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Metabolic modeling of single Th17 cells reveals regulators of autoimmunity
Open accessPublished version of a preprint
Abstract
Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.
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“the non-pathogenic/homeostatic and pathogenic Th17 cell phenotypes studied in the mouse models may mirror the physiological roles of human Th17 cell subsets that are important for immunity against distinct bacteria and fungi”
From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [65]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Self-citationBettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., & Kuchroo, V. K. (2006) · Nature · doi:10.1038/nature04753 · Cited by 6,992
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Items 501–523 of 523
Chen, H.-H., Ni, F.-X., Huang, D., & Jiang, Z. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1904338
A benchmarking framework for single-cell genome-scale metabolic model constructionPreprint
Yang, J., Deng, Y., Luo, J., Wang, Y., Li, F., & Chen, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.02.742346
Xu, Z., Liu, Y., Cheng, L., & Xu, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1915323
Sillito, F. E., Armbrecht, E., O'Neill, A. T., McIntyre, A., Nyatondo, M., Holler, A., Callender, L. A., Henson, S. M., Stauss, H. J., & Chakraverty, R. K. (2026) · The Journal of Immunology · doi:10.1093/jimmun/vkag206
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Yang, L., Xu, J., Wang, T.-L., Qi-ning, Z., Zhang, X., Qin, L., Zong, H.-J., Jia, H.-L., & Shen, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743252
Sun, J., Lei, Y., Yang, H., Li, S., & Wu, B. (2026) · Science Advances · doi:10.1126/sciadv.aee4337
Bae, S., Ávila-Pacheco, J., Clay, S. L., Bang, S., Scott, M. C., Andreeva, N., Michaud, M., Fonseca‐Pereira, D., Chun, E., Cao, Y. G., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.24.746719
Self-citationChronic stress promotes sex-divergent Type 1, 2 and 17 responses in brain resident lymphocytesPreprint
Paris, S., Cao, K., Park, Y., Lee, K. J., Zhou, S., Uthayakumar, D., Novelo, M., Tuncer, A., Qian, C., & Wang, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.31.748329
Self-citationUjagar, N., Poudel, S., Saraswat, S., Sureshchandra, S., Kim, J., Le, U.-V., Jones, A. R., Hopkins, N., Sriram, T., Matson, M. R., et al. (2026) · bioRxiv · doi:10.64898/2026.09.01.748572
Davidorf, B., Ho, D., Bouch, R., Yu, J., Dong, P., Liu, L., Song, Q., & He, Z. (2026) · Cell Insight · doi:10.1016/j.cellin.2026.100366
Extracting interpretable single-cell metabolic states with graph-guided representation learningPreprint
Lewinsohn, D. P., Dias, N., Chau, A., Koike, Y., Smith, Z. D., Ioannidis, N. M., & Wagner, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.17.751504
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Dias, N., Lewinsohn, D. P., Colgan, W. N., Wang, M., Kijima, Y., Villagrana, J., Hou, T.-C. J., Gowri, G., Chau, A., Aktaş, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.18.752622
Self-citationSpatial evolution of a cachexia-promoting microenvironment in pancreatic cancer
Hu, Y., Lv, G., Yuan, H., Zhou, Z., Zhang, S., Ye, Y., Chen, Q., Cai, Y., Yang, J., Zhang, Y., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.012 · Cited by 1
Zhu, H., Wan, C., Yang, M., Fang, Y., An, Z., Swaminathan, P., Dang, P., Li, Z., Wang, J., Wang, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.27.754711
Wang, W., Rui, B., Wang, G., Ruan, Z., Luo, Z., Zhang, Y., Shi, T., Huang, Y., & Chen, Y. (2026) · Advanced Materials · doi:10.1002/adma.75214
Decoding epithelial–mesenchymal transitions with multi-omics
Narwade, N. P., & Nieto, M. A. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-01015-4
Hong, S., Park, S., Kim, S., Park, Y. I., Lee, J. S., Kang, M., Lee, J., Hemberg, M., Kim, S. E., & Kim, K. (2026) · Science Advances · doi:10.1126/sciadv.aeh2362
Gene regulation at the early human maternal–fetal interface
Paredes, A. L., Kim, C. E., & Vento‐Tormo, R. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-01014-5
Bitar, A. (2026) · Functional & Integrative Genomics · doi:10.1007/s10142-026-02078-6
Yuan, Y., & Peng, Y. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1974223
Features of cancer cell metabolism linked to DNA repair mechanisms
Mikheeva, R., Leonov, P. A., Koryukov, M., Ruleva, E., Liu, X., & Андреевич, К. А. (2026) · DNA repair · doi:10.1016/j.dnarep.2026.103969
Zhao, T., Sun, Y., 江晓丹, Feng, Y., Ding, J., 李学民, & Deng, A. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1938863
Ye, X., Wang, Y., Li, Y., Zhu, H., Li, X. M., & Song, J. (2026) · Annals of Medicine · doi:10.1080/07853890.2026.2753327
Metadata
- Authors
- Wagner, A., Wang, C., Fessler, J., DeTomaso, D., Avila-Pacheco, J., Kaminski, J., Zaghouani, S., Christian, E., Thakore, P., Schellhaass, B., Akama-Garren, E., Pierce, K., Singh, V., Ron-Harel, N., Douglas, V. P., Bod, L., Schnell, A., Puleston, D., Sobel, R. A., Haigis, M., Pearce, E. L., Soleimani, M., Clish, C., Regev, A., Kuchroo, V. K., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Massachusetts Eye and Ear Infirmary (US)
- Max Planck Institute of Immunobiology and Epigenetics (DE)
- VA Palo Alto Health Care System (US)
- Harvard University (US)
- University of New Mexico (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Wang, Chao
- Kuchroo, Vijay K.
- Yosef, Nir
- ORCID
- Wagner, Allon
- Wang, Chao
- Fessler, Johannes
- DeTomaso, David
- Zaghouani, Sarah
- Christian, Elena
- Pierce, Kerry
- Singh, Vasundhara
- Douglas, Vivian Paraskevi
- Bod, Lloyd
- Schnell, Alexandra
- Puleston, Daniel
- Sobel, Raymond A.
- Haigis, Marcia
- Pearce, Erika L.
- Soleimani, Manoocher
- Clish, Clary
- Regev, Aviv
- Kuchroo, Vijay K.
- Yosef, Nir
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 184
- Issue
- 16
- Pages
- 4168-4185.e21
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC8621950
- OpenAlex
- W3174064560
- Cited by
- 524
- References
- 163
- Citations by year
- 2026: 105 · 2025: 118 · 2024: 111 · 2023: 92 · 2022: 75 · 2021: 18
- FWCI
- 27.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 369 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Chan Zuckerberg Initiative
- Australian Academy of Science
- Österreichischen Akademie der Wissenschaften
- NIH
- Canada First Research Excellence Fund (P01AI039671, P01AI056299, P01AI073748, R01AI144166, R01NS045937, RO1NS30843, TA-1605-08590)
- National Multiple Sclerosis Society
- National Institute of Mental Health (NIH5U19MH114821)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- Th17 cells, cellular metabolism, flux balance analysis, glycolysis, fatty acid oxidation, regulatory T cells, polyamine metabolism, arginine metabolism, Foxp3, central nervous system autoimmunity, multiple sclerosis, encephalitogenic T cells
- MeSH terms
- Epigenome, Diamine N-Acetyltransferase, Acetyltransferases, Adenosine Triphosphate, Aerobiosis, Algorithms, Eflornithine, Animals, Chromatin, Citric Acid Cycle, Encephalomyelitis, Autoimmune, Experimental, Fatty Acids, Glycolysis, Mice, Inbred C57BL, Models, Biological, Oxidation-Reduction, Putrescine, Autoimmunity, Cytokines, Mitochondrial Membrane Transport Proteins, T-Lymphocytes, Regulatory, Mice, Jumonji Domain-Containing Histone Demethylases, Th17 Cells, Single-Cell Analysis, Transcriptome
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