Haematopoietic stem cells (HSCs) differ from their committed progeny by relying primarily on anaerobic glycolysis rather than mitochondrial oxidative phosphorylation for energy production. However, whether this change in the metabolic program is the cause or the consequence of the unique function of HSCs remains unknown. Here we show that enforced modulation of energy metabolism impacts HSC self-renewal. Lowering the mitochondrial activity of HSCs by chemically uncoupling the electron transport chain drives self-renewal under culture conditions that normally induce rapid differentiation. We demonstrate that this metabolic specification of HSC fate occurs through the reversible decrease of mitochondrial mass by autophagy. Our data thus reveal a causal relationship between mitochondrial metabolism and fate choice of HSCs and also provide a valuable tool to expand HSCs outside of their native bone marrow niches.

We have integrated gene expression profiling with database and literature mining, mechanistic modeling, and cell culture experiments to identify intercellular and intracellular networks regulating blood stem cell self-renewal.Blood stem cell fate in vitro is regulated non-autonomously by a coupled positive–negative intercellular feedback circuit, composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9).The antagonistic signals converge in a core intracellular network focused around PI3K, Raf, PLC, and Akt.Model simulations enable functional classification of the novel endogenous ligands and signaling molecules.

Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans’ assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.

The in vitro expansion of long-term hematopoietic stem cells (HSCs) remains a substantial challenge, largely because of our limited understanding of the mechanisms that control HSC fate choices. Using single-cell multigene expression analysis and time-lapse microscopy, here we define gene expression signatures and cell cycle hallmarks of murine HSCs and the earliest multipotent progenitors (MPPs), and analyze systematically single HSC fate choices in culture. Our analysis revealed twelve differentially expressed genes marking the quiescent HSC state, including four genes encoding cell–cell interaction signals in the niche. Under basal culture conditions, most HSCs rapidly commit to become early MPPs. In contrast, when we present ligands of the identified niche components such as JamC or Esam within artificial niches, HSC cycling is reduced and long-term multipotency in vivo is maintained. Our approach to bioengineer artificial niches should be useful in other stem cell systems.