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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.

Recent studies of exosomes derived from mesenchymal stem cells (MSCs) have indicated high potential clinical applications in many diseases. However, the limited source of MSCs impedes their clinical research and application. Most recently, induced pluripotent stem cells (iPSCs) have become a promising source of MSCs. Exosome therapy based on iPSC-derived MSCs (iMSCs) is a novel technique with much of its therapeutic potential untapped. Compared to MSCs, iMSCs have proved superior in cell proliferation, immunomodulation, generation of exosomes capable of controlling the microenvironment, and bioactive paracrine factor secretion, while also theoretically eliminating the dependence on immunosuppression drugs. The therapeutic effects of iMSC-derived exosomes are explored in many diseases and are best studied in wound healing, cardiovascular disease, and musculoskeletal pathology. It is pertinent clinicians have a strong understanding of stem cell therapy and the latest advances that will eventually translate into clinical practice. In this review, we discuss the various applications of exosomes derived from iMSCs in clinical medicine.

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.

Hematopoietic stem cells (HSCs) sustain lifelong haematopoiesis as their progeny differentiate into all blood cell lineages. Homeostatic HSCs are mostly quiescent and only rarely divide, however their proliferation and differentiation rates can be modulated by external factors. Acute and chronic infections from a wide range of pathogens are known to challenge HSCs at the population level, being forced to respond to inflammation-mediated organismal demand to replenish the myeloid cell pool. However, less is known about the degree of heterogeneity in the HSCs’ response to inflammation at the single cell level. Here, using a natural murine malaria model and an NHS-ester biotin dilution assay we identify two subsets of HSCs, BiotinLo and BiotinHi, with distinct proliferation kinetics. Using combined functional, single-cell transcriptomics and phenotypic analyses, we uncover that BiotinHi HSCs remain highly functional despite expressing strong interferon response signatures. These infection-resistant HSCs express high levels of MHC II and are metabolically distinct from the remaining HSCs as they maintain less active mitochondria. These findings demonstrate that a likely reserve pool of HSCs remains highly functional during Plasmodium infection not because cells are shielded, but because they maintain a stemness associated metabolic profile despite effectively sensing inflammation.