A multi-omics transcriptional atlas of the human lung and airway with spatial resolution
To generate a comprehensive representation of the cell types in human lung and airway we applied scRNA-seq and snRNA-seq, VDJ sequencing and spatial transcriptomics to deep tissue samples (Fig. 1a, Tables S1-3). Tissue pieces (1–2 cm3) from healthy deceased transplant organ donors without previous lung disease were collected from the trachea, bronchi at the 1st or 2nd generation airway (Bronchi 2–3), bronchi at the 3rd to 4th generation airway (Bronchi 4), parenchyma from the upper left lobe (UpperPar) and the lower left lobe (LowerPar) to capture deep structures such as cartilage, muscle and the submucosal glands in the airways (Figure S1a).
In total 193,108 high quality transcriptomes were annotated into transcriptionally distinct broad cell type groups: epithelial (secreting, basal, alveolar type 1 and 2 - AT1 & AT2 - pneumocytes, ciliated, and SMG cells), immune (lymphoid, myeloid, B-cells, plasma, mast cells), erythroid, endothelial (vascular- VE, lymphatic - LE) and stromal cells (smooth muscle, fibroblasts, mesothelial and chondrocytes). These cell types were annotated according to widely accepted marker genes and consensus naming from other lung studies including the integrated Human Lung Cell Atlas14 and LungMAP24 (Fig. 1b, Figure S1b, Supplementary Table 4). We combined the analysis of suspension cells and nuclei with Visium Spatial Transcriptomics (ST) analysis to map the identified cell types back to tissue structures on 20 tissue sections from 5 locations (Figure S2). Whilst Visium ST does not yield single-cell resolution, we annotated distinct micro-anatomical tissue environments (Supplementary Table 5) to assess their transcriptomic make-up and test for co-location of cell types. As expected, we mapped ciliated epithelial cells to the lumen of the airway surrounded by basal cells, and AT1 and AT2 to lung parenchyma (Fig. 1c, d), using the Cell2location25 algorithm. In order to examine the benefits of different protocols and sampling locations, we used a Poisson linear mixed model to assess the relative effects of these variables on cell type composition (see Methods) (Fig. 1e). As expected, different enzymatic treatments enriched for specific cell type groups but had little effect, less than 1% of the total variance, on gene expression (Figure S1h).
Highlighting our comprehensive sampling approach, we transcriptionally defined chondrocytes for the first time in human lungs (ACAN, CHAD, COL9A3, HAPLN1 and CYTL1) (Figure S1c, d) and mapped these cells to the airway cartilage with Visium ST (Fig. 1c, d). Chondrocytes were mostly released using single nuclei sequencing from trachea (Fig. 1e, Figure S1e-g), demonstrating the utility of our multi-omics, multi-location atlasing of the human lung.
Overall, we generated a large scRNA-seq and snRNA-seq dataset with wide representation of distinct cell lineages and matching spatial gene expression data, allowing further exploration of cellular heterogeneity, communication and spatial dynamics to better understand lung function.
Identification of rare fibroblasts with immune recruiting properties
The sequential clustering of lung and airway fibroblasts identified 11 cell types with distinct marker genes (Fig. 2a, Figure S3a, b). We annotated previously described myofibroblasts, mesothelial, adventitial and alveolar fibroblasts 9,24,26−29, as well as multiple novel and less studied cell types that were not predicted with existing annotations, specifically in the airways (Figure S3c, d, e, Supplementary Table 4). While most of our cell types were isolated from all locations and protocols, there were clear differences in cell type proportions (Fig. 2b, Figure S3b). Adventitial fibroblasts (Fibro-adv) were enriched in the airway samples, while the alveolar fibroblasts (Fibro-alv) were depleted. We further observed a rare cell type recovered only from the single cells, but not from single nuclei. This rare type expressed the chemokines CCL19 and CCL21, also highly expressed in fibroblast reticular cells responsible for T-cell positioning in the lymph nodes, and GREM1 30, CXCL13 and FDCSP, a follicular dendritic cell (fDC) marker responsible for B-cell positioning 31,32 (Fig. 2c). These cells mapped to an area of immune cell infiltration on the bronchus with Visium ST, as well as with smFISH methods (Fig. 2d, Figure S4a, b) and were therefore annotated as immune recruiting fibroblasts (IR-fibro). Immune infiltrates were only found in a proportion of the donors and sections, but in keeping with the expected frequency of immune aggregates in donors with and without smoking history33. In support of IR-fibro similarity to stromal cells from secondary lymphoid organs, the gene signature of germinal centre fibroblast cell types from the Peyer’s Patches of human gut34 also map to the area of the immune infiltrate on lung Visium ST sections (Figure S4b). In conclusion, we describe an IR-fibro population with a likely role in immune cell recruitment to rare immune infiltrates in the healthy bronchus.
Perichondrial and peribronchial fibroblasts reveal disease associations
We next examined two fibroblast populations enriched in the trachea which we name peribronchial (PB-fibro) and perichondrial (PC-fibro) fibroblasts (Fig. 2b) based on their mapping in Visium ST data. For the first time, we provide the human transcriptome for PB-fibro with COL15A1 and ENTPD1 markers and localisation around the airway epithelium (Fig. 2e, Figure S5a). Functional GWAS analysis, that quantifies systematic associations between cell-type specific gene expression and disease-associated SNPs34, demonstrated that PB-fibros are linked to chronic obstructive pulmonary disease (COPD) (Fig. 2f). PB-fibro also express LGR5, as reported in mice35,36, which suggests the potential for analogous mesenchymal stem cell function in humans (Figure S5b), and share characteristics with the recently described LRG5 + fibroblasts in distal airways15. A COPD-related increase in WNT-5A downregulates Lgr5 37, which suggests PB-fibro could be targeted therapeutically to inhibit regenerative failure in COPD specifically in the airways.
The second airway-enriched cell type, PC-fibro, was detected only in the single nuclei data (Fig. 2b), likely due to difficulties in dissociating cartilage. The cell type was located around the chondrocytes within cartilage in our Visium ST (Fig. 2g) and could be identified by the expression of the marker gene COL12A1 in the human protein atlas (Figure S5c). Among the marker genes for PC-fibro, we detected bone development genes LGR4 and LGR6 38 (Figure S5b). PC-fibro, which expressed standard fibroblast markers, uniquely expressed genes relevant for bone development, placing these as an intermediate cell type in a trajectory from adventitial fibroblasts to chondrocytes (Figure S5d, e). Finally, PC-fibro markers were enriched in genes causing skeletal abnormalities in humans (Figure S5f), including FLNB and FGFR2 that are mutated in rare diseases causing skeletal malformations 39,40. This cell type could be relevant in bone differentiation, in supporting cartilage functions, and in skeletal abnormalities. In conclusion, we have newly identified perichondrial fibroblasts around the healthy human airway cartilage.
Resolution of four distinct cell types in peripheral nerves from the airway
Finally, in the fibroblast compartment we identify four rare cell clusters within the peripheral nerves in the airway: myelinating Schwann cells (NFASC, NCMAP, MBP, PRX), non-myelinating Schwann cells (nmScwann) (NGFR, SCN7A, CHD2, L1CAM, NCAM1) 41–44, endoneurial nerve-associated fibroblasts (NAF) (SOX9, OSR2)41 and perineurial NAF (SLC2A1, ITGA6)41,45 (Figure S6a). Localisation of these cell types in peripheral nerves is shown by analysis of bulk RNA-seq across tissues (Figure S6d), Visium ST (Fig. 2h, Figure S6e), protein staining (Figure S6f-h) and smFISH (Fig. 2i, Figure S6i). Specifically, we show the expression of perineurial NAF around, and endoneurial NAF along Schwann cells inside the nerve bundle in human airway samples. This further confirms the identity of myelinating and non-myelinating Schwann cells with marker gene set enrichment in myelination and cell adhesion categories respectively (Figure S6b, c). This analysis further reveals EVX1 - a homeobox gene with a role in spinal cord development - as a potential regulator of myelinating Schwann cells. NmSchwann cells expressed some genes involved in Schwann cell development, maintenance (SOX10) and myelination (ERBB3 and LGI4) (Figure S6a, Fig. 2i, Figure S6i). Finally, we observed specific expression of rare peripheral nervous system (PNS) disease genes in these populations (Figure S6j). Thus, our atlas discerns micro-anatomical location within peripheral nerves (Fig. 2j) and pinpoints disease associations, even for very rare cell types. The cell types described here exist in other tissues; therefore this data could have implications outside the lung.
Resolution of the vascular cell types in the systemic and pulmonary circulation
We next focused on the vasculature, which in the lung consists of both systemic circulation, providing oxygen to the tissue, and pulmonary circulation where gas exchange occurs. Uniquely in our dataset, we could distinguish clusters of pulmonary and systemic circulation by the distribution of cell types in the distinct locations we sampled, since pulmonary cells are enriched in parenchyma and systemic cells are enriched in the trachea (Fig. 3d, Figure S7e). We observed previously described endothelial venous (E-Ven-syst, E-Ven-pulm) and capillary (Cap, Car4-Aerocyte) cell types (Fig. 3a, Figure S7a)46–49. In addition, we now distinguish endothelial arterial cell types (systemic E-Art-syst and pulmonary E-Art-pulm), which have distinct localisation on Visium ST with E-Art-syst enriched in the trachea (Fig. 3a, b). The smooth muscle cells included non-vascular airway smooth muscle cells (ASM), pulmonary perivascular cell types (smooth muscle SM-pulm and pericytes Peri-pulm)9 and systemic perivascular cells (smooth muscle SM-Art-syst, pericytes Peri-syst and venous perivascular cells IR-Ven-Peri) (Fig. 3a, c Figure S7b). ASM was mainly captured in single nuclei data (Fig. 3d) enabling the detection of reliable marker genes for non-vascular smooth muscle cell types across tissues (Figure S7c, d).
In addition, we identified another perivascular cell type in the airways, expressing ABCC and ICAM1, but not CSPG4 - in a pattern similar to the postcapillary venous perivascular cells with a role in the homing of immune cells to peripheral lymph nodes50,51 (Fig. 3c). This cell type expressed known leukocyte-recruiting chemokines (Fig. 3e) and co-localised with a venous endothelial vessel (ACKR1+) in the Visium ST bronchi section (Fig. 3f) and in smFISH microscopy (Fig. 3g, Figure S7f). These cells were therefore annotated as immune recruiting venous perivascular cells (IR-Ven-Peri). The potential lymphocyte recruiting capacity in both venous endothelial and IR-Ven-Peri (Figure S7g) suggests a role in lymphocyte extravasation in the airway veins, similar to the postcapillary venules in peripheral lymph nodes.
In summary, we distinguish between cells of the systemic and pulmonary circulation, and describe a novel immune recruiting postcapillary venous pericyte (IR-Ven-Peri). We provide marker genes for all of these populations, map these cells back into the tissue context and thereby further define the relationship between the endothelial and perivascular cells in the systemic and pulmonary circulation (Fig. 3h).
Identification of duct cells in human airway submucosal glands (SMG)
Further analysis of the airway epithelial compartment (excluding AT1 and AT2 cells) identified expected ciliated, goblet, basal, suprabasal, dividing basal, club, SMG basal, mucous and serous populations along with rare deuterosomal, ionocyte & brush and neuroendocrine cells. In addition to these, we identified a novel population between serous, mucous and club/goblet cells (Fig. 4a, Figure S8a, b) with marker genes overlapping with SMG duct cells in human oesophagus and preferential localisation in trachea12,52. Thus, we hypothesise that these cells are the columnar non-ciliated duct cells of the human airway, previously only characterised in mice at the single cell level53–55. smFISH staining for ALDH1A3, MIA and RARRES1 validated localisation at the SMG and distinguished these cells from serous, mucous and other epithelial cells (Fig. 4b, Figure S8e, f). Cell2location integration of single-cell and Visium data could also distinguish distinct locations of SMG duct cells compared to SMG mucous and serous cells, providing orthogonal evidence of the identification of a new, distinct cell type (Figure S8g). In addition, Velocyto analysis suggested that these cells may lie on a trajectory towards the surface epithelial populations (Figure S8c), consistent with a potential role in airway surface regeneration, as has been demonstrated in mice 56,57.
An additional population was revealed from snRNA-seq, exhibiting both basal epithelium (TP63, KRT14) and muscle (ACTA2, TAGLN, CNN1 positive, but DES negative) markers (Fig. 4a, Figure S8a, j) with localisation around the glands ( Figure S8g-i), consistent with a myoepithelial cell signature previously reported as a small cluster of cells21. For this population we identified markers for cell-cell adhesion (FHOD3, LAMA1) as well as for nerve synapse signalling (NTRK2, PLD5). Co-localisation of FHOD3, epithelial marker KRT14 and muscle markers around the glands confirmed the myoepithelial cell signature by smFISH (Figure S8h) and in the Human Protein Atlas (Figure S8i). Interestingly, mouse myoepithelial cells have also been shown to regenerate the surface airway epithelium 56. This cell type is not as well described in humans, potentially due to difficulties in dissociating this cell type from the airways consistent with the exclusive recovery of myoepithelial cells from nuclei data. Overall, we provide the human transcriptome for SMG populations and mapped the constituents of the SMG and their spatial location using both spatial transcriptomics and smFISH.
Proportions and gene signatures of airway epithelial cells
Using statistical modelling that accounted for material, donor and dissociation protocol (see Methods), we examined the proportions of airway epithelial cells at the 5 different locations sampled (Figure S8d), with significance given as local true sign rate (LTSR). As expected, club cells were enriched in the parenchyma, whereas SMG epithelial cells were enriched in the trachea. While donor and protocol had little effect on the epithelial cell type proportions, the starting material of cells versus nuclei strongly influenced cell type composition (Figure S8d). SMG epithelial cell capture was increased in nuclei, while the capture of other epithelial cell types was better achieved with the dissociation into single cells (Figure S8d).
Taking advantage of our multi-location data, we compared gene expression for ciliated cells, as one of the most abundant cell types present, across the five locations. We avoided any artefacts due to differing ambient RNA contamination between locations using only snRNA-seq data where samples were pooled across locations for the 10X sequencing reaction (Fig. 1a). Using a linear mixed model58 (Methods) we detected 80 differentially expressed genes in ciliated cells from trachea compared to other locations (Local True Sign Rate (LTSR) > 0.9), many of which are upregulated in nasopharyngeal carcinoma including FBXL7, TSHZ2 and RAET1E (Figure S8k)59–61. We also examined ACE2 expression, a SARS-CoV-2 entry gene, which we found to be highest in ciliated cells from the trachea, and lower in ciliated cells in the distal regions of the lung, where expression of ACE2 is likely to be more relevant in AT2 cells as reported previously (Figure S8l)62.
Altogether, we describe transcriptomes for key cell types involved in the SMG structure in the human airways which also functions as an immunological niche which we describe below.
Immune cells in the lung and airways
Myeloid cells show previously undescribed heterogeneity
For the immune compartment, we identified all major populations including myeloid, T&NK, B lineage, mast cells and megakaryocytes which were analysed separately to reveal previously undescribed heterogeneity, especially in the myeloid cells (Fig. 4c, Figure S9a-c, j). We found all major myeloid cell types (DCs, monocytes and macrophages) including many known and novel subsets. Previously identified macrophage subsets included intravascular macrophages (expressing LYVE1 and MAF)63,64, Macro-AW-CX3CR1 63,65−67, Macro-CHIT1 (CHIT1 expressing with roles in asthma, COPD and lung fibrosis)9,68 and interstitial macrophages (Macro-interstitial expressing chemokines CXCL9, 10 and 11)64. We identified a new cluster expressing both monocyte CD14 and macrophage markers, termed Macro-intermediate (Figure S9a). Among alveolar macrophages, two more clusters appeared: dividing cells (Macro-alv-dividing), and a novel cell cluster expressing metallothioneins (Macro-alv-MT) including MT1G, MT1X and MT1F. Metallothioneins have a role in binding and metabolising metal ions69, in immunity and stress responses70,71, and therefore this population may have a function in response to air pollution. Finally we identified a rare novel population of macrophages expressing chemokines including CXCL8, CCL4 and CCL20 and was named Macro-CCL. While the expression of CCL4 was previously identified in interstitial macrophages64, the expression of CXCL8 and CCL20 distinguishes this novel subset. Dysregulation of CXCL8 expression is associated with multiple lung conditions including infection, asthma, IPF and COPD72 and was identified as a marker for a separate macrophage population in psoriatic skin73.
Overall, we have identified multiple known and novel myeloid populations in the healthy human lungs and airways, many of them expressing specific sets of chemokines, orchestrating the complex lung immune homeostasis.
Different subsets of T & NK cells in the lung and airways
T lymphocytes and natural killer (NK) cells included all major cell types (CD4, CD8, mucosal-associated invariant T (MAIT), NK, NKT, innate lymphoid cells (ILC)) and their subsets (Fig. 4c, Figure S9b). In the CD4 compartment we distinguished naive/central memory (CD4-naive/CM), effector memory/effector (CD4-EM/Effector), regulatory T cells (Treg) and tissue resident memory (CD4-TRM) cells. Within CD8 cells we found gamma-delta T cells (γδT), TRMs (CD8-TRM)74 and two distinct clusters analogous to populations found the lung in cross tissue analysis75 expressing CX3CR1 and GZMB (CD8-EM/EMRA) and CRTAM and GZMK (CD8-TRM/EM). The CD8-TRM cells, but not other CD8 subsets, nicely localised to airway epithelium in our spatial data as previously reported in the literature (Figure S9d)76,77. NK subsets included clusters with markers ITGAD/CD11d, LAG3, KLRC3/NKG3E, KLRC2/NKG2C (NK-CD11d), CD16+ (NK-CD16hi) and CD56 bright NK cells (NK-CD56 bright)78,79. NK cells positive for CD11d are activated in response to viral and other infections in both mouse and human 80–82 and were previously shown in human blood83. To our knowledge this is the first time this subset has been described in the healthy human lung.
The T and NK cells displayed striking donor-to-donor variability in cell type proportions compared to the myeloid clusters (Figure S9g-i), consistent with higher inter-individual variability in the adaptive immune compartment. The location of origin, material and protocol explained little variation for any of the cell type proportions, including for B cells.
We also obtained TCR VDJ sequencing data that confirmed MAIT cell type annotation (with preferential use of TRAJ33 and TRAV1-2)84 and showed low clonal expansion in naive and Treg populations compared to memory and effector subsets (Figure S9e). Lastly we show that, as expected, there was no clonal sharing between individuals, but expanded clones were found in multiple locations of the lung within one donor (Figure S9f).
Co-localisation of IgA plasma cells with the SMG
B cells included naive and memory B cells, and plasma cells that were further annotated into immunoglobulin (Ig) IgG or IgA secreting plasma cells and plasmablasts (Fig. 4c, Figure S9j, k), and this annotation was supported by VDJ-seq data via Scirpy B cell receptor (BCR) isotype analysis. IgA, which is important for mucosal immunity85,86, was the most frequent isotype in the airway samples, while only the third most abundant in the parenchyma (Fig. 4d, Figure S9l). Interestingly, we observe that proportions of IgA plasma cells, relative to IgG, are increased in COVID-19 patients versus healthy controls in single cell data from nasal and tracheal brush samples (Fig. 4e)87. The distinguishing markers for IgA versus IgG plasma cells included CCR10 and B-cell maturation antigen BCMA (TNFRSF17) (Figure S9 k), which are important for plasma cell localisation and survival, respectively86,88,89.
Using Visium ST, we observed the localisation of IgA plasma cells, but not B or IgG plasma cells, in the airway SMG in trachea and bronchi sections (Fig. 4f). Annotation of micro-anatomical tissue microenvironments across all Visium sections confirmed co-localisation of IgA plasma cells with duct, serous and mucous SMG cells, whilst IgG mapped to immune infiltrates (Fig. 4f). Enrichment of plasma cells to the SMG is confirmed in the Human Protein Atlas which shows an abundance of plasma cells (MZB1+) in the SMG region of the bronchus and the nasopharynx (Figure S10a, b), along with a study from the 1970s that showed localisation of IgA plasma cells in human airway SMG 90. We further distinguished enrichment of IgA plasma cells in the non-mucous areas of the glands by annotating exclusively mucous versus other seromucous glands on four FFPE Visium airway sections (Figure S10c).
To dissect this niche at single cell resolution, which is lacking with current ST technologies, we used multiplex IHC to confirm the specific presence of IgA2 plasma cells in the SMG at single cell resolution, while IgG positive cells were present in the airways mostly outside the SMG (Fig. 4g, Figure S10d), consistent with Visium ST (Fig. 4f). We also detected IgD + naive B cells and CD3 + CD4 + T helper cells in the SMG (Fig. 4g), suggesting that IgA plasma cells are supported by a complement of cell types that can orchestrate B cell maturation for IgA secretion directly into the airway mucous. We hypothesise that together these different cell types constitute an immune niche which we term the gland-associated immune niche (GAIN). Understanding the immunological mechanisms at the SMG can help understand disease, as increased plasma cell numbers have been shown in smokers90, patients with cystic fibrosis91, COPD92, Kawasaki disease 93 and, as we show here, in COVID-19 infection (Fig. 4e).
Cell-cell interactions and the SMG immune cell niche
To understand co-localisation of B cells, IgA plasma cells and T cells in the SMG (Fig. 4g, f), we explored the molecular mechanisms underpinning the SMG as a potential immune niche. We report that expression of pIgR, which facilitates transcytosis of polymeric Ig across the surface epithelium, was high across all SMG epithelial cells, as was Mucosal Epithelial Chemokine (MEC)/CCL28, known to recruit IgA plasma cells through CCR10 in other mucosal sites (Fig. 5a-d)88,94. We confirmed expression of CCL28 in SMG by smFISH and IHC (Fig. 5e, Figure S10f), and observed a gradient of expression along the proximal to distal axis, where CCL28 is highest in SMG-Duct and Serous cells of the trachea (Figure S10f, g). Using cell-cell interaction analysis tool CellChat95 on cells from the airways we saw that, in addition to the CCL28-CCR10 axis between SMG cells and B plasma cells (combined IgA, IgG and plasmablasts), SMG-Duct cells were predicted to interact with CCR6 on memory and naive B cells and CD4 T cells (combined CD4 subsets, excluding Tregs) through CCL2096–98 (Fig. 5b-d).
To maintain B-plasma and B-memory cells, A Proliferation Inducing Ligand (APRIL), a factor important for B cell survival, differentiation and class switching, was expressed by SMG-Duct and SMG-Serous cells and predicted to interact with the receptors TACI and BCMA on B cells (Fig. 5f, S10e). smFISH for APRIL in tissue confirmed expression in glands especially in duct and serous cells, correlating with the scRNA-seq results (Fig. 5g, Figure S10e, h, i). APRIL expression was detected at higher levels in SMG areas that contained B cells and IgA plasma cells (Figure S10i). In the colon, APRIL expression can be induced on intestinal epithelial cells and leads to IgA2 class-switch recombination (CSR) in the local tissue environment99. We also found that a portion of B memory cells express activation-induced cytidine deaminase (AICDA), suggesting the possibility of local CSR at the SMG through TACI-APRIL signalling (Figure S10j). SMG-Duct and SMG-Serous cells expressed IL-6 in scRNA-seq, which was predicted to interact with IL-6R/IL-6ST on B plasma and a subset of CD4-T cells, the CD4-naive/CM T cells (Fig. 5f, Figure S10e). Using smFISH we confirmed IL-6 expressing gland cells (Fig. 5g). In combination with APRIL, IL-6 induces and supports long lived plasma cells, and is a potent inducer of IgA secretion in IgA committed plasma cells100,101, and increases IgA secretion in COPD102. Furthermore, IL-6 has been shown as a required factor for CD4 T cell memory formation, and for overcoming Treg mediated suppression103. Salivary gland epithelial cells have been shown to induce CD4 T cell differentiation into Tfh cells in an IL-6 dependent manner104, which induced proliferation of B cells. In this study, proliferation of B cells was also induced in co-culture only with salivary gland epithelial cells, suggesting additional T-independent mechanisms. IL-6 is upregulated in serum and bronchoalveolar lavage fluid in asthma and COPD patients, suggesting that the balance of IL-6 signalling in the SMG is important for disease105–107.
CellChat also predicted interactions between HLA genes expressed by SMG epithelial cells, particularly SMG-Duct and SMG-Serous cells, and CD4-T cells (Fig. 5h, i). The expression of HLA-DRA and HLA-DRB1 in SMG-Duct and SMG-Serous cells was comparable to ciliated cells and, as expected, less than professional APCs such as B cells (Fig. 5i). However, HLA-DR protein expression was high in the glands, but not observed at all in the surface epithelium showing discrepancy between transcript and protein levels (Fig. 5j). We additionally found expression of CD40, a co-stimulatory molecule key for APC-T cell interactions, in SMG epithelial cells (Fig. 5i). Interestingly, we observed that CD4 T cells localised to HLA-DR high regions of glands (Fig. 5k), which are likely SMG-Duct and SMG-Serous cells (Figure S10k, l). CD4 T cells in the glands were mostly CD45RO+ (memory) cells with very few CD45RA+ (naive) cells, and could be seen closely interacting with HLA-DRhighSMG-epithelial cells, suggesting that SMG-epithelial cells are modulating CD4 T cells responses through MHCII (Fig. 5k, Figure S10m,n,o). Expression of HLA-DR and CD40 has previously been observed in airway and nasal epithelial cells, resulting in promotion of T cell proliferation in vitro108–112. Interestingly, CD40 expression has been observed in duct, but not other cells, of the salivary gland and is upregulated in Sjögren's syndrome, a systemic autoimmune exocrinopathy113. Antigen presentation by SGPlowMHCIIhigh epithelial cells in the parenchyma of mice has been shown to localise and regulate CD4 TRM responses, contributing to immune homeostasis114. HLA-DR/MHCIIhigh SMG epithelial cells may have a similar function in the airways, however the precise nature of antigen presentation by SMG epithelial cells is yet to be determined.
In conclusion, we have identified the localisation of IgA plasma cells, naive/memory B cells and T cells at the SMG along with a large number of molecular signalling pathways involved in B lymphocyte recruitment, antigen presentation and both T cell-dependent and -independent B cell maturation and survival. These pathways are known to be functional in secondary lymphoid organs, including within MALT, and we now show their possible involvement in establishing and maintaining the GAIN (Fig. 6) of the lung.