Summary of Study ST002986

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 ID	ST002986
Study Title	Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution: Study 2
Study 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	+8615201957271
Submit Date	2023-11-21
Raw Data Available	Yes
Raw Data File Type(s)	.txt
Analysis Type Detail	MALDI
Release Date	2023-11-29
Release Version	1

Select appropriate tab below to view additional metadata details:

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:	SU003099
Subject Type:	Mammal
Subject Species:	Mus musculus
Taxonomy ID:	10090

Factors:

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

mb_sample_id	local_sample_id	Celltype
SA323990	B_95	B
SA323991	B_96	B
SA323992	B_97	B
SA323993	B_94	B
SA323994	B_93	B
SA323995	B_91	B
SA323996	B_92	B
SA323997	B_98	B
SA323998	B_99	B
SA323999	B_104	B
SA324000	B_105	B
SA324001	B_103	B
SA324002	B_102	B
SA324003	B_100	B
SA324004	B_101	B
SA324005	B_90	B
SA324006	B_89	B
SA324007	B_79	B
SA324008	B_80	B
SA324009	B_78	B
SA324010	B_77	B
SA324011	B_75	B
SA324012	B_76	B
SA324013	B_81	B
SA324014	B_82	B
SA324015	B_87	B
SA324016	B_88	B
SA324017	B_86	B
SA324018	B_85	B
SA324019	B_83	B
SA324020	B_84	B
SA324021	B_106	B
SA324022	B_107	B
SA324023	B_128	B
SA324024	B_129	B
SA324025	B_127	B
SA324026	B_126	B
SA324027	B_124	B
SA324028	B_125	B
SA324029	B_130	B
SA324030	B_131	B
SA324031	B_136	B
SA324032	B_137	B
SA324033	B_135	B
SA324034	B_134	B
SA324035	B_132	B
SA324036	B_133	B
SA324037	B_123	B
SA324038	B_122	B
SA324039	B_112	B
SA324040	B_113	B
SA324041	B_111	B
SA324042	B_110	B
SA324043	B_108	B
SA324044	B_109	B
SA324045	B_114	B
SA324046	B_115	B
SA324047	B_120	B
SA324048	B_121	B
SA324049	B_119	B
SA324050	B_118	B
SA324051	B_116	B
SA324052	B_117	B
SA324053	B_74	B
SA324054	B_72	B
SA324055	B_30	B
SA324056	B_31	B
SA324057	B_32	B
SA324058	B_29	B
SA324059	B_28	B
SA324060	B_26	B
SA324061	B_27	B
SA324062	B_33	B
SA324063	B_34	B
SA324064	B_39	B
SA324065	B_40	B
SA324066	B_38	B
SA324067	B_37	B
SA324068	B_35	B
SA324069	B_36	B
SA324070	B_25	B
SA324071	B_24	B
SA324072	B_14	B
SA324073	B_15	B
SA324074	B_13	B
SA324075	B_12	B
SA324076	B_10	B
SA324077	B_11	B
SA324078	B_16	B
SA324079	B_17	B
SA324080	B_22	B
SA324081	B_23	B
SA324082	B_21	B
SA324083	B_20	B
SA324084	B_18	B
SA324085	B_19	B
SA324086	B_41	B
SA324087	B_42	B
SA324088	B_63	B
SA324089	B_64	B

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Collection:

Collection ID:	CO003092
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:	TR003108
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.

Treatment ID:

TR003108

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:	SP003105
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	AN004906
Analysis type	MS
Chromatography type
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:	CH003701
Chromatography Summary:	None
Instrument Name:	none
Column Name:	none
Column Temperature:	none
Flow Gradient:	none
Flow Rate:	none
Solvent A:	none
Solvent B:	none

MS:

MS ID:	MS004649
Analysis ID:	AN004906
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