Mathematical modelling of the kinetic parameters of haematopoietic stem cells and their progeny

Haematopoiesis, the process by which blood cells are formed, is extensively studied because of its relevance for animal life. Uncovering the mechanisms of blood formation and its regulation is fundamental to cope with anomalies or illnesses such as anaemia and leukaemia, or massive blood loss. Haematopoiesis is driven by the haematopoietic stem cells, HSCs. HSCs are able to reconstitute, upon transplantation, all blood lineages of an animal deprived of its haematopoietic cells (multipotency), and to generate one or two HSCs upon division (self-renewal). However, it is unclear how often they self-renew or differentiate into more mature compartments, according to which differentiation pathways, and how physiological and stressed conditions differ. Similarly, the kinetic properties of the progenies of the stem cells are mostly unknown. Here we present an approach to quantify the kinetics of the haematopoietic system via a deterministic mathematical model. The model is driven by two different sets of in vivo measurements: fate mapping of HSCs and BrdU accumulation data. In the first experiment we consider, an inducible, inheritable label is switched on in the stem cells without altering the physiological conditions. The fraction of labelled cells in the stem and in the downstream populations is measured over time. We build a model of population dynamics, which describes the time course increase of the labelled cells fraction in the progenies. The model has only one parameter, the time a cell resides in a population. Fitting reveals that the immediate progenies of stem cells have a long residence time, which suggest a small role of stem cells in normal haematopoiesis, sustained rather by early progenitors. We then infer the differentiation rate of a cell into its progeny by incorporating in the model the ratio of population sizes, and again confirm an infrequent contribution of stem cells. In the second experiment we consider, the thymidine analogue BrdU is fed to mice over time. BrdU labels the cells that undergo DNA replication. The fraction of labelled cells in the stem cells and in the downstream populations is measured over time. We adapt the population dynamics model of the previous part, incorporating the simplified assumption that cells are BrdU positive if and only if they have divided at least once. We fit the adapted model to BrdU and fate mapping data simultaneously and infer the rate at which cells divide, as well as the frequency at which division of different types (symmetric or asymmetric) happen. This analysis reveals infrequent and mainly symmetric divisions of the stem cells. Moreover, we investigate whether a subdivision of the stem cells and their immediate progeny into several heterogeneous sub-populations is compatible with the parameters inferred as described above. We adapt the model to again fit data that consider this subdivision. We find coherent estimates for quantities that are model-invariant, which supports the robustness of our approach. Finally, we adapt our model to describe fate mapping and cell-cycle-related data in non-stationary conditions, namely after irradiation. Contrary to normal conditions, stem cell proliferation and differentiation are significantly activated, demonstrating their importance in reconstituting a severely compromised system. In conclusion, we suggest via data-driven deterministic modelling that HSCs fuel but do not majorly sustain normal haematopoiesis, role played by their immediate progenies. On the contrary, they are very responsive in stressed conditions, rapidly replenishing the depleted cells via enhanced proliferation and differentiation

Reconciling Flux Experiments for Quantitative Modeling of Normal and Malignant Hematopoietic Stem/Progenitor Dynamics.

Hematopoiesis serves as a paradigm for how homeostasis is maintained within hierarchically organized cell populations. However, important questions remain as to the contribution of hematopoietic stem cells (HSCs) toward maintaining steady state hematopoiesis. A number of in vivo lineage labeling and propagation studies have given rise to contradictory interpretations, leaving key properties of stem cell function unresolved. Using processed flow cytometry data coupled with a biology-driven modeling approach, we show that in vivo flux experiments that come from different laboratories can all be reconciled into a single unifying model, even though they had previously been interpreted as being contradictory. We infer from comparative analysis that different transgenic models display distinct labeling efficiencies across a heterogeneous HSC pool, which we validate by marker gene expression associated with HSC function. Finally, we show how the unified model of HSC differentiation can be used to simulate clonal expansion in the early stages of leukemogenesis

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

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies

A time- and single-cell-resolved model of murine bone marrow hematopoiesis.

The paradigmatic hematopoietic tree model is increasingly recognized to be limited, as it is based on heterogeneous populations largely defined by non-homeostatic assays testing cell fate potentials. Here, we combine persistent labeling with time-series single-cell RNA sequencing to build a real-time, quantitative model of in vivo tissue dynamics for murine bone marrow hematopoiesis. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of differentiation at specific stages of erythroid and neutrophil production, illustrating how the 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 generally applicable to understanding tissue-scale dynamics at high resolution

Pharmacological inhibition of METTL3 impacts specific haematopoietic lineages.

Acknowledgements: KT and EY were supported by Wellcome Trust (grants RG94424, RG83195, G106133) and UKRI Medical Research Council (grant RG83195). KT and ME were supported by Leukaemia UK (grants G108148 and G117699). GSV was supported by Cancer Research UK (Senior Cancer Fellowship, grant no. C22324/A23015). Work in the Göttgens Laboratory is funded by grants from Wellcome (206328/Z/17/Z); Blood Cancer UK (18002); Cancer Research UK (C1163/A21762); UKRI Medical Research Council (G112574); and core support grants by the Cancer Research UK Cambridge Centre (C49940/A25117); and by the Wellcome Trust (203151/Z/16/Z) and the UKRI Medical Research Council (MC_PC_17230). KS is supported by Wellcome (204017/Z/16/Z). TI is supported by the Funai Foundation for Information Technology. The authors thank Reiner Schulte, Chiara Cossetti and Gabriela Grondys-Kotarba from the Cambridge Institute for Medical Research Flow Cytometry Core facility for their assistance with cell sorting. We would also like to thank the Cancer Research UK Cambridge Institute Genomics Core Facility for performing high-throughput sequencing. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.Recent efforts in understanding the epitranscriptome have shown that a diverse set of modifications to RNA represent a new pervasive mechanism of gene regulation, with roles in stem cell homeostasis and disease. N6-methyladenosine (m6A) is an evolutionarily conserved RNA modification and one of the most abundant found on polyadenylated RNA(1,2). The modification is predominantly deposited on mRNA by the METTL3/METTL14 methyltransferase complex(3,4). The majority of the reported phenotypes connected to METTL3/METTL14 function have so far utilised genetic knock-down or knock-out approaches which have been proven fairly pleiotropic, mainly due to the significant negative impact on the general m6A complex3,4. Lack of reagents and strategies to selectively block the catalytic activity of METTL3 without affecting any of its other functions and interactions has hindered investigation of catalysis-specific METTL3 activity. We recently showed that pharmacological inhibition of the catalytic activity of METTL3, using the first-in-class small molecule STM2457, is a novel therapeutic strategy against acute myeloid leukaemia (AML)(5). While no toxicity or long-term effects on normal blood counts were observed after in vivo pharmacological inhibition using STM2457, the potential impact of the isolated catalytic inhibition of METTL3 on normal haematopoiesis remained elusive. To address this, here we utilize a high-resolution single cell RNA sequencing (scRNA-seq) approach to understand: 1) the effect of catalytic inhibition of METTL3 on different lineages within normal haematopoiesis and 2) its specific impact on haematopoietic stem cell fate decisions in vivo

Pharmacological inhibition of METTL3 impacts specific haematopoietic lineages.

Recent efforts in understanding the epitranscriptome have shown that a diverse set of modifications to RNA represent a new pervasive mechanism of gene regulation, with roles in stem cell homeostasis and disease. N6-methyladenosine (m6A) is an evolutionarily conserved RNA modification and one of the most abundant found on polyadenylated RNA(1,2). The modification is predominantly deposited on mRNA by the METTL3/METTL14 methyltransferase complex(3,4). The majority of the reported phenotypes connected to METTL3/METTL14 function have so far utilised genetic knock-down or knock-out approaches which have been proven fairly pleiotropic, mainly due to the significant negative impact on the general m6A complex3,4. Lack of reagents and strategies to selectively block the catalytic activity of METTL3 without affecting any of its other functions and interactions has hindered investigation of catalysis-specific METTL3 activity. We recently showed that pharmacological inhibition of the catalytic activity of METTL3, using the first-in-class small molecule STM2457, is a novel therapeutic strategy against acute myeloid leukaemia (AML)(5). While no toxicity or long-term effects on normal blood counts were observed after in vivo pharmacological inhibition using STM2457, the potential impact of the isolated catalytic inhibition of METTL3 on normal haematopoiesis remained elusive. To address this, here we utilize a high-resolution single cell RNA sequencing (scRNA-seq) approach to understand: 1) the effect of catalytic inhibition of METTL3 on different lineages within normal haematopoiesis and 2) its specific impact on haematopoietic stem cell fate decisions in vivo

CITED2 coordinates key hematopoietic regulatory pathways to maintain the HSC pool in both steady-state hematopoiesis and transplantation.

Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic differentiation hierarchy and sustain multilineage hematopoiesis. Here, we show that the transcriptional regulator CITED2 is essential for life-long HSC maintenance. While hematopoietic-specific Cited2 deletion has a minor impact on steady-state hematopoiesis, Cited2-deficient HSCs are severely depleted in young mice and fail to expand upon aging. Moreover, although they home normally to the bone marrow, they fail to reconstitute hematopoiesis upon transplantation. Mechanistically, CITED2 is required for expression of key HSC regulators, including GATA2, MCL-1, and PTEN. Hematopoietic-specific expression of anti-apoptotic MCL-1 partially rescues the Cited2-deficient HSC pool and restores their reconstitution potential. To interrogate the Cited2→Pten pathway in HSCs, we generated Cited2;Pten compound heterozygous mice, which had a decreased number of HSCs that failed to reconstitute the HSC compartment. In addition, CITED2 represses multiple pathways whose elevated activity causes HSC exhaustion. Thus, CITED2 promotes pathways necessary for HSC maintenance and suppresses those detrimental to HSC integrity

Identification and characterization of in vitro expanded hematopoietic stem cells.

Funder: Biomedical Research Centre; Id: http://dx.doi.org/10.13039/100014461Funder: University of Cambridge; Id: http://dx.doi.org/10.13039/501100000735Funder: Biotechnology and Biological Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000268Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved > 200-fold expansion of functional HSCs, but their molecular characterization has not been possible since the majority of cells are non-HSCs and single cell-initiated cultures have 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. By directly linking single-clone functional transplantation data with single-clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to proliferating fetal HSCs and reveals a gene expression signature, including Esam, Prdm16, Fstl1, and Palld, that can identify functional HSCs from multiple cellular states. This "repopulation signature" (RepopSig) also enriches for HSCs in human datasets. Together, these findings demonstrate the power of integrating functional and molecular datasets to better derive meaningful gene signatures and opens the opportunity for a wide range of functional screening and molecular experiments previously not possible due to limited HSC numbers