Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets

Single-cell omics identifies inflammatory signaling as a trans-differentiation trigger in mouse embryos

Trans-differentiation represents a direct lineage conversion; however, insufficient characterization of this process hinders its potential applications. Here, to explore a potential universal principal for trans-differentiation, we performed single-cell transcriptomic analysis of endothelial-to-hematopoietic transition (EHT), endothelial-to-mesenchymal transition, and epithelial-to-mesenchymal transition in mouse embryos. We applied three scoring indexes of entropies, cell-type signature transcription factor expression, and critical transition signals to show common features underpinning the fate plasticity of transition states. Cross-model comparison identified inflammatory-featured transition states and a common trigger role of interleukin-33 in promoting fate conversions. Multimodal profiling (integrative transcriptomic and chromatin accessibility analysis) demonstrated the inflammatory regulation of hematopoietic specification. Furthermore, multimodal omics and fate-mapping analyses showed that endothelium-specific Spi1, as an inflammatory effector, governs appropriate chromatin accessibility and transcriptional programs to safeguard EHT. Overall, our study employs single-cell omics to identify critical transition states/signals and the common trigger role of inflammatory signaling in developmental-stress-induced fate conversions.

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.

Application of mass cytometry to characterize hematopoietic stem cells in apheresis products of patients with hematological malignancies

INTRODUCTION

Hematopoietic stem cell transplantation (HSCT) is a widely used therapy, but its success largely depends on the number and quality of stem cells collected. Current evidence shows the complexity of the hematopoietic system, which implies that, in the quality assurance of the apheresis product, the hematopoietic stem cells are adequately characterized and quantified, in which mass cytometry (MC) can provide its advantages in high-dimensional analysis.

OBJECTIVE

This research aimed to characterize and enumerate CD45dim/CD34+ stem cells using the MC in apheresis product yields from patients with chronic lymphoid malignant diseases undergoing autologous transplantation at the Abu Dhabi Stem Cells Center.

METHODS

An analytical and cross-sectional study was performed on 31 apheresis products from 15 patients diagnosed with multiple myeloma (n = 9) and non-Hodgkin lymphomas (n = 6) eligible for HSCT. The MC was employed using the MaxPar Kit for stem cell immunophenotyping. The analysis was performed manually in the Kaluza and unsupervised by machine learning in Cytobank Premium.

RESULTS

An excellent agreement was found between mass and flow cytometry for the relative and absolute counts of CD45dim/CD34+ cells (Bland-Altman bias: -0.029 and -64, respectively), seven subpopulations were phenotyped and no lineage bias was detected for any of the methods used in the pool of collected cells. A CD34+/CD38+/CD138+ population was seen in the analyses performed on four patients with multiple myeloma.

CONCLUSIONS

The MC helps to characterize subpopulations of stem cells in apheresis products. It also allows cell quantification by double platform. Unsupervised analysis allows results completion and validation of the manual strategy. The proposed methodology can be extended to apheresis products for purposes other than HSCT.

Complete miRNA-15/16 loss in mice promotes hematopoietic progenitor expansion and a myeloid-biased hyperproliferative state

Dysregulated apoptosis and proliferation are fundamental properties of cancer, and microRNAs (miRNA) are critical regulators of these processes. Loss of miR-15a/16-1 at chromosome 13q14 is the most common genomic aberration in chronic lymphocytic leukemia (CLL). Correspondingly, the deletion of either murine miR-15a/16-1 or miR-15b/16-2 locus in mice is linked to B cell lymphoproliferative malignancies. However, unexpectedly, when both miR-15/16 clusters are eliminated, most double knockout (DKO) mice develop acute myeloid leukemia (AML). Moreover, in patients with CLL, significantly reduced expression of miR-15a, miR-15b, and miR-16 associates with progression of myelodysplastic syndrome to AML, as well as blast crisis in chronic myeloid leukemia. Thus, the miR-15/16 clusters have a biological relevance for myeloid neoplasms. Here, we demonstrate that the myeloproliferative phenotype in DKO mice correlates with an increase of hematopoietic stem and progenitor cells (HSPC) early in life. Using single-cell transcriptomic analyses, we presented the molecular underpinning of increased myeloid output in the HSPC of DKO mice with gene signatures suggestive of dysregulated hematopoiesis, metabolic activities, and cell cycle stages. Functionally, we found that multipotent progenitors (MPP) of DKO mice have increased self-renewing capacities and give rise to significantly more progeny in the granulocytic compartment. Moreover, a unique transcriptomic signature of DKO MPP correlates with poor outcome in patients with AML. Together, these data point to a unique regulatory role for miR-15/16 during the early stages of hematopoiesis and to a potentially useful biomarker for the pathogenesis of myeloid neoplasms.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

EBF1 primes B-lymphoid enhancers and limits the myeloid bias in murine multipotent progenitors

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) generate all cells of the blood system. Despite their multipotency, MPPs display poorly understood lineage bias. Here, we examine whether lineage-specifying transcription factors, such as the B-lineage determinant EBF1, regulate lineage preference in early progenitors. We detect low-level EBF1 expression in myeloid-biased MPP3 and lymphoid-biased MPP4 cells, coinciding with expression of the myeloid determinant C/EBPα. Hematopoietic deletion of Ebf1 results in enhanced myelopoiesis and reduced HSC repopulation capacity. Ebf1-deficient MPP3 and MPP4 cells exhibit an augmented myeloid differentiation potential and a transcriptome with an enriched C/EBPα signature. Correspondingly, EBF1 binds the Cebpa enhancer, and the deficiency and overexpression of Ebf1 in MPP3 and MPP4 cells lead to an up- and downregulation of Cebpa expression, respectively. In addition, EBF1 primes the chromatin of B-lymphoid enhancers specifically in MPP3 cells. Thus, our study implicates EBF1 in regulating myeloid/lymphoid fate bias in MPPs by constraining C/EBPα-driven myelopoiesis and priming the B-lymphoid fate.

A novel Boolean network inference strategy to model early hematopoiesis aging

Hematopoietic stem cell (HSC) aging is a multifactorial event leading to changes in HSC properties and functions, which are intrinsically coordinated and affect the early hematopoiesis. To better understand the mechanisms and factors controlling these changes, we developed an original strategy to construct a Boolean model of HSC differentiation. Based on our previous scRNA-seq data, we exhaustively characterized active transcription modules or regulons along the differentiation trajectory and constructed an influence graph between 15 selected components involved in the dynamics of the process. Then we defined dynamical constraints between observed cellular states along the trajectory and using answer set programming with in silico perturbation analysis, we obtained a Boolean model explaining the early priming of HSCs. Finally, perturbations of the model based on age-related changes revealed important deregulations, such as the overactivation of Egr1 and Junb or the loss of Cebpa activation by Gata2. These new regulatory mechanisms were found to be relevant for the myeloid bias of aged HSC and explain the decreased transcriptional priming of HSCs to all mature cell types except megakaryocytes.

Murine foetal liver supports limited detectable expansion of life-long haematopoietic progenitors

Current dogma asserts that the foetal liver (FL) is an expansion niche for recently specified haematopoietic stem cells (HSCs) during ontogeny. Indeed, between embryonic day of development (E)12.5 and E14.5, the number of transplantable HSCs in the murine FL expands from 50 to about 1,000. Here we used a non-invasive, multi-colour lineage tracing strategy to interrogate the embryonic expansion of murine haematopoietic progenitors destined to contribute to the adult HSC pool. Our data show that this pool of fated progenitors expands only two-fold during FL ontogeny. Although Histone2B-GFP retention in vivo experiments confirmed substantial proliferation of phenotypic FL-HSC between E12.5 and E14.5, paired-daughter cell assays revealed that many mid-gestation phenotypic FL-HSCs are biased to differentiate, rather than self-renew, relative to phenotypic neonatal and adult bone marrow HSCs. In total, these data support a model in which the FL-HSC pool fated to contribute to adult blood expands only modestly during ontogeny.

Murine fetal bone marrow does not support functional hematopoietic stem and progenitor cells until birth

While adult bone marrow (BM) hematopoietic stem and progenitor cells (HSPCs) and their extrinsic regulation is well studied, little is known about the composition, function, and extrinsic regulation of the first HSPCs to enter the BM during development. Here, we functionally interrogate murine BM HSPCs from E15.5 through P0. Our work reveals that fetal BM HSPCs are present by E15.5, but distinct from the HSPC pool seen in fetal liver, both phenotypically and functionally, until near birth. We also generate a transcriptional atlas of perinatal BM HSPCs and the BM niche in mice across ontogeny, revealing that fetal BM lacks HSPCs with robust intrinsic stem cell programs, as well as niche cells supportive of HSPCs. In contrast, stem cell programs are preserved in neonatal BM HSPCs, which reside in a niche expressing HSC supportive factors distinct from those seen in adults. Collectively, our results provide important insights into the factors shaping hematopoiesis during this understudied window of hematopoietic development.

Relatively little is known about the first hematopoietic stem and progenitor cells to arrive in the fetal bone marrow. Here they characterize the frequency, function, and molecular identity of fetal BM HSPCs and their bone marrow niche, and show that most BM HSPCs have little hematopoietic function until birth.

Inhibition of mutant IDH1 promotes cycling of acute myeloid leukemia stem cells

Approximately 20% of acute myeloid leukemia (AML) patients carry mutations in IDH1 or IDH2 that result in over-production of the oncometabolite D-2-hydroxyglutarate (2-HG). Small molecule inhibitors that block 2-HG synthesis can induce complete morphological remission; however, almost all patients eventually acquire drug resistance and relapse. Using a multi-allelic mouse model of IDH1-mutant AML, we demonstrate that the clinical IDH1 inhibitor AG-120 (ivosidenib) exerts cell-type-dependent effects on leukemic cells, promoting delayed disease regression. Although single-agent AG-120 treatment does not fully eradicate the disease, it increases cycling of rare leukemia stem cells and triggers transcriptional upregulation of the pyrimidine salvage pathway. Accordingly, AG-120 sensitizes IDH1-mutant AML to azacitidine, with the combination of AG-120 and azacitidine showing vastly improved efficacy in vivo. Our data highlight the impact of non-genetic heterogeneity on treatment response and provide a mechanistic rationale for the observed combinatorial effect of AG-120 and azacitidine in patients.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Background

Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.

Objective

This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.

Methods

We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.

Results

Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.

Conclusions

The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

Embryonic Origins of the Hematopoietic System: Hierarchies and Heterogeneity

The hierarchical framework of the adult blood system as we know it from current medical and hematology textbooks, displays a linear branching network of dividing and differentiated cells essential for the growth and maintenance of the healthy organism. This view of the hierarchy has evolved over the last 75 years. An amazing increase in cellular complexity has been realized; however, innovative single-cell technologies continue to uncover essential cell types and functions in animal models and the human blood system. The most potent cell of the hematopoietic hierarchy is the hematopoietic stem cell. Stem cells for adult tissues are the long-lived self-renewing cellular component, which ensure that differentiated tissue-specific cells are maintained and replaced through the entire adult lifespan. Although much blood research is focused on hematopoietic tissue homeostasis, replacement and regeneration during adult life, embryological studies have widened and enriched our understanding of additional developmental hierarchies and interacting cells of this life-sustaining tissue. Here, we review the current state of knowledge of the hierarchical organization and the vast heterogeneity of the hematopoietic system from embryonic to adult stages.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Evaluating Pointwise Reliability of Machine Learning prediction

Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in many research papers, the increasing number of AI-based software products, and by the general interest in Artificial Intelligence to solve complex problems. It is therefore of importance to improve the quality of machine learning output and add safeguards to support their adoption. In addition to regulatory and logistical strategies, a crucial aspect is to detect when a Machine Learning model is not able to generalize to new unseen instances, which may originate from a population distant to that of the training population or from an under-represented subpopulation. As a result, the prediction of the machine learning model for these instances may be often wrong, given that the model is applied outside its "reliable" space of work, leading to a decreasing trust of the final users, such as clinicians. For this reason, when a model is deployed in practice, it would be important to advise users when the model's predictions may be unreliable, especially in high-stakes applications, including those in healthcare. Yet, reliability assessment of each machine learning prediction is still poorly addressed. Here, we review approaches that can support the identification of unreliable predictions, we harmonize the notation and terminology of relevant concepts, and we highlight and extend possible interrelationships and overlap among concepts. We then demonstrate, on simulated and real data for ICU in-hospital death prediction, a possible integrative framework for the identification of reliable and unreliable predictions. To do so, our proposed approach implements two complementary principles, namely the density principle and the local fit principle. The density principle verifies that the instance we want to evaluate is similar to the training set. The local fit principle verifies that the trained model performs well on training subsets that are more similar to the instance under evaluation. Our work can contribute to consolidating work in machine learning especially in medicine.

Identification of HSC/MPP expansion units in fetal liver by single-cell spatiotemporal transcriptomics

Limited knowledge of cellular and molecular mechanisms underlying hematopoietic stem cell and multipotent progenitor (HSC/MPP) expansion within their native niche has impeded the application of stem cell-based therapies for hematological malignancies. Here, we constructed a spatiotemporal transcriptome map of mouse fetal liver (FL) as a platform for hypothesis generation and subsequent experimental validation of novel regulatory mechanisms. Single-cell transcriptomics revealed three transcriptionally heterogeneous HSC/MPP subsets, among which a CD93-enriched subset exhibited enhanced stem cell properties. Moreover, by employing integrative analysis of single-cell and spatial transcriptomics, we identified novel HSC/MPP 'pocket-like' units (HSC PLUS), composed of niche cells (hepatoblasts, stromal cells, endothelial cells, and macrophages) and enriched with growth factors. Unexpectedly, macrophages showed an 11-fold enrichment in the HSC PLUS. Functionally, macrophage-HSC/MPP co-culture assay and candidate molecule testing, respectively, validated the supportive role of macrophages and growth factors (MDK, PTN, and IGFBP5) in HSC/MPP expansion. Finally, cross-species analysis and functional validation showed conserved cell-cell interactions and expansion mechanisms but divergent transcriptome signatures between mouse and human FL HSCs/MPPs. Taken together, these results provide an essential resource for understanding HSC/MPP development in FL, and novel insight into functional HSC/MPP expansion ex vivo.

De novo generation of macrophage from placenta-derived hemogenic endothelium

Macrophages play pivotal roles in immunity, hematopoiesis, and tissue homeostasis. In mammals, macrophages have been shown to originate from yolk-sac-derived erythro-myeloid progenitors and aorta-gonad-mesonephros (AGM)-derived hematopoietic stem cells. However, whether macrophages can arise from other embryonic sites remains unclear. Here, using single-cell RNA sequencing, we profile the transcriptional landscape of mouse fetal placental hematopoiesis. We uncover and experimentally validate that a CD44⁺ subpopulation of placental endothelial cells (ECs) exhibits hemogenic potential. Importantly, lineage tracing using the newly generated Hoxa13 reporter line shows that Hoxa13-labeled ECs can produce placental macrophages, named Hofbauer cell (HBC)-like cells. Furthermore, we identify two subtypes of HBC-like cells, and cell-cell interaction analysis identifies their potential roles in angiogenesis and antigen presentation, separately. Our study provides a comprehensive understanding of placental hematopoiesis and highlights the placenta as a source of macrophages, which has important implications for both basic and translational research.

Hematopoietic stem cells retain functional potential and molecular identity in hibernation cultures

Advances in the isolation and gene expression profiling of single hematopoietic stem cells (HSCs) have permitted in-depth resolution of their molecular program. However, long-term HSCs can only be isolated to near purity from adult mouse bone marrow, thereby precluding studies of their molecular program in different physiological states. Here, we describe a powerful 7-day HSC hibernation culture system that maintains HSCs as single cells in the absence of a physical niche. Single hibernating HSCs retain full functional potential compared with freshly isolated HSCs with respect to colony-forming capacity and transplantation into primary and secondary recipients. Comparison of hibernating HSC molecular profiles to their freshly isolated counterparts showed a striking degree of molecular similarity, further resolving the core molecular machinery of HSC self-renewal while also identifying key factors that are potentially dispensable for HSC function, including members of the AP1 complex (Jun, Fos, and Ncor2), Sult1a1 and Cish. Finally, we provide evidence that hibernating mouse HSCs can be transduced without compromising their self-renewal activity and demonstrate the applicability of hibernation cultures to human HSCs.

A comprehensive transcriptome signature of murine hematopoietic stem cell aging

We surveyed 16 published and unpublished data sets to determine whether a consistent pattern of transcriptional deregulation in aging murine hematopoietic stem cells (HSC) exists. Despite substantial heterogeneity between individual studies, we uncovered a core and robust HSC aging signature. We detected increased transcriptional activation in aged HSCs, further confirmed by chromatin accessibility analysis. Unexpectedly, using two independent computational approaches, we established that deregulated aging genes consist largely of membrane-associated transcripts, including many cell surface molecules previously not associated with HSC biology. We show that Selp, the most consistent deregulated gene, is not merely a marker for aged HSCs but is associated with HSC functional decline. Additionally, single-cell transcriptomics analysis revealed increased heterogeneity of the aged HSC pool. We identify the presence of transcriptionally "young-like" HSCs in aged bone marrow. We share our results as an online resource and demonstrate its utility by confirming that exposure to sympathomimetics, and deletion of Dnmt3a/b, molecularly resembles HSC rejuvenation or aging, respectively.

Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells

BACKGROUND

Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging, heterogeneity of HSCs changes, contributing to the deterioration of the immune system. In this study, we revisited mouse HSC compartment and its transcriptional plasticity during aging at unicellular scale.

RESULTS

Through the analysis of 15,000 young and aged transcriptomes, we identified 15 groups of HSCs revealing rare and new specific HSC abilities that change with age. The implantation of new trajectories complemented with the analysis of transcription factor activities pointed consecutive states of HSC differentiation that were delayed by aging and explained the bias in differentiation of older HSCs. Moreover, reassigning cell cycle phases for each HSC clearly highlighted an imbalance of the cell cycle regulators of very immature aged HSCs that may contribute to their accumulation in an undifferentiated state.

CONCLUSIONS

Our results establish a new reference map of HSC differentiation in young and aged mice and reveal a potential mechanism that delays the differentiation of aged HSCs and could promote the emergence of age-related hematologic diseases.

Two Aldehyde Clearance Systems Are Essential to Prevent Lethal Formaldehyde Accumulation in Mice and Humans

Reactive aldehydes arise as by-products of metabolism and are normally cleared by multiple families of enzymes. We find that mice lacking two aldehyde detoxifying enzymes, mitochondrial ALDH2 and cytoplasmic ADH5, have greatly shortened lifespans and develop leukemia. Hematopoiesis is disrupted profoundly, with a reduction of hematopoietic stem cells and common lymphoid progenitors causing a severely depleted acquired immune system. We show that formaldehyde is a common substrate of ALDH2 and ADH5 and establish methods to quantify elevated blood formaldehyde and formaldehyde-DNA adducts in tissues. Bone-marrow-derived progenitors actively engage DNA repair but also imprint a formaldehyde-driven mutation signature similar to aging-associated human cancer mutation signatures. Furthermore, we identify analogous genetic defects in children causing a previously uncharacterized inherited bone marrow failure and pre-leukemic syndrome. Endogenous formaldehyde clearance alone is therefore critical for hematopoiesis and in limiting mutagenesis in somatic tissues.

Manipulating niche composition limits damage to haematopoietic stem cells during Plasmodium infection

Severe infections are a major stress on haematopoiesis, where the consequences for haematopoietic stem cells (HSCs) have only recently started to emerge. HSC function critically depends on the integrity of complex bone marrow (BM) niches; however, what role the BM microenvironment plays in mediating the effects of infection on HSCs remains an open question. Here, using a murine model of malaria and combining single-cell RNA sequencing, mathematical modelling, transplantation assays and intravital microscopy, we show that haematopoiesis is reprogrammed upon infection, whereby the HSC compartment turns over substantially faster than at steady-state and HSC function is drastically affected. Interferon is found to affect both haematopoietic and mesenchymal BM cells and we specifically identify a dramatic loss of osteoblasts and alterations in endothelial cell function. Osteo-active parathyroid hormone treatment abolishes infection-triggered HSC proliferation and-coupled with reactive oxygen species quenching-enables partial rescuing of HSC function.

Advancing Stem Cell Research through Multimodal Single-Cell Analysis

Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.

Iterative Single-Cell Analyses Define the Transcriptome of the First Functional Hematopoietic Stem Cells

Whereas hundreds of cells in the mouse embryonic aorta transdifferentiate to hematopoietic cells, only very few establish hematopoietic stem cell (HSC) identity at a single time point. The Gata2 transcription factor is essential for HSC generation and function. In contrast to surface-marker-based cell isolation, Gata2-based enrichment provides a direct link to the internal HSC regulatory network. Here, we use iterations of index-sorting of Gata2-expressing intra-aortic hematopoietic cluster (IAHC) cells, single-cell transcriptomics, and functional analyses to connect HSC identity to specific gene expression. Gata2-expressing IAHC cells separate into 5 major transcriptomic clusters. Iterative analyses reveal refined CD31, cKit, and CD27 phenotypic parameters that associate specific molecular profiles in one cluster with distinct HSC and multipotent progenitor function. Thus, by iterations of single-cell approaches, we identify the transcriptome of the first functional HSCs as they emerge in the mouse embryo and localize them to aortic clusters containing 1-2 cells.