A hallmark of adult hematopoiesis is the continuous replacement of blood cells with limited lifespans. While active hematopoietic stem cell (HSC) contribution to multilineage hematopoiesis is the foundation of clinical HSC transplantation, recent reports have questioned the physiological contribution of HSCs to normal/steady-state adult hematopoiesis. Here, we use inducible lineage tracing from genetically marked adult HSCs and reveal robust HSC-derived multilineage hematopoiesis. This commences via defined progenitor cells, but varies substantially in between different hematopoietic lineages. By contrast, adult HSC contribution to hematopoietic cells with proposed fetal origins is neglible. Finally, we establish that the HSC contribution to multilineage hematopoiesis declines with increasing age. Therefore, while HSCs are active contributors to native adult hematopoiesis, it appears that the numerical increase of HSCs is a physiologically relevant compensatory mechanism to account for their reduced differentiation capacity with age.

The advent of single cell (Sc) genomics has challenged the dogma of haematopoiesis as a tree-like structure of stepwise lineage commitment through distinct and increasingly restricted progenitor populations. Instead, analysis of ScRNA-seq has proposed that the earliest events in human hematopoietic stem cell (HSC) differentiation are characterized by only subtle molecular changes, with hematopoietic stem and progenitor cells (HSPCs) existing as a continuum of low-primed cell-states that gradually transition into a specific lineage (CLOUD-HSPCs). Here, we combine ScRNA-seq, ScATAC-seq and cell surface proteomics to dissect the heterogeneity of CLOUD-HSPCs at different stages of human life. Within CLOUD-HSPCs, pseudotime ordering of both mRNA and chromatin data revealed a bifurcation of megakaryocyte/erythroid and lympho/myeloid trajectories immediately downstream a subpopulation with an HSC-specific enhancer signature. Importantly, both HSCs and lineage-restricted progenitor populations could be prospectively isolated based on correlation of their molecular signatures with CD35 and CD11A expression, respectively. Moreover, we describe the changes that occur in this heterogeneity as hematopoiesis develops from neonatal to aged bone marrow, including an increase of HSCs and depletion of lympho-myeloid biased MPPs. Thus, this study dissects the heterogeneity of human CLOUD-HSPCs revealing distinct HSPC-states of relevance in homeostatic settings such as ageing.

Ageing associates with significant alterations in somatic/adult stem cells and therapies to counteract these might have profound benefits for health. In the blood, haematopoietic stem cell (HSC) ageing is linked to several functional shortcomings. However, besides the recent realization that individual HSCs might be preset differentially already from young age, HSCs might also age asynchronously. Evaluating the prospects for HSC rejuvenation therefore ultimately requires approaching those HSCs that are functionally affected by age. Here we combine genetic barcoding of aged murine HSCs with the generation of induced pluripotent stem (iPS) cells. This allows us to specifically focus on aged HSCs presenting with a pronounced lineage skewing, a hallmark of HSC ageing. Functional and molecular evaluations reveal haematopoiesis from these iPS clones to be indistinguishable from that associating with young mice. Our data thereby provide direct support to the notion that several key functional attributes of HSC ageing can be reversed.

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.

BackgroundSingle cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.ResultsWe present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.ConclusionsSCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.