Coordinated Changes in Gene Expression Kinetics Underlie both Mouse and Human Erythroid Maturation

Abstract

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 applied this ‘RNA velocity concept’ to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Intriguingly, RNA velocity correctly identified epiblast cells as the starting point, but several trajectory predictions at later stages were inconsistent with both real time ordering and existing knowledge. The most striking discrepancy concerned red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes revealed 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 up-regulated 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 revealed 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. 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.

RNA velocity—current challenges and future perspectives

“…These can be governed by variations in splicing to degradation rates ratios and manifest as multiple trajectories in phase space. Second, as recently shown in mouse gastrulation (Pijuan‐Sala et al , 2019; Barile et al , 2021), a boost in expression has been observed in erythroid maturation, possibly induced by a change in transcription rate (Fig 2B). We made the same observation in human bone marrow CD34 + hematopoietic cells (Setty et al , 2019).…”

“…(B) Erythroid maturation in mouse gastrulation (top) and human bone marrow CD34 + hematopoietic cells (bottom) that show transcriptional boosts in expression possibly induced by a change in transcription rate. Data from Setty et al (2019), Barile et al (2021). (C) Peripheral blood mononuclear cells (PBMCs) from Zheng et al (2017) with mature cell types.…”

Mapping transcriptomic vector fields of single cells

“…See also Figure S1. 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 Figures 3D and 3E) 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 (Figures 3B and 3E).…”

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].…”