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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.

Recent lineage tracing based single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of barcoding systems to unravel how heterogeneous stem cell clones drive the overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate the clone and population-specific kinetics from both newly generated and publicly available LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields the summary of cell division dynamics across differentiation time-courses and reconstructs the lineage tree of the progenitor cells in a quantitative way. Moreover, clone-level behaviors can be grouped into characteristic types by pooling individual clones into meta-clones for analyses at various resolutions. Finally, we show that meta-clone specific cellular behaviors identified by CLADES originate from hematopoietic stem and progenitor cells in distinct transcriptional states. In conclusion, we report a scalable approach to robustly quantify clone-specific differentiation kinetics of cellular populations for time-series systems with static barcoding designs. Recent studies have traced haematopoiesis at the clonal level but lack a way to extract dynamical information. Here, authors develop CLADES, a tool to estimate cellular kinetics and the number of divisions to produce mature cells for each clone, in human cord blood and adult mouse haematopoiesis.

Inducible genetic labelling of haematopoietic stem cells (HSCs) and linked mathematical modelling show that at least 30% of all HSCs are productive, and that adult haematopoiesis is largely sustained by ‘short-term’ downstream stem cells that operate near self-renewal in the steady state; HSC fate mapping provides a quantitative model for better understanding of HSC functions in health and disease. Following haematopoiesis in vivo Most of what we know of the properties of haematopoietic stem cells (HSCs) is derived from transplantation and reconstitution of an emptied blood and immune system. Relatively little is known about how HSCs behave under physiological conditions. It was reported recently that normal haematopoeisis in adults is driven by thousands of long-lived progenitors rather than classic HSCs. Hans-Reimer Rodewald and colleagues have used inducible genetic labelling of primitive HSCs in a mouse model, combined with mathematical modelling, to show that although HSCs participate in establishment of the blood system in early life, steady-state haematopoiesis depends mainly on progenitors that are able to self-renew but also receive rare input from long-term HSCs. This input is increased following physiological challenges. Haematopoietic stem cells (HSCs) are widely studied by HSC transplantation into immune- and blood-cell-depleted recipients. Single HSCs can rebuild the system after transplantation 1 , 2 , 3 , 4 , 5 . Chromosomal marking 6 , viral integration 7 , 8 , 9 and barcoding 10 , 11 , 12 of transplanted HSCs suggest that very low numbers of HSCs perpetuate a continuous stream of differentiating cells. However, the numbers of productive HSCs during normal haematopoiesis, and the flux of differentiating progeny remain unknown. Here we devise a mouse model allowing inducible genetic labelling of the most primitive Tie2 + HSCs in bone marrow, and quantify label progression along haematopoietic development by limiting dilution analysis and data-driven modelling. During maintenance of the haematopoietic system, at least 30% or ∼5,000 HSCs are productive in the adult mouse after label induction. However, the time to approach equilibrium between labelled HSCs and their progeny is surprisingly long, a time scale that would exceed the mouse’s life. Indeed, we find that adult haematopoiesis is largely sustained by previously designated ‘short-term’ stem cells downstream of HSCs that nearly fully self-renew, and receive rare but polyclonal HSC input. By contrast, in fetal and early postnatal life, HSCs are rapidly used to establish the immune and blood system. In the adult mouse, 5-fluoruracil-induced leukopenia enhances the output of HSCs and of downstream compartments, thus accelerating haematopoietic flux. Label tracing also identifies a strong lineage bias in adult mice, with several-hundred-fold larger myeloid than lymphoid output, which is only marginally accentuated with age. Finally, we show that transplantation imposes severe constraints on HSC engraftment, consistent with the previously observed oligoclonal HSC activity under these conditions. Thus, we uncover fundamental differences between the normal maintenance of the haematopoietic system, its regulation by challenge, and its re-establishment after transplantation. HSC fate mapping and its linked modelling provide a quantitative framework for studying in situ the regulation of haematopoiesis in health and disease.

Inducible genetic labelling of haematopoietic stem cells (HSCs) and linked mathematical modelling show that at least 30% of all HSCs are productive, and that adult haematopoiesis is largely sustained by 'short-term' downstream stem cells that operate near self-renewal in the steady state; HSC fate mapping provides a quantitative model for better understanding of HSC functions in health and disease.

Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved more than 200-fold expansion of functional HSCs, but their molecular characterization has not been possible due to the substantial majority of cells being non-HSCs and single cell-initiated cultures displaying substantial clone-to-clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non-HSCs in expansion cultures. Linking single clone functional transplantation data with single clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to actively cycling fetal liver HSCs and shares a gene expression signature with functional HSCs from all sources, including Prdm16, Fstl1 and Palld. This new tool can now be applied to a wide-range of functional screening and molecular experiments previously not possible due to limited HSC numbers. Competing Interest Statement The authors have declared no competing interest.

A defining characteristic of all metazoan organisms is the existence of different cell states or cell types, driven by changes in gene expression kinetics, principally transcription, splicing and degradation rates. The RNA velocity framework utilizes both spliced and unspliced reads in single cell mRNA preparations to predict future cellular states and estimate transcriptional kinetics. However, current models assume either constant kinetic rates, rates equal for all genes, or rates completely independent of progression through differentiation. Consequently, current models for rate estimation are either underparametrised or overparametrised. Here we developed a new method (diffGEK) which overcomes this issue, and allows comparison of transcriptional rates across different biological conditions. diffGEK assumes that rates can vary over a trajectory, but are smooth functions of the differentiation process. Analysing Jak2 V617F mutant versus wild type mice for erythropoiesis, and Ezh2 KO versus wild type mice in myelopoiesis, revealed which genes show altered transcription, splicing or degradation rates between different conditions. Moreover, we observed that, for some genes, compensatory changes between different rates can result in comparable overall mRNA levels, thereby masking highly dynamic changes in gene expression kinetics in conventional expression analysis. Collectively, we report a robust pipeline for comparative expression analysis based on altered transcriptional kinetics to discover mechanistic differences missed by conventional approaches, with broad applicability across any biomedical research question where single cell expression data are available for both wild type and treatment/mutant conditions.

Recent lineage tracing single-cell techniques (LT-scSeq), e.g., the Lineage And RNA RecoverY (LARRY) barcoding system, have enabled clonally resolved interpretation of differentiation trajectories. However, the heterogeneity of clone-specific kinetics remains understudied, both quantitatively and in terms of interpretability, thus limiting the power of bar-coding systems to unravel how heterogeneous stem cell clones drive overall cell population dynamics. Here, we present CLADES, a NeuralODE-based framework to faithfully estimate clone-specific kinetics of cell states from newly generated and publicly available human cord blood LARRY LT-scSeq data. By incorporating a stochastic simulation algorithm (SSA) and differential expression gene (DEGs) analysis, CLADES yields cell division dynamics across differentiation timecourses and fate bias predictions for the early progenitor cells. Moreover, clone-level quantitative behaviours can be grouped into characteristic types by pooling individual clones into meta-clones. By benchmarking with CoSpar, we found that CLADES improves fate bias prediction accuracy at the meta-clone level. In conclusion, we report a broadly applicable approach to robustly quantify differentiation kinetics using meta-clones while providing valuable insights into the fate bias of cellular populations for any organ system maintained by a pool of heterogeneous stem and progenitor cells.

The chromatin regulator MLL2 (KMT2B) is the primary histone 3 lysine 4 (H3K4) trimethyltransferase acting at bivalent promoters in embryonic stem cells (ESCs) and is required for differentiation toward neuroectoderm. Here, we demonstrate that this requirement occurs during exit from naive pluripotency, days before neuroectoderm differentiation is impaired. During exit, the effect of MLL2 on transcription is subtle, increasing the expression of a few important neuroectodermal transcription factors. In contrast, MLL2's effect on chromatin architecture is substantial, stabilising loops associated with bivalent promoters in primed ESCs. MLL2 H3K4 catalytic activity is dispensable for stabilising these loops during ESC exit and for neuroectoderm differentiation. We therefore identify a non-catalytic function for MLL2 in stabilising 3D chromatin architecture, which has implications for lineage specification. Because MLL2 shares features with all four MLLs, we propose that chromatin tethering, rather than H3K4 methylation, represents a primary function for MLLs during lineage commitment decisions.Competing Interest StatementThe authors have declared no competing interest.

The paradigmatic tree model of hematopoiesis is increasingly recognized to be limited as it is based on heterogeneous populations and largely inferred from non-homeostatic cell fate assays. Here, we combine persistent labeling with time-series single-cell RNA-Seq to build the first real- time, quantitative model of in vivo tissue dynamics for any mammalian organ. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between single cell molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of neutrophil differentiation, illustrating how the new model can quantify the impact of perturbations. Our reconstruction of dynamic behavior from snapshot measurements is akin to how a Kinetoscope allows sequential images to merge into a movie. We posit that this approach is broadly applicable to empower single cell genomics to reveal important tissue scale dynamics information. Cell flux analysis reveals high-resolution kinetics of native bone marrow hematopoiesis Quantitative model simulates cell behavior in real-time and connects it with gene expression patterns Distinct lineage-affiliated progenitors have unique self-renewal and differentiation properties Transplanted HSCs display accelerated stage- and lineage-specific differentiation