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Metabolic programs of T cell tissue residency empower tumour immunity

Nature volume 621, pages 179–187 (2023)Cite this article

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Abstract

Tissue resident memory CD8+ T (TRM) cells offer rapid and long-term protection at sites of reinfection1. Tumour-infiltrating lymphocytes with characteristics of TRM cells maintain enhanced effector functions, predict responses to immunotherapy and accompany better prognoses2,3. Thus, an improved understanding of the metabolic strategies that enable tissue residency by T cells could inform new approaches to empower immune responses in tissues and solid tumours. Here, to systematically define the basis for the metabolic reprogramming supporting TRM cell differentiation, survival and function, we leveraged in vivo functional genomics, untargeted metabolomics and transcriptomics of virus-specific memory CD8+ T cell populations. We found that memory CD8+ T cells deployed a range of adaptations to tissue residency, including reliance on non-steroidal products of the mevalonate–cholesterol pathway, such as coenzyme Q, driven by increased activity of the transcription factor SREBP2. This metabolic adaptation was most pronounced in the small intestine, where TRM cells interface with dietary cholesterol and maintain a heightened state of activation4, and was shared by functional tumour-infiltrating lymphocytes in diverse tumour types in mice and humans. Enforcing synthesis of coenzyme Q through deletion of Fdft1 or overexpression of PDSS2 promoted mitochondrial respiration, memory T cell formation following viral infection and enhanced antitumour immunity. In sum, through a systematic exploration of TRM cell metabolism, we reveal how these programs can be leveraged to fuel memory CD8+ T cell formation in the context of acute infections and enhance antitumour immunity.

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Fig. 1: Functional genetics in vivo, metabolomics and transcriptional analysis of ex vivo populations of memory CD8+ T cells identify a graded upregulation of SREBP2-dependent metabolic programming across TRM cells.
Fig. 2: An SREBP2-dependent metabolic program controls SI TRM formation.
Fig. 3: Non-steroidal products of the mevalonate–cholesterol synthesis pathway mediate TRM cell adaptations.
Fig. 4: Pdss2 expression is required and sufficient to promote mitochondrial respiration and CoQ synthesis by CD8+ T cells.
Fig. 5: Increased production of non-steroidal products of the mevalonate–cholesterol synthesis pathway is a common requirement for SI TRM cells and TILs.

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Data availability

Bulk RNA-seq and single-cell RNA-seq data have been deposited to the Gene Expression Omnibus under the reference series GSE207044. In addition, the following published datasets were used: GSE107278 (ref. 17), GSE106107 (ref. 17), GSE182276 (ref. 24), GSE70813 (ref. 64), GSE131847 (ref. 65), GSE125527 (ref. 66), GSE137015 (ref. 28), GSE70813 (ref. 64) and GSE157072 (ref. 67). Source data are provided with this paper.

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Acknowledgements

We thank M. Pipkin for sharing the shSrebf2 plasmid, A. Reboldi for sharing the nSREBP2-OE plasmid, S. Crotty and K. Faliti for sharing the LsgA vector backbone, and G. Garcia and M. Sevilla for technical histological assistance. We also thank the Center for Advanced Technology at UCSF for sequencing support, L. Labarta-Bajo and the Goldrath laboratory members for technical advice, helpful discussion and critical reading of the manuscript, and the Immunological Genome Project (ImmGen) for reagents and sample and data processing. We thank A. M. Pinto and the Mass Spectrometry Core of the Salk Institute (supported by NIH-NCI CCSG: P30 014195 and the Helmsley Center for Genomic Medicine). The MS data described here were gathered on a Thermo Fisher Q Exactive hybrid quadrupole–Orbitrap mass spectrometer (supported by 1S10OD021815-01). Funding is as follows: National Institutes of Health (NIH) grants R01 AI067545, P01 AI132122, R01 AI150282 and R01 AI072117 (A.W.G.); NIH grant R01 CA197363 (M.F.K.); NIH grants P01 HL146358 and R01 HL157710 (S.J.B.); Cancer Research Institute Postdoctoral Fellowship CRI2943 (M.R.-C.); Cancer Research Institute Postdoctoral Fellowship CRI4145 (G.G.); HHMI fellowship GT14887 (K.K.); NCI grant P30 CA030199 (SBP Flow Cytometry Core); NIH grant P30 DK063720 (UCSF Parnassus Flow CoLab); NIH SIG grant S10 OD026929 (UCSD IGM Core).

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Authors and Affiliations

  1. School of Biological Sciences, Department of Molecular Biology, University of California, San Diego, San Diego, CA, USA

    Miguel Reina-Campos, Maximilian Heeg, Giovanni Galletti, Vida Luna, Quynhanh Nguyen & Ananda W. Goldrath

  2. Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Kelly Kennewick, Amy Vichaidit, Natalie Santillano & Steven J. Bensinger

  3. La Jolla Institute for Immunology, La Jolla, CA, USA

    Ian T. Mathews & Sonia Sharma

  4. Department of Medicine, University of California, San Diego, La Jolla, CA, USA

    Ian T. Mathews, Brigid S. Boland, John T. Chang & Mohit Jain

  5. Department of Pharmacology, University of California, San Diego, La Jolla, CA, USA

    Ian T. Mathews & Mohit Jain

  6. Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Hongling Huang & Hongbo Chi

  7. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA

    J. Justin Milner

  8. Department of Pathology, University of California, San Francisco, San Francisco, CA, USA

    Kenneth H. Hu & Matthew F. Krummel

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  1. Miguel Reina-Campos
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Contributions

Conceptualization: M.R.-C., A.W.G. Methodology: M.R.-C., K.K., I.T.M., K.H.H., G.G., A.W.G., S.S., M.J., M.F.K., H.C. Investigation: M.R.-C., K.K., I.T.M., V.L., Q.N., G.G., H.H., J.J.M., K.H.H., A.V., N.S., B.S.B. Visualization: M.R.-C., M.H., K.K., I.T.M., G.G. Funding acquisition: M.R.-C., A.W.G., J.T.C., S.S., M.F.K., H.C., S.J.B. Project administration: A.W.G. Supervision: M.R.-C., A.W.G. Writing – original draft: M.R.-C., A.W.G. Writing – review and editing: M.R.-C., M.H., G.G., H.C., S.S., S.J.B., A.W.G.

Corresponding author

Correspondence to Ananda W. Goldrath.

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Competing interests

A.W.G. serves on the scientific advisory boards of ArsenalBio and Foundery Innovations. H.C. is a consultant for Kumquat Biosciences. M.F.K. is a founder and shareholder of PIONYR Immunotherapeutic and Foundery Innovations. A provisional patent application has been filed by M.R.-C., G.G. and A.W.G. covering the methods of use of FDFT1 and PDSS2 manipulation for “Enhancing CAR-T cell performance for cancer immunotherapy” (US Provisional Application Serial No. 63/449,701). The rest of the authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Functional genetics in vivo, metabolomics and transcriptional analysis of ex vivo populations of memory CD8+ T cells identify a graded upregulation of a Srebp2-dependent metabolic programming across TRM.

a, Unsupervised hierarchical clustering heatmap of scaled GSVA scores for metabolic signatures across samples, p.i., post-infection. GSE107278. b, sgRNA library heterogeneity reported as Gini Index values. c, Upset plot showing intersection sizes and set sizes for positive and negative regulators of each P14 CD8+ T cell population in the in vivo CRISPR/Cas9-mediated loss-of-function screen. d, Sample acquisition and analysis workflow for LC-MS/MS-based untargeted metabolomics of ex vivo populations of CD8+ T cells in the context of LCMV infection. Gating strategy included for each cell type analyzed. e, Tandem mass spectra matching against commercial standards of mevalonate and mevalonolactone. Tandem mass spectra derived from the most abundant ion for each compound. Black spectra are derived from cell lysate, red mirrored spectra from commercial standard. f, Unsupervised hierarchical clustering of scaled relative abundances of identified annotated metabolites of P14 CD8+ T cells profiled by untargeted metabolomics at day 13 after LCMV infection. The images of the mice in d have been modified from ref. 17.

Extended Data Fig. 2 Upregulation of the mevalonate-cholesterol synthesis pathway in mouse and human TRM.

a, Mevalonate-cholesterol synthesis GSVA scores from averaged single cell expression of spleen and SI P14 CD8+ T cells at indicated time points after LCMV infection profiled by scRNA-seq, GSE131847. b, Mevalonate-cholesterol synthesis GSVA scores for indicated subsets of CD8+ T cells in the spleen or SI in the context of LCMV infection, GSE157072. c, Mevalonate-cholesterol synthesis GSVA scores for indicated subsets of CD8+ T cells at the indicated tissues in the context of LCMV and Herpes Simplex Virus (HSV), GSE70813. d, Gene expression of indicated genes in P14 CD8+ T cell populations from indicated tissues >30 days after LCMV infection, GSE182276e, Mevalonate-cholesterol synthesis pathway scores for all human immune cells grouped by indicated tissues (left), cell types (middle), or cell types of the Jejunum SI (right), from the tissue immune cell scRNA-seq atlas. f, Expression of selected genes on human CD8+ T cells from PBMC, rectum, and SI samples, pooled from 13 healthy donors, GSE125527. g, HMGCR protein expression in CD8+ T cells from human spleen, SI, and colon by immunofluorescence staining. Quantification of total HMGCR by CD8+ T cell area. Donor ID (#). Scale bar, 20 μm. h, GSEA of the mevalonate-cholesterol synthesis in Runx3 KO or control CD8+ T cells GSE106107. Data are pseudo-bulk averaged values (a), mean +/− s.e.m. (b, c, and d), or geometric distribution (f and g), with a total of n = 2 (b), n = 2–4 (c), n = 2–5 (d) mice, and n = 1-2 (g) samples. Two-sided unpaired t-Test (b, c, d, f, and g), and Gene Set Enrichment Analysis (h). *P < 0.05, **P < 0.01, ***P < 0.005.

Source data

Extended Data Fig. 3 A Srebp2-dependent metabolic program controls SI TRM formation.

a, Quantification of gene expression by qPCR of shRNAmir control (shCd19) or shSrebf2 shRNAmir P14 CD8+ T cells before adoptive transfer. b, Frequency of effector shRNAmir control (shCd19) or shSrebf2 shRNAmir P14 CD8+ T cell populations in the spleen 7 days after LCMV infection. c, Frequency of memory shRNAmir control (shCd19) or shSrebf2 shRNAmir P14 CD8+ T cell populations in the spleen 21 days after LCMV infection. d, CRISPR/Cas9-mediated indel efficiency of the Srebf2 sgRNA construct on sorted transduced P14 CD8+ T cells before adoptive transfer. e, Frequency of effector sgCd19 or sgSrebf2 P14 CD8+ T cell populations in the spleen 7 days after LCMV infection. f, Frequency of memory sgCd19 or sgSrebf2 P14 CD8+ T cell populations in the spleen 21 days after LCMV infection. g, Total cell quantification of CD44high CD8+ T cells (left) and Tet+ CD8+ T cells (right) in the blood of Scap WT or Scap KO at different time points after LCMV infection. h, Total cell quantification of Tet+ CD8+ T cells (upper) and CD44high CD8+ T cells (lower) in SI, kidney, and liver of Scap WT or Scap KO at different time points after LCMV infection. i, Ratio of congenically distinct P14 CD8+ T cells transduced with a constitutively active nuclear form of human Srebp268 (nSrebp2) or an empty vector (Empty) from indicated tissues 7 days after LCMV infection. j, Flow cytometry analysis of cholesterol content by Filipin stain (left), and LDLR expression in P14 CD8+ T cells transduced with a bicistronic construct encoding Thy1.1 alone or Thy1.1 and nSrebp2 (right). P14 CD8+ T cells from the kidney, liver, WAT, and SI were gated on the IV− population (h and i). Data are mean +/− s.e.m. and representative of at least two independent experiments, with a total of n = 2 (a), n = 4 (b-e), n = 5 (f), n = 6 (WT) and n = 7 (Scap KO) (g and h), n = 4 (i and j) mice and n = 2 (d) cell replicates. Two-sided unpaired (a, d, g, and h) and paired (j) t-Test. *P < 0.05, **P < 0.01, ***P < 0.005. Two-sided one-sample t-Test (i) #P < 0.05, ##P < 0.01, ###P < 0.005. Two-way ANOVA (h) ##P < 0.01.

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Extended Data Fig. 4 Limited effect of Srebp2 on transcriptional programs of CD8+ T cell differentiation.

a, Srebf2 expression values from the bulk Ultralow-input RNA-seq analysis of shRNAmir control (shCd19) and Srebf2 shRNAmir P14 CD8+ T cells from the spleen, kidney, liver, and SI from day 7 and 14 after LCMV infection. b, Mevalonate-cholesterol synthesis pathway GSVA scores from the bulk Ultralow-input RNA-seq analysis of shRNAmir control (shCd19) and Srebf2 shRNAmir P14 CD8+ T cells from the spleen, kidney, liver, and SI from day 7 and 14 after LCMV infection. c, Unsupervised hierarchically clustered heatmap of mevalonate-cholesterol synthesis pathway gene expression values from the bulk Ultralow-input RNA-seq analysis of shRNAmir control (shCd19) and Srebf2 shRNAmir P14 CD8+ T cells from the spleen, kidney, liver, and SI from day 7 and 14 after LCMV infection. d, Core TRM GSVA scores from the bulk Ultralow-input RNA-seq analysis of shRNAmir control (shCd19) and Srebf2 shRNAmir P14 CD8+ T cells from the spleen, kidney, liver, and SI from day 7 and 14 after LCMV infection. e, Circulating GSVA scores from the bulk Ultralow-input RNA-seq analysis of shRNAmir control (shCd19) and Srebf2 shRNAmir P14 CD8+ T cells from the spleen, kidney, liver, and SI from day 7 and 14 after LCMV infection. f, Volcano plots of differentially expressed genes between shCd19 and shSrebf2 SI P14 CD8+ T cells at days 7 and 14 after LCMV infection, adjusted (adj) p-value. P14 CD8+ T cells from the kidney, liver and SI were gated on the IV− population (a-f). TE were defined as KLRG1highCD127low P14 CD8+ T cells. Data are mean +/− s.e.m.. (a, b, d, and e) from one experiment with n = 2-3 (a-f), where every sample is 1×103 cells pooled from 2~3 mice. Two-sided unpaired t-Test (a, b, d, and e), and Deseq2 DEG testing with Benjamini-Hochberg multiple test correction. *P < 0.05, **P < 0.01, ***P < 0.005.

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Extended Data Fig. 5 SI TRM uptake and sense dietary cholesterol.

a, Quantification by flow cytometry of in vivo incorporation of Bodipy-cholesterol on indicated cell populations after 10 minutes after oral administration of Bodipy-cholesterol to memory P14 mice. LPL, lamina propia lymphocytes. b, Quantification by flow cytometry of incorporation of Bodipy-LDL on indicated cell types of the spleen 20 minutes (min) after IV delivery to memory P14 mice. c, Quantification of intracellular cholesterol by filipin staining in Tet+ populations of CD8+ T cells for various tissues >30 days post LCMV infection measured by imaging flow cytometry. d, Gene expression analysis by qPCR of selected SREBP2 targets on CD8+ T cells from the spleen and SI of mice on regular chow or subjected to low and high cholesterol-containing diets. e, Experimental design to measure transcriptional changes induced by dietary cholesterol on established SI TRM by Ultralow-input RNA-seq, f, SI TRM frequencies from total CD8+ T cells after 7 days of dietary intervention. g, Food consumption per day per mouse subjected to a low or high-cholesterol diet. h, Bodyweight of mice subjected to low or high cholesterol-containing diets. i, Total cholesterol in serum in mice subjected to low or high cholesterol-containing diets. j, Volcano plot of differentially expressed genes in SI TRM from mice fed a high vs a low cholesterol-containing diet. k, Mevalonate-cholesterol synthesis GSVA scores in SI TRM from mice fed a high versus low cholesterol-containing diet. l, Gene pathway analysis of top upregulated and top downregulated genes between SI TRM from mice fed a high versus low cholesterol-containing diet (MsigDb, Hallmark gene sets). P14 CD8+ T cells and CD8β+/CD44high/Tet+ from the kidney, liver and SI were gated on the IV− population (a, b, c, e, f, j, k, and l). Data are mean +/− s.e.m. and representative of at least two independent experiments, with a total of n = 4 (a), n = 3 (b, d), n = 5 (c), n = 3 (f) mice, or pooled from at least two independent experiments with a total of n = 5–10 (g-i) mice, or performed once (j-l) with a total of n = 3 samples each sample is 1 × 103 SI TRM pooled from 2~3 mice (j, k). Two-sided unpaired t-Test (a, b, d, f, i, and k), and Deseq2 DEG testing with Benjamini-Hochberg multiple test correction (j). *P < 0.05, **P < 0.01, ***P <  0.005. The images of the mice in a,b,e have been modified from ref. 17.

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Extended Data Fig. 6 Mevalonate-cholesterol synthesis pathway intermediates sustain the production of non-steroidal metabolites.

a, The mevalonate-cholesterol synthesis pathway with adjacent and downstream metabolic routes. Enzymes not included in the first CRISPR/Cas9-mediated loss-of-function screen in grey, downstream products and/or fates of cholesterol are boxed, key measured metabolites in bold, non-steroidal metabolites in blue, key enzymes are bold and colored by in vivo effect of deletion in P14 CD8+ T cells, gain-of-function in red, loss-of-function in blue, dashed lines indicate indirect or unknown mode of regulation. b, Abundance of 4-hydroxybenzaldehyde profiled by LC-MS/MS of ex vivo populations of P14 CD8+ T cells 13 days after LCMV infection. c, Tandem mass spectra matching against commercial standards of 4-hydroxybenzaldehyde. Tandem mass spectra derived from the most abundant ion for each compound: m/z = 121.0295 for 4-hydroxybenzaldehyde [M-H], black spectra are derived from cell lysate, red mirrored spectra from commercial standard. d, CRISPR/Cas9-mediated indel efficiency of the Hpd sgRNA construct on sorted transduced P14 CD8+ T cells before adoptive transfer. e, CRISPR/Cas9-mediated indel efficiency of the Fdft1 sgRNA construct on sorted transduced P14 CD8+ T cells before adoptive transfer. f, Gini index scores of sgRNA representation in each library. g, Schematic illustration of the contribution of mevalonate-cholesterol pathway intermediates to non-steroidal products. h, Quantification of Pdss2 mRNA by qPCR in transduced Empty and Pdss2 OE P14 CD8+ T cells. Data are mean +/− s.e.m. (b and h), and representative of at least two independent experiments (d, e, and h), with a total of n = 2 (d, e), and n = 3 (h) cell replicates, or one experiment (b), with n = 3 (SI), n = 4 (TN), and n = 5 (rest) (b) samples, where each sample contains cells pooled from 2 to 5 mice. Two-sided unpaired t-Test (b, d, e and h). *P < 0.05, **P < 0.01, ***P < 0.005.

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Extended Data Fig. 7 Increased production of non-steroidal products of the mevalonate-cholesterol synthesis pathway is a common requirement between SI TRM and TIL.

a, Dimensionality reduction plot by UMAP of P14 CD8+ T cells profiled from spleens and subcutaneously implanted MC38-GP33–41 tumors by scRNAseq 7 days after ACT. b, Mevalonate-cholesterol synthesis signature values and expression of indicated genes in P14 CD8+ T cells from the spleen and MC38-GP33–41 tumors profiled by scRNAseq. MAGIC-imputed gene values are shown. c, Hierarchically clustered heatmap of scaled averaged gene expression values by cluster of P14 CD8+ T cells profiled from spleens and MC38-GP33–41 tumors by scRNAseq 7 days after adoptive transfer. d, Dimensionality reduction plot by UMAP of CD8+ T cell metaclusters (left) with “meta.cluster.coarse” labels and hierarchically clustered heatmap of mevalonate-cholesterol synthesis pathway scores grouped by “meta.cluster.coarse” labels and cell type. NPC, nasopharingeal carcinoma, ESCA, esophageal cancer, LC, lung cancer, HCC, hepatocellular carcinoma, CHOL, cholangiocarcinoma, RC, renal carcinoma, CRC, colorectal cancer, AML, acute myeloid leukemia, BCL, B-cell lymphoma, MM, multiple myeloma, HNSCC, head and neck squamous cell carcinoma, THCA, thyroid carcinoma, MELA, melanoma, BCC, basal cell carcinoma, SCC, squamous cell carcinoma, BRCA, breast cancer, STAD, stomach adenocarcinoma, PACA, pancreatic cancer, OV, ovarian cancer, FTC, fallopian tube carcinoma, UCEC, uterine corpus endometrial carcinoma. EMRA, effector memory CD45RA+, mem, memory. e, sgRNA library heterogeneity reported as Gini Index values. f, CRISPR/Cas9-mediated indel efficiency of the Pdss2 sgRNA construct on sorted transduced P14 CD8+ T cells. g, Frequency of PD1highTim3high and PD1highTim3low subpopulations for sgCd19 and sgFdft1 Cas9 P14 CD8+ T cells in MC38-GP33–41 tumors at day 5–7 after adoptive transfer. h, Frequency of PD1highTim3high and PD1highTim3low subpopulations for Empty and Pdss2 OE P14 CD8+ T cells in MC38-GP33–41 tumors at day 5–7 after adoptive transfer. i, Quantification of gene expression by qPCR of control (shNT) and Fdft1 deficient (shFdft1) P14 CD8+ T cells before adoptive transfer. j, Restimulation capacity of control (shNT) and Fdft1 deficient (shFdft1) P14 TIL 4 days after in vivo transfer treated with GP33–41 peptide ex vivo. k, MC38-GP33–41 tumor growth curves of mice receiving ZAA or vehicle. l, Survival of mice implanted with MC38-GP33–41 tumors receiving ZAA or vehicle. m, MC38-GP33–41 tumor growth curves of mice receiving ZAA in combination with CD8+ T cell depletion. Data are mean +/− s.e.m. (g, h, i, j, k, and m), raw values (d), geometrical distributions of single-cell gene expression values (b) or averaged gene expression (c and e) and pooled or representative of at least two independent experiments, with a total of n = 2 (f) cell replicates, and n = 8 (g), n = 9 (h), n = 3 (i), n = 10 (j), n = 12/group (k, l), n = 5/group (m) mice. Two-sided unpaired t-Test (b, f and i). Log-rank test (l). Two-way ANOVA (m). *P < 0.05, **P < 0.01, ***P < 0.005.

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Extended Data Fig. 8 A requirement for the mevalonate-cholesterol synthesis enzymes for TIL persistence and heightened antitumor activity in a melanoma model.

a, GSEA of the mevalonate-cholesterol synthesis pathway in genes pre-ranked by their impact on TIL accumulation in B16-OVA tumors from a published CRISPR/Cas9-mediated loss-of-function screen. b, Dimensionality reduction plot by UMAP of WT and Regnase-1 KO OT-I cells profiled from MC38-GP33–41 tumors by scRNA-seq 7 days after adoptive transfer (left) and unsupervised hierarchical clustering heatmap of selected genes related to CD8+ T cell cytotoxicity (Ifng) stemness (Tcf7), proliferation (Mki67), and the mevalonate-cholesterol synthesis pathway (Srebf2, Hmgcs1, Fdft1, Hmgcr, and Ldlr) grouped by cell type and cluster. c, Comparison of the effect of targeting the 50 genes of the mevalonate-cholesterol pathway included in our targeted screen in WT TIL or Regnase-1 KO TIL (sgZc3h12a) from a published CRISPR/Cas9-mediated loss-of-function screen in B16 melanoma tumors32. Relevant enzymes are labeled.

Supplementary information

Reporting Summary

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Supplementary Table 1

Gene-level enrichment scores for all samples versus the input for each sample and library.

Supplementary Table 2

Curated list of metabolic signatures.

Supplementary Table 3

Pathway-level enrichment scores for all samples versus the input for each sample and library for the metabolic CRISPR–Cas9 screen.

Supplementary Table 4

Polar metabolites identified by LC–MS/MS.

Supplementary Table 5

Statin usage information for the human cohort profiled by single-cell RNA-seq.

Supplementary Table 6

Differentially expressed genes in P14 CD8+ T cells isolated from the SI of mice fed a high- or low-cholesterol diet for 7 days and profiled by ultralow-input RNA-seq.

Supplementary Table 7

Targeted library sgRNA information.

Supplementary Table 8

Gene-level enrichment scores for all sgCd19 and sgFdft1 samples versus corresponding inputs in the mevalonate–cholesterol synthesis pathway-targeted CRISPR–Cas9-mediated loss-of-function screen.

Supplementary Table 9

Gene-level enrichment scores for TIL, dLN and SI TRM samples versus corresponding inputs in the mevalonate–cholesterol synthesis pathway-targeted CRISPR–Cas9-mediated loss-of-function screen.

Source data

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Reina-Campos, M., Heeg, M., Kennewick, K. et al. Metabolic programs of T cell tissue residency empower tumour immunity. Nature 621, 179–187 (2023). https://doi.org/10.1038/s41586-023-06483-w

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