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The subfornical organ is a nucleus for gut-derived T cells that regulate behaviour

Nature volume 643, pages 499–508 (2025)Cite this article

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Abstract

Specialized immune cells that reside in tissues orchestrate diverse biological functions by communicating with parenchymal cells1. The contribution of the innate immune compartment in the meninges and the central nervous system (CNS) is well-characterized; however, whether cells of the adaptive immune system reside in the brain and are involved in maintaining homeostasis is unclear2,3,4. Here we show that the subfornical organ (SFO) of the brain is a nucleus for parenchymal αβ T cells in the steady-state brain in both mice and humans. Using unbiased transcriptomics, we show that these extravascular T cells in the brain are distinct from meningeal T cells: they secrete IFNγ robustly and express tissue-residence proteins such as CXCR6, which are required for their retention in the brain and for normal adaptive behaviour. These T cells are primed in the periphery by the microbiome, and traffic from the white adipose and gastrointestinal tissues to the brain. Once established, their numbers can be modulated by alterations to either the gut microbiota or the composition of adipose tissue. In summary, we find that CD4 T cells reside in the brain at steady state and are anatomically concentrated in the SFO in mice and humans; that they are transcriptionally and functionally distinct from meningeal T cells; and that they secrete IFNγ to maintain CNS homeostasis through homeostatic fat–brain and gut–brain axes.

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Fig. 1: Brain T cells are distinct and are localized in the SFO.
Fig. 2: There is a fat–brain T cell axis at steady state.
Fig. 3: There is a gut–brain T cell axis at steady state.
Fig. 4: Comparative analysis of T cells isolated from human CSF, blood and GI tissue identifies CXCR6 as a CNS homing receptor for T cells.
Fig. 5: IFNγ-secreting brain CD4 T cells control food-seeking behaviour after food deprivation.

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Data availability

The data that support the findings of this study are available on request from the corresponding authors. Sequence files and metadata for all samples used in this study have been deposited in the NCBI Sequence Read Archive (SRA). Single-cell RNA-seq data generated from T cells isolated from steady-state adult mouse brain, meninges, white fat, colon lamina propria, lung and blood (referred to in Figs. 1–3) are available in the NCBI SRA under PRJNA1240821. Single-cell RNA-seq data generated from T cells isolated from steady-state adult mouse brain, meninges, white fat, colon lamina propria, mLNs and blood (referred to in Extended Data Fig. 5) are available in the NCBI SRA under PRJNA1074276. Bulk RNA-seq data generated from CD4 T cells isolated by FACS from steady-state adult mouse brain, meninges and white fat (referred to in Extended Data Fig. 5) are available in the NCBI SRA under PRJNA1073984. Bulk RNA-seq data generated from CD4 T cells isolated from steady-state adult mouse brain, lungs and lymph nodes (referred to in Extended Data Fig. 5) are available in the NCBI SRA under PRJNA1074381. Single-cell RNA-seq data generated from healthy human adult CSF, colon, duodenum and blood (referred to in Fig. 4 and Extended Data Fig. 8) are available in the NCBI SRA under PRJNA1074056. Bulk RNA-seq data generated from microglia isolated from steady-state adult wild-type and Tbx21 KO mouse brain (referred to in Extended Data Fig. 10) are available in the NCBI SRA under PRJNA1073982.

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Acknowledgements

We thank the Yale Legacy Tissue Donation Program and Center for Human Brain Discovery for the human autopsy tissue samples; D. Wolfsohn for performing the colonoscopy for our human study collection; D. Littman for sharing 7B8 mice and an aliquot of FITC rat anti-mouse Vβ 14 T cell receptor antibody; E. Schaubeck and others from LifeCanvas Technologies for assistance with brain-clearing and image acquisition; E. Menet, B. Shonts, A. Khalil, J. Bonin and D. Trotta for cell sorting through the Yale Flow Cytometry Core Facility; I. Klenovskiy for helping with Python-coding-related tasks; the Yale Center for Cellular and Molecular Imaging Confocal Facility for use of the Lecia Stellaris microscope; the Yale Flow Cytometry facility for assistance with cell sorting and instrument maintenance; and the YCGA for assistance with library preparation and genomic sequencing services. The Flow Cytometry Core is supported in part by an NCI Cancer Center Support Grant NIH P30 CA016359; the BD Symphony was funded by shared instrument grant NIH S10 OD026996; and YCGA-related research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under award number 1S10OD030363-01A1. A.W. was supported by NIH grants R01 AI162645 and R01 AR080104, the Smith Family Foundation, the Colton Center for Autoimmunity at Yale, the Food and Allergy Science Initiative, the PEW Charitable Trusts, the Chan Zuckerberg Initiative, and gifts from the Mathers Family Foundation, the Ludwig Family Foundation and the Knights of Columbus; D.A.H. was supported by NIH grants P01 AI073748, R01 AI22220, UM 1HG009390, P01 AI039671, P50 CA121974 and R01 CA227473, Race to Erase MS and the National MS Society; T.M.Y. was supported by the Smith Family Foundation and NIH grant 1F31NS130957-01A1; J.L. was supported by NIH grants T32MH019961 and R25MH071584. B.Y.L. was supported by NIH grants T32 CA233414 and K12 CA215110; Y.S.M. and M.R.P. were supported by DP1 DA050986; and J.E.C. was supported by NIH grant R37 AR40072. We acknowledge BioRender for their platform to generate schematics.

Author information

Authors and Affiliations

  1. Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA

    Tomomi M. Yoshida, Mytien Nguyen, Benjamin Y. Lu, Di Xu, Elizabeth Lin, Daniel A. Waizman, Mackenzie E. Coden, Yifan Ma, Kavita Israni-Winger, Wenzhi Song, Jafar Al Souz, Joseph E. Craft, Noah W. Palm, David A. Hafler & Andrew Wang

  2. Department of Rheumatology, Allergy and Immunology, Yale School of Medicine, New Haven, CT, USA

    Tomomi M. Yoshida, Cuiling Zhang, Di Xu, Elizabeth Lin, Daniel A. Waizman, Mackenzie E. Coden, Yifan Ma, Kavita Israni-Winger, Wenzhi Song, Jafar Al Souz, Joseph E. Craft & Andrew Wang

  3. Department of Neurology, Yale School of Medicine, New Haven, CT, USA

    Tomomi M. Yoshida, Le Zhang, Benjamin Y. Lu, Katie N. Murray, Elizabeth Lin, Anthony Russo, Haowei Wang, Jaime Grutzendler & David A. Hafler

  4. Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA

    Le Zhang

  5. Department of Medicine (Section of Medical Oncology and Hematology), Yale School of Medicine, New Haven, CT, USA

    Benjamin Y. Lu

  6. Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

    Biqing Zhu & Hongyu Zhao

  7. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Yann S. Mineur, Joseph Luchsinger & Marina R. Picciotto

  8. Department of Pathology, Yale School of Medicine, New Haven, CT, USA

    Sagar Bhatta & Marcello Distasio

  9. Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT, USA

    Sagar Bhatta & Marcello Distasio

  10. Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA

    Hongyu Zhao

Authors
  1. Tomomi M. Yoshida
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  2. Mytien Nguyen
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  3. Le Zhang
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Contributions

Conceptualization: T.M.Y., A.W. and D.A.H. Investigation: T.M.Y., M.N., L.Z., D.X., E.L., M.E.C., K.I.-W., D.A.W., J.L. and M.D. Formal analysis: T.M.Y., L.Z., B.Y.L., B.Z., J.L. and M.D. Funding acquisition: A.W., D.A.H. and T.M.Y. Methodology: T.M.Y., M.N., L.Z., K.N.M., Y.S.M., D.X., Y.M. and M.D. Project administration: A.W. and D.A.H. Supervision: A.W. and D.A.H. Resources: M.N., L.Z., Y.S.M., C.Z., S.B., A.R., H.W., W.S., J.A.S., H.Z., J.E.C., M.R.P., J.G., M.D. and N.W.P. Writing (original draft): T.M.Y. Writing (review and editing): A.W., D.A.H., T.M.Y., B.Y.L., E.L., D.A.W., M.E.C., Y.M., W.S., M.R.P., J.G. and N.W.P.

Corresponding authors

Correspondence to David A. Hafler or Andrew Wang.

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Competing interests

D.A.H. has received research funding from Bristol Myers Squibb, Novartis, Sanofi and Genentech, and has been a consultant for Bayer Pharmaceuticals, Repertoire, Bristol Myers Squibb, Compass Therapeutics, EMD Serono, Genentech, Juno Therapeutics, Novartis, Proclara Biosciences, Sage Therapeutics and Sanofi Genzyme. A.W. has received research funding from NGM Biopharmaceuticals and AstraZeneca; has been a consultant for Seranova Bio, the Column Group and Soleil Labs; and is on the scientific advisory board for NGM Biopharmaceuticals.

Peer review

Peer review information

Nature thanks Dan Littman, Scott Mueller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 T cells are present in the mouse and human brain.

a, Coronal brain sections from 8–10-week-old C57BL/6 mouse stained with CD31 for blood vessels, CD4 and CD3. Representative image of extravascular CD4 T cells in the brain in the grey matter, specifically the dentate gyrus of the hippocampus. b, Representative image of extravascular CD4 T cells from whole-mount dura meninges peeled from skullcaps. LYVE1 stains for lymphatic vessels. c, Representative image of vascular adjacent CD4 T cell in the brain in the white matter, specifically the stria terminalis as indicated by an arrow. Quantification of vascular adjacent CD3+ CD4+ T cells by brain region. (n = 3 per brain area; pooled experiments from 8 female and 7 male mice). d, Representative image of intravascular CD4 T cells in the brain in the choroid plexus. e, Quantification of intravascular CD3+ CD4+ T cells by brain region. (n = 3 per brain area; pooled experiments from 8 female and 7 male mice). **P < 0.01, ****P < 0.0001 by one-way ANOVA test for c,e. f, Brain from a CD4–tdTomato fate-map reporter mouse (Ai14 CD4-Cre) was processed for whole-brain clearing, imaged using a light-sheet microscope, and registered to the Allen Mouse Brain Atlas. Analysis of tdTomato expression by brain area was calculated by LifeCanvas. R was used to generate the graph displaying the average density of tdTomato+ cells by mm3 across all registered brain areas. (n = 1). g, Representative image of the coronal slice containing the SFO h, IGL and SubG i, and AON from the cleared whole brain generated using Imaris. j, Same as in f, except for all CVOs of the brain. (n = 1) k, Deidentified post-mortem human brain samples were obtained from individuals without acute neurological disease, structures of interest were dissected by a neuropathologist and submitted for histology. Patient 6 from Table 1. (n = 1).

Extended Data Fig. 2 Mouse brain T cells are effector memory cells and secrete IFNγ.

a, Intravascular labelling strategy of circulating leukocytes via retro-orbital (R.O.) injection of a fluorophore-conjugated CD45 antibody. Representative FACS strategy to isolate extravascular tissue CD4 T cells from the mouse brain. Gating strategy was kept the same across all organs used for the experiment. b, Quantification of absolute numbers of extravascular CD4 αβ T cells in the brain with different incubation times of CD45 intravascular labelling. c, Representative FACS strategy of CD45 IV-negative extravascular CD4 T cells in the mouse brain. d, Representative FACS expression of T-bet in the steady-state brain, lung, liver and inguinal lymph node. e, FACS analysis of brain T cell population in Il15ra KO mice. **P < 0.01, ***P < 0.001 by unpaired parametric t-test (n = 5 per group). Grubbs’ outlier test (P < 0.05) was used to determine outliers, which were excluded. f, IFNγ reporter with endogenous polyA transcript” (GREAT) mice were used to look at IFNγ-YFP expression in various immune cells in steady-state tissue. g, Comparison of IFNγ-YFP expression in CD4 T cells (CD45 IV-, CD11b-, CD3+, CD4+, TCRβ+), CD8 T cells (CD45 IV-, CD11b-, CD3+, CD8+, TCRβ+), NK cells (CD45 IV-, CD11b-, CD3-, NK1.1+), γδ T cells (CD45 IV-, CD11b-, CD3+, TCRγδ+), and T and NK cell negative lymphocytes (CD45 IV-, CD11b-, CD3-, NK1.1-). (n = 6; repeated twice). h, Comparison of IFNγ-YFP expression across various immune cells subsets in the brain. i, Quantification of IFNγ-YFP+ cells in CD4 T cells, CD8 T cells and NK cells in the brain with or without 4-hour cell stimulation (PMA–ionomycin with Brefeldin A). *P < 0.05, ***P < 0.001, ****P < 0.0001 by two-way ANOVA with Bonferroni’s multiple comparisons test (n = 2 per group).

Extended Data Fig. 3 Mouse brain T cells secrete various cytokines and express co-inhibitory receptors.

a, Single-cell suspensions isolated from brain, meninges, white fat and spleen were stimulated with PMA–ionomycin and incubated with Brefeldin A at 37 °C for four hours. Representative flow-cytometric plots of IFNγ and IL-17A double producing cells present in the brain at steady-state. b, Representative flow-cytometric expression of IL-17A from brain, meninges, white fat and spleen. Single-cell suspensions isolated from brain, meninges, white fat, colon, brown fat, liver, lung, ovaries, spleen, and lymph nodes (mesenteric (m)LN, inguinal (i)LN and deep cervical (dc)LN) were stimulated with PMA–ionomycin and incubated with GolgiPlug at 37 °C for four hours. ****P < 0.0001 one-way ANOVA with Tukey’s multiple comparison’s test (n = 4 per group pooled from 3 experiments). c, Representative flow-cytometric expression of transcription factor FOXP3 and cytokine IL-10 from steady-state brain, meninges, white fat and spleen. d, Representative flow-cytometric expression of transcription factor GATA3 and cytokine IL-4 staining in the steady-state brain, spleen and lung. e, Representative flow-cytometric expression of co-inhibitory receptors PD-1, TIGIT and LAG3 in the steady-state brain, spleen and lung.

Extended Data Fig. 4 Flow-cytometric analysis of T cells from human tissue.

a, Flow-cytometric analysis was performed on single-cell suspensions isolated from fresh brain samples (n = 2) obtained 4–5 h post-mortem. Representative flow gating strategy to isolate CD4 and CD8 T cells from the human autopsy samples; the CSF data was used as the example. CXCR3 and CXCR6 gating on CD4 and CD8 T cells were displayed for CSF and Blood samples. Gating strategy was kept the same across all organs used for the experiment. b, The frequency of CXCR3 of CXCR6-expressing CD8 T cells from patient 6 (Table 1) was graphed. The density of CD4 and CD8 T cells in the brain were generated from absolute counts using counting beads divided by weight of the original tissue sample. (n = 1) c, The frequency and density of CXCR3 or CXCR6-expressing CD4 T cells from patient 7 (Table 1) were graphed. The density of CD4 and CD8 T cells in the brain were generated from absolute counts using counting beads divided by weight of the original tissue sample. (n = 1) d, The frequency and density of CXCR3 or CXCR6-expressing CD4 T cells from patient 7 (Table 1) was graphed. The density of CD4 and CD8 T cells in the brain were generated from absolute counts using counting beads divided by weight of the original tissue sample. (n = 1).

Extended Data Fig. 5 RNA-seq analyses of T cells from various tissues.

a, UMAP generated from single-cell RNA-seq of T cells (FACS: CD45 intravenous label−, CD11b−, CD19−, B220− and RNA: Cd3e > 1) sorted from brain, meninges, gonadal white fat, colon lamina propria, mLN and blood of 5 pooled 10-week-old C57BL/6 mice. Comparison of gene expression across all tissue T cells representing various T cell differentiation states. b, Schematic of bulk RNA-seq experimental setup. c, PCA analysis of the CD4 T cells from brain, fat and meninges. (n = 8 per biological replicate represented as a dot in the PCA). DEGs (cut-off FDR < 0.05) from comparing brain/white fat and brain/meningeal CD4 T cells were used for QIAGEN IPA. Volcano plots represent DEGs (cut-off FDR < 0.05) from comparing brain/white fat, brain/meninges and white fat/meninges. d, PCA analysis of the CD4 T cells from brain, iLN and lung. (n = 8 per biological replicate represented as a dot in the PCA) The circles in the PCA plot represent the 95% confidence interval. Volcano plot representing DEGs (cut-off FDR < 0.05) from bulk RNA-seq analysis of brain/LN and brain/lung CD4 T cells. e, DEGs (cut-off FDR < 0.05) from comparing brain/white fat, brain/meningeal and white fat/meningeal CD4 T cells were used for QIAGEN IPA. Images in b were created in BioRender.

Extended Data Fig. 6 Fat–brain and gut–brain T cell axes exist at homeostasis.

a, Body fat percentage of mice fed a HFD (n = 3 per group; pooled 2 experiments). b, Tissue CD4 T cell numbers HFD-fed mice. (n = 7 per group; pooled 2 experiments). c, Body fat percentage of mice fasted for 48 h. (n = 9 per group; pooled 2 experiments). d, CD4 T cell numbers in mice fasted for 48 h. (n = 3 per group; brain, white fat, brown fat pooled 2 experiments; repeated twice). e, Body fat percentage changes in mice fed a HFD and then fasted. (n = 3 per group) f, Spleen and bone marrow CD4 T cell numbers as in e. (n = 3 per group; spleen = pooled 2 experiments). g, Representative FACS plots from fat-photoconverted mice. h, Brain IFNγ+ and total CD8 T cell numbers at various ages. (n = 3 per group; repeated once) i, Total CD4 and CD8 T cells and IFNγ-secreting CD8 T cell numbers in brains of various-aged GF and SPF mice. (n = 3 per group; repeated once). j, FACS analysis of brain CD4 T cells post antibiotic (ABX)-treatment. k, Same as in j, except for total CD4 T cells in other tissues. (n = 4 per group; repeated once). l, Representative FACS plots of adult and m, newly weaned mice that underwent gut or fat photoconversion. Proportion of photoconverted T cells quantified. (n = 3 per group; pooled 2 experiments) n, Brain CD4 T cell numbers in weaning-aged mice treated with anti-VLA4 and anti-LFA1. (n = 4 per group) o, Brain CD4 T cell numbers in weaning-aged mice treated with FTY720. (n = 4 per group). Statistical tests: unpaired t-test for a–d,j,k,n,o. Two-way ANOVA with Bonferroni’s multiple comparisons test for e,f,i. One-way ANOVA with Tukey’s multiple comparisons test for h,m. For all graphs, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Extended Data Fig. 7 TCRs in the brain are clonally expanded and highly overlap with certain tissues.

a, Percentage of unique T cell clones per tissue and line graph representing raw number of unique TCR clones in each tissue from the mouse single-cell TCR-seq dataset. b, Demultiplexed brain sample according to unique Hashtag antibody barcode (TotalSeq-C Hashtag 1–6) labelling each mouse. c, TCR clonal sharing as measured by the Morisita index (n = 5). d, FACS analysis of CD4 T cells in Smarta TCR (Vα2Vβ8) transgenic mouse. *P < 0.01 by unpaired t-test (n = 4 per group, pooled 2 experiments in white fat and mLN; repeated once). e, FACS analysis of Smarta Rag1–/– mice that were immunized using LPS as an adjuvant and co-administered with vehicle or GP61–80 peptide by oral route to prime antigens through the gut. (n = 4 per group) f, Same as in e, with an additional boost of vehicle, LPS or LPS and GP61–80 peptide (n = 3 per group). g, 50,000 naive 7B8 T cells were transferred into gnotobiotic mice administered GF or SFB faecal slurry. FACS analysis of the absolute number and frequency of TH17 7B8 T cells from white fat, mLN, small intestine and spleen 5 weeks later. *P < 0.05, **P < 0.01 by unpaired parametric t-test (n = 5 per group; repeated twice). h, Same as in g, except 50,000 naive 7B8 T cells were transferred into Jackson Laboratory (no SFB) or Taconic Biosciences (have SFB) mice. *P < 0.05 by unpaired parametric t-test *P < 0.05 by unpaired parametric t-test (n = 4 per group).

Extended Data Fig. 8 A gut–brain T cell axis is present in a healthy human.

a, Degree of TCR clonal expansion from T cell clones in the human single-cell RNA-seq dataset (CSF, blood 1 and 2, colon, duodenum) was analysed using ScRepertoire. Inverse Simpson Index, Chao Index, and Inverse Pielou Index applied to TCR clonal expansion. **P < 0.01, ***P < 0.001 and ****P < 0.0001 by one-way ANOVA (n = 5) b, Highlighted the T cells with shared TCR clone between CSF, colon and blood in each tissue’s UMAP graph. c, Volcano plot of DEGs after comparing clusters containing the shared TCR clone from each tissue (colon vs blood 2 and colon vs CSF). d, Trajectory analysis on mouse single-cell RNA-seq dataset from Extended Data Fig. 5 to identify genes highly expressed in brain T cells along the Slingshot pseudotime trajectory. e, FACS analysis on CXCR6-deficient mice for IFNγ and IL-17A-secreting CD4 T cells in the brain, white fat, and meninges and spleen. **P < 0.01 and ****P < 0.0001 by one-way ANOVA test (n = 3 per group) f, FACS analysis on IP10 (CXCL10)-deficient mice for total CD4 T cell numbers and proportion of CXCR3+IFNγ+ CD4 T cells in the brain. **P < 0.01 by unpaired t-test (n = 5 per group, outliers were calculated using Grubbs’ outlier test and excluded).

Extended Data Fig. 9 Behavioural tests performed in transgenic mice deficient in brain T cells.

a, Tcrb KO mice subjected to the LDT, b, OFT, c, TST and two days of FST. (Pooled 3 experiments (males) and 3 experiments (females)), d, Brain CD4 and CD8 T cell numbers in Ifng KO mice. (n = 4 per group pooled; repeated twice). e, Ifng KO mice subjected to LDT, f, OFT g, TST and FST. (Pooled 3 experiments (males) and 4 experiments (females)). h, Total and IFNγ+ brain CD4 and CD8 T cell numbers in Tbet KO mice (n = 3 per group; repeated twice). i, subjected to LDT, j, OFT, k, TST and FST (Pooled 3 experiments (males) and 4 experiments (females)). l, β-hydroxybutyrate (BHOB), triglycerides (TG) and non-esterified fatty acids (NEFA) measured in the serum. (n = 5 per group). m, CD4 and CD8 T cell numbers in Il17a–Il17f double-KO mice (n = 3 per group) n, subjected to the NSFT, o, LDT, p, OFT, q, TST and FST (Pooled 2 experiments (males and females), except OFT males). r, Representative FACS plot and bar graph of Tcrb KO mice adoptively transferred with wild-type (CD45.1) or Ifng KO CD4 T cells for 5 weeks. (n = 12 (males), 6 (females) per group; pooled 2 experiments) s, LDT, t, OFT u, TST and FST. (Pooled 4 experiments (males) and 5 experiments (females)). v, Same as in r, except with Ifng KO recipient mice. (n = 5 per group) w, LDT, x, NSFT, y, OFT, z TST and FST. (Pooled 3 experiments (males) and 2 experiments (females)). Statistical tests: unpaired t-test for d,h,m. Two-way ANOVA with Bonferroni’s multiple comparisons test for a–c,e–g,i–l,n–z. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Outliers were calculated using the ROUT outlier test (Q = 1%) and excluded.

Extended Data Fig. 10 Behavioural tests in additional transgenic mice deficient in brain T cells or IFNGR.

a, Smarta Rag KO or heterozygous (het) mice were subjected to the NSFT, b, LDT, c, OFT, d, TST and two days of FST. (Pooled 3 experiments for males and females). e, Cxcr6 KO mice were subjected to the NSFT, f, LDT, g, OFT, h, TST and FST. (Pooled 3 experiments for males and females). i, Ifngr1f/fCx3cr1-cre mice were subjected to the LDT, j, OFT, k, TST and FST. (Pooled 4 experiments for males and females) l, Bulk RNA-seq of FACS sorted microglia from wild-type and Tbet KO mice. Separate PCA plot by sex. m, Volcano plot of DEGs. DEGs (cut-off FDR < 0.05) were used for QIAGEN IPA. n, Ifngr1f/fNestin-cre mice were subjected to the LDT, o, OFT, p, TST and FST. q, Ifngr1f/fGfap-cre mice were subjected to the LDT, r, OFT, s, TST and FST. t, Single-cell suspensions were isolated from the brains of Ifng KO mice and prepared for FACS to identify microglia and astrocytes. (n = 3 per group; pooled 2 experiments for females) Statistical tests: *P < 0.05, **P < 0.01 and ***P < 0.001 by two-way ANOVA with Bonferroni’s multiple comparisons test for a–k,n–t. Outliers were calculated using the ROUT outlier test (Q = 1%) and excluded. Images in l were created in BioRender.

Supplementary information

Reporting Summary

Supplementary Table 1

Differentially expressed gene list from single-cell RNA-seq of mouse T cells from brain and dura meninges. Legend: p_val= p-value; avg_log2FC= average log2 fold change; pct.1= Proportion of cells in group 1 expressing the gene; pct.2= Proportion of cells in group 1 expressing the gene; p_val_adj= adjusted p-value; cluster= assigned cluster number; gene = gene name.

Supplementary Table 2

Differentially expressed gene list from single-cell RNA-seq of mouse T cells from brain, meninges, white fat, colon, lung and blood. Legend: p_val= p-value; avg_log2FC= average log2 fold change; pct.1= Proportion of cells in group 1 expressing the gene; pct.2= Proportion of cells in group 1 expressing the gene; p_val_adj= adjusted p-value; cluster= assigned cluster number; gene = gene name.

Supplementary Table 3

Differentially expressed gene list from single-cell RNA-seq of mouse T cells from brain, meninges, white fat, colon, mesenteric lymph node and blood. Legend: p_val= p-value; avg_log2FC= average log2 fold change; pct.1= Proportion of cells in group 1 expressing the gene; pct.2= Proportion of cells in group 1 expressing the gene; p_val_adj= adjusted p-value; cluster= assigned cluster number; gene = gene name.

Supplementary Table 4

Differentially expressed gene list from bulk RNA-seq of mouse CD4 T cells from brain vs lung. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name ; mean_BrCD4= mean gene expression of all Brain CD4 T cell samples; mean_LungCD4= mean gene expression of all Brain CD4 T cell samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; BrCD4_1_A= Brain CD4 T cell sample 1 (8 mice pooled); BrCD4_1_B= Technical replicate of Brain sample 1; BrCD4_5_A= Brain sample 5 (8 mice pooled); BrCD4_5_B= Technical replicate of Brain sample 5 (8 mice pooled); BrCD4_7_A= Brain sample 7 (8 mice pooled); BrCD4_7_B= Technical replicate of Brain sample 7 (8 mice pooled); Lung4_1= Lung CD4 T cell sample 1; Lung4_2= Lung CD4 T cell sample 2; Lung4_3= Lung CD4 T cell sample 3; Lung4_4= Lung CD4 T cell sample 4; Lung4_5= Lung CD4 T cell sample 5; Lung4_6= Lung CD4 T cell sample 6; Lung4_7= Lung CD4 T cell sample 7.

Supplementary Table 5

Differentially expressed gene list from bulk RNA-seq of mouse CD4 T cells from brain vs inguinal lymph node. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name ; mean_BrCD4= mean gene expression of all Brain CD4 T cell samples; mean_LnCD4= mean gene expression of all Lymph node CD4 T cell samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; BrCD4_1_A= Brain CD4 T cell sample 1 (8 mice pooled); BrCD4_1_B= Technical replicate of Brain sample 1; BrCD4_5_A= Brain sample 5 (8 mice pooled); BrCD4_5_B= Technical replicate of Brain sample 5 (8 mice pooled); BrCD4_7_A= Brain sample 7 (8 mice pooled); BrCD4_7_B= Technical replicate of Brain sample 7 (8 mice pooled); Ln4_1= Lymph node CD4 T cell sample 1; Ln4_2= Lymph node CD4 T cell sample 2; Ln4_3= Lymph node CD4 T cell sample 3; Ln4_4= Lymph node CD4 T cell sample 4; Ln4_5= Lymph node CD4 T cell sample 5; Ln4_6= Lymph node CD4 T cell sample 6; Ln4_7= Lymph node CD4 T cell sample 7.

Supplementary Table 6

Differentially expressed gene list from bulk RNA-seq of mouse CD4 T cells from brain vs white fat. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name; mean_Brain= mean gene expression of all Brain CD4 T cell samples; mean_Fat= mean gene expression of all Fat CD4 T cell samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; X9_11_Brain= Brain CD4 T cell sample 1 (8 mice pooled); X9_12_Brain= Brain sample 2 (8 mice pooled); X9_13_Brain= Brain sample 3 (8 mice pooled); X9_5_Brain= Brain sample 4 (8 mice pooled); X9_11_Fat= White gonadal fat CD4 T cell sample 1 (8 mice pooled); X9_12_Fat= White gonadal fat CD4 T cell sample 2 (8 mice pooled); X9_13_Fat= White gonadal fat CD4 T cell sample 3 (8 mice pooled); X9_5_Fat= White gonadal fat CD4 T cell sample 4 (8 mice pooled).

Supplementary Table 7

Differentially expressed gene list from bulk RNA-seq of mouse CD4 T cells from brain vs dura meninges. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name; mean_Brain= mean gene expression of all Brain CD4 T cell samples; mean_Meningeal= mean gene expression of all Meningeal CD4 T cell samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; X9_11_Brain= Brain CD4 T cell sample 1 (8 mice pooled); X9_12_Brain= Brain sample 2 (8 mice pooled); X9_13_Brain= Brain sample 3 (8 mice pooled); X9_5_Brain= Brain sample 4 (8 mice pooled); X9_11_M= Meningeal CD4 T cell sample 1 (8 mice pooled); X9_12_M= Meningeal CD4 T cell sample 2 (8 mice pooled); X9_13_M= Meningeal CD4 T cell sample 3 (8 mice pooled); X9_5_M= Meningeal CD4 T cell sample 4 (8 mice pooled).

Supplementary Table 8

Differentially expressed gene list from bulk RNA-seq of mouse CD4 T cells from white fat vs dura meninges. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name; mean_Fat= mean gene expression of all Fat CD4 T cell samples; mean_Meningeal= mean gene expression of all Meningeal CD4 T cell samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; X9_11_Fat= White gonadal fat CD4 T cell sample 1 (8 mice pooled); X9_12_Fat= White gonadal fat CD4 T cell sample 2 (8 mice pooled); X9_13_Fat= White gonadal fat CD4 T cell sample 3 (8 mice pooled); X9_5_Fat= White gonadal fat CD4 T cell sample 4 (8 mice pooled); X9_11_M= Meningeal CD4 T cell sample 1 (8 mice pooled); X9_12_M= Meningeal CD4 T cell sample 2 (8 mice pooled); X9_13_M= Meningeal CD4 T cell sample 3 (8 mice pooled); X9_5_M= Meningeal CD4 T cell sample 4 (8 mice pooled).

Supplementary Table 9

Differentially expressed gene list from single-cell RNA-seq of human T cells from CSF vs duodenum. Legend: names= canonical gene names; scores=; pvals= p-value; pvals_adj= adjusted p-value; logfoldchanges= average log2 fold change.

Supplementary Table 10

Differentially expressed gene list from single-cell RNA-seq of human T cells from CSF vs PBMCs. Legend: names= canonical gene names; scores=; pvals= p-value; pvals_adj= adjusted p-value; logfoldchanges= average log2 fold change.

Supplementary Table 11

Differentially expressed gene list from bulk RNA-seq of microglia from Tbet KO and wild-type mice. Legend: Genever = Ensemble gene ID with specific gene version; GeneID= Ensemble gene ID; GeneName= canonical gene name; mean_TbetF= mean gene expression of all Tbet KO microglia samples; mean_B6F= mean gene expression of all wild-type microglia samples; log2FoldChange= average log2 fold change; pvalue= p-value; padj= adjusted p-value; Tbet_4= Tbet KO microglia sample 4; Tbet_5= Tbet KO microglia sample 5; Tbet_6= Tbet KO microglia sample 6; B6_4= Wild-type microglia sample 4; B6_5= Wild-type microglia sample 5; B6_6= Wild-type microglia sample 6.

Supplementary Video 1

z-stack video generated on ImageJ for a representative image of an extravascular T cell in the white matter corpus callosum (Fig. 1a).

Supplementary Video 2

z-stack video generated on ImageJ for a representative image of extravascular T cells in dura meninges (Extended Data Fig. 1b).

Supplementary Video 3

z-stack video generated on ImageJ for a representative image of T cells in the SFO (Fig. 1d).

Supplementary Video 4

3D-projection video generated on LAS/X software of the SFO (Fig. 1d and Supplementary Video 3).

Supplementary Video 5

z-stack video generated on ImageJ for a representative image of vascular adjacent T cells in the white matter stria terminalis (Extended Data Fig. 1c).

Supplementary Video 6

z-stack video generated on ImageJ for a representative image of T cells in the choroid plexus (Extended Data Fig. 1d).

Supplementary Video 7

Video of the whole-brain imaging conducted on Ai14 CD4-Cre mice, in which cells that have previously or currently express CD4 also express tdTomato. The 3D images were rendered using Imaris and iMovie was used to generate the composite video.

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Yoshida, T.M., Nguyen, M., Zhang, L. et al. The subfornical organ is a nucleus for gut-derived T cells that regulate behaviour. Nature 643, 499–508 (2025). https://doi.org/10.1038/s41586-025-09050-7

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