar clusters were reported in a scRNA-seq comparison of fibroids and myometrium (36). The depth of sequencing for each cell was close to saturation allowing us to identify a small cell population with stem cell characteristics, including the expression of MSC markers SUSD2, MCAM, PDGFRβ, and CSPG4, a quiescent (G0) cell cycle state (37) and low transcriptomic activity/low RNA velocity (37)(38)(39). CRIP1 + /PECAM1cells were primarily located in the perivascular region, a known stem cell niche (42-44), particularly by the larger myometrial blood vessel.…”

Comprehensive cell atlas of the first-trimester developing human brain

“…Pebworth et al identified two transcriptional subtypes of IPCs; radial glia-like cells that express SOX2 and neuron-like cells that express neuronal genes like NEUROD6 ( 18 ). To explore these subtypes in cortical IPCs, we resolved the cell cycle trajectory for IPCs and early differentiating IPCs in samples from two donors (8.5 and 14 p.c.w) using DeepCycle ( 23 ), yielding a projection of each cell to an offset along the cell cycle. As predicted, RNA molecule counts (UMIs) increased gradually along the cell cycle, with a sudden drop at mitosis, corresponding to cell growth followed by cell division (Fig.…”

Quantifying common and distinct information in single-cell multimodal data with Tilted-CCA

“…The majority of the computational tools used in this manuscript consisted of (v4.1.1), (v1.7.0), and (v2.3.1). We derived the cell-cycle genes for humans via Seurat::cc.genes, while we used the lists of human housekeeping genes 59 or mouse cell-cycling genes 60 from particular published work.…”