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Active aldehydes accelerate CD8+ T cell exhaustion by metabolic alteration in the tumor microenvironment

Nature Immunology (2026)Cite this article

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Abstract

Glycolysis and mitochondrial fatty acid oxidation (FAO) regulate CD8+ T cell differentiation, but how this metabolic balance regulates T cell exhaustion is unclear. PD-1 signaling inhibits glycolysis and enhances FAO. Here, we show that CD8+ T cells in tumors adhere to glycolysis with attenuated FAO despite high PD-1 expression. Active aldehydes, final products of lipid peroxidation, accumulate in CD8+ T cells in proportion to their level of exhaustion, defined by mitochondrial mass and potential. Aldehydes promote glycolysis and inhibit FAO in T cells. Mice deficient in an FAO enzyme in T cells generate more acrolein, a representative aldehyde, enhancing T cell exhaustion and attenuating antitumor immunity. Acrolein is generated partly from mitochondria and damages mitochondrial architecture. Inhibitors of lipid peroxidation or aldehydes enhanced PD-1-blockade by rectifying metabolic imbalance. Therefore, active aldehydes resulting from FAO impairment can cause a vicious cycle of metabolic imbalance that leads to T cell exhaustion.

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Fig. 1: CD8+ T cells undergo differentiation accompanied by changes in mitochondrial status.
Fig. 2: Acrolein accumulation in CD8+ T cells is dependent on exhaustion depth in the TME.
Fig. 3: Acrolein increases glycolysis and reduces FAO in vitro.
Fig. 4: Endogenous acrolein shapes the metabolic state of TEX cells in the TME.
Fig. 5: FAO dysfunction in T cells accelerates exhaustion and acrolein production.
Fig. 6: Lipid radical and acrolein scavengers recover antitumor immunity attenuated by FAO dysfunction.
Fig. 7: Acrolein scavengers restore antitumor immunity by reactivating FAO.

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Data availability

ScRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus under accession code GSE307051. Source data are provided with this paper.

Code availability

Custom scripts supporting the data analysis were developed using standard computational tools and are available from the corresponding author upon reasonable request.

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Acknowledgements

We sincerely thank members of the Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology, Graduate School of Medicine, Kyoto University for sample preparation and discussion; S. Fagarasan and T. Carvalho for productive discussion; the staff of the Clinical Bio-Resource Center at Kyoto University Hospital for collecting the human samples; and the staff of the Division of Electron Microscopic Study, Center for Anatomical Studies, Graduate School of Medicine, Kyoto University, for preparing TEM slides. We are grateful to the Radioisotope Research Center, Agency for Health, Safety and Environment, Kyoto University, for supporting the experiments involving thymidine incorporation assays. This work was supported by the Japan Agency for Medical Research and Development under grant number JP25ama221330 (K.C.); JSPS KAKENHI under grant number JP23KJ1378 (K.K.); Yanai Fund (T. Honjo), Meiji Holdings Co. (T. Honjo and K.C.), Meiji Seika Pharma Co. (T. Honjo and K.C.) and Shimadzu Corporation (T. Honjo and K.C.). This manuscript was edited by Life Science Editors.

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  1. These authors contributed equally: Yasuharu Haku, Koji Kitaoka.

Authors and Affiliations

  1. Department of Immunology and Genomic Medicine, Center for Cancer Immunotherapy and Immunobiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Yasuharu Haku, Koji Kitaoka, Koki Ichimaru, Tomoko Hirano, Jun Wang, Shuhei Hirose, Yu Wang, Katsuhiro Ito, Tomohiro Kozuki, Keiko Yurimoto, Mai Kiyono, Tomonori Yaguchi, Tasuku Honjo & Kenji Chamoto

  2. Department of Otolaryngology-Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Yasuharu Haku & Koichi Omori

  3. Department of Immuno-Oncology PDT, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Koki Ichimaru, Tomonori Yaguchi & Kenji Chamoto

  4. Department of Dermatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Tomoko Hirano

  5. Technology Research Laboratory, Shimadzu Corp., Nakagyo-ku, Japan

    Kazuhiro Sonomura & Asuka Maruo

  6. Department of Urology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Katsuhiro Ito & Takashi Kobayashi

  7. Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Tomohiro Kozuki

  8. Division of Cell Signaling, Institute of Advanced Medical Sciences, Tokushima University, Tokushima, Japan

    Hidetaka Kosako

  9. Department of Thoracic Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Toshi Menju & Hiroshi Date

  10. Department of Head and Neck Oncology and Innovative Treatment, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Koichi Omori

  11. Department of Immune metabolism, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Tomonori Yaguchi

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  1. Yasuharu Haku
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Contributions

Y.H., K.K. and K.C. designed research; Y.H., K.K., K. Ichimaru, J.W., K.S., A.M., S.H., Y.W., K.Y., M.K. and H.K. performed research; Y.H., K.K., K. Ichimaru, T. Hirano, J.W., S.H., Y.W., T. Kozuki, T.Y., and K.C. analyzed data; Y.H., K.K., K. Ichimaru, J.W., K. Ito, T.M., H.D., T. Kobayashi, K.O. and T.Y. collected human samples; and Y.H., K.K., T. Honjo and K.C. wrote the paper. All authors reviewed the results and approved the manuscript.

Corresponding author

Correspondence to Kenji Chamoto.

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Competing interests

T. Honjo and K.C. received research funding from Shimadzu Corporation. All other authors declare no competing interests.

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Nature Immunology thanks Navdeep Chandel, Ping-Chih Ho and the other anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Surrogate markers and phenotypic characterization of D1–D4 populations.

(A) A representative gating strategy: FSC-A/SSC-A profiles and frequencies of FSC-Alow cells of D1–D4 populations among PD-1+ CD8+ TILs in MC38 tumors on day 14 (n = 6 mice). (B) MC38-OVA cells were injected intradermally into CD45.1 congenic male and female mice on day 0. Male and female mice were equally distributed across groups. On day 9 after tumor inoculation, isolated naïve (CD44low) OT-1 CD8⁺ T cells (CD45.2⁺) were intravenously transferred into the tumor-bearing CD45.1 recipient mice. Prior to transfer, the isolated naïve CD8⁺ T cells (CD45.2⁺) were also analyzed by flow cytometry (representative profiles shown). Tumor-infiltrating donor PD-1⁺ CD8⁺ T cells (CD45.2⁺) were analyzed by flow cytometry on days 13, 16, and 20 after tumor inoculation (that is, days 4, 7, and 11 after transfer). Representative flow cytometry profiles (male recipients) and frequencies of D1–D4 cells, based on mitochondrial mass and mitochondrial potential (all recipients, n = 6 mice). (C) Among PD-1+ CD8+ T cells in MC38 tumors, the FSC-Alow population is regarded as D4’ whereas the FSC-Ahigh population is divided into 3 populations (D1’ to D3’) according to their mitochondrial potential intensity. Distributions of D1’-D4’ populations are compared with D1–D4 gates. (D) Representative histogram and MFI of IFN-γ, TNF-α, granzyme B and perforin in D1’-D4’ populations among PD-1+ CD8+ T cells in MC38 tumors on day 14 (n = 6 mice). (E) Frequencies of CD38+, CD39+ and CD73+ cells in D1–D4 populations among PD-1+ CD8+ T cells in tumors on day 14 (n = 8 mice). (F) MFI of IFN-γ, TNF-α, granzyme B and perforin in D1’-D4’ populations among naïve CD8+ T cells on day 8 of repetitive stimulation (n = 5 technical replicates). (G) MFI of Annexin V, Tim-3 and LAG-3 in D1’-D4’ or D1–D4 populations among naïve CD8+ T cells on day 8 of repetitive stimulation (n = 5 technical replicates). (H) Based on the data shown in Fig. 1e, UMAP projection revealed six distinct clusters. (I) Violin plots showing exhausted or stem-like signature scores for each cluster. Data are means ± SEMs (A and B). P values were determined using paired two-tailed Student’s t tests (D to G) or one-way ANOVA with Tukey’s multiple comparisons tests (B and I). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. All data are representative of three or more independent experiments with similar results, except (H) and (I).

Extended Data Fig. 2 Active aldehyde accumulates in CD8+ T cells in an exhaustion depth-dependent manner in vitro and in vivo.

(A) Flow cytometry profile and MFI of lipid radical in Tim-3− and Tim-3+ populations among PD-1+ CD8+ T cells in tumors on day 14 (n = 8 mice). (B) Flow cytometry profile and MFI of acrolein or PC-Acro in Tim-3− and Tim-3+ populations among PD-1+ CD8+ T cells in tumors on day 14 (acrolein, n = 5 mice; PC-Acro, n = 7 mice). (C) MFI of protein-conjugated malondialdehyde (PC-MDA) and protein-conjugated 4-hydroxynonenal (PC-4-HNE) in Tim-3− and Tim-3+ populations among PD-1+ CD8+ T cells in tumors on day 14 (n = 7 mice) and on day 17 (n = 8 mice), respectively. (D) MFI of lipid radical, acrolein and PC-Acro in D1’-D4’ populations among naïve CD8+ T cells on day 8 of repetitive stimulation (n = 5 technical replicates). (E) MFI of acrolein in naïve-derived CD8+ T cells on day 0, 2, 6 and 10 of repetitive stimulation (n = 5 technical replicates). P values were determined using paired two-tailed Student’s t tests (A to D) or one-way ANOVA with Tukey’s multiple comparisons tests (E). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. All data are representative of three or more independent experiments with similar results.

Extended Data Fig. 3 Active aldehydes, but not hydrogen peroxide (H2O2), enhance glycolysis and proliferation of CD8+ T cells.

(A) Acrolein concentration (μM) in MC38 tumor tissue on day 14 (n = 6 mice). An experimental control group was not included, as the experiment aimed to determine the physiological acrolein levels in tumor tissues. (B) Frequencies of D1–D4 populations among CD8+ T cells 21 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 2 μM acrolein (ACR) (n = 5 technical replicates). (C) MFI of pAkt, pmTOR and pS6 in naïve-derived CD8+ T cells on day 8 of repetitive stimulation in the presence or absence of ACR (n = 5 technical replicates). (D) Frequencies of PD-1− Tim-3− and PD-1+ Tim-3+ cells among naïve-derived CD8+ T cells on day 8 of repetitive stimulation in the presence or absence of ACR (n = 5 technical replicates). (E) Western blotting images of enzymes associated with glycolysis and FAO in CD8+ T cells stimulated with anti-CD3/CD28 beads for 3 hours in the presence or absence of 5 μM ACR. (F) Seahorse measurement of real-time ECAR of naïve CD8+ T cells 3 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 0.5 μM 4-hydroxynonenal (4-HNE) (n = 4 technical replicates). (G) Thymidine incorporation of CD8+ T cells 7 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 0.5 μM 4-HNE (n = 4 technical replicates). The control data are shared with those in Fig. 3f to compare results under the same conditions in the same experiment, as supportive data. (H) Seahorse measurement of real-time ECAR of CD8+ T cells 3 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 120 μM H2O2 (n = 4 technical replicates). (I) Frequencies of D1–D4 populations among CD8+ T cells 7 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 120 μM H2O2 (n = 6 technical replicates). (J) Frequencies of D1–D4 populations among CD8+ T cells 14 hours after stimulation with anti-CD3/CD28 beads in the presence or absence of 60 μM H2O2 (n = 5 technical replicates). Data are means ± SEMs. P values were determined using unpaired two-tailed Student’s t tests. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. All data are representative of two or more independent experiments with similar results.

Source data

Extended Data Fig. 4 Suppressed T cells exhaustion by acrolein scavengers and expression level of Aldh2 in D1–D3.

(A) Frequencies of PD-1− Tim-3− and PD-1+ Tim-3+ cells among naïve-derived CD8+ T cells on day 8 of repetitive stimulation in the presence or absence of N-benzylhydroxylamine (NBHA) (n = 5 technical replicates). The control data are shared with those in Extended Data Fig. 3d to compare results under the same conditions in the same experiment, as supportive data. (B) Expression level of Aldh2 in D1–D3 of PD-1+ CD8+ T cells sorted from MC38 tumors on day 14, evaluated by scRNA-seq. Data are means ± SEMs (A). P values were determined using unpaired two-tailed Student’s t tests (A). ****P < 0.0001. Data are representative of two or more independent experiments with similar results, except (B).

Extended Data Fig. 5 Unnecessary fatty acid uptake boosts FAO dysfunction in TME CD8+ T cells.

(A) MFI of lipid radical, acrolein and PC-Acro in naïve (CD62Lhigh CD44low) CD8+ T cells in PBMCs from Hadhaflox/flox (Ctrl) and Hadhaflox/flox Cd4-Cre (KO) male mice (Ctrl, n = 7 mice; KO, n = 5 mice). (B) MFI of pAkt, pS6 and 2-NBDG in naïve CD8+ T cells in PBMCs from Ctrl and KO male mice (Ctrl, n = 7 mice; KO, n = 5 mice). (C) CD36 expression and BODIPY C5, C12, and C16 (fatty acid uptake) in naïve CD8+ T cells in PBMCs from Ctrl and KO male mice. (Ctrl, n = 7 mice; KO, n = 5 mice). (D) BODIPY C5, C12, and C16 in Tim-3− and Tim-3+ populations among PD-1+ CD8+ T cells in MC38 tumors on day 14 (n = 5 mice). (E) The positive loop toward intensive oxidative stress by FAO dysfunction in CD8+ T cells in TME. Acrolein production following lipid peroxidation damages mitochondria and reduces the FAO activity. FAO dysregulation promotes fatty acid uptake and drives further accumulation of lipid peroxidation and acrolein. Created with BioRender.com, elements adapted and assembled using PowerPoint. Data are means ± SEMs (A to C). P values were determined using unpaired two-tailed Student’s t tests (A to C) or paired two-tailed Student’s t tests (D). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Data are representative of two independent experiments with similar results.

Extended Data Fig. 6 Acrolein scavenger attenuates Akt/mTOR/S6 pathway of CD8+ T cells in TME.

MFI of pAkt, pmTOR, pS6 and Glut1 in PD-1+ CD8+ T cells in MC38 tumors on day 10 from WT female mice treated with anti-PD-L1 mAb and N-benzylhydroxylamine (NBHA) as indicated in Fig. 7b (Ctrl, n = 8 mice; the other groups, n = 6 mice). Data are means ± SEMs. P values were determined using unpaired two-tailed Student’s t tests. *P < 0.05. Data are representative of two independent experiments with similar results.

Extended Data Fig. 7 Vicious loop toward metabolic exhaustion by active aldehyde accumulation in the process of CD8+ T cell exhaustion in TME.

(A) In CD8+ T cells of TME, lipid peroxidation gradually proceeds due to mitochondrial ROS production. Lipid peroxidation generates active aldehydes from mitochondria, attenuating the mitochondrial FAO function. A positive loop of active aldehydes accumulation is then established as shown in Extended Data Fig. 5e. (B) Active aldehydes boost the glycolysis pathway forcedly even if the cell expresses PD-1. (C) Active aldehyde accumulation attenuates FAO and boosts glycolysis, shaping the metabolic exhaustion state. Created with BioRender.com, elements adapted and assembled using PowerPoint.

Supplementary information

Reporting Summary

Supplementary Table 1

Genes upregulated in D3 compared to D1 (GO analysis).

Supplementary Table 2

Phosphoproteomics data comparing ACR and Ctrl groups.

Supplementary Table 3

Canonical pathway analysis of IPA.

Supplementary Table 4

The targeted metabolite for metabolic trace analysis of 13C-labeled glucose and palmitate.

Source data

Source Data Fig. 3

Uncropped immunoblot data for Fig. 3k.

Source Data Extended Data Fig. 3

Uncropped immunoblot data for Extended Data Fig. 3e.

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Haku, Y., Kitaoka, K., Ichimaru, K. et al. Active aldehydes accelerate CD8+ T cell exhaustion by metabolic alteration in the tumor microenvironment. Nat Immunol (2026). https://doi.org/10.1038/s41590-025-02370-w

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