Summary of Study ST002085

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 PR001324. The data can be accessed directly via it's Project DOI: 10.21228/M8K989 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 IDST002085
Study TitleA genome-scale gain-of-function CRISPR screen in CD8 T cells identifies proline metabolism as a means to enhance CAR-T therapy(Part 1)
Study SummaryChimeric antigen receptor (CAR)-T cell-based immunotherapy for cancer and immunological diseases has made great strides, but it still faces multiple hurdles. Finding the right molecular targets to engineer T cells toward a desired function has broad implications for the armamentarium of T cell-centered therapies. Here, we developed a dead-guide RNA (dgRNA)-based CRISPR activation screen in primary CD8+ T cells, and identified gain-of-function (GOF) targets for CAR-T engineering. Targeted knock-in or overexpression of a lead target, PRODH2, enhanced CAR-T-based killing and in vivo efficacy in multiple cancer models. Transcriptomics and metabolomics in CAR-T cells revealed that augmenting PRODH2 expression re-shaped broad and distinct gene expression and metabolic programs. Mitochondrial, metabolic and immunological analyses showed that PRODH2 engineering enhances the metabolic and immune functions of CAR-T cells against cancer. Together these findings provide a system for identification of GOF immune boosters, and demonstrate PRODH2 as a target to enhance CAR-T efficacy.
Institute
Yale University
Last NameYe
First NameLupeng
Address850 West campus drive
Emaillupeng.ye@yale.edu
Phone2035436568
Submit Date2022-02-12
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2022-02-28
Release Version1
Lupeng Ye Lupeng Ye
https://dx.doi.org/10.21228/M8K989
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001324
Project DOI:doi: 10.21228/M8K989
Project Title:Proline metabolism in T cells
Project Summary:T cell metabolites detection after PRODH2-CD22-CAR knock-in.
Institute:Yale University
Last Name:Ye
First Name:Lupeng
Address:850 West campus drive
Email:lupeng.ye@yale.edu
Phone:2035436568

Subject:

Subject ID:SU002169
Subject Type:Cultured cells
Subject Species:Homo sapiens
Taxonomy ID:9606

Factors:

Subject type: Cultured cells; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Genotype Treatment
SA19831020181221_hProdh2_OE_1_M62PRODH2_OE PRODH2
SA19831120181221_hProdh2_OE_5_M62PRODH2_OE PRODH2
SA19831220181221_hProdh2_OE_4_M62PRODH2_OE PRODH2
SA19831320181221_hProdh2_OE_2_M62PRODH2_OE PRODH2
SA19831420181221_hProdh2_OE_3_M62PRODH2_OE PRODH2
SA19831520181221_hProdh2_OE_Vector5_M62Vector_OE Control
SA19831620181221_hProdh2_OE_Vector4_M62Vector_OE Control
SA19831720181221_hProdh2_OE_Vector1_M62Vector_OE Control
SA19831820181221_hProdh2_OE_Vector2_M62Vector_OE Control
SA19831920181221_hProdh2_OE_Vector3_M62Vector_OE Control
Showing results 1 to 10 of 10

Collection:

Collection ID:CO002162
Collection Summary:cell were counted, washed twice with PBS then used for metabolite extraction.
Sample Type:T-cells

Treatment:

Treatment ID:TR002181
Treatment Summary:T cells were transduced with lentivirus, then collected for metabolite extraction.

Sample Preparation:

Sampleprep ID:SP002175
Sampleprep Summary:After normalizing cell counts, 800 μL of 80 % (vol / vol) HPLC-grade methanol (Sigma) (precooled to -80 oC on dry ice) was added to fresh cells in a 1.5-mL microcentrifuge tube, then tubes were put on dry ice for 30 minutes. The tubes were then incubated on ice for 20 minutes and centrifuged at 15,000 x g for 15 min at 4 oC to pellet the cell debris. The metabolite-containing supernatant was transferred to a new 1.5-mL microcentrifuge tube on dry ice. Metabolite extraction was repeated with 400 μL of 80 % (vol / vol) HPLC-grade methanol. The cell lysate / methanol mixtures were dried by Speedvac at room temperature. The metabolites were dissolved again with 80 % (vol / vol) methanol, then centrifuged at 18,000 x g for 10 min to remove any particulates, and the metabolite mixtures were stored at -80 oC until LC-MS analysis.

Combined analysis:

Analysis ID AN003402 AN003403
Analysis type MS MS
Chromatography type HILIC HILIC
Chromatography system Agilent 6490 Agilent 6550
Column Phenomenex Kinetex C18 (150 x 2.1mm,2.6um) Phenomenex Kinetex C18 (150 x 2.1mm,2.6um)
MS Type ESI ESI
MS instrument type Triple quadrupole QTOF
MS instrument name Agilent 6490 QQQ Agilent 6550 QTOF
Ion Mode POSITIVE POSITIVE
Units ppm ppm

Chromatography:

Chromatography ID:CH002515
Instrument Name:Agilent 6490
Column Name:Phenomenex Kinetex C18 (150 x 2.1mm,2.6um)
Chromatography Type:HILIC
  
Chromatography ID:CH002516
Instrument Name:Agilent 6550
Column Name:Phenomenex Kinetex C18 (150 x 2.1mm,2.6um)
Chromatography Type:HILIC

MS:

MS ID:MS003169
Analysis ID:AN003402
Instrument Name:Agilent 6490 QQQ
Instrument Type:Triple quadrupole
MS Type:ESI
MS Comments:Two metabolomics strategies were adopted, i.e. untargeted metabolomics (aiming to unbiasedly detect all detectable metabolites) and targeted approaches (aiming to detect specifically defined metabolites, such as related metabolites in the proline metabolism and T cell metabolism). For untargeted metabolomics analysis, the optimized workflow consists of automated peak detection and integration, peak alignment, background noise subtraction, and multivariate data analysis. These steps were carried out for comprehensive metabolite phenotyping of the two groups using Agilent Mass Hunter Qualitative Analysis Software (Version B.07.0.0, build 7.0.7024.0) and Agilent Mass Profiler Professional (Version 14.5-Build 2772). The metabolites were first putatively identified based on accurate mass match (accurate mass ± 30 ppm error) and fragmentation pattern match. Putative structural annotation was carried out by searching the metabolite databases HMDB (http://www.hmdb.ca/) and METLIN (http:// metlin.scripps.edu) using the mass-to-charge ratio of the metabolic features.
Ion Mode:POSITIVE
  
MS ID:MS003170
Analysis ID:AN003403
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Two metabolomics strategies were adopted, i.e. untargeted metabolomics (aiming to unbiasedly detect all detectable metabolites) and targeted approaches (aiming to detect specifically defined metabolites, such as related metabolites in the proline metabolism and T cell metabolism). For untargeted metabolomics analysis, the optimized workflow consists of automated peak detection and integration, peak alignment, background noise subtraction, and multivariate data analysis. These steps were carried out for comprehensive metabolite phenotyping of the two groups using Agilent Mass Hunter Qualitative Analysis Software (Version B.07.0.0, build 7.0.7024.0) and Agilent Mass Profiler Professional (Version 14.5-Build 2772). The metabolites were first putatively identified based on accurate mass match (accurate mass ± 30 ppm error) and fragmentation pattern match. Putative structural annotation was carried out by searching the metabolite databases HMDB (http://www.hmdb.ca/) and METLIN (http:// metlin.scripps.edu) using the mass-to-charge ratio of the metabolic features.
Ion Mode:POSITIVE
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