Differentiation tracing identifies hematopoietic regeneration from multipotent progenitors but not stem cells.

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) generate the immune system in development, and contribute to its maintenance under steady-state conditions. How stem and progenitor cells respond to increased demand for mature cells upon injury is a fundamental question of stem cell biology. Several studies of murine hematopoiesis have reported increased proliferation of HSCs in situ when exposed to inflammatory stimuli, which has been taken as a proxy for increased HSC differentiation. Such surplus generation of HSC may fuel enhanced HSC differentiation or, alternatively, maintain HSC cellularity in the face of increased cell death without enhanced HSC differentiation. This key question calls for direct measurements of HSC differentiation in their natural niches in vivo. Here, we review work that quantifies native HSC differentiation by fate mapping and mathematical inference. Recent differentiation tracing studies show that HSC do not increase their differentiation rate upon a wide range of challenges, including systemic bacterial infection (sepsis), blood loss, and transient or persistent ablation of specific mature immune cells. By contrast, MPPs differentiate more rapidly in response to systemic infection to accelerate the production of myeloid cells. These new in vivo data identify MPPs as a major source of hematopoietic regeneration; HSCs might not contribute to regeneration while remaining protected.

Flt3- and Tie2-Cre tracing identifies regeneration in sepsis from multipotent progenitors but not hematopoietic stem cells.

In response to infections and stress, hematopoiesis rapidly enhances blood and immune cell production. The stage within the hematopoietic hierarchy that accounts for this regeneration is unclear under natural conditions in vivo. We analyzed by differentiation tracing, using inducible Tie2- or Flt3-driven Cre recombinase, the roles of mouse hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs). During polymicrobial sepsis, HSCs responded transcriptionally and increased their proliferation and cell death, yet HSC differentiation rates remained at steady-state levels. HSC differentiation was also independent from the ablation of various cellular compartments-bleeding, the antibody-mediated ablation of granulocytes or B lymphocytes, and genetic lymphocyte deficiency. By marked contrast, the fate mapping of MPPs in polymicrobial sepsis identified these cells as a major source for accelerated myeloid cell production. The regulation of blood and immune cell homeostasis by progenitors rather than stem cells may ensure a rapid response while preserving the integrity of the HSC population.

CycleFlow simultaneously quantifies cell-cycle phase lengths and quiescence in vivo.

Populations of stem, progenitor, or cancer cells show proliferative heterogeneity in vivo, comprising proliferating and quiescent cells. Consistent quantification of the quiescent subpopulation and progression of the proliferating cells through the individual phases of the cell cycle has not been achieved. Here, we describe CycleFlow, a method that robustly infers this comprehensive information from standard pulse-chase experiments with thymidine analogs. Inference is based on a mathematical model of the cell cycle, with realistic waiting time distributions for the G1, S, and G2/M phases and a long-term quiescent G0 state. We validate CycleFlow with an exponentially growing cancer cell line in vitro. Applying it to T cell progenitors in steady state in vivo, we uncover strong proliferative heterogeneity, with a minority of CD4+CD8+ T cell progenitors cycling very rapidly and then entering quiescence. CycleFlow is suitable as a routine method for quantitative cell-cycle analysis.

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.

Resolving Fates and Single-Cell Transcriptomes of Hematopoietic Stem Cell Clones by PolyloxExpress Barcoding.

Lineage tracing reveals hematopoietic stem cell (HSC) fates, while single-cell RNA sequencing identifies snapshots of HSC transcriptomes. To obtain information on fate plus transcriptome in the same cell, we developed the PolyloxExpress allele, enabling Cre-recombinase-dependent RNA barcoding in situ. Linking fates to single HSC transcriptomes provided the information required to identify transcriptional signatures of HSC fates, which were not apparent in single-HSC transcriptomes alone. We find that differentiation-inactive, multilineage, and lineage-restricted HSC clones reside in distinct regions of the transcriptional landscape of hematopoiesis. Differentiation-inactive HSC clones are closer to the origin of the transcriptional trajectory, yet they are not characterized by a quiescent gene signature. Fate-specific gene signatures imply coherence of clonal HSC fates, and HSC output toward short-lived lineage progenitors indicates stability of HSC fates over time. These combined analyses of fate and transcriptome under physiological conditions may pave the way toward identifying molecular determinants of HSC fates.