Ex vivo expansion potential of murine hematopoietic stem cells is a rare property only partially predicted by phenotype.

The scarcity of hematopoietic stem cells (HSCs) restricts their use in both clinical settings and experimental research. Here, we examined a recently developed method for expanding rigorously purified murine HSCs ex vivo. After 3 weeks of culture, only 0.1% of cells exhibited the input HSC phenotype, but these accounted for almost all functional long-term HSC activity. Input HSCs displayed varying potential for ex vivo self-renewal, with alternative outcomes revealed by single-cell multimodal RNA and ATAC sequencing profiling. While most HSC progeny offered only transient in vivo reconstitution, these cells efficiently rescued mice from lethal myeloablation. The amplification of functional HSC activity allowed for long-term multilineage engraftment in unconditioned hosts that associated with a return of HSCs to quiescence. Thereby, our findings identify several key considerations for ex vivo HSC expansion, with major implications also for assessment of normal HSC activity.

Implications of stress-induced gene expression for hematopoietic stem cell aging studies.

A decline in hematopoietic stem cell (HSC) function is believed to underlie hematological shortcomings with age; however, a comprehensive molecular understanding of these changes is currently lacking. Here we provide evidence that a transcriptional signature reported in several previous studies on HSC aging is linked to stress-induced changes in gene expression rather than aging. Our findings have strong implications for the design and interpretation of HSC aging studies.

Single-cell multiomics of human fetal hematopoiesis define a developmental-specific population and a fetal signature.

Knowledge of human fetal blood development and how it differs from adult blood is highly relevant to our understanding of congenital blood and immune disorders and childhood leukemia, of which the latter can originate in utero. Blood formation occurs in waves that overlap in time and space, adding to heterogeneity, which necessitates single-cell approaches. Here, a combined single-cell immunophenotypic and transcriptional map of first trimester primitive blood development is presented. Using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), the molecular profile of established immunophenotype-gated progenitors was analyzed in the fetal liver (FL). Classical markers for hematopoietic stem cells (HSCs), such as CD90 and CD49F, were largely preserved, whereas CD135 (FLT3) and CD123 (IL3R) had a ubiquitous expression pattern capturing heterogenous populations. Direct molecular comparison with an adult bone marrow data set revealed that the HSC state was less frequent in FL, whereas cells with a lymphomyeloid signature were more abundant. An erythromyeloid-primed multipotent progenitor cluster was identified, potentially representing a transient, fetal-specific population. Furthermore, differentially expressed genes between fetal and adult counterparts were specifically analyzed, and a fetal core signature was identified. The core gene set could separate subgroups of acute lymphoblastic leukemia by age, suggesting that a fetal program may be partially retained in specific subgroups of pediatric leukemia. Our detailed single-cell map presented herein emphasizes molecular and immunophenotypic differences between fetal and adult blood cells, which are of significance for future studies of pediatric leukemia and blood development in general.

Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.

As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf .