Antigen presentation by lung epithelial cells directs CD4+ TRM cell function and regulates barrier immunity

Shenoy, Anukul T.; Lyon De Ana, Carolina; Arafa, Emad I.; Salwig, Isabelle; Barker, Kimberly A.; Korkmaz, Filiz T.; Ramanujan, Aditya; Etesami, Neelou S.; Soucy, Alicia M.; Martin, Ian M. C.; Tilton, Brian R.; Hinds, Anne; Goltry, Wesley N.; Kathuria, Hasmeena; Braun, Thomas; Jones, Matthew R.; Quinton, Lee J.; Belkina, Anna C.; Mizgerd, Joseph P.

doi:10.1038/s41467-021-26045-w

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Published: 05 October 2021

Antigen presentation by lung epithelial cells directs CD4⁺ T_RM cell function and regulates barrier immunity

Nature Communications volume 12, Article number: 5834 (2021) Cite this article

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Abstract

Barrier tissues are populated by functionally plastic CD4⁺ resident memory T (T_RM) cells. Whether the barrier epithelium regulates CD4⁺ T_RM cell locations, plasticity and activities remains unclear. Here we report that lung epithelial cells, including distinct surfactant protein C (SPC)^lowMHC^high epithelial cells, function as anatomically-segregated and temporally-dynamic antigen presenting cells. In vivo ablation of lung epithelial MHC-II results in altered localization of CD4⁺ T_RM cells. Recurrent encounters with cognate antigen in the absence of epithelial MHC-II leads CD4⁺ T_RM cells to co-express several classically antagonistic lineage-defining transcription factors, changes their cytokine profiles, and results in dysregulated barrier immunity. In addition, lung epithelial MHC-II is needed for surface expression of PD-L1, which engages its ligand PD-1 to constrain lung CD4⁺ T_RM cell phenotypes. Thus, we establish epithelial antigen presentation as a critical regulator of CD4⁺ T_RM cell function and identify epithelial-CD4⁺ T_RM cell immune interactions as core elements of barrier immunity.

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Introduction

Tissue-resident memory T (T_RM) cells operate as front-line adaptive immune sentinels for barrier tissues, such as the skin, lungs, vagina, and intestine^1,2. Barrier tissues experience unavoidable, transient, repeated exposures to environmental antigens. T_RM cell formation and responses depend on diverse natures of antigenic stimuli, types of antigen-presenting cells (APC), and costimulatory signals and cytokines within the challenged tissue^3,4. These signals diversify T_RM cell responses beyond their initial phenotype specification, yielding T cell plasticity that enhances anti-microbial immunity and tissue barrier integrity^3,5,6. Because T_RM cells and T cell plasticity have been linked to barrier tissue pathologies like asthma, psoriasis, and inflammatory bowel disease (IBD)^7,8,9,10, tissue-resident regulators of T_RM cell plasticity have become an important knowledge gap.

As direct interfaces between the environment and the body, epithelial cells are poised to be critical regulators of barrier immunity^4,11. Barrier epithelial cells recruit and maintain CD8⁺ T_RM cells near the sites of antigen encounter, and reactivate them in the tissues via local antigen presentation^12,13. In contrast to CD8⁺ T_RM cell effects, epithelial roles governing CD4⁺ T_RM locations, plasticity, and activities remain speculative. Distinct barrier epithelial cells, such as alveolar type 2 (AT2) cells of the lungs¹⁴, Lgr5⁺ stem cells of the small intestinal crypts¹⁵, enterocytes of the villi¹⁶, and keratinocytes of the skin all express MHC-II¹⁷. Although the only known function of MHC-II is antigen presentation to CD4⁺ T cells, whether barrier epithelial cells direct CD4⁺ T_RM cell activities during exposures to relevant cognate antigens remains speculative.

Streptococcus pneumoniae (Spn) is a leading cause of pneumonia and infectious deaths worldwide¹⁸. Serotype replacement and antibiotic resistance have contributed to the classification of Spn as a priority pathogen by WHO in need of better antibiotics and vaccines. The protection offered by Spn-specific CD4⁺ T_RM cells is T_H17 polarized and serotype-independent¹⁹, representing a potential avenue for vaccination against all Spn serotypes. Especially considering that up to 65% of children and ~10% of adults are exposed to this pathobiont at any given time¹⁸, understanding the biology of Spn-specific CD4⁺ T_RM cells is imperative to improving the outomes of recurrent Spn exposures.

In this work, we postulate that lung epithelial cells (LECs) mediate antigen presentation to pulmonary CD4⁺ T_RM cells in order to regulate their localization and activities after Spn infection. We define molecular factors involved in this communication axis and show a physiological role of LEC MHC-II related to CD4⁺ T_RM cell plasticity and immunity.

Results

Identification of SPC^lowMHC^high LECs

We sought to identify LECs with antigen-presenting abilities during homeostasis. Because MHC-II has been localized to AT2 cells¹⁴ and AT2 cells have no known cell-specific surface markers useful for flow cytometry, we used mice expressing GFP under control of human surfactant protein C (SPC) promoter (SPC-GFP mice)²⁰ in order to discriminate the SPC-GFP^high AT2 cells. At baseline, LECs from elastase digested lungs of (Fig. 1a) separated into two clusters based on MHC-II profiles. The MHC-II^low cluster was largely comprised of airway LECs (i.e., club and multiciliated cells) with minimal contribution of alveolar type 1 (AT1) cells (Fig. 1a and Supplementary Fig. 1a). The MHC-II^high cluster was comprised of SPC-GFP^high AT2 cells and a comparable-sized fraction of SPC-GFP^low cells (Fig. 1a and Supplementary Fig. 1a). Interestingly, the SPC-GFP^low cells expressed more MHC-II than AT2 cells, almost comparable to levels observed on CD45⁺CD24⁺MHC-II⁺ professional APCs (Fig. 1b and Supplementary Fig. 1b). These SPC-GFP^low cells exhibited smaller forward scatter, comparable to club cells (Fig. 1c), and higher β4 integrin (Fig. 1d) and stem cell antigen Sca-1 (Fig. 1e) when compared to AT2 cells. We sought to define the SPC-GFP^low cells using mRNA for lineage-specifying markers. The SPC-GFP^low cells coexpressed markers characteristic of AT2 cells (Spc) and club cells (Scgb1a1), but not markers for multiciliated cells (Foxj1) or AT1 cells (Aqp5) (Fig. 1f). To further characterize this LEC type, we used bronchioalveolar stem cell (BASC) reporter mice that express YFP and mCherry inserted into Spc and Scgb1a1 loci, respectively²¹ (Fig. 1g). These mice confirmed a population of SPC^low cells with high levels of MHC-II, and revealed that they were distinguishable from club cells (SCGB1A1^high) that were MHC-II^low, bronchioalveolar stem cells²² (BASC; SCGB1A1^medium SPC^medium) that were MHC-II^medium, and AT2 cells (SPC^high) that were MHC-II^high (Fig. 1g). The SPC-YFP⁺ cells were consistently found within alveoli and not in conducting airways, suggesting an alveolar location for SPC^low LECs (Fig. 1h). Transmission electron microscopy (TEM) of sorted SPC^low LECs revealed these cells contain both the AT2 cell-defining lamellar bodies, as well as electron-dense secretory vesicles characteristic of club cells (Fig. 1i). Lamellar bodies were exclusively observed in alveoli and never in airways upon inspection of >100 anatomically distinct areas of C57BL/6J mouse lungs (Supplementary Fig. 1c), leading us to conclude that SPC^lowMHC-II^high cells localize to alveoli rather than conducting airways. The SPC^low cells also possessed a transcript profile, including Sox2, MHC-I molecule H2-K1, long non-coding RNA Aw112010, and cell cycle regulator Cdkn1a (Supplementary Fig. 1d), reminiscent of distal lung stem cells²³. Thus, these data suggest a discrete set of SPC^low LECs that express high amounts of MHC-I and MHC-II, exhibit transcriptional features of lung progenitor cells, and surface marker, transcriptional, and structural characteristics distinct from conventional LEC types, hereafter referred to as SPC^lowMHC^high LECs.

**Fig. 1: In addition to AT2 cells, MHC-II is highly expressed by SPC^lowMHC^high LECs.**

LECs exhibit anatomically segregated and temporally dynamic APC activities

To establish if antigen presentation is a feature of LECs, we examined their antigen uptake and processing abilities by intratracheal instillation of DQ-Ovalbumin (DQ-OVA). Consistent with antigen uptake and proteolytic degradation of DQ-OVA to release the quenched fluorophore, we observed fluorescence in all LECs at baseline and after infection with Spn serotype 19F (Fig. 2a and Supplementary Fig. 1a). Because antigen processing abilities were observed in cells with very low MHC-II at baseline, we tested whether MHC-II levels were altered in infection settings where antigen presentation becomes necessary. Indeed, Sp19F infection increased MHC-II on MHC-II^low LECs while further enhancing its expression on distal LECs, which remained the highest MHC-II expressers at all times (Fig. 2b). Further supporting APC functions, all LECs sorted from infected lungs displayed cell-specific patterns of MHC-II related accessory genes (Supplementary Fig. 2a) and activated antigen-specific CD4⁺ T cells in MHC-II dependent fashions in ex vivo co-cultures (Supplementary Fig. 2b). Finally, to explicitly establish LECs as APCs, we generated mice lacking MHC-II in LECs by crossing H2-Ab^fl/fl mice²⁴ to Nkx2.1^Cre-ERT2 mice²⁵ (henceforth MHC-II^ΔEpi) (Fig. 2c). Administration of tamoxifen efficiently deleted all baseline or induced MHC-II on LECs (Supplementary Fig. 2c, d) while leaving MHC-II intact on other cell types (Supplementary Fig. 2e). Stimulation of antigen-specific CD4⁺ T cells by antigen-fed LECs was compromised by MHC-II gene targeting (Fig. 2c). Thus, our results reveal that all LECs respond to infection by increasing MHC-II on their surface, and LEC MHC-II mediates antigen presentation to CD4⁺ T cells.

**Fig. 2: LECs display anatomically segregated and temporally-dynamic APC activity.**

Antigen presentation is modulated by costimulatory signals, so we examined if such signals were expressed by which LECs and when. Repeated exposures to Spn induces formation of protective lung CD4⁺ T_RM cells¹⁹. We collected LECs from multiple time-points after Spn infections (Fig. 2d) and subjected them to a 17-parameter high-dimensional multispectral flow cytometry (MSFC) panel that included antibodies to MHC-II, costimulatory molecules (CD80, CD86, CD40, ICOS-L), cell adhesion molecules with costimulatory functions (ICAM1 and VCAM1), and coinhibitory molecules (PD-L1 and PD-L2). Approximately 2 million CD45⁻EpCAM⁺ LECs from 84 C57BL/6J mice across timepoints were concatenated, projected into 2-dimensional optimized t-distributed stochastic neighbor embedding (opt-SNE) space²⁶ and subjected to unsupervised clustering using Phenograph algorithm²⁷. This empowered us to (i) identify distinct LEC clusters based on expression profiles integrating all measurements rather than individual surface markers, and (ii) quantify the relative abundances of these clusters over time. Phenograph clustering identified 34 distinct LEC clusters that aggregated into 6 metaclusters corresponding to each LEC type (Fig. 2e and Supplementary Fig. 3a, b). One metacluster did not cleanly correspond to recognized LEC types and shared club and multiciliated features likely representing a club-to-multiciliated transitional state (clusters 14, 18, 19), consistent with our timeline encompassing lung epithelial damage and regeneration²⁸. Opt-SNE visualization confirmed the identity of these metaclusters (Supplementary Fig. 3c)²⁶. As expected, some clusters were products of dynamic signaling events and not all clusters were present simultaneously; instead within each metacluster certain clusters dominated at distinct time points (Supplementary Fig. 3d). These observations demonstrate that LECs undergo dynamic modulation of surface APC phenotypes during microbial infections concurrent with the formation of lung CD4⁺ T_RM niches.

Next, we tracked the surface expression levels of T cell-directed molecules on the LEC metaclusters (Fig. 2e) and discovered that expression of costimulatory/ coinhibitory molecules on LECs was dynamic and highly cell type-specific (Fig. 2f–l and Supplementary Table 1–6). Club cells and multiciliated cells consistently expressed higher levels of T cell costimulatory CD40 (Fig. 2f, h) and CD86 (Fig. 2f, i), respectively, compared to any other LECs. SPC^lowMHC^high LECs expressed the highest levels of the T cell coinhibitory molecule PD-L1, followed by AT2 cells then all other LECs (Fig. 2f, k). Multiciliated cells were high expressers of PD-L2 (Fig. 2f, l). ICAM1 was elevated on all LECs during active inflammation, but especially on the AT2 cells (Fig. 2j). Throughout the time-course, little to no CD80, ICOS-L, or VCAM1 were observed on any LEC (Supplementary Fig. 3c). The modulation of APC molecules on LECs on day 8 was microbe-induced, since the absence of infection did not incite such changes (Supplementary Fig. 4). These data show that all LECs express costimulatory molecules in a cell-specific fashion. Columnar LECs of the conducting airways (including club and multiciliated cells) have inducible MHC-II and constitutive expression of costimulators (CD40 and CD86, respectively), whereas the lamellar body-containing alveolar LECs (AT2 cells and SPC^lowMHC^high LECs) express constitutive MHC-II with PD-L1 (both of which increase during infection).

LEC antigen presentation facilitates CD4⁺ T_RM cell niches around conducting airways

We hypothesized that the anatomically segregated LEC antigen presentation pattern instructs the formation of CD4⁺ T_RM niches within the bronchovascular bundles along the airways²⁹. To test this, we first tracked the temporal dynamics of CD4⁺ T cells at timepoints coinciding with LEC antigen presentation using flow cytometry (as in Fig. 2d). To accurately discriminate between circulating versus lung CD4⁺ T cells, we used an in vivo antibody labeling approach³⁰ and generated comprehensive snapshots of the dynamics of CD4⁺ T cell recruitment, expansion, and contraction leading to the formation of CD4⁺ T_RM niches (Fig. 3a and Supplementary Fig. 5a). One Sp19F infection instigated a modest but transient increase in CD4⁺ T cells numbers by day 3 (Fig. 3a). We do not know the antigen specificity of these T cells, but at such an early time-point we suspect they were most likely not Spn-specific but were instead a mixture of naïve and unrelated cells that were recruited to the lung due to inflammation. Consistent with this, a single Sp19F infection did not induce lung CD4⁺ T_RM cells (Supplementary Fig. 5b). A second Sp19F infection at day 7; however, elicited rapid recruitment of all conventional CD4⁺ T cell subsets including naïve T (T_Naive), central memory T (T_CM), effector memory T (T_EM), and effector T (T_eff) cells (Fig. 3a and Supplementary Fig. 5c). The CD4⁺ T cell numbers peaked by day 10, with dominating subsets comprised of CD4⁺ T_Naive, T_CM, and T_EM cells along with activated and expanding T_eff cells. At this point, ~17% of lung CD4⁺ T_eff cells expressed IL-2Rα (CD25), suggesting dependence on IL-2 signaling. Between days 10 and 24, the lung CD4⁺ T cell compartment contracted until CD4⁺ T_RM cells and a small pool of patrolling CD4⁺ T_EM cells remained. CD4⁺ T_eff cells were the highest PD-1 expressers, with most surface PD-1 on day 10 (Fig. 3b, c). Interestingly, LEC PD-L1 expression spiked on day 8 (Fig. 2k) preceding the CD4⁺ T_eff PD-1 peak (Fig. 3c). Of note, only CD4⁺ T_RM cells were PD-1^high in the resting but experienced lungs at day 35 (Fig. 3d).

**Fig. 3: LEC MHC-II facilitates seeding of CD4⁺ T_RM niches around conducting airways.**

We used immunofluorescence to localize CD4⁺ cells during the establishment of CD4⁺ T_RM niches (Fig. 3e, f). The second Sp19F infection at day 7 elicited rapid recruitment of CD4⁺ cells on days 8–10 in the loose interstitium of the bronchovascular bundles, surrounding the coordinated arterial and airway branching trees of the lower respiratory tract (Fig. 3e, f and Supplementary Fig. 5c). Resolution of infection between days 14 and 24 was associated with contraction of CD4⁺ cells in parenchyma and bronchovascular bundles, except along the airway LECs where there appeared to be a stable CD4⁺ cell niche (Fig. 3e, f). By day 35, 4 weeks after infection, all remaining CD4⁺ cells were CD69⁺ T_RM cells in these resting lungs (Fig. 3a), and they localized exclusively around the conducting airways (Fig. 3e, f). These observations suggest that the airway niche promotes survival and/or maintenance of recruited CD4⁺ T cells (perhaps via MHC-II plus the costimulatory CD40 and/or CD86 of the club and multiciliated cells) to form CD4⁺ T_RM cells, while the parenchymal niches instigate contraction of CD4⁺ T cells (perhaps via LEC MHC-II plus coinhibitory molecules like PD-L1) during resolution of infection. If true, deletion of LEC MHC-II should disrupt the anatomical distribution of CD4⁺ T_RM niches within the lungs.

To test this, we generated CD4⁺ T_RM niches within MHC-II^fl/fl and MHC-II^ΔEpi lungs and systematically quantified their anatomical distribution (Fig. 3g, h). The sustained absence of MHC-II was confirmed (Supplementary Fig. 5d). Consistent with the notion that LEC MHC-II facilitates the formation of CD4⁺ T_RM cell niches within bronchovascular bundles, MHC-II^ΔEpi lungs exhibited sparser CD4⁺ T_RM niches along the airway epithelium and within bronchovascular interstitium (Fig. 3h). However, loss of LEC MHC-II did not change the parenchyma CD4⁺ T cell numbers, suggesting the involvement of other mechanisms in their contraction. For conducting airway LECs, MHC-II overlapped with CD40 better than CD86 (Fig. 2f–i), leading us to test whether the frequency of CD4⁺ T_RM cells in the lungs was dependent on CD40 signals offered by club cells in addition to other canonical CD40⁺ cells. We examined this using the MR-1 antibody to block CD40-CD40L interaction during Spn-specific CD4⁺ T_eff cell recruitment (Fig. 3i). Consistent with the requirement for CD40 signals for the formation/maintenance of CD4⁺ T_RM cells, blockade of CD40-CD40L interactions significantly reduced lung CD4⁺ T cell, CD4⁺ T_RM cell, and CD103⁺ CD4⁺ T_RM cell numbers (Fig. 3i). Taken together, our results indicate that antigen presentation by LECs facilitates the formation of CD4⁺ T_RM niches around the conducting airways, but is dispensable for their exclusion from the lung parenchyma.

CD4⁺ T_RM cells display multipotent phenotypes that are constrained by LEC MHC-II

Residence of CD4⁺ T_RM cells within barrier tissues as frontline adaptive immune sentinels involves inevitable and recurrent stimulations by cognate antigens over their lifespan. We next asked whether antigen presentation by LECs instructs CD4⁺ T_RM cell activities during repeated memory recall encounters. To test this, we generated CD4⁺ T_RM cells in MHC-II^ΔEpi and MHC-II^fl/fl lungs as in Fig. 3g, followed by recurring exposures to a serotype-mismatched nonlethal strain of Spn serotype 23A (Sp23A) (Fig. 4a). Deletion of LEC MHC-II did not impede reactivation of CD4⁺ T_RM cells nor affected rapid clearance of Spn (Supplementary Fig. 6a–d) and as such, these infections mimic homeostatic nonlethal inhalation of microbes to which adult lungs would already possess CD4⁺ T_RM cells. Measurements of airway cellularity revealed no ongoing inflammation as evidenced by <1% neutrophil content of the bronchoalveolar lavage (BAL) fluid on day 105 (Supplementary Fig. 6e). Sustained deletion of LEC MHC-II was confirmed (Supplementary Fig. 6f).

**Fig. 4: LEC MHC-II directs CD4⁺ T_RM cell activities.**

To examine CD4⁺ T_RM phenotypes in the absence of LEC MHC-II, we used a 21-parameter high-dimensional MSFC panel including antibodies to CD4⁺ T_RM markers, T cell lineage-defining transcription factors (LDTFs), and markers of activation and proliferation (Fig. 4a). Analysis of total lung CD4⁺ T cell and CD4⁺ T_RM cell numbers on day 105 revealed comparable frequencies between genotypes (Supplementary Fig. 6g). Next, we used Phenograph to identify distinct clusters of CD4⁺ T cells based on unique expression profiles, and quantified their relative abundance in genotype-dependent fashion. Phenograph clustering of concatenated lung (i.v.CD45.2⁻) CD44⁺CD11a⁺ CD4⁺ T cells identified 21 distinct memory CD4⁺ T cell clusters (Supplementary Fig. 7a), none being unique to either genotype and all being negative for CD62L. For further analyses, we focused on the 20 clusters at >0.5% frequency (Fig. 4b). Remarkably, 17 of the 20 memory CD4⁺ T cell clusters co-expressed ≥2 LDTFs (cluster 17 was positive for all LDTFs tested) (Fig. 4b). Among these, clusters 10 and 19 were CD69⁻ and represented T_EM cells while the remaining 15 clusters encompassed T_RM cells comprised of 2 CD103⁺ T_reg-³¹, 1 T_H1/17-, 1 T_H2/17-, 8 T_H1/2-, and 3 T_H1/2/17- lung T_RM clusters at homeostasis. Thus, our results highlight the presence of complex lineage plasticity and multipotency within lung CD4⁺ T_RM cells. Of note, clusters 9 and 12 were negative for all tested LDTFs; however, positivity for other LDTFs not included in our panel, like BCL6, cannot be excluded.

Quantification of relative frequencies revealed significant perturbations in 8 of the 20 memory CD4⁺ T cell clusters due to deletion of LEC MHC-II (Fig. 4b). While MHC-II^ΔEpi mice exhibited significant expansion of T_H1/2/17 T_RM clusters 1 and 4, and the T_H1/17 T_RM cluster 8, they revealed a significant contraction in 3 T_H1/2 T_RM clusters (3, 5, and 7) and the T_H1 T_RM cluster 6 (Fig. 4b). Of note, all CD4⁺ T_RM clusters enlarged in MHC-II^ΔEpi mice were Ki67⁻, suggesting they were not the result of uninhibited proliferation at tissue homeostasis. Furthermore, neither of the T_reg T_RM cell clusters were affected by LEC MHC-II deletion, implying that the observed effects were probably independent of T_reg-suppressive functions. Our findings, therefore suggest that LEC MHC-II imposes inhibitory constraints on CD4⁺ T_RM cell plasticity and multipotency (defined as coexpression of ≥2 LDTFs).

Next, we investigated whether deletion of LEC MHC-II induces perturbations in the circulating CD4⁺ T cell compartment by performing Phenograph clustering on the intravascular (i.v.CD45.2⁺) CD44⁺CD11a⁺ CD4⁺ T cells collected from the lungs of the same mice. We identified 21 distinct clusters (Supplementary Fig. 7b), none being unique to either genotype. In stark contrast to the lungs, only 11 of the 21 blood memory CD4⁺ T cell clusters exhibited multipotency (Fig. 4c) with 10 of the 21 clusters being positive for only one of these LDTFs (Fig. 4c). Comparisons of relative frequencies for multipotency revealed stark differences in the permitted degree of plasticity conferred to memory CD4⁺ T cells based on: (i) the status of LEC MHC-II and (ii) the anatomical site of T cell residence. For example, while 81% of MHC-II^fl/fl lung CD4⁺ T_RM cells exhibited multipotency (of which 20% were T_H1/2/17), a significantly higher proportion (88%) of MHC-II^ΔEpi lung CD4⁺ T_RM cells coexpressed ≥2 LDTFs (of which 38% were T_H1/2/17) (Supplementary Fig. 7c). Irrespective of their LEC MHC-II status, only ~61% of all blood memory CD4⁺ T cells displayed such plasticity (none of which were T_H1/2/17) (Supplementary Fig. 7c). Comparing phenotypes of extravascular (tissue) and intravascular (circulating) CD4⁺ T cells in the lungs of MHC-II^fl/fl and MHC-II^ΔEpi mice revealed that the tissue cells had greater complexity than the circulating pools (evidenced by Tbet⁺Gata3⁺RORγT⁺ triple-positive CD4⁺ T cells corresponding to T_H1/2/17 skewing)(Supplementary Fig. 7c) with consistently high multipotency (Fig. 4d) that was exacerbated by the loss of LEC MHC-II (Fig. 4d and Supplementary Fig. 7c). Our results, thus (i) ascertain antigen presentation by LECs as a critical regulator of CD4⁺ T_RM cell plasticity and (ii) emphasize the existence of distinct selective pressures against memory CD4⁺ T cell plasticity in circulation versus barrier tissues of the same immunocompetent host.

LEC MHC-II may govern CD4⁺ T_RM cell plasticity by two ways: (i) by constraining plasticity of pre-existing T_H17-specified CD4⁺ T_RM cells during memory recalls or instead (ii) by regulating the development of multipotent CD4⁺ T_RM cells from T_eff cells²⁹. To test these possibilities, we first generated CD4⁺ T_RM cells in C57BL/6J, MHC-II^fl/fl, and MHC-II^ΔEpi mice and sought to examine if newly established CD4⁺ T_RM cells were exclusively T_H17-specified (Supplementary Fig. 8a). Consistent with the notion that CD4⁺ T_RM cells are not solely T_H17-specified, we observed heterogeneity and plasticity in CD4⁺ T_RM cells even at day 56 (Supplementary Fig. 8b, c). Furthermore, we detected modest but significant expansion of T_H1/2/17 CD4⁺ T_RM cells in MHC-II^ΔEpi mice even without subsequent Sp23A memory recalls (Supplementary Fig. 8c–e), suggesting that CD4⁺ T_RM cells are innately plastic and LEC MHC-II might regulate multipotency development from tissue-infiltrating T_eff cells. To confirm this, we generated CD4⁺ T_RM cells in MHC-II^fl/fl and MHC-II^ΔEpi mice before deletion of LEC MHC-II, and then provided Sp23A memory recall exposures in presence of the lymph node egress inhibitor FTY720 (Supplementary Fig. 9a). This denied CD4⁺ T cells access to the systemic circulation (Supplementary Fig. 9b–d) so that only pre-established lung CD4⁺ T_RM cells could contribute to the memory recall responses and plasticity depending on LEC MHC-II status. No differences in CD4⁺ T_RM cell phenotypes between MHC-II^fl/fl and MHC-II^ΔEpi lungs were observed due to LEC MHC-II deletion (Supplementary Fig. 9e–h). Together, these observations suggest that LEC MHC-II does not govern phenotypes of established T_RM cells but rather constrains the multipotency of CD4⁺ T_RM cells developing from T_eff cells during primary and memory recall encounters.

LEC MHC-II directs skewing of lung CD4⁺ T_RM cell responses

We next investigated the activity of the multipotent T_RM cells on restimulation with a nonlethal mismatched Spn serotype (Sp35B) in order to elicit a heterotypic response. We profiled lung CD4⁺ T cells 8 hours after infection (Fig. 4a), when Spn-specific T_RM cells already bolster neutrophil recruitment^11,19. Concatenated lung CD62L⁻CD44⁺CD11a⁺ CD4⁺ T cells included 26 distinct clusters (Supplementary Fig. 10a), none being unique to either genotype. Unlike resting T_RM cells, no CD69⁺ clusters in the infected lung were triple positive for Tbet, Gata3, and RORγT (Supplementary Fig. 10b, c), suggesting a rapid transition away from extensive multipotency towards more limited phenotypes with one or two dominant LDTF.

Quantification of relative frequencies revealed significant perturbations in 4 of 15 CD69⁺ T cell clusters (Supplementary Fig. 10b) within MHC-II^ΔEpi lungs. Of these, T_H17 clusters 3 and 8 and T_H1 cluster 7 were enriched in MHC-II^ΔEpi lungs, while the T_H1/17 cluster 6 was reduced, compared to MHC-II^fl/fl littermates. Quantification of cumulative frequencies identified a significant expansion of T_H17 cells with a concomitant reduction of the less-specified T_H1/17 cells within the MHC-II^ΔEpi lungs (Supplementary Fig. 10c). Cumulative frequencies of each individual transcription factor within RORγT⁺ T cells further suggested a bias towards T_H17 and away from T_H1 in the absence of LEC MHC-II (Fig. 4e), based on LDTF content. This was further supported by the strong negative correlation of Tbet⁺ T_H17 cells with RORγT⁺ single-positive (SP) T_H17 cells on memory recall (Supplementary Fig. 10d). Of note, these differences were independent of blood CD4⁺ T cell involvement since no genotype-dependent differences were observed in the pulmonary intravascular memory cells (i.v.CD45.2⁺CD62L⁻CD44⁺CD11a⁺CD4⁺) of the same mice (Supplementary Fig. 11). Thus, based on LDTFs, the absence of LEC MHC-II exaggerates lung T_H17 cell responses, at the cost of T_H1 activity.

To define T cell lineages by cytokine expression, we used intracellular cytokine staining with PMA/Ionomycin stimulated lung and blood CD4⁺ T cells from experienced MHC-II^ΔEpi and MHC-II^fl/fl mice. Across conditions, the majority of these CD4⁺ T cells were either IFN-γ⁺ or IL-17A⁺, followed by IFN-γ⁺IL-17A⁺ dual expressor cells (Fig. 4f). Only very few cells expressed IL-5 or IL-13. Consistent with the absence of MHC-II on LECs leading to reprogrammed T_RM cells, lung CD4⁺ T cells from MHC-II^ΔEpi mice expressed increased IL-17A and decreased IFN-γ when stimulated (Fig. 4f). The latter finding was recapitulated by antigen-specific ex vivo stimulation of CD4⁺ T cells (Supplementary Fig. 12). Very consistently, T_H1 activity was blunted in CD4⁺ T cells from lungs devoid of LEC MHC-II.

The absence of LEC MHC-II dysregulates barrier immune landscape

CD4⁺ T_RM cells confer microbial clearance by accelerating innate immunity^1,2. We examined if LEC MHC-II and its regulation of CD4⁺ T_RM cell activities instructs innate immune cellular responses to memory recall infections. While both the MHC-II^fl/fl and MHC-II^ΔEpi lungs exhibited comparable Sp35B clearance (Fig. 5a) and BAL cells (Fig. 5b), quantification of extravascular myeloid cells in the infected lung using flow cytometry revealed significant perturbations in the lung immune landscape of MHC-II^ΔEpi mice within 8 hours of heterotypic infection (Fig. 5c, and Supplementary Fig. 13, 14). MHC-II^ΔEpi lungs possessed higher frequencies of eosinophils and tissue-resident alveolar macrophages, but they exhibited reduced accumulation of multiple monocyte-related cells including Ly6C⁺ inflammatory monocytes, monocyte-derived interstitial macrophages, and monocyte-derived DCs, in addition to reduced CD11b⁺ conventional DCs and plasmacytoid DCs (Fig. 5c). These results suggest that deletion of LEC MHC-II has far-reaching consequences on barrier immunity. Of note, these changes were downstream of CD4⁺ T_RM cell reactivation, since uninfected lungs exhibited no inflammation (Supplementary Fig. 6e) or decrements in monocyte-related cell compartments (Supplementary Fig. 15a, b), although eosinophil enrichment was apparent (Supplementary Fig. 15a, b). To connect CD4⁺ T cell clusters to the observed dysregulation in barrier immune landscape on memory recall, we performed correlation analyses for each CD69⁺CD4⁺ T cell lineage and all the myeloid cell changes from the same mice (Fig. 5d). Consistent with the notion that RORγT⁺ T_reg cells suppress T_H17 responses³², RORγT⁺ T_reg cells inversely correlated with neutrophil responses. Furthermore, consistent with Tbet promoting monocyte influx³³, T_H1/17 exhibited direct correlation with Ly6C⁺ monocytes and monocyte-derived CD11b⁺ DCs. Interestingly, we observed strong and direct correlations between eosinophils and T_H17 cells and lung neutrophils with LDTF-negative (TN) CD4⁺ T_H cells, suggesting possible T cell influences on myeloid cells.

**Fig. 5: MHC-II^ΔEpi mice exhibit dysregulated barrier immunity.**

Reduced lung monocytes and persistently high eosinophils in the MHC-II^ΔEpi lungs evoke features reported in patients with checkpoint blockade immunotherapy (CBI)^34,35,36,37 and checkpoint inhibitor pneumonitis (CIP)³⁸. We probed the leukocyte frequencies within the blood fraction (i.v.CD45.2⁺) of MHC-II^fl/fl and MHC-II^ΔEpi lungs for features proposed as robust clinical biomarkers for CBI^{34,35,36,37,39,40}. Consistently, blood fraction of MHC-II^ΔEpi lungs phenocopied features of CBI as manifested by presence of elevated eosinophil frequencies, reduced monocyte frequencies, elevated lymphocyte to monocyte ratios, elevated eosinophil-to-monocyte ratios, and elevated eosinophil-to-lymphocyte ratios during infection (Fig. 5e, f and Supplementary Fig. 15c, d). Our results thus demonstrate that memory recall of CD4⁺ T_RM cells expanded in absence of LEC MHC-II triggers dysregulated barrier immunity phenocopying features of CBI, emphasizing the complex (and incompletely understood) intra- and interleukocyte interactions mediating barrier immunity.

LEC MHC-II functions in a post-translational coupling with PD-L1

The similarities of immune dysregulations in infected MHC-II^ΔEpi mice and patients with CBI and CIP led us to further investigate checkpoint molecules on LECs. LEC MHC-II correlated strongly with LEC PD-L1 throughout all timepoints and across all LEC-types (Fig. 6a). Higher PD-L1 levels on MHC-II^high LECs were also confirmed in human lungs, which had similar positive correlations between the 2 signals (Fig. 6b). When we examined surface PD-L1 expression on LECs of MHC-II^ΔEpi mice, we unexpectedly observed loss of this immune checkpoint on alveolar LECs during homeostasis and pneumonia (Fig. 6c and Supplementary Fig. 16a). This coupling also extended to other lung challenges (Supplementary Fig. 17), suggesting an evolutionarily conserved coupling between MHC-II and PD-L1 on LECs leading to an unanticipated LEC-specific interruption of checkpoint signaling. We aimed to elucidate the mechanism underlying the loss of PD-L1 on MHC-II^ΔEpi LECs. There was a comparable expression of Pd-l1 mRNA in MHC-II^ΔEpi and MHC-II^fl/fl LECs (Supplementary Fig. 16b), suggesting post-transcriptional effects. We posited that PD-L1 depended on LEC MHC-II for surface localization, and tested this using a monoclonal antibody that binds to PD-L1 extracellular domains⁴¹. Consistent with our hypothesis, while most PD-L1 of MHC-II^fl/fl alveolar LECs was surface exposed, almost all PD-L1 within MHC-II^ΔEpi alveolar LECs was trapped intracellularly (Fig. 6d). In addition to failed PD-L1 delivery to the cell surface, distal LECs exhibited decreased content of intracellular PD-L1 (Supplementary Fig. 16c), consistent with PD-L1 instability and degradation in MHC-II^ΔEpi LECs. These data suggest that surface presentation of the checkpoint molecule PD-L1 is entirely dependent upon the expression levels of MHC-II in LECs. This absence of LEC PD-L1 may contribute to phenotypes resulting from LEC MHC-II loss, as evidenced in PD-1 deficient mice⁴² with relevant pneumonia history (Fig. 6e) showing greater memory CD4⁺ T_RM cell plasticity (Fig. 6f and Supplementary Fig. 18) and multipotency (Fig. 6g) in their lungs but not their blood, akin to MHC-II^ΔEpi mice. Taken together, MHC-II on LECs emerges as having two-pronged effects on immunity, directing adaptive immunity via antigen presentation and regulating adaptive immunity via PD-L1.

**Fig. 6: LEC PD-L1 functions in a post-translational coupling with MHC-II.**

Discussion

Herein, we show that diverse LECs, including a distinct SPC^lowMHC^high LEC, function as APCs to CD4⁺ T cells in anatomically segregated fashions (Supplementary Figure 19a). We discover that LEC MHC-II is dynamically regulated on all LECs in conjunction with CD4⁺ T cell recruitment and activation (Supplementary Figure 19b) and that LEC MHC-II imposes constraints on aberrant expansion and plasticity of CD4⁺ T_RM cells (Supplementary Figure 19c) via the PD-L1:PD-1 signaling axis wherein epithelial PD-L1 depends on MHC-II. Further, we show that disruption of LEC MHC-II has significant consequences on the barrier immune landscape, thus, establishing LEC-CD4⁺ T_RM cell immune synapses as core elements of barrier immunity. Epithelial antigen presentation, thus, plays a pivotal role in localizing and directing CD4⁺ T_RM cell immunity.

MHC-II expression is high on AT2 cells and SPC^lowMHC^high LECs. SPC^lowMHC^high LECs possess features of traditional club cells and MHC-I^high club-like stem cells²³, but they resemble AT2 cells in their high MHC-II, lamellar bodies, and abundance within the lung. They may represent an AT2 cell subset. Given their overlaps with stem cells of the lung^23,43, and reported connections of MHC-II to epithelial stem cell biology in other barrier tissues^15,17, the immunological synapse may be a component of tissue repair and SPC^lowMHC^high LECs may contribute to resolution, repair, or regeneration. If and how SPC^lowMHC^high LECs compare to Wnt-active AT2-stem cells^44,45 and AT2-signaling cells⁴⁶ remains to be determined. CD4⁺ T_RM cells can regulate stem cell renewal, proliferation, differentiation fate, and tissue repair in the skin and the gut^6,15. Similar roles may apply for MHC-II in regulating lung stem cell functions⁴⁷.

Epithelial MHC-II has been connected to regeneration, but its immune significance is less established. Our results identify antigen presentation as a pan-epithelial feature with major roles in lung and CD4⁺ T_RM biology. MHC-II on LECs was envisioned as restricted to AT2 cells^14,48, but now is expanded to SPC^lowMHC^high LECs and to club cells and multiciliated cells during inflammation. Possible immune roles for epithelial MHC-II were controversial and speculative^20,49,50,51. Here, we provide in vivo evidence of functional significance whereby deletion of LEC MHC-II led to decreased seeding and aberrant phenotypes of CD4⁺ T_RM cells around conducting airways, with diminished recruitment of monocyte-derived cells during subsequent infections. Thus, LEC MHC-II is a critical tissue-localized cue for resident memory cells.

Our results identified a surprising degree of transcriptional plasticity and multipotency within CD4⁺ T_RM cells, more than previously appreciated^6,19,29. Adult humans daily inhale ~11,000 liters of air with its contaminants, regularly microaspirate, and experience frequent respiratory infections, a tremendous environmental exposure for lung memory cells. Factors constraining CD4⁺ T_RM cell plasticity driven by this environmental pressure were unknown. Our studies reveal 3 major findings. (i) CD4⁺ T_RM cells possess extensive flexibility, wherein many T_RM cells co-express ≥2 classically antagonistic LDTFs. Such plasticity within T_H17 effector cells has been reported⁵² but our work extends these paradigms to T_RM cells and suggests a stem-cell-like uncommitted property for otherwise “primed” T_RM cells that might allow flexibility to choose effector pathways suited to the nature of perceived threats³. Epigenetic accessibility of LDTFs to target genes defines T cell responses, and future studies using single-cell epigenetic and transcriptomic profiling of multipotent T_RM cells are warranted^5,53. Consistent with T_H17 effector cells, we observed a tendency for T_H17 T_RM cell clusters to acquire a T_H2 Gata3 signal at homeostasis⁵², but this was not sustained at the cytokine level post-restimulation. Whether Gata3 expression reflects bivalent epigenetic marks adjacent to Gata3 gene within T_RM cells⁵³ or indicates a poised T_H2 transcriptome that would translate into T_H2 cytokines upon licensing by alarmins such as IL-18 and IL-33 (which are precluded in our 8-h challenge model with Spn)⁶ needs further investigation. (ii) LECs impose constraints on the aberrant expansion of T_RM cells co-expressing Tbet, Gata3, and RORγT via PD-L1:PD-1 signaling axis. Our study establishes PD-1 as a checkpoint molecule that restrains CD4⁺ T cell phenotypic boundaries in peripheral non-lymphoid tissues. In doing so, we distinguish these restraints as distinct from those imposed by CTLA-4 during initial priming within the lymph nodes in that they curtail expansion, but not de novo generation, of multipotent T_RM cell clusters⁵⁴. Given that MHC-II and PD-L1 expression in LECs is anatomically segregated and enriched in alveolar LECs, the physical locations of the most multipotent T_RM cells within the lung becomes an important next question. (iii) Our findings show that the lungs provide a permissive environment for memory CD4⁺ T cell plasticity compared to the blood. We propose that this may reflect distinct selective pressures in barrier tissues where antigenic encounters are more diverse in terms of quantity, quality, and frequency.

Deletion of LEC MHC-II altered the barrier immune landscape. Reactivated CD4⁺ T_RM cells expanded in the absence of LEC MHC-II show an intrinsic bias to deviate away from T_H1 responses, precipitating consequences on host immunity that include increased alveolar macrophages and eosinophils accompanied by reduced monocytes, monocyte-derived interstitial macrophages, monocyte-derived DCs, CD11b⁺ cDCs, and pDCs within MHC-II^ΔEpi lungs. While molecular mechanisms are likely multifactorial, it is reasonable to posit that reduced T_H1 responses tilted the barrier immune landscape towards weaker monocyte recruitment and more eosinophilia^33,55. This remodeled immunity phenocopies biomarkers of successful CBI^{34,35,36,37,39,40} and is in line with higher incidences of CIP in PD-1/PD-L1 CBI patients⁵⁶. Our observations thus suggest that PD-1/PD-L1 CBI may interfere with epithelial constraints on CD4⁺ T_RM cells and unleash unrestrained T_RM cell activation leading to immunopathologies like CIP, colitis, IBD, and skin inflammation, all of which may involve CD4⁺ T cell plasticity^7,8,9,56,57. PD-L1 blockade results in unrestrained immunopathology of the lungs in response to viral antigens⁵⁸, and a similar axis for restraining CD8⁺ T_RM cell-driven pathology in the human pancreas is exploited by PD-L1-expressing pancreatic macrophages⁵⁹. The exact biological role for the exhaustion marker PD-1 on CD4⁺ T_RM cells that are otherwise highly responsive to antigenic restimulation is incompletely understood¹. Our results suggest that T_RM cell PD-1 may act akin to a molecular switch that an organ like the lung can exploit to limit the plasticity of T_RM cells arising there.

Our findings have implications that pervade fields beyond barrier epithelial and CD4⁺ T_RM cell biology. High levels of MHC-II on AT2 cells¹⁴, keratinocytes¹⁷, and Lgr5⁺ intestinal stem cells¹⁵ connect antigen presentation to diverse tumor-initiating stem cells (TISCs)^60,61,62. While PD-L1 expression on Lgr5⁺ ISCs is uncertain, keratinocytes, like AT2 cells and SPC^lowMHC^high LECs, do express PD-L1⁶³. Given our observations, it is tempting to posit that MHC-I, MHC-II, and PD-L1 co-expression on TISCs may jointly be responsible for evasion of early antitumor T cell immunosurveillance during tumorigenesis. Furthermore, high MHC-II and PD-L1 levels might also provide a tumor-conducive immunosuppressive microenvironment wherein expansion of antitumor CD4⁺ T cells would be restricted⁶⁴. Indeed, murine lungs deficient in MHC-II within non-hematopoietic cells are resistant to metastatic tumorigenesis⁶⁵. Studies investigating the tumorigenic potential of SPC^lowMHC^high LECs and elucidating the role of MHC-II in epithelial stem cells are merited. Detailed understanding of stem cell antigen presentation will help guide the development of innovative immunomodulatory agents and inform development of molecular targets that could be valuable biomarkers for early cancer detection and immunotherapy outcomes, such as MHC-II for melanoma⁶⁶.

Taken together, our study identifies antigen presentation as a pan-epithelial feature that exhibits division of labor in anatomically segregated and temporally dynamic fashions within mammalian lungs. We identify epithelial antigen presentation as a tissue-resident regulator of CD4⁺ T_RM cell locations, plasticity, and activities, and implicate disruption of epithelial restraints on CD4⁺ T_RM cells as underlying checkpoint blockade induced immune dysregulation. Distinct epithelial cells in other barrier tissues may have similar rheostat functions in T_RM cell immunity. Epithelial MHC is emerging as relevant to stem cell biology, cancer, and immunomodulatory therapy. Thus, considering epithelial antigen presentation may inform future strategies to curb immunopathology during chronic diseases and checkpoint blockade.

Methods

Mice

C57BL/6J (Stock# 000664), OT-II TCR transgenic (B6.Cg-Tg(TcraTcrb)425Cbn/J) (Stock # 004194), and B6.Cg-Pdcd1^tm1.1Shr/J (PD-1^−/−, Stock #028276)⁴² mice were obtained from The Jackson laboratories (USA) at 6 weeks old age, all on the C57BL/6 background. SPC-GFP mice²⁰ were back-crossed more than 15 generations onto C57BL/6 and bred in Boston University animal facilities. Mice for targeted deletion of MHC-II on LECs were generated in-house at BU-ABSL2 facility. Briefly, male B6.129X1-H2-Ab1^tm1Koni/J (Stock# 013181; C57BL/6 background) and female Nkx2-1^{tm1.1(cre/ERT2)Zjh}/J mice (Stock# 014552; B6.129SF2 background) were obtained from The Jackson Laboratories (USA) and crossed to create Nkx2.1^creERT2H2-Ab1^fl/fl mice. Both founder mice lack I-E due to mutation in H2-Ea locus allowing deletion of H2-Ab1 to abrogate MHC-II entirely. Nkx2-1 is expressed in all LECs and some cells of telencephalon, thyroid, and pituitary^67,68,69 and Nkx2-1-driven Cre efficiently targets all LECs⁷⁰. All breeders were homozygous floxed for H2-Ab1. For experiments, all mice were homozygous for loxP sites flanking H2-Ab1 exon1 and were identified as Cre-positive or negative based on the presence or absence of Nkx2.1^cre/ERT2. Cage and littermate controls of both sexes were used for the studies. BASC Reporter mice including the BASC viewer and the BASC v-race mice were on a mixed hybrid C57BL/6 x 129/SV background²¹. All mice were housed in specific pathogen-free environment on a 12-h light-dark cycle at 30–70%, humidity and temperature of 20–26 °C, with ad libitum access to standard chow and water. for all experiments, 7–14-week-old mice were used. Mice were euthanized using isoflurane overdose and death confirmed using pneumothorax before organ collections. Animal procedures were performed with compliance to all relevant ethical regulations for animal testing and research, in the United States in accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health (after review and approval by the Institutional Animal Care and Use Committee of Boston University) and in Germany according to the German animal protection law (after review and approval by the Regierungspräsidium Darmstadt, Veterinärdezernat of Hesse, Germany).

Human lung biopsy samples

De-identified pathologically “disease-free” samples from lung tissue wedge resections or lobectomies were used for experiments. None of the patients included received any checkpoint blockade therapy. The Boston University Medical Center Institutional Review Board reviewed and approved the study, deeming that it did not meet the definition of “human subjects research” (Protocol # H-32271).

Experimental pneumonia

Mice were anesthetized via intraperitoneal (i.p.) injection of ketamine and xylazine before infection. To induce experience, Streptococcus pneumoniae (Spn) suspended in sterile saline was instilled via a 24-gauge angiocatheter directed to the left lobe after surgical exposure of the trachea. Mice were infected intratracheally (i.t.) with 1–5 × 10⁶ CFU of serotype 19F Spn (Strain EF3030) or saline and euthanized 48 h postinfection. To generate Spn-specific lung-resident CD4⁺ T_RM cells, mice were infected intratracheally (i.t.) with 1–5 × 10⁶ CFU of serotype 19F Spn (Strain EF3030) or saline one week apart, and then allowed to recover for 28–35 days¹⁹. Mice with such infection histories exhibited a lung-resident CD4⁺ T_RM cell-dependent protection on memory recall challenge with a lethal serotype-mismatched Spn¹⁹. To test CD4⁺ T_RM cell phenotypes on repeated memory recall, 2X Sp19F experienced mice were infected with 1–3 × 10⁶ CFU of serotype 23A Spn (Sp23A) on Days 35 and 49 before allowing recovery for another 5 weeks. For final challenge, 3–5 × 10⁶ CFU of serotype Sp35B Spn (Sp35B) was intratracheally instilled. Of note, Sp35B (like Sp19F and Sp23A) causes nonlethal pneumonia and was chosen to mimic nonlethal homeostatic bacterial exposures experienced by humans. For analyses of MHC-II and PD-L1 coupling, mice were infected with 50 PFU of Influenza A virus PR8 or 1 × 10⁵ CFU of Klebsiella pneumoniae (ATCC43816).

Mouse experimental procedures

For in vivo DQ-Ovalbumin uptake and processing assay, 20 μg of DQ-Ovalbumin, DQ-OVA (Molecular Probes, Cat# D-12053) in 50 μL of sterile saline was instilled i.t. via angiocath directed to the left lung 3 h before euthanasia. For Tamoxifen administration, tamoxifen (Sigma) was dissolved in corn oil (Sigma) to 20 mg/mL stock concentration and stored at 4 °C. Mice were i.p. injected at 100 mg/kg of body weight for five consecutive days. Treated mice were allowed to rest for at least 2 weeks for tamoxifen to wash out before infecting them. For in vivo CD40L blockade, Mice received i.p. injections of 400 μg antimouse CD40L (Clone MR-1, BioXCell, Cat# BE0017-1) or IgG control (BioXCell, Cat# BE0091) antibody on day 6. This was followed by 100 μg i.n. instillations and 250 μg i.p. injections of the respective antibodies on days 7, 9, 11, and 13. For FTY720 administration, Mice were given 1 mg/kg of FTY720 (Sigma) every alternate day by intraperitoneal injections. For recombinant IFN-γ administration, anesthesized mice received a bolus of 100 ng of recombinant IFN-γ (R&D systems) in 100 μL intranasally.

Murine lung digestion for epithelial cell flow cytometry and FACS sorting

For high throughput flow cytometry of lung epithelial cells, elastase digestion of murine lung was performed. Briefly, euthanized mice were exsanguinated, trachea cannulated and lungs lavaged thrice with 1 mL Ca-Mg free DPBS containing 0.5 mM EDTA solution. The lungs were then inflated with 1 mL digestion media (RPMI 1640 containing 10% Dextran, 4.5 units/mL of Elastase (Worthington Biochemicals, Lakewood, NJ), and 150 μg/mL of DNase I) and plugged with 0.5 mL of 1% low melting temperature agarose in PBS. The heart-lung blocks were then placed on petri dishes and covered with ice to allow agarose to solidify for at least 5 min. Left lobes of the enzyme-instilled and plugged lungs were dissected and kept for enzymatic digestion in additional 2 mL of digestion media for 1 h at 37 °C at 200 rpm. The digested lungs were then minced finely using disposable razor blades, washed down with 1 mL digestion media, and transferred to 50 mL conical tubes. The slurry was then kept on a shaker for 20 mins at 37 °C at 200 rpm before adding 10mL of RPMI 1640 with 50% heat-inactivated FBS and 100 μL of 10 mg/mL DNase I. This suspension was then gently vortexed to allow cell aggregate dissociation and incubated on ice for 5 mins. Ten-milliliter RPMI 1640 was then added and the suspension was shaken vigorously at 300 rpm at 37 °C before sequential gravity-mediated filtering through 100 and 70 μm cell strainers. The single-cell suspensions were then centrifuged at 300 g for 10 mins to pellet cells before RBC lysis and resuspension in FACS buffer for flow cytometry as per usual procedures. Cells were blocked with TruStain αCD16/CD32 Fc-Block (BioLegend). Flow cytometry was performed on LSR II Flow Cytometer (BD Biosciences). High-dimensional multiparameter spectral flow cytometry was performed on Aurora (Cytek), SpectraFlo (Cytek) software was utilized for spectral unmixing of the data using an ordinary least square algorithm, and data were analyzed with FlowJo software (BD Biosciences). Gating strategies are provided in the Supplemental figures and were based on use of Fluorescence minus one (FMO) controls.

For data generated using the BASC reporter mice, dispase digestion was used²¹. Briefly, the lungs of euthanized mice were perfused with PBS, cannulated via the trachea, and instilled with 1 ml prewarmed dispase solution (Corning). The trachea was ligated, the lungs extracted from the thorax, and then finely minced using scissors. Two milliliters dispase and DNase I (20 U/ml, Roche) were added to the resulting slurry, and samples digested at 37 °C shaker for 20–30 min. Resulting cell suspensions were gently mixed and sequentially filtered through 100- and 40-μm strainers followed by wash with excess DMEM through the filter. The single-cell suspensions were centrifuged (5 min, 300 g), pellets resuspended in 1 mL pre-cooled FACS buffer. Cell suspensions were resuspended in MACS buffer, mixed with anti-CD45, anti-CD31, and anti-Ter119 microbeads (Miltenyi Biotec), and incubated at 4 °C for 15 min. After washing, cells were loaded onto pre-conditioned MS columns that had been placed in the magnetic field of a MACS separator and the flow-through containing unlabeled cells was collected. These enriched LECs were then blocked using CD16/CD32 Fc-Block (BD Biosciences), stained, and washed before flow cytometry was performed on LSRFortessa Flow Cytometer (BD Biosciences). Gating was based on the use of Fluorescence minus one (FMO) controls. The list of antibodies used in this study are provided in Supplementary Table 7.

For RNA-profiling, epithelial subsets from stained single-cell suspensions isolated from elastase digested lungs were sorted into RPMI 1640 with 20% FBS on ice using FACS-Aria II SORP (BD Biosciences) before proceeding to RNA extraction.

For TEM analyses, stained LEC cell suspensions were fixed using 2% paraformaldehyde in PBS before sorting using the MoFlo Astrios cell sorter (Beckman Coulter).

Murine lung digestion for leukocyte flow cytometry

To allow enumeration of extravascular versus the intravascular fraction of lung CD4⁺ T cells, anesthetized mice were retro-orbitally administered 2 μg anti-CD45.2 antibody 3 min prior to euthanasia. Lungs were collected in RPMI 1640 with 10% FBS before processing for flow cytometry. Single-cell suspensions were prepared by digestion of lungs in type 2 collagenase (Worthington Biochemicals, Lakewood, NJ) and DNase I¹⁹. Cells were blocked with TruStain αCD16/CD32 Fc-Block (BioLegend). Flow cytometry was performed on LSR II Flow Cytometer (BD Biosciences). High-dimensional multiparameter spectral flow cytometry was performed on Aurora (Cytek), SpectraFlo (Cytek) software was utilized for spectral unmixing of the data using ordinary least square algorithm and data were analyzed with FlowJo software (BD Biosciences). Gating strategies are provided in the Supplemental figures and were based on the use of Fluorescence minus one (FMO) controls. The list of antibodies used in this study is provided in Supplementary Table 7.

Lung processing for human lung flow cytometry

Pathologically deemed normal segments of lung biopsies obtained from tumors resected from patients were collected in RPMI 1640. The lung pieces were minced in 2 mL of Digestion media (RPMI containing 10% Dextran, 10 units/mL of elastase, and 150 μg/mL of DNase I) with a disposable razor blade to a fine slurry. The slurry was then washed down with 3 mL more digestion media and transferred to 50 ml conical tubes. The dish was washed with additional 5 mL digestion media and transferred to the 50 mL tube to have a total of 10 mL cell slurry. The suspension was shaken at 250 rpm for 1 h at 37 °C with intermittent vortexing every 20 mins after which 10 mL of RPMI 1640 with 50% heat-inactivated FBS and 200 μL of 10 mg/mL DNAse I was added. The suspension was vortexed to dissociate aggregates and kept on ice for 5 mins. Additional 10 mL RPMI 1640 media was added to bring the final liquid volume to 30 mL and the tube shaken at 300 rpm for 10 min at 37 °C. The lung minces were sequentially filtered through 100 and 70 μm cell strainers using gravity (no scraping). The single-cell suspensions then centrifuged at 300 g for 10 mins to pellet cells before RBC lysis and resuspension in FACS buffer for flow cytometry as per usual procedures. Cells were blocked with Human TruStain FcX- Block (BioLegend). Flow cytometry was performed on LSR II Flow Cytometer (BD Biosciences) and data were analyzed with FlowJo software (BD Biosciences). Gating strategies are provided in the Supplemental figures and are based on the use of Fluorescence minus one (FMO) controls. The list of antibodies used in this study is provided in Supplementary Table 7.

Intracellular cytokine, protein, and transcription factor staining for flow cytometry

For intracellular staining of transcription factors and Ki67, eBioscience Foxp3/ Transcription Factor Staining Buffer Set (Cat# 00-5523-00) was used as per manufacturer’s protocols. For intracellular cytokine staining and subcellular PD-L1 localization, eBioscience Intracellular Fixation & Permeabilization Buffer Set (Cat# 88-8824-00) was used as per manufacturer’s protocols. The list of antibodies used in this study is provided in Supplementary Table 7.

Transmission electron microscopy

The sorted cells or mouse lungs were fixed for 2 h in 2% glutaraldehyde plus 1% paraformaldehyde in 0.1 M Na Cacodylate buffer, pH 7.4. After washing in buffer, samples were postfixed in 1.5% osmium tetroxide, block stained in 1.5% uranyl acetate, and dehydrated in acetone. They were then embedded in Epon 812, sectioned and stained with uranyl acetate and lead citrate. Images were captured using Tecnai F20 microscope.

RNA extraction and Real-time PCR

RNA was extracted from sorted LECs using RNAeasy Micro Kit (Cat# 74004) as per manufacturer’s protocols and stored at −80 °C. qRT-PCR was performed using the RNA-to-Ct kit (Life Technologies, Cat# 4392938). Commercially available predesigned TaqMan gene expression assays for Spc (Mm00488144_m1), Scgb1a1 (Mm00442046_m1), Foxj1 (Mm01267279_m1), Aqp5 (Mm00437578_m1), Aw112010 (Mm01197675_m1), Sox2 (Mm03053810_s1), H2-K1 (Mm01612247_mH), Cdkn1a (Mm00432448_m1), Cd74 (Mm00658576_m1), H2-DMa (Mm00439226_m1), H2-DMb1 (Mm04213366_s1), H2-DMb2 (Mm00783707_s1), H2-Oa (Mm00468476_m1), H2-Ob (Mm00468801_m1), Cd274 (Mm03048248_m1), and 18S rRNA (Cat# 4319413E) from Applied Biosystems were used. The probe context sequence for these assays are detailed in Supplementary Table 8. The quantity of the detectable mRNA was calculated by normalizing to 18S rRNA from the respective sample and then expressed as fold change over mRNA levels of the whole lung of naive mice of pertinent genotype.

Ex vivo antigen presentation assay

FACS-sorted LECs from Sp19F-infected mice were co-cultured with OT-II CD4⁺ T cells (1:10 ratio) with 25μg/mL of OVA_323-339 peptide (Anaspec) in media containing 25% DMEM (Gibco), 25% F-12 (Gibco), 50% RPMI 1640 (Gibco) supplemented with 10% Heat inactivated FBS, Penicillin/Streptomycin (100 U/mL, Gibco), 1X Non-essential amino acids (Gibco), 2 mM L-Glutamine (Glutamax, Gibco), 1 mM Na-pyruvate (Gibco), 10 mM HEPES (Gibco), 0.2 μM beta-mercaptoethanol, 10 μg/mL Insulin, 50 mM Hydrocortisone, 100 μg/mL Transferrin, 1 mM Na-selenite, and 1 mM beta-estradiol for 8 h at 37 °C 5% CO₂ incubator. For assays with C57BL/6J LECs, EasySep™ Mouse CD4⁺ T Cell negative selection and Isolation Kit (StemCell Technologies, Cat# 19852) was used to enrich OT-II CD4⁺ T cells from spleens of OT-II TCR transgenic mice and 10 μg/mL of antimouse MHC-II antibody (Biolegend) was used for MHC-II blockade. For assays with MHC-II^ΔEpi LECs, EasySep™ Mouse CD4⁺ T Cell negative selection and Isolation Kit (StemCell Technologies, Cat# 19852) was used to enrich OT-II CD4⁺ T cells from spleens of OT-II TCR transgenic mice. This was followed by MHC-II blockade of the enriched CD4⁺ T cell suspension to block any excess MHC-II expressing splenocytes before rigorous washing and coculture with LECs for 8 h.

Ex vivo stimulation and Intracellular Cytokine staining of CD4⁺ T cells

To allow identification of extravascular versus intravascular fraction of lung CD4⁺ T cells, anesthetized mice were retro-orbitally administered 2 μg anti-CD45 antibody 3 min prior to euthanasia. Single-cell suspension of lung leukocytes was prepared as described before.

For PMA/Ionomycin stimulation, 2 × 10⁶ cells were stimulated ex vivo in 12 well plate with 250 ng/mL Phorbol Myristate Acetate (PMA)(LC Laboratories, Woburn, MA) and 1.5 μg/mL Ionomycin (Sigma, St. Louis, MO) in T cell stimulation media (RPMI 1640 (Gibco) supplemented with 10% Heat inactivated FBS, Penicillin/Streptomycin (100U/mL, Gibco), 1X Non-essential amino acids (Gibco), 2 mM L-Glutamine (Glutamax, Gibco), 1 mM Na-pyruvate (Gibco), 10 mM HEPES (Gibco), and 0.2 μM beta-mercaptoethanol) for 1 h at 37 °C and 5% CO₂. Monensin (Biolegend, Cat# 420701) and Brefeldin A (Biolegend, Cat# 420601) both at 1X final concentration were added to the cell suspension for last 5 h at 37 °C and 5% CO₂. Cells were then processed for intracellular cytokine staining, as per manufacturer’s protocols.

For Spn-specific stimulation, 2 × 10⁶ lung cells were stimulated with beta-propiolactone killed Sp35B (ratio of 1:10 of cells:bacteria) or 1 mg/mL chicken egg ovalbumin (Sigma) ex vivo in 12 well plate for 3 h followed by addition of Monensin and Brefeldin A at 1X final concentration for 14 h at 37 °C and 5% CO₂. Cells were then processed for intracellular cytokine staining, as per manufacturer’s protocols. The list of antibodies used in this study is provided in Supplementary Table 7.

Immunofluorescent staining

The trachea of euthanized mice were cannulated with an 18-gauge angiocath and 1 mL of Tissue-Tek Optimal Cutting Temperature (O.C.T.) compound (Sakura Finetek). Once the lungs were inflated, the left bronchus was tied using a suture, the lung washed in sterile HBSS before embedding in cryomolds with O.C.T. and flash freezing at −80 °C until sectioning. Frozen 8 μm thin coronal sections of the left lungs that contained the whole face of the lungs including the entire airway tree structure were collected for further analyses. Sections with folds and/or tears were rejected. The sections that met our inclusion criteria were first fixed, washed, and permeabilized (with 0.2% Triton X) before blocking with Blocking buffer (PBS with 10% normal donkey serum and 3% BSA) followed by overnight incubation with rabbit antimouse CD4 (abcam, Cat# ab183685) at 4 °C in a humidified chamber. Next day, sections were vigorously washed before incubation with Alexa 594 conjugated Affinipure donkey antirabbit IgG (Jackson Immunoresearch) at room temperature for 1 hr in a dark humidified chamber. All slides were counterstained with DAPI (Molecular Probes by Life Technologies, R37606) before mounting the sections with FluorSave (Millipore Calbiochem: 345789) and covering with coverslip for visualization. Of note, a “no anti-mouse CD4” treated primary control was used to identify true CD4⁺ events; a section of day 10 mouse lung (i.e., 3 days post 2x Sp19F infection when the lung possesses most CD4 T cells, Fig. 3A) was chosen for the “no anti-CD4 primary control” to ensure that the inclusion criteria for CD4⁺ events was stringent. Images were captured using a Leica DM4 microscope equipped with Leica DFC 7000T camera and processed using ImageJ 2.0.0-rc-69.

Quantification of anatomical distribution of CD4⁺ cells

Fifteen images of randomly selected fields per coronal section from each mouse lung were identified in a raster fashion, from the top left to bottom right, and were captured at 10x objective with Numerical Aperture of 0.30. Images included autofluorescence of lung tissue in the GFP channel to allow identification of lung anatomical structures. ImageJ was used for automated quantification of lung tissue area identified as airway (along the conducting airways), bronchovascular interstitium (interstitial area around major arteries within the bronchovasculature) and the parenchyma (alveoli) within the GFP channel which was permissible for tissue autofluorescence (AF). Once the areas of distinct lung structures were identified, ImageJ was used to computationally enumerate CD4⁺ events within the same manually selected, anatomically distinct areas of the imaged fields within the Alexa 594 channel to minimize the inclusion of artefacts as false-positive signals. This was done by setting a standardized minimum threshold value which excluded 99.9% of the background signal on the “no anti-CD4 primary control” slide. Images within Alexa 594 channel underwent watershed segmentation to differentiate overlapping CD4⁺ events. Once this was done for every one of the 15 images per lung, the number of CD4⁺ events per unit area of the identified lung structure (N_X) was identified as (1):

$${N}_{X}=\frac{\#CD{4}^{+}event{s}_{X}}{Are{a}_{X}}$$

(1)

where X = the anatomical structure of the lung wherein the CD4⁺ events were identified.

If at least 1 of the 15 images per mouse lung contained a structure of interest (i.e., an instance of X), the dynamics of N_X, i.e D(N_X) for that mouse lung was calculated as (2):

$$D({N}_{X})=\frac{1}{n}\mathop{\sum }\limits_{a=1}^{n}{({N}_{x})}_{a}$$

(2)

where n = number of images per lung containing instances of X

Since each group contained a total of at least four mice per time points collected across two experiments, the group dynamics of N_X for the whole group of mice at their specific time points, i.e., G(D(N_X)) was then calculated as (3):

$$G(D({N}_{X}))=\frac{1}{m}\mathop{\sum }\limits_{b=1}^{m}{[D({N}_{X})]}_{b}$$

(3)

where m = total number of mice per group for that timepoint.

Algorithmic analysis of single-cell fluorescence datasets

Data processing pipeline was established using the Omiq.ai cloud computation platform (Omiq). For temporal LEC MSFC data analysis, live CD45⁻EpCAM⁺ single-cell datasets containing equal quantities (25,000 events per animal) of cells from all experimental timepoints were concatenated and asinh transformed (cofactor = 6000). Datapoints were projected into two-dimensional space with opt-SNE algorithm²⁶ incorporating fluorescence, forward scatter and side scatter parameters (perplexity = 50, theta = 0.5, opt-SNE endpoint = 7500; PCA preinitialization embedding). Intensities of each fluorescence parameter were overlaid to represent expression levels. Same dataset was clustered with Phenograph algorithm (k = 20, distance metric = euclidean)²⁷. Groupings of clusters based on hierarchical clustering of median fluorescence intensities (MFI) across multiple protein markers were annotated and color-overlaid on the opt-SNE projection of multidimensional data. For T cell flow cytometry profiling, live CD45⁺CD4⁺CD19⁻CD45.2⁺ for lung intravascular (‘blood’) cells or live CD45⁺CD4⁺CD19⁻CD45.2⁻ for lung extravascular (‘lung’) T cells were concatenated, asinh transformed (cofactor = 6000) and clustered with Phenograph (k = 20, distance metric = euclidean). CD44⁺CD11a⁺ clusters (as determined based on the MFI cutoff) were subsampled as memory T cell data. These data were re-clustered with Phenograph (k = 20, distance metric = euclidean) and projected into opt-SNE space (perplexity = 30, theta = 0.5, opt-SNE endpoint = 5000; PCA preinitialization embedding). Clusters were color-coded and overlaid on the opt-SNE projections. Each marker MFIs of Phenograph clustered datasets were organized into hierarchically clustered heatmaps. Clusters were classified as positive for specific lineage-defining transcription factors (LDTF) based on the MFI value cutoffs set by measuring the MFIs of LDTF-negative non-T cell populations sampled from the same dataset. This approach was preferred over the FMO-based cutoff calculation to alleviate the MFI difference caused by nonspecific binding of anti-LDTF antibodies to cells that do not express them. Frequencies of each cell type and MFI values for individual markers were calculated from corresponding clusters per each animal and data were plotted and compared using Prism 8.0 (Graphpad).

Quantification of Relative Multipotency Index

Relative multipotency indices for CD4⁺ CD44⁺CD11a⁺ memory T cells in different anatomical sites were calculated as (4):

$$Relative\,Multipotency\,Index=ln\left(\frac{ \% \,of\,memory\,CD4+T\,cells\,expressing\ge 2\,LDTFs}{ \% \,of\,memory\,CD4+T\,cells\,expressing\,1\,LDTF}\right)$$

(4)

Where memory CD4+ T cells = CD4⁺CD62L⁻CD44⁺CD11a⁺ CD4⁺ T cells, and LDTF = lineage-defining transcription factors.

i.v.CD45.2⁻ CD4⁺ memory T cells were identified as the lung memory T cells while the i.v.CD45.2⁺ CD4⁺ memory T cells were identified as the blood memory T cells.

Statistical Analyses

Statistical analyses were performed using Prism 8.0 (GraphPad). Differences were deemed statistically significant if the p value or FDR q value was ≤0.05. Each figure legend communicates the number of mice used per experiment, the number of experiment replicates performed and the statistical tests used to make comparisons. For all figures, data are represented as mean ± SEM.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Original data generated in this study are available within the paper and associated supplementary files, with additional information available from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank Riley Pihl and the Boston University School of Medicine Flow Cytometry Core Facility (BU-FCCF) for assistance with flow cytometry and FACS sorting. We thank Dr. Esther Bullitt for assistance with electron microscopy and Dr. Mary Williams for her expertize with electron microscopy of LECs. We would also like to thank Dr. Jason Rock, Dr. Anna Engler and Hanne Richardson for advice with tamoxifen procedures and Cheryl Spencer for help with acquisition of consented human lung samples. Finally, we would like to thank all the consenting donors for their generosity. This work was supported by NIH grants including HL147397 to EIA, HL142199 to KAB, HL147461 to FTK, HL136725 to MRJ, GM120060 and HL111449 to LJQ, AI115053, HL135756, and HL137081 to JPM and T32 HL007035 for support of trainees in addition to support by the German Research Foundation (DFG) Clinical Research Group KFO309 TP08, the Excellence Cluster Cardio-Pulmonary Institute (CPI), and the German Center for Lung Research (DLZ) to TB.

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Pulmonary Center, Boston University School of Medicine, Boston, MA, 02118, USA
Anukul T. Shenoy, Carolina Lyon De Ana, Emad I. Arafa, Kimberly A. Barker, Filiz T. Korkmaz, Aditya Ramanujan, Neelou S. Etesami, Alicia M. Soucy, Ian M. C. Martin, Anne Hinds, Wesley N. Goltry, Hasmeena Kathuria, Matthew R. Jones, Lee J. Quinton, Anna C. Belkina & Joseph P. Mizgerd
Department of Microbiology, Boston University School of Medicine, Boston, MA, 02118, USA
Carolina Lyon De Ana, Kimberly A. Barker, Neelou S. Etesami, Hasmeena Kathuria, Lee J. Quinton & Joseph P. Mizgerd
Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA
Emad I. Arafa, Anne Hinds, Matthew R. Jones, Lee J. Quinton & Joseph P. Mizgerd
Department of Cardiac Development and Remodeling, Max-Planck-Institute for Heart and Lung Research, Member of the German Center for Lung Research (DZL), Bad Nauheim, Germany
Isabelle Salwig & Thomas Braun
Flow Cytometry Core Facility, Boston University School of Medicine, Boston, MA, 02118, USA
Brian R. Tilton & Anna C. Belkina
Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, 02118, USA
Lee J. Quinton & Anna C. Belkina
Department of Biochemistry, Boston University School of Medicine, Boston, MA, 02118, USA
Joseph P. Mizgerd

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Contributions

A.T.S. and J.P.M. conceived the project, designed the experiments and supervised the study. A.T.S. performed experiments and analyzed the data with assistance from C.L.D.A., E.I.A., K.A.B., F.T.K., A.R., N.S.E., A.M.S., I.M.C.M., B.R.T., A.H., W.N.G. Experiments with BASC-reporter mice were performed by I.S. with guidance from T.B. A.C.B. provided guidance with multispectral cytometry and performed computational analyses including the Phenograph clustering and optSNE visualization on the data. H.K., T.B., M.R.J., L.J.Q., A.C.B. and J.P.M. contributed resources used in this study. A.T.S. and J.P.M. wrote the manuscript, which was edited and approved by C.L.D.A., E.I.A., K.A.B., I.S., F.T.K., A.R., N.S.E, A.M.S., I.M.C.M., B.R.T., A.H., W.N.G., H.K., T.B., M.R.J., L.J.Q. and A.C.B.

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Shenoy, A.T., Lyon De Ana, C., Arafa, E.I. et al. Antigen presentation by lung epithelial cells directs CD4⁺ T_RM cell function and regulates barrier immunity. Nat Commun 12, 5834 (2021). https://doi.org/10.1038/s41467-021-26045-w

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Received: 21 October 2020
Accepted: 31 August 2021
Published: 05 October 2021
DOI: https://doi.org/10.1038/s41467-021-26045-w

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Subjects

Abstract

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Introduction

Results

Identification of SPClowMHChigh LECs

LECs exhibit anatomically segregated and temporally dynamic APC activities

LEC antigen presentation facilitates CD4+ TRM cell niches around conducting airways

CD4+ TRM cells display multipotent phenotypes that are constrained by LEC MHC-II

LEC MHC-II directs skewing of lung CD4+ TRM cell responses

The absence of LEC MHC-II dysregulates barrier immune landscape

LEC MHC-II functions in a post-translational coupling with PD-L1

Discussion

Methods

Mice

Human lung biopsy samples

Experimental pneumonia

Mouse experimental procedures

Murine lung digestion for epithelial cell flow cytometry and FACS sorting

Murine lung digestion for leukocyte flow cytometry

Lung processing for human lung flow cytometry

Intracellular cytokine, protein, and transcription factor staining for flow cytometry

Transmission electron microscopy

RNA extraction and Real-time PCR

Ex vivo antigen presentation assay

Ex vivo stimulation and Intracellular Cytokine staining of CD4+ T cells

Immunofluorescent staining

Quantification of anatomical distribution of CD4+ cells

Algorithmic analysis of single-cell fluorescence datasets

Quantification of Relative Multipotency Index

Statistical Analyses

Reporting Summary

Data availability

References

Acknowledgements

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Identification of SPC^lowMHC^high LECs

LEC antigen presentation facilitates CD4⁺ T_RM cell niches around conducting airways

CD4⁺ T_RM cells display multipotent phenotypes that are constrained by LEC MHC-II

LEC MHC-II directs skewing of lung CD4⁺ T_RM cell responses

Ex vivo stimulation and Intracellular Cytokine staining of CD4⁺ T cells

Quantification of anatomical distribution of CD4⁺ cells