Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation

Abstract

Background Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Results Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. Conclusions By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

Mapping transcriptomic vector fields of single cells

“…By contrast, with dynamo ’s modeling framework, the labeling data (labelled and total RNA) yielded velocity flows that closely recapitulate the established knowledge of hematopoiesis ( Figure 3B right). Previous studies have reported that biased capture of intron regions via mispriming in droplet-based scRNA-seq libraries ( La Manno et al, 2018 ; Qiu et al, 2020a ) and dynamic RNA transcription rates ( Barile et al, 2021 ; Bergen et al, 2021 ) may result in inaccurate RNA velocity flow. Indeed, when inspecting the expression kinetics of lineage marker genes, such as PF4 , a Meg lineage marker ( Paul et al, 2016 ), we found that the spliced and unspliced RNAs were undetectable in progenitors, but its expression switched on rapidly in the Meg lineage ( Figure 3C , left subpanels of Figure 3D , E ) with the unspliced RNA present at a much lower level, consistent with the unsuccessful capture of its introns.…”

“…In contrast to the implicit assumption of a constant transcription rate for cscRNA-seq data ( Barile et al, 2021 ; Bergen et al, 2020 ; La Manno et al, 2018 ), dynamo models the transcription rate for labeling data as a variable that depends on measured new RNA and can therefore vary across genes and cells. Collectively, the unbiased measurements of the nascent RNA and the modeling assumption of a transcription rate that differs for each gene in each cell correctly led to positive velocities of PF4 for Meg lineage cells and more broadly corrected the velocity flow ( Figure 3B , E ).…”

“…In the first stage of kinetic parameter estimation, because our approach implements a universal modeling system, it is broadly compatible with existing RNA metabolic labeling strategies, as well as new labeling protocols that may be developed, such as dual labeling with 4sU and 6-thioguanine (6-TG) ( Kiefer et al, 2018 ) to directly measure RNA acceleration. Furthermore, we collected a high-quality tscRNA-seq dataset for the human hematopoiesis and establish that the total RNA velocity estimated from this and other tscRNA-seq datasets with dynamo overcomes intrinsic limitations of conventional RNA velocity estimation, which can lead to inaccurate velocity measurements ( Barile et al, 2021 ; Bergen et al, 2021 ), thereby enabling more accurate downstream absolute vector field analyses.…”

BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

“…Our results showed that, in the presence of cell annotations, BRIE2 can be a useful tool to select relevant genes (differential momentum genes) which provide a smoother and more interpretable description of cell transitions within RNA velocity studies. The importance of selecting trajectory-informed genes for RNA velocity is also evidenced in another recent study [ 23 ].…”

IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

“…The TF Gata1 plays an important role in erythropoiesis as it regulates multiple downstream genes in erythroid cell development [ 15 ]. In vitro and in vivo studies of hematopoietic cells that lack the normal expression level of Gata1 , show that the lack of Gata1 halts erythroid differentiation [ 48 , 49 ]. Our model also predicts a block of erythroid differentiation in the absence of Gata1 with a single attractor state resembling the MEPs ( Fig 6F ).…”