Summary of Study ST002983

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001858. The data can be accessed directly via it's Project DOI: 10.21228/M8JM83 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002983
Study TitleDeciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution
Study SummaryMetabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations, and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.
Institute
Shanghai Jiao Tong University
Last NameCAO
First NameJING
Address1954 Huashan Road, Shanghai, Shanghai, 200030, China
Emailcaojing1@sjtu.edu.cn
Phone+8615201957271
Submit Date2023-11-21
Raw Data AvailableYes
Raw Data File Type(s).txt
Analysis Type DetailMALDI
Release Date2023-11-28
Release Version1
JING CAO JING CAO
https://dx.doi.org/10.21228/M8JM83
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001858
Project DOI:doi: 10.21228/M8JM83
Project Title:Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution
Project Type:Cell Metabolism
Project Summary:Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations, and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.
Institute:Shanghai Jiao Tong University
Last Name:CAO
First Name:JING
Address:1954 Huashan Road, Shanghai, Shanghai, 200030, China
Email:caojing1@sjtu.edu.cn
Phone:15201957271
Funding Source:This work was supported by the National Key R&D Program (2022YFE0103500, 2018YFA0107200, 2021YFF0703500, and 2022YFC2502800), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021ZD09, YG2022QN107, YG2023ZD08), Haihe Laboratory of Cell Ecosystem Innovation Fund (22HHXBSS00016), the National Natural Science Foundation of China (32270785, 31771637, 81730006, 81971771, 22074044, 22122404) and the CAS Youth Interdisciplinary Team (JCTD-2021-12). This work was also sponsored by Shanghai Municipal Science and Technology Major Project Fund, Shanghai Science and Technology Commission (22XD1424000, 22ZR1469000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Innovation Group Project of Shanghai Municipal Health Comission (2019CXJQ03), Innovation Research Plan of Shanghai Municipal Education Commission (ZXWF082101), National Research Center for Translational Medicine Shanghai (TMSK-2021-124, NRCTM(SH)-2021-06), and Fundamental Research Funds for the Central Universities.

Subject:

Subject ID:SU003096
Subject Type:Mammal
Subject Species:Mus musculus
Taxonomy ID:10090
Species Group:Mammals

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id local_sample_id Celltype
SA323635HSCa_27HSCa
SA323636HSCa_28HSCa
SA323637HSCa_29HSCa
SA323638HSCa_26HSCa
SA323639HSCa_24HSCa
SA323640HSCa_22HSCa
SA323641HSCa_23HSCa
SA323642HSCa_31HSCa
SA323643HSCa_25HSCa
SA323644HSCa_33HSCa
SA323645HSCa_38HSCa
SA323646HSCa_39HSCa
SA323647HSCa_1HSCa
SA323648HSCa_37HSCa
SA323649HSCa_36HSCa
SA323650HSCa_21HSCa
SA323651HSCa_34HSCa
SA323652HSCa_35HSCa
SA323653HSCa_32HSCa
SA323654HSCa_30HSCa
SA323655HSCa_7HSCa
SA323656HSCa_8HSCa
SA323657HSCa_9HSCa
SA323658HSCa_10HSCa
SA323659HSCa_6HSCa
SA323660HSCa_5HSCa
SA323661HSCa_2HSCa
SA323662HSCa_20HSCa
SA323663HSCa_4HSCa
SA323664HSCa_11HSCa
SA323665HSCa_3HSCa
SA323666HSCa_17HSCa
SA323667HSCa_18HSCa
SA323668HSCa_12HSCa
SA323669HSCa_16HSCa
SA323670HSCa_19HSCa
SA323671HSCa_13HSCa
SA323672HSCa_14HSCa
SA323673HSCa_15HSCa
SA323674HSCb_28HSCb
SA323675HSCb_30HSCb
SA323676HSCb_23HSCb
SA323677HSCb_29HSCb
SA323678HSCb_25HSCb
SA323679HSCb_31HSCb
SA323680HSCb_24HSCb
SA323681HSCb_26HSCb
SA323682HSCb_27HSCb
SA323683HSCb_36HSCb
SA323684HSCb_39HSCb
SA323685HSCb_40HSCb
SA323686HSCb_22HSCb
SA323687HSCb_38HSCb
SA323688HSCb_37HSCb
SA323689HSCb_33HSCb
SA323690HSCb_34HSCb
SA323691HSCb_35HSCb
SA323692HSCb_32HSCb
SA323693HSCb_11HSCb
SA323694HSCb_6HSCb
SA323695HSCb_7HSCb
SA323696HSCb_8HSCb
SA323697HSCb_9HSCb
SA323698HSCb_5HSCb
SA323699HSCb_4HSCb
SA323700HSCb_1HSCb
SA323701HSCb_21HSCb
SA323702HSCb_3HSCb
SA323703HSCb_10HSCb
SA323704HSCb_2HSCb
SA323705HSCb_17HSCb
SA323706HSCb_18HSCb
SA323707HSCb_20HSCb
SA323708HSCb_16HSCb
SA323709HSCb_19HSCb
SA323710HSCb_12HSCb
SA323711HSCb_15HSCb
SA323712HSCb_13HSCb
SA323713HSCb_14HSCb
SA323714HSCc_27HSCc
SA323715HSCc_29HSCc
SA323716HSCc_26HSCc
SA323717HSCc_28HSCc
SA323718HSCc_23HSCc
SA323719HSCc_30HSCc
SA323720HSCc_22HSCc
SA323721HSCc_24HSCc
SA323722HSCc_25HSCc
SA323723HSCc_36HSCc
SA323724HSCc_37HSCc
SA323725HSCc_38HSCc
SA323726HSCc_39HSCc
SA323727HSCc_21HSCc
SA323728HSCc_35HSCc
SA323729HSCc_32HSCc
SA323730HSCc_33HSCc
SA323731HSCc_34HSCc
SA323732HSCc_31HSCc
SA323733HSCc_2HSCc
SA323734HSCc_6HSCc
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Collection:

Collection ID:CO003089
Collection Summary:Bones were rapidly dissected and stored on ice in Ca2+- and Mg2+-free HBSS (ThermoFisher) plus 2% heat-inactivated fetal bovine serum (Gibco). Bone marrow cells were flushed out from the bone and then dissociated to single-cell suspension by gently passing through the needle then filtering through a 70-μm nylon mesh. The following antibodies were used to isolate hematopoietic cells: TER-119-FITC, CD3-FITC, CD5-FITC, CD8-FITC, B220-FITC, Gr-1-FITC, TER-119-APC780, CD3e-APC780, CD5-APC780, CD8-APC780, B220-APC780, Gr-1-APC780, TER-119-biotin, CD3-biotin, B220-biotin, Gr-1-biotin, c-kit-biotin, TER-119-PerCP/Cy5.5, Sca-1-PerCP/Cy5.5, MAC-1-APC, CD48-APC, CD16/32-APC, CD135-APC, CD127-PE, CD34-PE and CD3-PE, CD150-PE. FITC streptavidin, APC/Cy7 streptavidin and/or APC-R700 streptavidin were used for biotin-labeled antibodies. All reagents were acquired from BD Biosciences, eBiosciences or BioLegend. For isolation of HSPCs, cells were incubated with c-kit-biotin and paramagnetic microbeads, and then passed through an autoMACS magnetic separator. For isolation of CLPs, lineage was stained with Ter119-biotin, CD3-biotin, B220-biotin and Gr-1-biotin and paramagnetic microbeads. Then an autoMACS magnetic separator was used to enrich lineage- populations. To minimize metabolic changes, antibody-stained cells were fixed with 2% PFA (Sigma) for 15 minutes on ice. Flow cytometric analysis was performed with a BD LSRFortessa cytometer. To measure ROS levels of HSCs, antibody-stained cells were incubated with 5μm DCFDA for 15 minutes at 37°C before flow cytometric analysis. For metabolic detection, cells were sorted with a FACSAria SORP cytometer into 384-well plates containing 2.5-μl 80% methanol (Sigma) per well, and then centrifuged at 1500g for 5 mins at 4°C. Plates were sunk in liquid nitrogen for 10 mins to lyse cells and kept at -80°C before MS analysis.
Sample Type:Stem cells

Treatment:

Treatment ID:TR003105
Treatment Summary:The following antibodies were used to isolate hematopoietic cells: TER-119-FITC, CD3-FITC, CD5-FITC, CD8-FITC, B220-FITC, Gr-1-FITC, TER-119-APC780, CD3e-APC780, CD5-APC780, CD8-APC780, B220-APC780, Gr-1-APC780, TER-119-biotin, CD3-biotin, B220-biotin, Gr-1-biotin, c-kit-biotin, TER-119-PerCP/Cy5.5, Sca-1-PerCP/Cy5.5, MAC-1-APC, CD48-APC, CD16/32-APC, CD135-APC, CD127-PE, CD34-PE and CD3-PE, CD150-PE. FITC streptavidin, APC/Cy7 streptavidin and/or APC-R700 streptavidin were used for biotin-labeled antibodies. All reagents were acquired from BD Biosciences, eBiosciences or BioLegend. For isolation of HSPCs, cells were incubated with c-kit-biotin and paramagnetic microbeads, and then passed through an autoMACS magnetic separator. For isolation of CLPs, lineage was stained with Ter119-biotin, CD3-biotin, B220-biotin and Gr-1-biotin and paramagnetic microbeads. Then an autoMACS magnetic separator was used to enrich lineage- populations.

Sample Preparation:

Sampleprep ID:SP003102
Sampleprep Summary:For metabolic detection, cells were sorted with a FACSAria SORP cytometer into 384-well plates containing 2.5-μl 80% methanol (Sigma) per well, and then centrifuged at 1500g for 5 mins at 4°C. Plates were sunk in liquid nitrogen for 10 mins to lyse cells and kept at -80°C before MS analysis.

Combined analysis:

Analysis ID AN004902
Analysis type MS
Chromatography type None (Direct infusion)
Chromatography system none
Column none
MS Type MALDI
MS instrument type MALDI-TOF-MS
MS instrument name Bruker Autoflex MALDI-TOF (/TOF)-MS
Ion Mode POSITIVE
Units intensity

Chromatography:

Chromatography ID:CH003697
Chromatography Summary:None
Instrument Name:none
Column Name:none
Column Temperature:none
Flow Gradient:none
Flow Rate:none
Solvent A:none
Solvent B:none
Chromatography Type:None (Direct infusion)

MS:

MS ID:MS004645
Analysis ID:AN004902
Instrument Name:Bruker Autoflex MALDI-TOF (/TOF)-MS
Instrument Type:MALDI-TOF-MS
MS Type:MALDI
MS Comments:MS acquisition Comments: MS experiments were conducted on the Autoflex MALDI-TOF (/TOF)-MS (Bruker Autoflex Speed) with Nd:YAG lasers (355 nm) and smart beam system. All MS experiments were performed in the reflector positive ion mode, with 2000 laser shots per analysis and 5 independent experiments per sample. Data processing Comments:MS data preprocessing was carried out on python version 3.8, including peak detection, peak filtration, standardization, and batch effect removal by ComBat algorithm. The heatmap, hierarchical clustering analysis, Pearson correlation analysis and PCA analysis were performed on MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). Machine learning and t-SNE clustering were conducted on Orange (version 3.25.0, the Bioinformatics Lab at University of Ljubljana, Slovenia). Software/procedures used for feature assignments: Python
Ion Mode:POSITIVE
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