MassIVE MSV000097228

Partial Public

GNPS - Metabolic profile of human bone marrow hematopoietic stem and progenitor cells upon differentiation, aging and leukemia

Description

Metabolic cues are crucial for regulating hematopoietic stem and progenitor cells (HSPCs). However, the metabolic profile of human HSPCs remains poorly understood due to the limited number of cells and the scarcity of bone marrow samples. Here, we present the integrated metabolome, lipidome and transcriptome of human adult HSPCs (lineage-, CD34+, CD38-) upon differentiation, aging and acute myeloid leukemia (AML). The combination of low-input targeted metabolomics with our newly optimized low-input untargeted lipidomics workflow allows us to detect up to 219 metabolites and lipids from a starting material of 3,000 and 5,000 HSPCs, respectively. Among other findings, we observe elevated levels of the essential nutrient choline in HSPCs compared to downstream progenitors, which decline upon aging and further decrease in AML. Functionally, we show that choline supplementation fuels lipid production in HSPCs and enhances stemness. Overall, our study provides a comprehensive resource identifying metabolic changes that can be utilized to promote and enhance human stem cell function. [doi:10.25345/C5V11VZ2V] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: hematopoiesis ; stem cell ; leukemia ; aging ; DatasetType:Metabolomics ; DatasetType:Other (Lipidomics, 13C label tracing)

Contact

Principal Investigators:
(in alphabetical order)
Joerg Buescher, MPI Immunbiology and Epigenetics, Germany
Nina Cabezas-Wallscheid, MPI Immunbiology and Epigenetics, Germany
Submitting User: joergbuescher
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GNPS content goes here (MSV000097228 [task=b989df95d313469498f50d74d35250e6])
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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