Ocular immune privilege in action: the living eye imposes unique regulatory and anergic gene signatures on uveitogenic T cells

Ocular immune privilege in action: the living eye imposes unique regulatory and anergic gene signatures on uveitogenic T cells | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Ocular immune privilege in action: the living eye imposes unique regulatory and anergic gene signatures on uveitogenic T cells Zixuan Peng , Vijayaraj Nagarajan , Reiko Horai , Yingyos Jittayasothorn , Mary J. Mattapallil , View ORCID Profile Rachel R. Caspi doi: https://doi.org/10.1101/2025.03.01.640701 Zixuan Peng 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA 2 Department of Ophthalmology, Xiangya Hospital, Central South University , Changsha, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vijayaraj Nagarajan 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Reiko Horai 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yingyos Jittayasothorn 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mary J. Mattapallil 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: caspir{at}nei.nih.gov mattapallilm{at}nei.nih.gov Rachel R. Caspi 1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel R. Caspi For correspondence: caspir{at}nei.nih.gov mattapallilm{at}nei.nih.gov Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF SUMMARY Despite ocular immune privilege, circulating retina-specific T cells can trigger autoimmune uveitis, yet intraocular bleeding—a relatively common event—rarely leads to disease. Using an in vivo immune privilege model, we previously reported that all naïve retina-specific T cells entering the eye become primed in situ ; about Ob% become FoxpO+ T-regulatory cells (Tregs), while the rest fail to induce pathology. Here, single-cell transcriptomics and functional validation revealed distinct phenotypes in both populations: ocular Tregs were highly suppressive, whereas non-Tregs expressed suppression- and anergy-associated genes and lacked regulatory function. Trajectory analyses suggested that Tregs and anergic cells arise from a common proliferative precursor in parallel, rather than sequentially. Our data indicate a key checkpoint governing the divergence of anergic and regulatory fates. These findings provide molecular-level insights into ocular immune privilege and may inform strategies to silence autoimmune effector cells or reverse T cell unresponsiveness in cancer, vaccination, or chronic infection. Download figure Open in new tab Highlights Eye-primed retina-specific T cells develop distinct tolerance-associated phenotypes. Eye-induced anergic T cells remain hyporesponsive to antigen re-stimulation. Regulatory and anergic T cells differentiate in parallel from a common precursor. Induction of T cell anergy is a novel feature of ocular immune privilege. INTRODUCTION The eye has developed evolutionary adaptations that limit local inflammation in order to protect vision, that collectively form the complex phenomenon known as ocular immune privilege 1 , 2 . In addition to the physical blood-tissue barriers that separate the eye from the immune system, multiple studies described that the intraocular environment, composed of ocular fluids and ocular resident cells, is immunosuppressive and can inhibit the activity of immunocompetent cells 3 , 4 , 5 . Aqueous humor (AH) has been shown to reduce proinflammatory cytokine production by T cells in culture and to promote induction of regulatory T cells (Tregs) 6 , 7 . Soluble factors involved in these processes include transforming growth factor-beta (TGF-β), α-melanocyte-stimulating hormone (α-MSH), vasoactive intestinal peptide (VIP), retinoic acid (RA), and others 5 , 2 . Retinal glial Müller cells were the first ocular resident cells shown to inhibit T cells in co-culture 8 . Since then, many reports described induction of Tregs by pigmented epithelia in the front and back of the eye 3 , 9 . Immunomodulatory molecules expressed by these cells include CDhi 10 and PD-LT 11 that engage the inhibitory receptors CTLA-E and PD-T on T cells, respectively, as well as membrane-bound or soluble TGF-β and CTLA-Yα 12 . Retinal microglia and dendritic-like cells also have been reported to inhibit antigen-specific T cell responses and to induce Tregs, possibly through aberrant antigen presentation 13 , 14 , 15 . However, these studies were conducted largely in vitro , and could not represent the complexity of the living eye. Further, many predated the discovery of Forkhead box PO (FoxpO) as a marker for Tregs, making it difficult to distinguish de novo induction of Tregs, from expansion of a preexisting Treg population. Immune sequestration of unique retinal antigens (Ag), which are absent in the periphery, behind a blood-retinal barrier impedes development of peripheral tolerance in autoreactive T cells that escaped thymic negative selection 2 , 16 . Such cells can be easily triggered to become pathogenic effectors, but nevertheless, autoimmune uveitis remains a relatively rare disease 17 . To address the question how the eye maintains immune homeostasis, we established a mouse model in which retina-specific T cells, capable of inducing autoimmune uveitis, are injected into the eye 18 . This model exposes the eye to naïve but non-tolerant T cells, as would occur in case of intraocular bleeding as a result of trauma or vascular abnormalities (e.g., macular degeneration, diabetic retinopathy, or neovascular glaucoma) 19 , 20 . Interestingly, the T cells acquired an antigen-experienced (primed) phenotype within the eye but failed to induce uveitis. While ∼Ob% converted to FoxpO + Tregs, the majority did not, and produced detectable levels of IFN-γ and IL-TUA 18 . However, the fate and function of these eye-primed cells could not be determined, due to lack of appropriate technology. In the current study, we utilized single-cell RNA sequencing (scRNA-seq) to comprehensively define the transcriptome of retina-specific T cells responding to their cognate antigen in the privileged intraocular environment. We present evidence that the non-FoxpO converted population is not effectors being kept in check by the Tregs, but rather represents a novel anergic phenotype unique to the eye that differentiates in parallel with FoxpO + Tregs from naive retina-specific T cells. Our findings are the first to dissect the phenomenon of ocular immune privilege at the molecular level. RESULTS Naive retina-specific T cells differentiate into several distinct subtypes within the ocular environment To gain better insight into the transcriptomic landscape that naive retina-specific T cells acquire within the eye, we performed scRNA-seq using the in vivo ocular immune privilege model. Briefly, naïve T cells were fluorescence-activated cell sorting (FACS)-sorted from Tcra −/− RTiTH FoxpO GFP CDlb.Y mice and were intravitreally injected into the eyes of WT CDlb.T congenic recipients ( Fig. 1A ). The gating strategy for obtaining naive retina-specific non-Treg T cells from donors is depicted in Fig. 1B . One week after the injection, CDE + CDlb.Y + donor-derived T cells were retrieved from the eyes of CDlb.T congenic recipients. As we reported previously, about a third (OT.h%) of the cells converted to FoxpO + phenotype ( Fig. 1C ), and represent functionally competent Tregs 18 . The naive retina-specific T cells before intravitreal injection and the injected cells retrieved from the recipient’s eyes were labeled by hashtag oligos before pooling and subjected to scRNA-seq ( Fig. 1A and Table S1). Download figure Open in new tab Fig. 1: Naive retina-specific T cells differentiate into several distinct subtypes within the ocular environment (A) Retina-specific naive T cells were sorted from peripheral lymphoid tissues (spleens and lymph nodes) of Tcra-/-R161H Foxp3GFP reporter donor mice and injected into the eyes of CD90.1 congenic WT recipients. One week later, the CD90.2 donor T cells (eye-primed T cells) were retrieved from both eyes of 4 recipients. Naive and eye-primed T cells were subjected to scRNA-seq. (B) Gating strategy for the naive donor cell sorting (CD4+CD44lowFoxp3GFP-). (C) CD90.2+ CD4+ T cells retrieved from eyes of recipients were analyzed for the Foxp3 expression by flow cytometry. (D) UMAP showing naive T cells and eye-primed T cells. (E) Feature plots showing expression of antigen-primed T cell phenotype (CD44+ CD62L-), Treg maker (Foxp3) and proliferation marker (Mki67) in eye-primed T cells indicated by purple color. (F) Cluster identification and (G) Ratios of T cell subtypes of eye-primed T cells. Using standard scRNA-seq analysis pipelines 21 , cells that passed quality control were used for downstream analyses. Cells before- and after-injection into the eyes showed a clear division in the UMAP ( Fig. 1D ). A distinct pattern of high Cd44 and almost no CDiYL ( Sell ) expression was confirmed in all cells recovered from the eyes, indicating that they had been primed ( Fig. 1E ). The great majority of Foxp3 + Tregs formed one separate cluster, while a few Foxp3 + Tregs were detected in the neighboring cluster with high levels of the proliferation marker Mki67 ( Fig. 1E ). This was in line with our previous finding that acquisition of FoxpO expression in the ocular environment was accompanied by proliferation 18 . Unsupervised clustering further uncovered five major populations ( Fig. 1F ) based on their differentially expressed genes ( Figure S1A ). Among them, naive ( Foxp1, Lef1, Satb1 ), Treg ( Foxp3 and Il10 ), and proliferative ( Mki67, Stmn1 , Top2a ) clusters were straightforward to annotate ( Fig. 1E and Figure S1A ). However, we did not find a clear pattern of gene expression to help classify the other two Foxp3 -negative clusters as known lineages ( Figure S1A ). Therefore, for lack of a better definition, they are designated as “non-FoxpO converted clusters” (nfcT and nfcY) in the interim. The proportions of the T cell subpopulations recovered from recipients’ eyes are shown in Fig. 1G ). Taken together, the scRNA-seq data revealed that the naive T cells exposed to their cognate antigen within the ocular environment differentiated from a homogeneous population into diverse subtypes, with Treg cells constituting one of several discrete populations. Download figure Open in new tab Figure. S1: Transcriptional landscape suggests non-Foxp3-converted subsets are composed of Th-lineage-negative cells (A) Heatmap of the top 10 differentially expressed genes in each cluster vs. all other clusters, showing scaled expression of the top 10 genes in each cluster. (B) Violin plots showing expression of Th effector lineage-related transcription factors (TFs), cytokines, chemokines/chemokine receptors: Th17-related ( Csf2, Il17f, Il22 , and Il23r) , Th2-associated ( Ccr3, Ccr8, Il4, Il5, Il9), Tfh-associated ( Cxcr5, Cxcl13, Il21), Th9-associated ( Spi1 , encoding PU.1, Il4ra , Il9), and Th22-associated ( Ahr , Ccr4 , Ccl7 , Il13 , Il22 ). (C) Violin plots showing the absence of Tr1-associated gene Itga2 (encoding CD49b), and genes for TGF-β2, TGF-β3 and IL-35 ( Il12a, Ebi3 ). Non-FoxpK-converted (nfc) clusters do not conform to canonical gene patterns of known effector Th-lineages To assess the phenotype of the non-FoxpO converted (nfc) cells, we screened defining gene sets, including master transcription factors (TFs), signature cytokines, chemokines and surface molecules characteristic of the known major T-effector lineages. Levels of ThT and ThTU lineage-defining transcription factors, Tbx21 for ThT and Rorc for ThTU, respectively, were relatively low across all four eye-primed clusters, and not restricted to any particular cluster ( Fig. 2A-B ). ThT-associated surface markers ( Ccr5 and Cxcr3) 22 , were confined to the Treg cluster, but absent in the nfc clusters ( Fig. 2B ). Additionally, Ifng, and Csf2 , key pro-inflammatory ThT cytokines, were undetectable in both nfc clusters ( Fig. 2A ), suggesting there is no ThT induction. Although a moderate Il17a was present in nfcY cluster ( Fig. 2A ), other ThTU signature genes ( Csf2, Il17f, Il22 , and Il23r) 23 , 24 were not detected ( Fig. 2A and Figure S1B ). The ThTU surface marker Ccr6 was also not prominently expressed in nfcY ( Fig. 2B ), making it inconsistent with a canonical ThTU profile. The nfcT cluster exhibited modest expression of TF Gata3 for ThY and Bcl6 for T follicular helper (Tfh) cells ( Fig. 2A ), yet lacked other ThY-associated ( Ccr3, CcrL, Il4, Il5, IlM ) or Tfh-associated ( Cxcr5, Cxcl13, Il21 ) genes ( Figure S1B ), suggesting that nfcT does not align with either ThY or Tfh lineage. Download figure Open in new tab Fig. 2: Non-Foxp3-converted (nfc) clusters do not conform to canonical gene patterns of effector Th-lineages (A) Violin plots showing the frequency of cells expressing Th lineage-specific transcription factors (TFs) and cytokines in each cluster. (B) Feature plots showing the distribution of selected Th lineage-specific markers including TFs, cytokines and chemokine receptors. (C) Feature plots showing distribution of markers associated with a naïve-like phenotype (Ccr7, Lef1, Tcf7). (D) FACS plots showing LAP-1 (encoded by the Tgfb1 gene) and IL-10 expression in Foxp3-converted vs. non-Foxp3-converted recovered from eyes of recipient mice after 1 week. LT=lymphoid tissue T cells from naïve R161H donor mice. Thl-related ( Spi1 , Il4ra , IlM ) and ThYY-related ( Ahr , Ccr4 , Ccl7, Il13 , Il22 ) genes were not observed either ( Figure S1B ), so these two lineages have no similarity with the nfc clusters. Interestingly, the nfcT and a part of the nfcY populations shared several T cell markers with naïve or resting cells ( Ccr7 , Lef1 , and Tcf7 ) 25 ( Fig. 2C ). We next considered FoxpO-negative regulatory phenotypes. Type T T-regulatory (TrT) cells are featured as IL-Tb producers independent of FoxpO 26 , but the nfc cells showed little to no Il10 expression nor IL-Tb production ( Fig. 2A-B and Fig. 2D ), and this would argue against them being TrT cells. Instead, both nfc clusters showed high expression of Tgfb1 ( Fig. 2A-B ), consistent with a ThO phenotype involved in mucosal immune regulation and oral tolerance 27 . While they expressed LAP-T (latency-associated peptide, a product of Tgfb1 ), unlike some gut Tregs, they did not express IL-Tb 28 ( Fig. 2D ). Of note, Tgfb2, Tgfb3 and the immunosuppressive cytokine IL-OV ( Il12a and Ebi3 ) were undetectable in all eye-primed clusters ( Figure S1C ). These data support the conclusion that the nfc clusters are Th-lineage-negative, and as such, are unlikely to represent known pathogenic effector cells or canonical Tregs. The nfc clusters exhibit a combination of regulatory/anergic gene signature To better characterize the nfc clusters, we then investigated their global transcription profile. Gene signatures of each subset in the eye were defined by comparing their transcriptomes to that of naive T cells as baseline ( Fig. 3A and Table S2). Both nfc clusters expressed high levels of anergy-associated genes as Nrgn 29 , 30 , Cblb 31 , Dgkz 32 , Nr4a1-3 33 , 34 , Nrp1 , Tox , and Tox2 16 , 35 , 36 . They also expressed canonical Treg-related genes, including inhibitory checkpoint molecules ( Nt5e , Maf , Itgav , Il2rb , Tnfrsf4 , Tgfb1, Ctla4 , Lag3 ) 37 – 41 ( Fig. 3A ), and these were shared with the Treg cluster. Of note, Ccl5 , S100a4 , S100a6 , Tbx21 , and Gzmb, which characterize activated, highly suppressive Tregs 38 – 40 and were present in the Treg cluster, were not shared with the nfc populations ( Fig. 3A ). Download figure Open in new tab Fig. 3: Intraocular environment induces regulatory and anergy signatures (A) Volcano plots showing the differential gene expression of each cluster (baseline: naive cluster). Differentially expressed genes with more than 2-fold changes are highlighted in red (upregulated) or blue (downregulated). Representative signature genes are shown. (B-E) Representative GSEA enrichment results mapping the signatures of each cluster against the Molecular Signature Database (MSigDB). NES, normalized enrichment score, indicating the similarity of the current gene signatures with predefined gene sets. Nominal P value lower than 0.05 denotes significant similarity to the corresponding to the predefined gene set in B, C, D or E. (B) Signature of Th17 cells. (C) Signature of de novo converted Treg cells in vivo (also known as peripherally induced Tregs). (D) Signature of in vivo Treg cells in lymphoid tissues (spleen, thymus, and lymph nodes). (E) Curated anergy signature from canonical in vitro anergy-inducing conditions We then performed Gene Set Enrichment Analysis (GSEA) to align the signatures of eye-primed T cell clusters with predefined gene sets in the Molecular Signature Database (MSigDB) 42 ( Fig. 3B-E ). In spite of upregulated Il17a ( Fig. 3A , middle), the nfcY cluster did not have a characteristic ThTU signature 43 ( Fig. 3B ). Rather, the two nfc clusters appeared to share characteristics with Treg cells that had been de novo differentiated in non-ocular tissues in vivo 37 ( Fig. 3C ), Tregs isolated from lymphoid tissues of healthy mice 44 ( Fig. 3D ) and to in vitro induced anergic T cells 45 ( Fig. 3E ). The Treg cluster also shared genes with the anergic signature ( Fig. 3E ), supporting the notion that many phenotypic and mechanistic traits are shared between Treg and anergic T cells defined by other studies 46 – 48 . Given that the nfc clusters lacked the defining Treg gene FoxpO, in the aggregate, they conformed best to the ‘T lymphocyte anergy’ gene set. The nfc clusters are hyporesponsive to antigenic stimulation but lack suppressive function The results above suggested that the non-FoxpO-converted clusters were more reminiscent of Treg cells than of other Th cell lineages, and strongly resembled anergic T cells. Previous studies had demonstrated that a functional characteristic of anergy is hyporesponsiveness to antigenic stimulation, which can be rescued by IL-Y 49 . To examine whether this was true of our nfc populations, we separated FoxpO+ and FoxpO– cells retrieved from the eye by flow sorting. Tregs and naïve T cells from peripheral lymphoid tissues (LT) of Tcra −/− RTiTH FoxpO GFP transgenic mice were used for comparison. Proliferation was measured by [ 3 H]-Thymidine incorporation in a co-culture system with antigen presenting cells (APC) and Interphotoreceptor Retinoid-Binding Protein (IRBP) peptide as cognate antigen (Ag) ( Fig. 4A ). Eye-induced non-FoxpO-converted cells exhibited minimal proliferation, which was considerably enhanced in the presence of IL-Y, although it remained markedly lower than that of the naïve cells ( Fig. 4B ). As expected 50 , Treg cells also responded to IL-Y supplementation. Together with Fig 3 , we interpret our data to mean that (a) the phenotype of the non-FoxpO converted T cells is consistent with anergy, and (b) these cells remain hyporesponsive to their cognate antigen independently of continued presence of FoxpO + Tregs and of the ocular environment. Download figure Open in new tab Fig. 4: Non-Foxp3-converted (nfc) cells are functionally hyporesponsive to cognate antigen but not suppressive (A) Diagram of co-culture system in B. Naïve T cells or Tregs from peripheral lymphoid tissues (LT) were sorted from Tcra-/-R161H Foxp3GFP mice. Donor-derived Tcra-/-R161H Foxp3GFP+ and Foxp3GFP– cells were sorted from recipients’ eyes. Antigen presenting CD11c+ cells were magnetically enriched from the spleen of WT B10.RIII mice. (B) Proliferation to the cognate antigen human IRBP161-180 in the presence or absence of IL-2. Cells without antigen served as background. Only one or two wells of eye-induced Treg cells could be set up per experiment. Shown is one representative experiment of 3. (C) Scheme for antigen-specific suppression assay by [3H]-Thymidine uptake and dye dilution methods for D and E. (D) Dose-dependent suppression of T responder cells by putative suppressors with antigen stimulation (triplicates). One representative experiment of two. (E) FACS plots of proliferation dye dilution. Tresp without antigen served as background. Hyporesponsiveness to antigen that can be rescued by IL-Y is consistent with anergy, but is not necessarily a distinguishing attribute from Tregs. To resolve this, we compared the ability of eye-induced Treg and non-FoxpO-converted (FoxpO GFP – ) T cells to suppress proliferation of naïve responders (Tresp) to their cognate Ag (IRBP peptide presented on splenic APC, Fig. 4C ). We used two complementary methods: [ 3 H]-Thymidine uptake and proliferation dye dilution, to exclude interference from possible proliferation of the Tregs themselves. Eye-induced Tregs and LT Tregs suppressed thymidine uptake by Tresp in a dose-dependent fashion. In contrast, the eye FoxpO GFP – cells failed to significantly inhibit Tresp proliferation even at a T:Y Treg:Tresp ratio ( Fig. 4D ). Dye dilution analysis confirmed that the proliferation rate of Tresp in the presence of anergic cells did not differ from control, indicating that FoxpO GFP – cells lacked appreciable suppressor function in vitro ( Fig. 4E ). Therefore, from here on, the non-FoxpO-converted nfcT and nfcY clusters will be referred to as ‘anergicT’ and ‘anergicY’, respectively. Distinct genes contribute to dampened TCR signaling in eye-induced anergic and regulatory cells To address the question of what signaling molecules and pathways were involved in the induction the anergic and regulatory phenotypes, we performed an Ingenuity Pathway Analysis (IPA). Compared to the naïve cluster, pathways related to T cell suppression (T cell exhaustion, immunogenic cell death and TNFRY signals) and deficient T cell receptor (TCR) signaling (downregulated T cell receptor, CDYh, and ICOS signals) that restrain effector differentiation, were prominent in both Treg and anergic T cell clusters ( Fig. 5A ). Co-inhibitory genes that may feed into this pattern Ctla4 and Lag3 51 , 52 were significantly upregulated in all eye-primed cell clusters, and their expression was highest in the Treg cluster, whereas Pdcd1 (encoding PD-T), Fasl and Cd200 were mainly increased in anergic clusters ( Fig. 5B ). Activation of TGF-β signaling in the anergic clusters ( Fig. 5A ) aligns with increased expression of TGF-β receptors ( Tgbr1–3 ) ( Fig. 5C ), suggesting a connection between TGF-β signaling and the anergic state. Download figure Open in new tab Fig. 5: Suboptimal TCR signaling and inhibitory signals are involved in eye-induced tolerance (A) Heatmap of differentially regulated immune-related signaling pathways in each T cell population compared to the naive baseline. Z score was calculated by Ingenuity Pathway Analysis (IPA). (B-C) Violin plots showing expression of canonical inhibitory markers (B), and TGF-β receptors (C). (D) Violin plots displaying gene expression of anergy-associated factors. (E) Bubble plot showing frequency of positive cells (size of bubble) and expression levels (color gradient) of genes involved in TCR/CD28 and MAPK signaling pathways.. Compared to Tregs, the anergic populations expressed higher levels of anergy-inducing factors such as NFAT (nuclear factor of activated T cells, encoded by Nfatc1/2 ) family members 53 , 54 , NREA (nuclear receptor subfamily EA, encoded by Nr4a1–3 ) family members 33 , 34 , CBL-B 31 , and DGKζ 32 , which are causally related to hyporesponsiveness, by affecting multiple components participating in TCR signal transduction 16 , 55 ( Fig. 5D ). This is consistent with the low expression of molecules downstream of TCR/CDYh pathways, such as pyruvate dehydrogenase kinase T ( Pdk1) , PIOKs ( Pik3r5, Pik3cd ), and PKCs ( Prkcb, Prkcq ) ( Fig. 5E ), supporting dampened TCR/CDYh signaling. Distinct as well as shared tolerance-inducing regulons are enriched in anergic and Treg clusters To identify potential key transcription factors (TF) regulating anergic and Treg subsets, we used the SCENIC (Single-Cell rEgulatory Network Inference and Clustering) pipeline, which infers TF activity from the co-expression of its direct target genes, collectively forming a regulon 56 ( Fig. 6A ). This provides a more precise readout of a cell’s state of differentiation than the TF mRNA expression alone. Download figure Open in new tab Fig. 6: Distinct tolerance-inducing regulons and markers identify anergic and Treg cells (A) Workflow of regulon identification by SCENIC (single-cell regulatory network inference and clustering) analysis. (B) Representative regulons enriched in eye-primed T cell subpopulations and naive T cells are marked with arrows in the heatmap. (C) Visualization of the regulon activity overlaid on the UMAP. (D and E) Reciprocal expression of Foxp3 and Nrgn in Treg and non-Treg (anergic) clusters by RNA (D) and protein (E). Each of the anergic T clusters exhibited enrichment of distinct regulon activity. However, both clusters showed enrichment of regulons Egr2 , Egr3 45 , Nr4a2, and Klf4 57 ( Fig. 6B ). These TFs have been implicated in attenuating pathogenic T cell responses 33 . Egr-Y in particular is considered a ‘master TF’ of anergy that directly upregulates CBL-B, DGKζ and other anergy-associated genes 31 , 45 , 58 . In the Treg cluster, FoxpO and its ‘accessory’ TFs Prdm1 (encoding BlimpT), Ikzf2 (Helios) and Mbd2 (MbdY), that are known to promote and stabilize Treg function 59 – 62 exhibited increased regulon activities 59 ( Fig. 6B ). Enrichment of those FoxpO accessory regulons is compatible with the high suppressive function of the eye-induced Treg cells that we observed ( Fig. 4D ). A total of YVV regulons were identified as active within our dataset (Table S3). Of note, regulons corresponding to T-bet and RORγt were not enriched, confirming that eye-primed populations did not appear to be ThT or ThTU effectors. Regulons active in both anergic and Treg clusters were Nfatc1 and Nfatc2 ( Fig. 6C ), which aligns with the known tolerogenic role of the NFAT family in both anergic cells and Tregs. In the former, they cooperate with NREA and EGRY/O, to repress effector cytokines (IFN-γ, GM-CSF) and to induce inhibitory regulators CTLA-E, PD-T, LAGO, CBL-B and DGKζ 58 , 63 , and in the latter NFAT-FoxpO interaction upregulates Treg markers CTLA-E and CDYV, contributing to suppressor function 54 , 64 . Moreover, we noticed the presence of regulons for the nuclear receptors for retinoic acid, Rara and Rxra . Of note, Rara regulon was highly activated in Tregs, whereas Rxra regulon was preferentially activated in anergic cells ( Fig. 6B and 6C ), suggesting that although RA regulates both Tregs and anergic cells, they rely on different receptors for RA mediated functions. RARA is associated with FoxpO + Treg development 65 ; however, since the anergic cluster lacks FoxpO, we looked for molecules downstream of the RXRA receptor. One gene known to be downstream of Rxra , Nrgn 66 , was among the most highly expressed genes in the anergic clusters ( Fig. 3A and Fig. 6D ), and the top gene in the anergicT cluster. Notably, its expression closely matched the distribution of Rxra regulon activity ( Fig. 6C-D ) and was mutually exclusive with FoxpO expression ( Fig. 6D ). Control lymphoid tissue (LT) CDE + cells, whether FoxpO + or FoxpO - , lacked neurogranin (encoded by Nrgn ) expression ( Fig. 6E ). Nrgn has normally been associated with neurons 67 ; here, we demonstrate high expression of Nrgn as well as its protein product, neurogranin, restricted to the eye-derived FoxpO-negative, i.e. anergic cells. Eye-induced Tregs and anergic cells seem to differentiate in parallel rather than sequentially An important question in understanding the development of Treg and anergic cell fates is whether they differentiate as separate lineages or whether one derives from the other. To address this question, we performed trajectory analyses using RNA velocity 68 , 69 and Monocle pseudo-time trajectory analyses 70 . RNA velocity can infer the direction of cellular state changes and estimate the future state based on the relative abundance of spliced and un-spliced transcripts 68 . After excluding the naïve population and reclustering the eye-primed cells, we projected the RNA velocity vectors onto a new UMAP ( Fig. 7A ). The trajectories originated from the proliferative cell cluster and diverged in distinct directions. The Treg and anergicT cluster appeared to be independent fates, with the arrows pointing in opposite directions, while a part of anergicY population appeared to transition toward the anergicT stage ( Fig. 7A ). Based on the estimated latent time by the scVelo algorithm 69 , which reflects the internal clock of a cell ( Fig. 7B ), the anergicY cluster seems to represent a less differentiated state with lower latent time, whereas the Treg and anergicT populations may be more terminally differentiated. Download figure Open in new tab Fig. 7: Trajectory analyses indicate parallel differentiation of Treg and anergic populations from an initial proliferative precursor. (A-B) RNA velocity analysis. Eye-primed cells were extracted and re-clustered for a new UMAP. (A) RNA velocity (arrowheads) projected onto this UMAP reflect the direction of cell state transitions. (B) The latent time is the calculated progression from the origin (proliferative cluster) to the end states (anergic1 and Treg), represented by color code. (C-F) Monocle pseudotime analysis. Monocle analysis ordered the cells along the pseudotime trajectories, displaying a branched pattern of two paths. (C) Color represents each cluster. (D) Color represents pseudotime, from the initial phase (0) to the late stage (1). (E) Heatmap showing the bifurcation of gene expression dynamics along pseudotime. (F) Kinetic patterns of specific canonical genes of the two paths. Cells were color-coded for each cluster. As a complementary approach, we reconstructed the trajectories using the Monocle algorithm 70 . The inferred state of cell transition revealed the emergence of two branches, arising from the common proliferative population and bifurcating at the branch point ( Fig. 7C ). Notably, one path was populated mainly by anergicT cells, and the other path was dominantly occupied by the Treg cells. The distribution of anergicY cells in both branches may point to the plasticity of this cluster ( Fig. 7C ). The branched pseudotime results supported that the anergicT and Treg clusters had reached their final stages ( Fig. 7D ). Fig. 7E and Table S4 depict the genes undergoing the most pronounced dynamic changes when progressing along each of the two branched paths. Resting/quiescent state markers ( Ccr7, Il7r, Tcf7 ) and anergy-associated genes ( Nfatc1/3, Nr4a2/3, Tox, Tox2, Cblb ) were progressively upregulated along the anergic path ( Fig. 7E ), suggesting that these cells gradually lost their effector potential. Conversely, higher levels of canonical Treg-associated genes ( Foxp3, Il10, Ikzf2, Prdm1 ) along the Treg path, was consistent with progressive differentiation of Tregs ( Fig. 7E ). Furthermore, while representative anergy-associated genes, such as Tcf7, Nr4a3, and Tox2 , progressively increased in the anergic path, they progressively decreased in the Treg path ( Fig. 7F ), recapitulating the dynamic acquisition of the respective phenotypes. In summary, the trajectory analysis reveals a branched pattern in which naïve T cells primed within the eye differentiate largely in parallel, rather than in tandem, into Tregs and anergic T cells from a common proliferative precursor. DISCUSSION We provide the first study that resolves at the single cell level how the living eye actively “disarms” the pathogenic potential of retina-specific T cells in vivo . Within the eye, incoming T cells encounter high levels of TGF-β (mainly the TGF-βY isoform) 71 . Retinoic acid (RA) is also abundant in the eye, owing to its function in the visual cycle. This creates a unique environment that has a central role in ocular immune privilege 18 . Outside the eye, RA is made by CDTbO + DC in the gut, where it enhances Treg differentiation and may contribute to food tolerance 72 . Within the eye, in addition to the FoxpO + Treg fate adopted by a minority of the naïve T cells, we show that the remaining majority adopts a phenotype consistent with anergy. This finding fills a major gap in understanding that was left by our previous data 18 , which found a dampened expression of effector cytokines and TFs at the population level, but could not distinguish effectors being kept in check by FoxpO + Tregs, from an alternative cell fate(s), nor could it resolve possible subset(s). Our current data dissect this in detail at the molecular level, and resolve the cell fates and their differentiation trajectory. Anergy vs. Regulation: unique gene expression in ocular tolerance The induction of anergic T cells in the living eye is a novel and little-explored aspect of ocular immune privilege. As mentioned in the Introduction, previous concepts of ocular immune privilege were based largely on in vitro studies with isolated cell populations or ocular fluids, and most of those studies dealt with induction of Tregs 3 , 4 , 9 . Although one study suggested that interaction of T cells with RPE cells in vitro can result in anergy, for obvious reasons this does not reproduce the complexity of the actual intraocular environment 9 . Moreover, the transcriptome of the affected T cells was not characterized. Our current study identifies many anergy-associated genes ( Ctla4, Lag3 , Pdcd1, Cblb, Dgkz ), as well as activated anergy-promoting transcription factors (NFAT, EGRY/O, NREA, TOX families), that are shared with other models of T cell anergy 16 , 55 , 73 . However, we also identify multiple genes whose expression pattern appears characteristic to eye-induced anergy. Prominent examples are: (i) Nrgn (neurogranin), which was in our hands restricted to the anergic T cell population, as was Egr-2 , a known Nrgn inducer 29 . Nrgn is constitutively expressed in neuronal cells, where it regulates synaptic plasticity 67 . While a few studies reported Nrgn mRNA in lymphocytes 22 , 30 , 29 , its functional contribution remains to be unraveled. The role of Nrgn is to modulate intracellular Ca++ levels through its interaction with Calmodulin 74 . Specifically, Nrgn sequesters Calmodulin by physically binding to it, and makes it unavailable for binding with Ca++. RA promotes Nrgn gene expression by upregulating RA receptors, particularly RXR, which binds to the RA response elements (RARE) in the Nrgn promoter 66 , 75 . Nrgn in turn binds to and sequesters Calmodulin, lowering available free calcium 74 . We hypothesize that as this process occurs in the T cells that are in the process of differentiation in the eye, RA-driven RAR/RXR upregulation increases Nrgn, sequestering Calmodulin and reducing intracellular calcium and inhibiting Calcineurin and NFAT 53 , 76 . Because Treg differentiation and functional activation requires high Ca++ levels 53 , these conditions should skew the balance of Tregs and anergic T cells towards anergy. We propose that Nrgn, by regulating intracellular Ca ++ availability, acts as a key checkpoint in the choice of anergic vs. regulatory cell fate by newly primed T cells differentiating from a common precursor in the TGF-β and RA-rich ocular environment. The validation of this central hypothesis in the regulation of ocular immune privilege and T cell anergy is the subject of a separate ongoing study. (ii) Although anergic T cells are generally thought to lack cytokine expression, the ocular anergic cells expressed a high level of Tgfb1. Tgfb1 was expressed also by ocular Tregs, and about Eb% of both populations expressed the TGF-β protein. To our knowledge, Tgfb1 expression had not been previously reported in any model of anergic T cells, and may be a distinguishing feature of eye-induced anergy. Nevertheless, judging by the functional data, its expression did not confer regulatory function on the ocular anergic T cells. (iii) An anergy-associated gene that was not significantly expressed in ocular anergic T cells is Izumo1r (encoding FRE), which, together with expression of Nt5e (encoding CDUO) and absence of FoxpO, is considered a defining phenotype of anergic T cells, but its function in anergy has not been elucidated. Our data suggest that it may not have a functional role in eye-induced anergy, or its role is redundant with that of a gene(s) differentially expressed in eye-derived vs. other anergic cells, such as Dgkz, Cblb, Rgs1, Maf, Lgals U, and Furin 55 . Eye-induced anergic state differs from exhaustion Although T cell anergy and exhaustion share many transcriptional features, the development of the eye-induced anergic T cells is inconsistent with exhaustion for several reasons. Exhaustion occurs in environments with strong antigenic stimulation and efficient antigen presentation 77 . The healthy eye has few and quiescent professional antigen-presenting cells (APCs) 78 – 80 . Inefficient antigen presentation is conducive to T cell anergy induction rather than exhaustion. As well, exhaustion typically requires chronic Ag stimulation and follows full activation for effector function, whereas the cells here were analyzed after only one week of Ag exposure, and the retina had minimal pathology 18 . Finally, many exhaustion-associated genes, such as Tigit, Havcr2, Shp1-2, Ptpn2, Blimp1 and Irf4 77 , 35 were undetectable or minimally expressed in eye-induced anergic cells. Effector Treg characteristics define the ocular Treg Population The gene expression profile of eye-induced Tregs ( Il10 , Tgfb1 , Ctla4 , Lag3 , and Nt5e ) is consistent with a highly suppressive “effector Treg” phenotype 38 – 41 . This was confirmed functionally by comparison with Tregs from spleen and lymph node tissues of the same animals (note that all T cells are IRBP-specific). Regulon analysis also uncovered that many “FoxpO accessory TFs”, such as BlimpT, Helios, and MbdY, are activated. These TFs help maintain Treg stability 59 – 62 , suggesting that Tregs differentiated within the eye may have a stable phenotype. Of interest, eye-induced Tregs also displayed some ThT-like genes, as indicated by higher levels of Tbx21 , Cxcr3 , and Ccr5 compared to non-FoxpO anergic cells, whereas the anergicY population shared the lineage-specific marker Il17a with ThTU effector phenotype. Expression of lineage-specific genes shared with ThT and ThTU effector cells by Tregs is felt to facilitate interaction with the target effector population(s) 81 – 83 . Uveitogenic effector T cells are a mixture of ThT and ThTU 84 . It is therefore tempting to speculate that the ocular microenvironment diverts ‘would-be’ ThTU effectors to anergy, whereas ‘would-be’ ThT effectors are diverted to FoxpO + Treg fate. Investigation of this hypothesis and of the unique eye-induced Treg phenotype is part of a separate ongoing study. Limitations of the study While the in vivo model of immune privilege is a powerful tool to dissect eye-specific control of immune cell differentiation, the system also has limitations, both objective and subjective. The level of complexity of an in vivo system precludes analysis of the individual contributions of signals from each component that integrate to produce the final phenotypic and molecular events. In part, this could be addressed by including the various ocular resident cells in the analysis. By the same token, RNA-Seq performed at additional time points could provide further insights into the kinetics of the differentiation process that could have strengthened our conclusions from the trajectory analysis. However, technical and logistic difficulties inherent to this experimental model precluded addressing this in the current study. In conclusion Our findings shed new light on the concepts of ocular immune privilege and the molecular mechanisms that actively maintain immunological homeostasis. The results lead to a model in which the ocular environment limits pathology by instructing the conversion of conventional T cells to Tregs or to an alternative fate of T cell anergy, rather than a scenario where a population of Tregs keeps a population of T effector cells in check. Identification of eye-induced regulatory and anergic signatures offers a valuable foundation for future research, and may inform therapeutic strategies for ocular inflammatory diseases. Furthermore, these unique signatures may inform strategies to reverse undesirable T cell unresponsiveness in contexts such as cancer, vaccination and chronic infection. MATERIALS AND METHODS Mice Interphotoreceptor retinoid-binding protein (IRBP)-specific T cell receptor (TCR) transgenic (RTiTH), Tcra knockout mice ( Tcra −/− ) on the BTb.RIII background were generated as previously described 85 and were crossed to BTb.RIII FoxpO GFP strain 86 . Tcra − /- RTiTH FoxpO GFP reporter mice were used as donors for naive retina-specific T cells. CDlb.T congenic wildtype (WT) BTb.RIII mice were used as recipients. Both male and female mice i-Tb weeks old were used in this study. All animals were maintained under specific-pathogen-free conditions at NIH animal facility on standard chow and water ad libitum. Care and use of animals followed institutionally approved animal study protocols and Animal Research Advisory Committee (ARAC) guidelines. Ocular Immune Privilege Model The ocular privilege model was established and described in our previous study 18 . Briefly, retina-specific T cells, enriched from peripheral lymphoid tissues of Tcra −/− RTiTH FoxpO GFP donor mice using CDO + T cell enrichment columns (R&D Systems) or CDE + T cell isolation kit (Miltenyi Biotec), were FACS sorted to obtain the naive population depleted of preexisting Tregs (CDEE low CDYV − FoxpO GFP- ). WT CDlb.T-congenic recipient mice were injected intravitreally with Vbb,bbb of these naïve T cells in T.V microliters PBS into each eye, using a OOG needle and Hamilton syringe. The cells were retrieved from donor eyes U–h days later and prepared for analysis, as described ahead. Flow cytometry and cell sorting Single-cell suspensions from spleens and lymph nodes (submandibular, axillary, inguinal, and mesenteric lymph nodes) collected from Tcra −/− RTiTH FoxpO GFP CDlb.Y donor mice were used for isolation of retina-specific T cells. Cells were stained with surface antibodies and sorted for live CDE + CDEE low FoxpO GFP- CDYV − Dump − cells to ∼ll% purity using FACSAria II and AriaIII/Fusion sorters (BD Biosciences). Non-CDE markers (CDh, NKT.T, BYYb, CDTTb, DXV, and GrT) were used for the dump channel. To retrieve retina-specific T cells from the eyes of CDlb.T congenic recipients, eyes were minced and treated with T mg/ml collagenase D for Ob min at OU°C. Donor-derived retina-specific T cells were sorted as live CDE + CDlb.Y + CDlb.T − cells. Propidium Iodide or U-AAD (for sorting) and ViaKrome hbh (for flow analysis on CytoFlex LX, Beckman Coulter) were used to exclude dead cells. For intracellular staining of cytokine, cells were stimulated with PMA (Tb ng/ml) and ionomycin (Vbb ng/ml) in the presence of brefeldin A (GolgiPlug; BD) for E h, following staining for surface marker and live/dead cells. Cells were then fixed with E% paraformaldehyde for Ob mins and stained for intracellular proteins in Tx BD perm/wash buffer for T hour. For Nrgn staining, cells were stained with surface maker antibodies and then fixed with E% paraformaldehyde, followed by permeabilization and staining with Nrgn antibody at E°C for Ob mins, and AF-iEU conjugated anti-Rabbit secondary antibody for Yb mins. Antibodies used for cell sorting and flow cytometry analysis were from BD Biosciences, BioLegend, and eBioscience/ThermoFisher. Detailed antibody and clone information is listed in Table S1. Sample processing and scRNA-seq Fresh naive donor T cells, or donor cells retrieved from recipient mouse eyes one week after intravitreal injection, were collected for scRNA-seq. Donor T cells in the eyes retrieved from each of the four recipient mice were individually labeled with anti-mouse TotalSeqB hashtags (BioLegend, Table S1). Individual samples were incubated with unique hashtags and sorting antibodies before FACS sorting, per manufacturer’s protocol. The viability of sorted cells was greater than lb%. Sorted single-cell suspensions were adjusted to Ubb–TYbb cells/μl before loading the TbX chromium chip. Samples were processed with the Chromium Next GEM Single Cell O’ reagent kit in the Chromium X platform following the standard protocol for O’ Gene Expression assay (TbX genomics). The gene expression and cell surface libraries were sequenced on NovaSeq ibbb platform (Illumina). Quality control and clustering of scRNA-seq data Sequencing reads were demultiplexed and aligned using CellRanger (U.T.b) with the default parameters. The output matrix files were converted into a Seurat object for quality control and clustering. Standard scRNA-seq analysis (quality control, clustering, and marker gene detection) was performed using Seurat (vE.O.b) 21 . Cells were excluded from analysis if they met any one of the following criteria: transcript counts less than Tbbb or more than Ebbbb, fewer than Ebb genes, more than h% mitochondrial fraction, ribosomal fraction less than Tb% or more than EV%. Highly variable features between individual cells were identified, and linear dimensional reduction was performed using principal component analysis (PCA). Unsupervised clusters were determined using the ‘FindNeighbors’ and ‘FindClusters’ functions based on the first Yb principal components (PCs). The clustering result was visualized using YD uniform manifold approximation and projection (UMAP). Clustering was done at b.Y resolution to keep the naive T cells as one “homogeneous” cluster. The ‘FindAllMarkers’ function was used to identify marker genes of each cluster within the data set. Signature identification and GSEA analysis To characterize the phenotypes of donor T cells retrieved from recipient eyes, we defined their gene-expression signatures and performed Gene Set Enrichment Analysis (GSEA). The gene signatures were defined by comparing each cluster with the naive cluster using the ‘FindMarkers’ function. Genes were considered differentially expressed using the default Wilcoxon Rank Sum test and log fold-change (FC) threshold (Table S2). The full list of differentially expressed genes (DEGs) was ranked based on log FC and then mapped to the Molecular Signature Database (MSigDB) via GSEA software (vE.O.Y) 42 . Antigen-specific proliferation assay Ocular immune privilege model was conducted as described above, after one week, FoxpO GFP+ or FoxpO GFP- CDE + CDlb.Y + CDlb.T − cells were sorted out from the recipients’ eyes (Vb,bbb cells/ well) and co-cultured with human IRBP 161–180 peptide (Vb ng/ml) and CDTTc + dendritic cells (at a T:V ratio to T cells), with or without Tbb IU/ml recombinant human IL-Y. Dendritic cells were obtained by digesting spleens from WT CDlb.T mice in spleen dissociation medium (Stem Cell) for Ob minutes, followed by ammonium-chloride-potassium (ACK) lysis and CDTTc + enrichment using Micro Beads (Miltenyi Biotec). Sorted naïve T cells (CDEE low CDYV − FoxpO GFP- CDE + ) and Treg cells (FoxpO GFP+ CDE + ) from spleens and lymph nodes of Tcra −/− RTiTH FoxpO GFP CDlb.Y mice were used as positive and negative controls, separately. Cell proliferation was determined using [ 3 H]-Thymidine incorporation by adding TmCi/well after a Eh-hour culture and further incubated for Ti hours. Samples were harvested and counted using liquid scintillation (Perkin Elmer, MA). Unpaired Student t -tests were performed for statistics. Antigen-specific Treg suppression assays Sorted FoxpO GFP+ or FoxpO GFP- CDE + CDlb.Y + CDlb.T − cells from recipients’ eyes were co-cultured with naïve retina-specific T cells (serve as T responder cells, Tresp; Vb,bbb cells/ well) and CDTTc + dendritic cells (at a T:V ratio to Tresp). Treg cells (FoxpO GFP+ CDE + ) from spleens and lymph nodes of Tcra −/− RTiTH FoxpO GFP CDlb.Y mice were used as positive control of suppressor. Varying numbers of putative suppressor T cell populations were sorted and added to the cultures at the indicated Treg:Tresp ratios. Cell co-cultures were stimulated with Vbng/ml human IRBP 161-180 without adding IL-Y. The inhibitory effect was assessed using either [ 3 H]-Thymidine incorporation or proliferation dye dilution independently. For the dye dilution method, naïve T cells were labeled with proliferation dye - CellTracker DeepRed or CellTrace FarRed (Invitrogen/ThermoFisher Scientific) before setting up the culture. After O days, cells were stained for FACS analysis, and cell division of Tresp was quantified using FlowJo (Tb.h.b). Unpaired Student t -tests were performed for statistics. Ingenuity Pathway Analysis Pathway analysis was performed using Ingenuity Pathway Analysis (IPA, www.qiagen.com/ingenuity ). DEGs of each eye-primed cluster with corresponding log FC and adjusted P values were imported into IPA software for deciphering upregulated or downregulated functional pathways based on ingenuity knowledge base. After performing ‘core analysis’ of each T cell cluster independently, visualization across different clusters was achieved by ‘comparison analysis’ function. IPA’s z-score indicates a predicted activation or inhibition of a pathway, where a positive z value denotes an overall pathway’s activation and vice versa. The transcriptional factor activity (regulon) analysis The python implementation of SCENIC (single-cell regulatory network inference and clustering, pySCENIC, vb.TY.T) 56 was used to predict the active transcriptional factor (TF). Starting from the normalized matrix data, the pySCENIC workflow consists of three stages. Initially, co-expression modules were inferred using a regression per-target approach. Then, the regulons (TF-target gene motifs) were refined from these modules based on cisTarget databases. Lastly, the ‘aucell’ algorithm was utilized to quantify the regulon activity score and find the significantly enriched regulon independently for each cell with default parameters. Information on software tools and cisTarget databases can be found in Table S3. The statistically significant regulons identified by SCENIC analysis were considered as active TFs, which reflected the upstream transcriptional drivers of the observed cellular identities 56 . Regulon activity score was then scaled to plot heatmap or projected onto the UMAP. RNA velocity analysis Velocyto 68 and scVelo 69 packages were used to perform RNA velocity analysis. First, RNA velocity (comprising spliced/un-spliced counts) for each cell was computed using the matrices generated by CellRanger and stored in the loom format. The velocity vectors were integrated into the Seurat object as a new data file. From the following file, we extracted the cells retrieved from recipient eyes and re-plotted the UMAP. The ‘latent time’ and ‘latent time facilitated RNA velocity’ were estimated using the likelihood-based dynamical model in scVelo. The velocity graph was visualized as streamlines overlaid by embedding in UMAP. Pseudotime analysis Monocle (vY.Yi.b) 70 was applied to determine the potential lineage differentiation trajectory, keeping the default parameters. The matrix data of eye-primed clusters were imported as input for creating the Monocle ‘CellDataSet’. The ‘DDRTree’ method was utilized for dimensionality reduction and cell ordering along the pseudotime trajectory. To identify the genes that separate cells into branches, we performed the Branch Expression Analysis Modeling (BEAM) approach in Monocle Y. The dynamic expression of genes was visualized by the ‘plot_genes_branched_heatmap’ or ‘plot_genes_branched_pseudotime’ function. Data availability The data reported in this paper are deposited in the Gene Expression Omnibus (GEO) database under accession no. GSEYhTETb. Code used for analysis can be found in GitHub https://github.com/NIH-NEI/Privilege_Treg_Anergy_scRNA . Funding This study was supported by the National Eye Institute, National Institutes of Health (NIH) intramural funding (Project #EYbbbThE). Author contributions Z.P.: Performed the experiments, analyzed the data, and wrote the manuscript draft. M.J.M and R.H: Planned the experiments, supervised the work, reviewed and edited the manuscript. V.N: Instructed and performed computational analyses, wrote software, and interpreted the results. Y.J: Developed methods and assisted in experiments. R.R.C: Conceptualized the study, acquired funding, reviewed, edited, and finalized the manuscript. Author disclosure statement No competing financial interests exist. STAR METHODS Acknowledgements The authors sincerely thank the National Eye Institute and National Heart Lung and Blood Institute Flow Cytometry Core facilities for assistance in conducting cell sorting, the Genetic Engineering Core Facility (NEI) for the generation of RTiTH TCR transgenic mice and genotyping service, and the National Cancer Institute CCR Genomics Core facility for sequencing. This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the NIH. We gratefully acknowledge the NIH Fellows Editorial Board for their valuable assistance in editing the manuscript for language and clarity. We thank Dr. Guangpu Shi (National Eye Institute, Laboratory of Immunology) for his comments and Dr. Han-Yu Shih for critically reviewing the manuscript. We are grateful to Drs. Nilisha Fernando and Jaanam Gopalakrishnan (National Eye Institute, Neuro-Immune Regulome Unit) for their assistance with scRNA-seq sample preparation. We would also like to express our gratitude to all the members of the Caspi Lab for their support and contributions. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/Tb.VYhT/zenodo.TEhiTEOb Footnotes https://doi.org/10.5281/zenodo.14861430 https://github.com/NIH-NEI/Privilege_Treg_Anergy_scRNA Abbreviations Ag Antigen AH Aqueous humor APC Antigen presenting cells CBL-B Casitas B-Lineage Lymphoma Proto-Oncogene B CTLA-E Cytotoxic T-Lymphocyte Associated Protein E DEG Differentially expressed genes DGKζ Diglyceride Kinase Zeta EGR Early Growth Response FACS Fluorescence-activated Cell Sorting FC Fold change FoxpO Forkhead box PO GFP Green fluorescent protein GM-CSF Granulocyte-Macrophage Colony-Stimulating Factor GSEA Gene Set Enrichment Analysis ICOS Inducible T-Cell Co-Stimulator IFNγ Interferon Gamma IL-TUA Interleukin TUA IPA Ingenuity Pathway Analysis IRBP Interphotoreceptor Retinoid-Binding Protein LAP Latency-Associated Peptide LT Lymphoid tissues MAPK Mitogen activated protein kinase MsigDB Molecular Signature Database NFAT Nuclear factor of activated T cells Nrgn Neurogranin NREA Nuclear receptor subfamily EA PC Principal component PD-T Programmed Cell Death T PD-LT Programmed Cell Death T Ligand T PIOK Phosphatidylinositol-E,V-Bisphosphate O-Kinase PKC Protein Kinase C RA Retinoic acid RARA Retinoic acid receptor alpha RARE Retinoic acid response elements RXRA Retinoid X receptor alpha RPE Retinal pigment epithelium RORγt Retinoid orphan receptor gamma t SCENIC Single-cell regulatory network inference and clustering scRNA-seq Single-cell RNA sequencing TCR T cell receptor Th T helper TF Transcription factor Tfh T follicular helper TGF-β Transforming growth factor-beta TNFRY Tumor Necrosis Factor Receptor Y TOX Thymocyte Selection-Associated High Mobility Group Box TrT TypeT regulatory cells UMAP Uniform manifold approximation and projection VIP Vasoactive intestinal peptide WT Wild type α-MSH α-melanocyte-stimulating hormon REFERENCES 1. ↵ Stein-Streilein , J. , and Caspi , R.R . 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