1. Immunology and Inflammation
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Tissue environment, not ontogeny, defines murine intestinal intraepithelial T lymphocytes

  1. Alejandro J Brenes
  2. Maud Vandereyken
  3. Olivia J James
  4. Harriet Watt
  5. Jens Hukelmann
  6. Laura Spinelli
  7. Dina Dikovskaya
  8. Angus I Lamond
  9. Mahima Swamy  Is a corresponding author
  1. Centre for Gene Regulation and Expression, University of Dundee, United Kingdom
  2. Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, United Kingdom
  3. MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, United Kingdom
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Cite this article as: eLife 2021;10:e70055 doi: 10.7554/eLife.70055

Abstract

Tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) patrol the gut and have important roles in regulating intestinal homeostasis. T-IEL include both induced T-IEL, derived from systemic antigen-experienced lymphocytes, and natural T-IEL, which are developmentally targeted to the intestine. While the processes driving T-IEL development have been elucidated, the precise roles of the different subsets and the processes driving activation and regulation of these cells remain unclear. To gain functional insights into these enigmatic cells, we used high-resolution, quantitative mass spectrometry to compare the proteomes of induced T-IEL and natural T-IEL subsets, with naive CD8+ T cells from lymph nodes. This data exposes the dominant effect of the gut environment over ontogeny on T-IEL phenotypes. Analyses of protein copy numbers of >7000 proteins in T-IEL reveal skewing of the cell surface repertoire towards epithelial interactions and checkpoint receptors; strong suppression of the metabolic machinery indicating a high energy barrier to functional activation; upregulated cholesterol and lipid metabolic pathways, leading to high cholesterol levels in T-IEL; suppression of T cell antigen receptor signalling and expression of the transcription factor TOX, reminiscent of chronically activated T cells. These novel findings illustrate how T-IEL integrate multiple tissue-specific signals to maintain their homeostasis and potentially function.

Introduction

The presence of tissue-resident immune cells enables a quick response to either local stress, injury, or infection. Understanding the functional identity of immune cells and their shaping by the tissue environment is therefore critical to understanding tissue immunity. Tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) reside within the intestinal epithelium and consist of a heterogenous mix of natural and induced T-IEL (Olivares-Villagómez and Van Kaer, 2018). All T-IEL express a T cell antigen receptor (TCR), consisting of either αβ or γδ chains, alongside TCR co-receptors, that is, CD8αβ or CD8αα and to a lesser extent CD4(+/-). The most prevalent IEL subsets within the epithelium of the murine small intestine are derived directly from thymus progenitors, the so-called natural or unconventional T-IEL. These natural T-IEL express either TCRγδ and CD8αα (TCRγδ CD8αα T-IEL), which account for ~50% of the total T-IEL pool, or express TCRαβ and CD8αα (TCRβ CD8αα Τ-IEL), which account for ~25% of the total T-IEL. TCRβ CD8αα Τ-IEL are derived from CD4-CD8- double negative (DN) progenitors in the thymus by agonist selection. Conversely, induced T-IEL are antigen-experienced, conventional CD4+ or CD8αβ+ αβ T cells that are induced to establish tissue residency within the intestinal epithelium, most likely in response to cues from dietary antigens and the microbiota, as evidenced by a strong reduction in their numbers in germ-free and protein antigen-free mice (Di Marco Barros et al., 2016). These induced T-IEL (TCRβ CD8αβ T-IEL) are believed to have substantial overlap with tissue-resident memory T (TRM) cells (Sasson et al., 2020) and are present in high numbers in human intestines. How these induced T-IEL are formed, their functional importance, and the role of the gut environment in deciding their fate are still the focus of intense study.

Residing at the forefront of the intestinal lumen, T-IEL are exposed to a range of commensal bacteria and their metabolites, dietary metabolites and antigens, and potential pathogens. These immune cells are therefore faced with the conflicting tasks of protecting the intestinal barrier, while also preventing indiscriminate tissue damage. Previous gene expression studies have identified T-IEL as having an ‘activated-yet-resting’ phenotype, with the expression of several activation markers, such as Granzymes and CD44, along with inhibitory receptors, such as the Ly49 family and CD8αα (Denning et al., 2007; Fahrer et al., 2001; Shires et al., 2001). Yet, it is still unclear how T-IEL are kept in check at steady state (Vandereyken et al., 2020). T-IEL effector responses can get dysregulated in chronic inflammatory conditions, such as celiac disease and inflammatory bowel diseases, therefore we need insight into the regulation of these cells. Moreover, we lack an understanding of how T-IEL are programmed to respond to specific epithelial signals, and how this is dictated and regulated by the tissue microenvironment.

In this study, we use quantitative proteomics to explore the differences between induced T-IEL and systemic T cells from lymph nodes (LN), from which induced T-IEL are ostensibly derived. We also compare induced T-IEL with the natural TCRγδ and TCRαβ T-IEL subsets in the gut. Our findings suggest that the tissue environment largely overrides any developmental imprinting of the cells to define the proteomic landscape of intestinal-resident T-IEL, and reveal important metabolic and protein translation constraints to T-IEL activation. Importantly, we also uncover evidence of chronic T cell activation potentially driving a partially exhausted phenotype in both the induced and natural T-IEL subsets.

Results

Tissue microenvironment defines T-IEL as distinct from systemic T cells

CD8+ T-IEL subsets were purified from wild-type (WT) murine small intestinal epithelial preparations to >95% purity by cell sorting (Figure 1—figure supplement 1). Next, high-resolution mass spectrometry (MS) was performed to obtain an in-depth characterisation of the proteomes of the three main CD8+ T-IEL subsets in the intestine. Tandem mass tags (TMT) were used with synchronous precursor selection (SPS) to obtain the most accurate quantifications for all populations (Figure 1a). To evaluate how T-IEL related to other immune populations, we first compared the proteomes of T-IEL with other TMT-based proteomes of various T cell populations currently available within the Immunological Proteome Resource (ImmPRes, http://immpres.co.uk), an immune cell proteome database developed in-house (Howden et al., 2019). Even though T-IEL are thought to have an effector-like phenotype, by using principal component analysis (PCA), we found that T-IEL were much more similar to ex vivo naïve CD8+ T cells, than to in vitro activated, effector cytotoxic T cells (CTL) (Figure 1b). Hence, we did an in-depth, protein-level comparison of the three T-IEL subsets with two naïve CD8+ T cells from the LN. The two LN naïve CD8+ T cells used here were derived either from WT mice, similar to the T-IEL, or from P14 transgenic mice, which express a TCR specific for a peptide derived from lymphocytic choriomeningitis virus. P14 T cells were included as a genetically and developmentally distinct comparator for WT LN T cells. To enable cross-comparisons, all five populations were acquired using the same TMT-based SPS-MS3 method and they were all analysed together using MaxQuant (Cox and Mann, 2008). The data were searched using a 1% false discovery rate (FDR) at the protein and peptide spectrum match (PSM) level (for more details, see Materials and methods), both raw and processed data were uploaded to PRIDE (Perez-Riverol et al., 2019) under accession PXD023140. The dataset provided an in-depth overview of the proteome, with over 8200 proteins detected in total, where each of the five populations showed similar coverage, ranging from 6500 to 7500 proteins detected in all of them (Figure 1c).

Figure 1 with 1 supplement see all
Quantitative proteomic analyses of induced and natural tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) subsets.

(a) Schematic of the mass spectrometry (MS)-based proteomics workflow. The data were acquired at the MS3 level with synchronous precursor selection (see Materials and methods). (b) Principal component analysis comparing the tandem mass tags (TMT)-based estimated protein copy numbers of conventional naïve and effector T cells with T-IEL. CTL, cytotoxic T lymphocytes. (c) Bar plot showing the number of proteins identified across all replicates in the five populations used for this study. (d) Box plot showing the MS-based protein content estimation for all replicates used across the five populations. (e) Principal component analysis comparing the estimated protein copy numbers across conventional naïve lymph node (LN) T cells and T-IEL subsets. (f–i) Scatter plot comparing the estimated copy numbers for (f) TCRαβ CD8αβ T-IEL and wild-type (WT) LN TCRαβ CD8αβ T cells, (g) TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL, (h) TCRαβ CD8αβ T-IEL and TCRγδ CD8αα T-IEL, (i) WT LN TCRαβ CD8αβ T cells and P14 LN TCRαβ CD8αβ T cells. Pearson correlation coefficient are included within all the scatter plots. The proteomics data displayed on the plots include CTL (n = 3 biological replicates), conventional naïve LN T cells (both WT and P14 n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates) and TCRγδ CD8αα T-IEL (n = 4 biological replicates). For box plots, the bottom and top hinges represent the first and third quartiles. The top whisker extends from the hinge to the largest value no further than 1.5× IQR from the hinge; the bottom whisker extends from the hinge to the smallest value at most 1.5× IQR of the hinge. The bar plots show the mean. Total number of proteins identified and total protein content across all populations are available in Figure 1—source data 1.

Figure 1—source data 1

Total protein identifications and total protein content across all populations.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig1-data1-v4.xlsx

For all downstream analyses we converted the raw MS intensity values into estimated protein copy numbers using the ‘proteomic ruler’ (Wisniewski et al., 2014). First, these copy numbers were used to estimate the total protein content for all five populations, which revealed no major differences in most, except for the TCRγδ CD8αα T-IEL, which displayed a slightly higher protein content than the rest (Figure 1d). Next, the copy numbers were used as input for a second dimensionality reduction analysis via PCA, focussed now on comparing the T-IEL and LN populations. The results indicated that across the first component, which explains 44% of variance, there was a clear separation between T-IEL and LN populations (Figure 1e), highlighting that the three T-IEL subsets share much closer identity to each other, than to the naïve LN T cell populations.

To explore these results further we compared each population to each other. As induced TCRαβ CD8αβ T-IEL are thought to be derived from systemic T cells that respond to antigen in organised lymphoid structures, and then migrate into intestinal tissues, we first compared their proteome to the systemic WT LN TCRαβ CD8αβ T cells. Unexpectedly, the Pearson correlation coefficient comparing the estimated protein copy number of TCRαβ CD8αβ T- IEL and the WT LN TCRαβ CD8αβ T cells was only 0.85, the lowest value in all the comparisons (Figure 1f). In contrast, the proteomes of induced T-IEL and the so-called natural Τ-IEL populations showed greater similarity with a correlation >0.93 (Figure 1g and h), while the correlation between LN T cells from WT to P14 TCR transgenic mice was 0.92 (Figure 1i). These analyses indicated that induced T-IEL share a very similar expression profile to natural T-IEL. The comparisons to LN T cells revealed that even LN T cells derived from two different strains of mice were much more similar to each other than to induced T-IEL.

To further explore similarities and differences between induced T-IEL and LN T cells, we focussed on the TCRαβ CD8αβ T-IEL and the WT LN TCRαβ CD8αβ T cells (LN). We first performed a global analysis of the most abundant protein families that represent the top 50% of the proteome. This overview revealed some similarities and some important proteomic differences between the two cell types. Though the histone content and the glycolytic enzymes looked very similar, there were big differences in proteins related to the ribosomes, the cytoskeleton and the cytotoxic granules (Figure 2a). The LN population had nearly double the number of ribosomal proteins, while the T-IEL displayed higher cytoskeletal and cytotoxic proteins. These proteomic differences were not exclusive to TCRαβ CD8αβ T-IEL, as the same pattern was observed within both natural T-IEL subsets. Perhaps, the most striking difference between naïve LN T cells and T-IEL was the expression levels of Granzymes (Figure 2b). Granzyme A (GzmA) was expressed at >20 million copies per cell in each of the natural T-IEL subsets, and at 9–10 million molecules per cell in the induced T-IEL population. This was more than double what was previously identified in cytotoxic CD8+ T cells (Howden et al., 2019). Granzyme B (GzmB), which was expressed at ~20 million copies per cell in CTL, was expressed at between 4 and 10 million copies per cell in all three T-IEL subsets. T-IEL also express Granzyme C (GzmC) and K (GzmK), although at <100,000 molecules per cell each (Figure 2b), making their general expression of Granzymes either comparable to, or higher than, in vitro-generated CTL. This substantial commitment to Granzyme expression is consistent with the expression of the whole cytotoxic machinery, including perforin and key molecules involved in degranulation (James et al., 2021), all of which are either barely detectable, or altogether absent, in the naïve T cells. Thus, these data support the hypothesis that all T-IEL in the gut are geared towards cytotoxic activity.

Gene ontology analyses of the induced tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) proteome.

(a) Treemap showing the abundance of proteins classified into histones, ribosomal proteins, cytoskeletal proteins, glycolytic enzymes, chaperones and granzymes across wild-type (WT) lymph node (LN) TCRαβ CD8αβ T cells and TCRαβ CD8αβ T-IEL. Rectangle size is proportional to the median estimated copy numbers. Median copy numbers across all categories are available in Figure 2—source data 1. (b) Bar plots showing the estimated copy numbers for all granzymes across WT LN TCRαβ CD8αβ T cells (n = 6) and all T-IEL (n = 3 or 4). Symbols on the bars represent the biological replicates. The bars show the mean and SEM. The p-values have been calculated on R with LIMMA where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations. (c) Bar plot showing the results of the DAVID functional annotation clustering (false discovery rate [FDR] < 0.05; see Materials and methods for details) enrichment analysis for all proteins exclusive to or significantly increased in expression within TCRαβ CD8αβ T-IEL. (d) Bar plot showing the results of the PANTHER GO Biological process (FDR < 0.05; see Materials and methods for details) enrichment analysis for all proteins exclusive to or significantly increased in expression within TCRαβ CD8αβ T-IEL (blue) or within WT TCRαβ CD8αβ T cells (red).

To obtain an unbiased overview of the differences between the induced T-IEL and the LN T cell populations, we performed an overrepresentation analysis (ORA) focussed on gene ontology (GO) terms related to biological processes (Figure 2c–d; Supplementary files 2 and 3). The data indicated that proteins which were significantly increased in expression in induced T-IEL were highly enriched in cholesterol and lipid metabolism, intestinal absorption and xenobiotic metabolism, and processes involving cytoskeletal proteins, such as cell-cell adhesion and integrin-mediated signalling. Conversely, proteins which were significantly higher expressed in LN T cells were enriched for terms relating to ribosomal proteins and ribonuclease P activity.

Downregulation of protein synthesis in T-IEL

Based on the results obtained from the ORA, we next focussed on the protein machinery involved with the ribosomes and protein synthesis. A comparison of the total estimated copy numbers for ribosomal proteins indicated that LN T cells express almost double the amount expressed in any of the T-IEL subsets (Figure 3a). This was true for both cytoplasmic and mitochondrial ribosomal proteins, with the latter being the most reduced in T-IEL, compared to LN T cells. The decreased expression of ribosomal proteins in T-IEL was mirrored by the decreased expression of RNA polymerases I (Pol1) and III (Pol3), which transcribe, respectively, ribosomal RNA and transfer RNA (Figure 3b). For the subunits of both the Pol1 and Pol3 complexes, the median fold reduction in T-IEL was greater than fivefold when compared to LN T cells (Figure 3—figure supplement 1). Strikingly, the subunits specific for RNA polymerase II (Pol2), which transcribes protein-coding genes, did not display a reduction in median expression levels. These data suggest that while ribosomal expression is reduced, mRNA pools could potentially still be maintained in T-IEL.

Figure 3 with 1 supplement see all
Downregulation of protein synthesis in tissue-resident intestinal intraepithelial T lymphocytes (T-IEL).

(a) Box plots showing the estimated total cytoplasmic (left) and mitochondrial (right) ribosomal protein copies for lymph node (LN) TCRβ CD8αβ T cells and all T-IEL subsets. The sums of all copy numbers are available in Figure 3—source data 1. (b) Box plots showing the estimated summed total protein copies for the protein subunits that are exclusive to RNA polymerases I, II, and III, respectively, for LN TCRβ CD8αβ T cells and all T-IEL subsets. The sum of all copy numbers are available in Figure 3—source data 1. (c) Bar plots showing the estimated protein copy numbers of the amino acid transporters, SLC7A5 and SLC38A2, for wild-type (WT) LN TCRβ CD8αβ and all three subsets of T-IEL. (d) Bar plots showing the estimated protein copy numbers of arginase-2 (ARG2; left) and alanine aminotransferase (glutamic-pyruvic transaminase [GPT]; right) for WT LN TCRβ CD8αβ and all three subsets of T-IEL. (e) Bar plots showing the estimated protein copy numbers of PKR-like endoplasmic reticulum kinase (PERK) for WT LN TCRβ CD8αβ and all three T-IEL subsets. (f) Bar plots showing the O-propargyl puromycin (OPP) incorporation (n = 3 biological replicates) in ex vivo WT LN TCRβ CD8αβ and T-IEL. As a negative control, OPP incorporation was inhibited by cycloheximide (CHX) pre-treatment. OPP incorporation was assessed by flow cytometry 15 min after administration. Bar graph represents the geometric MFI of the OPP-AlexaFluor 647 in each T cell subsets normalised to the geometric MFI of the CHX pre-treated T cells. The p-values were calculated using ordinary one-way ANOVA with Dunnett’s multiple comparisons. For all box plots, the bottom and top hinges represent the first and third quartiles. The top whisker extends from the hinge to the largest value no further than 1.5× IQR from the hinge; the bottom whisker extends from the hinge to the smallest value at most 1.5× IQR of the hinge. All bar plots show the mean and SEM. Symbols on the bars represent the biological replicates. The proteomics data displayed on the plots include WT TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8αα T-IEL (n = 4 biological replicates). The p-values for individual proteins (c,d,e) were calculated in R with LIMMA, where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations, and in (a,b) in R with Welch’s t-test.

Figure 3—source data 1

Sum of median copy numbers for the cytoplasmic and mitochondrial ribosomes.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig3-data1-v4.xlsx

To maintain protein synthesis, consistent uptake of amino acids is generally required. However, our data show that T-IEL express low levels (<2000 copies per cell) of three key amino acid transporters, that is, SLC1A5, SLC7A5, and SLC38A2 (Figure 3c), all of which are highly upregulated upon T cell activation with SLC7A5 being expressed at >400,000 copies in effector T cells (Howden et al., 2019). SLC7A5 expression levels have been reported to directly control the expression of ribosomal proteins and other important translation machinery components (Marchingo et al., 2020; Sinclair et al., 2019). The very low levels of amino acid transporters detected in T-IEL is therefore expected to limit protein synthesis. Furthermore, despite having low levels of amino acid transporters, enzymes involved in amino acid catabolism, such as arginase-2 (ARG2) and alanine aminotransferase (glutamic-pyruvic transaminase [GPT]), are highly expressed in T-IEL, also suggesting reduced protein synthesis in T-IEL (Figure 3d). Interestingly, high expression of ARG2 was also accompanied by upregulated expression of other enzymes from the urea cycle (Figure 3—figure supplement 1). Finally, it is also notable that T-IEL express significantly higher levels of PKR-like Endoplasmic Reticulum Kinase (PERK) than LN T cells. PERK functions as a global protein synthesis inhibitor, either in the presence of unfolded proteins or upon low amino acid availability (Figure 3e). We therefore decided to measure the protein synthesis rates in T-IEL and LN T cells by O-propargyl puromycin (OPP) incorporation into nascent peptide chains and compared with cycloheximide (CHX)-treated controls. The data from the OPP assay highlighted almost undetectable levels of protein translation in all three T-IEL subsets (Figure 3f), which correlated well with the reduced ribosomal content, low expression of amino acid transporters, and high catabolic enzymes identified within the proteomes of T-IEL. In contrast, LN T cells contain more actively translating ribosomes than T-IEL, providing orthogonal validation of the proteomic data. It should be noted that naïve T cells have been reported to have low protein synthesis rates (Wolf et al., 2020), however, our data indicate even lower synthesis rates in T-IEL. Thus, multiple mechanisms appear to be active in T-IEL to keep protein synthesis at a minimum.

T-IEL have a unique metabolic profile

Recent studies have shown a direct correlation between metabolic activity and the rates of protein synthesis in T cells (Argüello et al., 2020). The very low levels of protein synthesis in all three T-IEL subsets therefore prompted us to further explore the bioenergetic profile of T-IEL. Globally, we did not find any major differences in the proportion of the T-IEL proteomes dedicated to the major metabolic pathways compared to naïve T cells (Figure 4a). We did however find that all three T-IEL subsets express substantial levels of the GLUT2 (~5000 copies) and GLUT3 (~35,000 copies), both facilitative glucose transporters (Figure 4b). GLUT2 is normally found in intestinal and other epithelial cells, not in immune cells, and is a low affinity bidirectional glucose transporter. GLUT3 is a high affinity glucose transporter that is thought to be particularly important in CD8 T cell activation (Geltink et al., 2018). Glucose can be utilised in T cells through either glycolysis or oxidative phosphorylation (OXPHOS) and the tricarboxylic acid (TCA) cycle that also provides biosynthetic intermediates (Ma et al., 2019). We therefore examined the expression of proteins involved in these pathways in T-IEL. We find that T-IEL express most of the proteins of the glycolytic and TCA pathways at similar levels to naïve T cells (Figure 4c). With one exception being the lactate transporters, SLC16A1 and SLC16A3, which even though they are significantly higher than in naïve T cells, are still expressed at very low levels, indicating a low glycolytic potential within these cells. Furthermore, T-IEL have also been shown to have comparably low OXPHOS potential as in naïve T cells (Konjar et al., 2018). Thus, the function of the glucose being taken up through the T-IEL glucose transporters remains unclear.

Metabolic profiling of the tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) proteome.

(a) Stacked bar plots comparing the proportional representation of metabolic pathways in lymph node (LN) TCRβ CD8αβ and all T-IEL subsets. (b) Bar plots showing the estimated protein copy numbers of the glucose transporters, GLUT1, GLUT2, and GLUT3, for wild-type (WT) LN TCRβ CD8αβ and all three subsets of T-IEL. (c) Heatmaps displaying the Log2 fold change (T-IEL/ LN CD8 T cells) for all proteins involved in glycolysis, tricarboxylic acid (TCA) cycle, and fatty acid oxidation (FAO). (d) Bar plots showing the sum of the estimated protein copy numbers of the electron transport chain (ETC) components, for WT LN TCRβ CD8αβ and all three subsets of T-IEL. Sum of the copy numbers across all ETC complexes are available in Figure 4—source data 1. (e) Schematic representation of the glycerol-3-phosphate shuttle with heatmaps showing protein expression of cytosolic glycerol-3-phosphate dehydrogenase (cGpDH) and the Log2 fold change of mitochondrial glycerol-3-phosphate dehydrogenase (mGpDH) (T-IEL/LN CD8 T cells) in, from left to right, T-IEL TCRβ CD8αβ, T-IEL TCRβ CD8αα, and T-IEL TCRγδ CD8αα. All bar plots show the mean and SEM. Symbols on the bars represent the biological replicates. The proteomics data displayed on the plots include WT TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8αα T-IEL (n = 4 biological replicates). The p-values for individual proteins (b–c) were calculated in R with LIMMA, where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations, and in (d) in R with Welch’s t-test. For full protein names, see Supplementary file 4.

Figure 4—source data 1

Sum of median copy numbers for all the electron transport chain (ETC) complexes.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig4-data1-v4.xlsx

We also examined the mitochondrial protein content of T-IEL. The total mitochondrial protein content appeared to be significantly reduced, however, all the components of the electron transport chain (ETC) were expressed at similar levels in all T-IEL, as in LN T cells (Figure 4d). These data suggest that T-IEL mitochondria have similar respiratory capacity to naïve T cells. Naïve T cells use OXPHOS and fatty acid oxidation (FAO) to maintain their cellular functions. Therefore, we assessed FAO enzyme expression in T-IEL and found this was also largely similar to naïve T cells (Figure 4c). Interestingly, some proteins involved in peroxisomal FAO, including the transporter ABCD4, the key peroxisomal beta-oxidation enzymes acyl-CoA oxidase ACOX1, and carnitine O-acetyltransferase, were more highly expressed in T-IEL than in naïve T cells. Peroxisomal FAO produces acetyl CoA, which can be used within the TCA cycle, and NADH, which can be utilised in the ETC, to contribute to energy production. NADH produced during FAO and OXPHOS needs to be transported into the mitochondria through a redox shuttle, and in this context, we find that the glycerol-3-phosphate shuttle is only expressed in T-IEL (Figure 4e). Put together, these data suggest that peroxisomes may be a source of fuel to support the low levels of energy produced in T-IEL and indicate key differences in the metabolic pathways active in T-IEL.

T-IEL have increased lipid biosynthesis and cholesterol metabolism

Our data would seem to indicate that T-IEL have low bioenergetic production and requirements. However, functional annotation of proteins enriched in induced T-IEL indicate overrepresentation of cholesterol and steroid metabolism pathways, and the metabolism of chemicals and inorganic compounds (Figure 2c). T-IEL are highly enriched in proteins involved in xenobiotic metabolism, including members of the UDP glucuronosyl transferase family, glutathione S-transferase (GST), and cytochrome P450 enzymes (Supplementary file 1). Detailed examination of the cholesterol biosynthetic pathway indicates almost all the enzymes are expressed highly in all T-IEL, as compared to LN T cells (Figure 5a). This pathway is controlled by the master regulator sterol-regulatory element binding protein 2 (Madison, 2016), which the data showed is exclusively expressed within the three T-IEL populations (Figure 5b). We therefore measured cholesterol content in T-IEL and found that indeed all three subsets have greater than 2.5-fold more cholesterol than naïve LN CD8 T cells (Figure 5c).

Tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) have enhanced cholesterol and lipid metabolism.

(a) Schematic representation of proteins involved of the cholesterol biosynthetic pathway. Heatmap squares represent the Log2 fold change (T-IEL/lymph node [LN] CD8 T cells) in, from left to right, T-IEL TCRβ CD8αβ, T-IEL TCRβ CD8αα, and T-IEL TCRγδ CD8αα. Proteins expressed only by T-IEL are highlighted by red squares, representing the mean estimated protein copy numbers (from at least three biological replicates). (b) Bar plot showing the estimated protein copy number of SREBP2 for wild-type (WT) LN TCRβ CD8αβ and all three subsets of T-IEL. (c) Bar plot showing a comparison of total cellular cholesterol content in sorted WT LN TCRβ CD8αβ and all three subsets of T-IEL (n = 4 biological replicates). The p-values calculated using ordinary one-way ANOVA with Dunnett’s multiple comparison test. Data for the total cholesterol content are available in Figure 5—source data 1. (d) Bar plots showing the estimated protein copy numbers of the fatty acid transporters FATP1, FATP2, and FATP4 for WT LN TCRβ CD8αβ and all three subsets of T-IEL. (e) Bar plots showing the estimated protein copy numbers of the fatty acid binding proteins FABP1, FABP2, FABP5, and FABP6 for WT LN TCRβ CD8αβ and all three subsets of T-IEL. (f) Schematic representation of the triacylglycerol synthesis pathways and lipoprotein assembly. Coloured squares represent the Log2 fold change (T-IEL/LN CD8 T cells) in, from left to right, T-IEL TCRβ CD8αβ, T-IEL TCRβ CD8αα, and T-IEL TCRγδ CD8αα. Proteins expressed only by T-IEL are highlighted by red squares, representing estimated protein copy numbers (mean from at least three biological replicates). All bar plots show the mean and SEM. Symbols on the bars represent the biological replicates. The proteomics data displayed on the plots include WT TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8αα T-IEL (n = 4 biological replicates). The p-values for individual proteins (a,b,d,e,f) were calculated in R with LIMMA, where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations. For full protein names, see Supplementary file 4.

Figure 5—source data 1

Total cholesterol content across all populations.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig5-data1-v4.xlsx

T-IEL also express the fatty acid transport proteins (FATP2(Slc27a2) and FATP4 (Slc27a4)), which are necessary for uptake and transport of long chain fatty acids, as well as fatty acid binding proteins (FABP1, -2, -5, and -6), which also contribute to uptake and transport of fatty acids to the endoplasmic reticulum (Figure 5d and e). In addition to the intestinal specific family member, FABP2 (>350,000 copies/cell), the liver FABP, FABP1 (>200,000 copies/cell), which is highly expressed in the proximal intestine, and the ileal FABP, FABP6, or gastrotropin (>10,000 copies/cell), are all also highly expressed in all three T-IEL subsets (Figure 5e). It is interesting to note that FABP5, which was previously identified as being expressed in skin TRM cells, but not in intestinal TRM at the mRNA level (Frizzell et al., 2020), was detected at >200,000 molecules per cell in all three T-IEL subsets. Skin TRM appear to use increased exogenous fatty acids uptake to feed into mitochondrial FAO, thus supporting their maintenance and survival (Pan et al., 2017). However, carnitine O-palmitoyl transferase (CPT1A), the rate-limiting enzyme for mitochondrial FAO of long chain fatty acids is expressed at lower levels in T-IEL compared to naïve LN T cells (Figure 4c). This suggests that the highly increased lipid transporter expression in T-IEL is not solely used to drive FAO.

T-IEL are also enriched in proteins involved in the two major pathways of triacylglycerol (TAG or triglyceride) synthesis expressed in the intestine (Figure 5f; Yen et al., 2015). TAG is hydrophobic and is either stored transiently in the cytosol in lipid droplets or assembled and secreted from enterocytes in apolipoprotein B (ApoB)-containing chylomicrons, or lipoproteins that also contain cholesterol and cholesteryl esters. Surprisingly, T-IEL also express high levels of a key cholesterol esterification enzyme, Acyl CoA:cholesterol acyl transferase 2, ACAT-2 (Soat2), which is thought to be specifically expressed in enterocytes (Pan and Hussain, 2012). Esterification of cholesterol increases its hydrophobicity for efficient packaging into lipoproteins. We therefore also explored the expression of enzymes involved in lipoprotein assembly. Lipoprotein assembly involves the packaging of TAG and cholesteryl esters by the microsomal triglyceride transfer protein (MTP, Mttp) into ApoB-lipid conjugates, followed by export out of the cells by the core protein complex II (COPII) (Hussain et al., 2012). MTP was highly expressed in T-IEL with over 80,000 copies per cell, while <100 copies were identified in LN T cells. Similarly, ApoB and the GTPase SAR1b, a key component of the COPII complex, were also expressed in T-IEL at higher copies than in LN T cells (Figure 5f). Together, these data suggest that T-IEL also take up and metabolise fatty acids and cholesterol, and further, have the capacity to package these lipids into lipid droplets and potentially even transport them out of the cells.

Intestinal T-IEL proteome contains cell surface receptors for epithelial and neuroimmune interactions

We also explored the expression of proteins uniquely identified in T-IEL and found several proteins involved in cell adhesion, cytoskeleton remodelling, and integrin signalling. Strikingly, all T-IEL subsets expressed numerous epithelial cell adhesion molecules and integrins which are not found on naïve LN T cells (Figure 6a). Although these results are consistent with the localisation of T-IEL within the gut epithelial layer, we were surprised to find T-IEL proteomes also contained many tight junction, adherens junction, and desmosome-associated proteins, which are normally expressed on intestinal epithelial cells, such as E-cadherin (E-Cad), ZO-2, desmoplakin, villin-1, and JAM-A (F11R) (Figure 6a and b). These proteins could potentially be contaminants from epithelial cells in the sample preparation, however, E-Cad, occludin, and EpCAM have been detected both at the RNA and protein level in T-IEL (Figure 6—figure supplement 1, Inagaki-Ohara et al., 2005; Nochi et al., 2004). Moreover, immunofluorescence imaging and flow cytometry confirmed expression of ZO-2, E-Cad, and EpCAM in T-IEL, suggesting that T-IEL could use these molecules to navigate the tissue environment (Figure 6c, Figure 6—figure supplement 1). Conversely, endothelial cell adhesion molecules such as PECAM-1 and L-selectin, that facilitate T cell migration into secondary lymphoid organs, were highly expressed on naïve T cells, but not on T-IEL, as befits their tissue-resident status (Figure 6d).

Figure 6 with 1 supplement see all
Cell surface proteins expressed on tissue-resident intestinal intraepithelial T lymphocytes (T-IEL).

(a) Schematic representation of proteins involved in cell-cell adhesion that are only expressed by the different T-IEL subsets. (b) Heatmaps displaying the estimated protein copy numbers of adhesion molecules, co-signalling receptors, neuropeptide receptors, and purinergic receptors expressed only by T-IEL. Data represent the mean of at least three biological replicates. (c) Purified lymph node (LN) CD8 T cells (left) and isolated T-IEL (right) were immunostained for ZO-2 (top, red), E-cadherin (bottom, green), and CD8α (not shown) and counterstained with DAPI to mark the nuclei (blue). Representative (of two independent experiments) maximal intensity projections of confocal sections spanning the entire cell thickness of selected CD8+ cells of each type are shown. Size bars = 2 µm. See also Figure 6—figure supplement 1. (d) Heatmap displaying Log2 fold change (T-IEL/ LN CD8 T) cells of adhesion molecules, co-signalling receptors, and purinergic receptors. The proteomics data displayed on the plots show the mean values and were calculated from wild-type (WT) TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8α T-IEL (n = 4 biological replicates). The p-values were calculated in R with LIMMA, where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations.

T-IEL proteomes also suggest that T-IEL could be communicating with the enteric nervous system. TCRγδ CD8αα T-IEL express two neural cell adhesion molecules, NCAM1 (CD171) and NrCAM, both implicated in homophilic adhesion and in axonal growth and guidance. Furthermore, two neuropeptide receptors, GPR171 and VIPR2, were also identified in T-IEL proteomes (Figure 6b). BigLEN and vasoactive intestinal peptide (VIP) bind to GPR171 and VIPR1/VIPR2, respectively (Delgado et al., 2004; Gomes et al., 2013). BigLEN and VIP are neuropeptides with multiple physiological effects, including gut motility, nutrient absorption, food intake regulation, and immune responses (Yoo and Mazmanian, 2017). VIPR2 expression on intestinal innate lymphoid cells was shown to regulate their immune response (Seillet et al., 2020; Talbot et al., 2020). In addition, we also found that T-IEL express GLP1R and GLP2R (Figure 6b), receptors for the glucagon-like peptides 1 and 2 (GLP1 and GLP2), which are intestinal peptides involved in regulating appetite and satiety. Both of these receptors were previously mainly found on enteroendocrine cells and enteric neurons. However, recently GLP1R expression on T-IEL was shown to contribute to metabolic syndrome development in mice (He et al., 2019; Yusta et al., 2015). Together, these data suggest that T-IEL may be involved in regulating immune responses and potentially also metabolic responses to food intake through their communication with epithelial cells.

T-IEL share a common signature with exhausted T cells

T-IEL also express many signalling receptors that are absent on naïve T cells and that potentially regulate their poised activated state (Vandereyken et al., 2020). The proteomic analyses here confirmed that all three T-IEL subsets express many inhibitory receptors, including LAG-3, CD200R1, CD244 and NK receptors, such as members of the Ly49 family, but also showed that a wider range of these inhibitory receptors are found on innate T-IEL compared to induced T-IEL (Figure 6b). Furthermore, T-IEL, regardless of ontogeny, uniformly expressed CD38 and CD73 (Nt5e) (Figure 6b and d). Indeed, co-expression of CD38 and CD73 is seen to provide a better marker for identifying T-IEL than CD103 expression (Figure 7a and b). These receptors are tightly linked to purinergic signalling through their regulation of P2RX7, and as previously found on TRM cells (Borges da Silva et al., 2018; Stark et al., 2018), P2RX7 and CD39 are also highly expressed on T-IEL, although less uniformly than CD38 and CD73 (Figure 7a and b).

Tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) share similarities with exhausted T cells.

(a) Flow cytometry dot plots comparing the expression of purinergic receptors (CD38/CD73, top and P2RX7/CD39, bottom) in wild-type (WT) lymph node (LN) TCRβ CD8αβ and all three subsets of T-IEL. (b) Stacked doughnut plot showing the percentages of cells from LN CD8 T cells and T-IEL expressing the indicated purinergic receptors, quantified by flow cytometry (n = 1 biological replicate). (c) Flow cytometric histograms comparing the protein expression of the exhaustion markers, CD244, LAG-3, CD160, and CD96 in all three subsets of T-IEL. (d) Stacked doughnut plot showing the percentage of cell from LN CD8 T cells and T-IEL expressing the indicated exhaustion markers quantified by flow cytometry (n = 4 biological replicates). (e) Bar plot showing the result of the T cells exhaustion overrepresentation analyses in LN TCRβ CD8αβ and in TCRβ CD8αβ T-IEL. (f) Venn diagrams showing the commonality of proteins upregulated (top) and downregulated (bottom) during exhaustion and in TCRβ CD8αβ T-IEL. (g) Heatmap displaying the Log2 fold change (T-IEL/LN CD8 T cells) of transcription factors associated with exhaustion in T cells. (h) Bar plots showing the estimated protein copy number of TOX (left) and flow cytometry derived MFI for TOX (right) for WT LN TCRβ CD8αβ and all three subsets of T-IEL. The proteomics data displayed on the plots include WT TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8αα T-IEL (n = 4 biological replicates). The flow cytometry was performed on three biological replicates, representative of two independent experiments. The p-values for individual proteins (g–h) were calculated in R with LIMMA where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations, for the flow cytometry data (h) in GraphPad Prism using one-way ANOVA with Dunnett’s multiple comparisons test. For full protein names, see Supplementary file 4.

Figure 7—source data 1

Flow cytometry-based percentage of cells expressing exhaustion markers and TOX MFI.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig7-data1-v4.xlsx

CD38 and CD39 have recently been identified as markers of T cell exhaustion, along with expression of PD-1, LAG-3, CD244, CD160 among other inhibitory receptors. As all these molecules are highly expressed on T-IEL (Figure 7a–d), with the exception of PD-1, T-IEL appear to share some similarities with exhausted T cells (Alfei et al., 2019; Khan et al., 2019; Scott et al., 2019). An ORA using a database of T cell exhaustion markers confirmed that T-IEL are enriched in markers of exhaustion (Figure 7e), with at least 76 proteins that were upregulated in exhausted T cells also being upregulated in T-IEL (Figure 7f, top and Supplementary file 5). During exhaustion of systemic T cells, several proteins are downregulated. Interestingly, a significant proportion of these downregulated proteins are also downregulated in induced T-IEL (Figure 7f, bottom and Supplementary file 5). We therefore further examined the expression of transcription factors associated with T cell exhaustion (Figure 7g). Indeed, two transcription factors recently identified to be key to imprinting the ‘exhausted’ T cell phenotype, that is, TOX and NR4A2, were preferentially expressed in all T-IEL, whereas other transcription factors that show reduced expression in exhausted T cells, including TCF1 and LEF1, were also downregulated in T-IEL. We further confirmed expression of TOX in all T-IEL subsets by flow cytometry (Figure 7h). However, T-IEL still express high levels of T-bet, as do effector T cells, which most likely helps to maintain expression of cytolytic effector molecules, such as granzymes, while repressing PD-1 expression on T-IEL. Overall, both natural and induced T-IEL appear to have a hybrid phenotype combining features of exhausted T cells and effector T cells, while also bearing unique hallmarks imprinted by the intestinal microenvironment.

Modifications in the TCR signalosome in T-IEL

Given the connection between T-IEL and exhausted T cells, one key question we wanted to address was whether T-IEL are also unresponsive to TCR stimulation. Indeed, we find that only a small percentage (<10%) of both natural T-IEL subsets are able to respond to TCR cross-linking as measured by induction of phosphorylation of ERK1/2 and S6 ribosomal protein (Figure 8a and b). However, induced TCRαβ CD8αβ T-IEL responded even better than LN T cells to TCR stimulation. It has previously been recognised that cross-linking of the TCR on TCRγδ T-IEL does not induce calcium flux and downstream signalling (Malinarich et al., 2010; Wencker et al., 2014). This reduced TCR signalling capacity has been attributed to chronic TCR signalling in the tissue. However, how TCR signalling is dampened at a mechanistic level has not yet been addressed. We sought to evaluate whether there were changes in the TCR signalosome in T-IEL that were blocking TCR signals, and how conserved it was across the different subsets (Figure 8c). Strikingly, several proteins were differentially expressed, not just in TCRγδ T-IEL, but in all T-IEL subsets including induced TCRαβ CD8αβ T-IEL. Quantitative analysis of the immediate TCR signalling elements confirmed previous studies showing exclusive expression of FcεR1γ and LAT2 (NTAL/LAB) on T-IEL, and downregulation of LAT and CD3ζ as compared to LN T cells (Figure 8c). Replacement of the CD3ζ chain with the FcεR1γ chain reduces the number of immunoreceptor tyrosine-based activation motifs in the TCR. LAT2 is reported to play a dominant negative role in TCR signalling by competing with LAT for binding partners, but is unable to couple to PLCγ (Fuller et al., 2011).

Rewiring of the T cell antigen receptor (TCR) signalosome in tissue-resident intestinal intraepithelial T lymphocytes (T-IEL).

(a–b) Responses of wild-type (WT) lymph node (LN) CD8 T cells and T-IEL to TCR stimulation. Bar plots show the percentage of cells positive for (a) phospho-ERK1/2 (T202/Y204) and (b) phospho-S6 (S235/236) before and after anti-CD3 stimulation. The Src kinase inhibitor PP2 was added as a control to show that induction of ERK1/2 and S6 phosphorylation was specific. n = 3 biological replicates, p-values were calculated by two-way ANOVA with Dunnett’s multiple comparisons test. Data are available in Figure 8—source data 1. (c–d) Schematic representation of the main TCR signalling pathways comparing the expression of selected proteins in T-IEL and LN naïve T cells. (c) TCR and LAT signalosome. (d) Signalling pathways downstream TCR receptor. All heatmap squares represent the Log2 fold change (T-IEL/LN CD8 T cells) in, from left to right, T-IEL TCRβ CD8αβ, T-IEL TCRβ CD8αα, and T-IEL TCRγδ CD8αα. Proteins expressed only by T-IEL are highlighted by red squares, representing estimated protein copy numbers (mean from at least three biological replicates). The proteomics data displayed on the plots was calculated from WT TCRαβ CD8αβ T cells (n = 6 biological replicates), TCRαβ CD8αβ T-IEL and TCRαβ CD8αα T-IEL (n = 3 biological replicates), and TCRγδ CD8αα T-IEL (n = 4 biological replicates). The p-values were calculated in R with LIMMA, where ** = p < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations. For protein names, see Supplementary file 4.

Figure 8—source data 1

Flow cytometry-based percentage of cells positive for phospho ERK1/2 and phospho-S6.

https://cdn.elifesciences.org/articles/70055/elife-70055-fig8-data1-v4.xlsx

In addition to LAT and CD3ζ, several other proteins were differentially expressed (Figure 8c and d). Surprisingly, many proteins normally found in B cells and involved in BCR signal transduction were identified as expressed in T-IEL, for example, Lyn, Syk, LAT2, PLCγ2, Themis2, and many of these are also often found in exhausted T cells (Supplementary file 5, Khan et al., 2019; Schietinger et al., 2016). We also noted the expression of several negative regulators of TCR signalling, including STS-1 (Ubash3b) that dephosphorylates Zap70/Syk, CD148 (PTPRJ), and DUSP6, which negatively regulates MAPK signalling (Gaud et al., 2018; Figure 8c and d). These negative regulators of signalling are also highly expressed in tumour-associated exhausted T cells (Schietinger et al., 2016). Conversely, key TCR signalling intermediates, such as protein kinase C θ and Rac, were very poorly expressed. Importantly, many of these changes were not confined to the natural CD8αα T-IEL subsets but were also identified in induced T-IEL.

In summary, these data suggest that the rewiring of the TCR signalosome in T-IEL occurs independently of the developmental pathway through which the three different subsets are derived, and is instead shaped by the intestinal environment. However, as induced T-IEL also express LAT2 and FcεR1γ chain, but still respond to TCR signals, the loss of TCR responsiveness in natural T-IEL cannot be solely attributed to these proteins. Further evaluation of the TCR signalling pathways is necessary to provide an explanation for the loss of TCR responses in natural T-IEL.

Discussion

Both TCRγδ and TCRαβ CD8αα natural T-IEL have long been considered unconventional T cells, due to their unique developmental pathways and their strict restriction to the intestinal epithelium. In contrast, induced T-IEL that arise from systemic antigen-experienced T cells are considered conventional and more like memory T cells in their ability to respond rapidly to activation signals. Yet, our unbiased analyses clearly show that induced T-IEL share far greater similarity to other intestinal T cell subsets than to the systemic T cells they arise from. The T-IEL signature most strikingly contains several proteins thought to be exclusively, or very highly, expressed by enterocytes. These include cognate proteins involved in mediating adherens and tight junction formation, showing that T-IEL are strongly integrated into the intestinal epithelium, by interactions that extend well beyond the CD103:E-cadherin interaction. T-IEL also share metabolic similarities with enterocytes, including a strong enrichment in proteins required for cholesterol, lipid, and xenobiotic metabolism. Many of these genes are aryl hydrocarbon receptor (AHR) targets (Stockinger et al., 2014; Tanos et al., 2012), suggesting that one reason why AHR is essential for T-IEL survival (Li et al., 2011) is to protect them from toxins and bacterial metabolites in the gut. Other potential indicators of tissue adaptation include expression of the intestine-specific GLUT2 glucose transporter, and the high expression of the urea cycle that could be important for detoxifying the large quantities of ammonia that is present in the intestinal lumen (Romero-Gomez et al., 2009). Furthermore, we find that despite having very low energy requirements, T-IEL have a distinct metabolic signature, with high expression of proteins such as GLUT3, glycerol-3-phosphate shuttle, and peroxisomal FAO enzymes. Interestingly, the glycerol phosphate shuttle is normally only expressed in highly glycolytic cells to maintain cellular redox balance by recycling NAD in the cytosol, with most other mammalian cells using the malate-aspartate shuttle for this purpose (Mráček et al., 2013; Spinelli and Haigis, 2018). Thus, we find that T-IEL, far from being metabolically quiescent, have instead a metabolism tailored to their environment, to protect T-IEL from the harsh intestinal environment and actively limit proliferation and activation of these cells.

Ribosomal content was the one area where T-IEL seemed truly deficient in comparison to naïve LN T cells. This was surprising, since naïve T cells, like T-IEL, are not actively cycling cells. However, it was recently shown that a subset of proteins in naïve T cells have short half-lives and are rapidly turned over (Wolf et al., 2020). These included transcription factors that maintain the naïve state, but that need to be rapidly degraded upon T cell activation, allowing T cells to differentiate. Naïve T cells were also found to express a large number of idling ribosomes ready to translate mRNAs required for T cell activation. Thus, the non-existent ribosomal activity in T-IEL subsets is possibly a reflection of their terminally differentiated status. It is also interesting to note that amino acid transporters were expressed at very low levels in T-IEL, thus limiting amino acid availability for protein translation. In this context, we recently showed that activation of T-IEL with IL-15 involves both upregulation of ribosome biogenesis and upregulation of amino acid transporters (James et al., 2021). The low rates of protein translation also highlight the importance of studying the proteome in T-IEL as there may be a significant disconnect between protein and mRNA expression.

In identifying proteins that were expressed solely in T-IEL, but not LN T cells, we uncovered a clear signature of T cell exhaustion within the proteome. Like exhausted T cells, T-IEL have diminished capacity to proliferate in response to TCR triggering and increased expression of co-inhibitory molecules. However, unlike exhausted T cells, T-IEL maintain high levels of cytotoxic effector molecules, suggesting that they are still capable of killing, although it is unclear what signals are required to trigger full degranulation in T-IEL. Interestingly, despite identifying more than 30 cell surface proteins on T-IEL that were not expressed in LN T cells, no one marker was exclusive to T-IEL, as they were either proteins that were normally expressed in intestinal epithelial cells or those expressed on activated or exhausted T cells. Thus, the proteomic profiles of T-IEL reveal an interesting mixture of various T cell types; naïve, effector, and exhausted, as well their unique tissue-specific signatures.

T-IEL also display several hallmarks of exhausted or suppressed T cells, including a major rewiring of the TCR signalosome. We were surprised to find that LAT2 and many negative regulators of signalling, such as DUSP6, were also expressed in induced T-IEL. These data suggest that the changes in the TCR signalosome are induced by the gut environment, rather than being developmentally regulated. In addition, we found that all T-IEL express ACAT2, a key protein involved in cholesterol esterification, that potentially sequesters cholesterol away from the plasma membrane. Previously, ACAT1, but not the closely related ACAT2, was found to be upregulated in activated CD8+ T cells (Yang et al., 2016). Genetic ablation of ACAT1 lead to increased response from activated T cells in both infection and cancer, and this was attributed to the increased cholesterol in the plasma membrane leading to increased TCR clustering (Molnár et al., 2012; Yang et al., 2016). Indeed, increased cholesterol content has also been shown to potentiate γδ T cell activation (Cheng et al., 2013). It would be interesting to see if the high levels of ACAT2 expressed in T-IEL prevent accumulation of cholesterol in the T-IEL membranes, thus increasing the activation threshold of T-IEL. On a similar note, ARG2, which was highly expressed in T-IEL, has also been shown to block T cell activation (Geiger et al., 2016; Martí i Líndez et al., 2019), and expression of the alanine metabolising enzyme, GPT1, may limit alanine availability for protein synthesis in T cell activation (Ron-Harel et al., 2019). Thus, multiple lines of evidence support the notion that both natural and induced T-IEL are tightly regulated through inhibition of signalling.

In summary, we have presented an in-depth proteomic analyses and comparisons of induced and natural T-IEL and systemic T cells. These data provide key insights into the nature of T-IEL as well as the underappreciated similarities between both induced and natural T-IEL. New findings related to cholesterol metabolism, a high energy and translation barrier to activation, and transcription factors that potentially regulate T-IEL function suggest new ways to investigate how the different T-IEL subsets contribute to tissue and organismal homeostasis.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)P14PMID:2573841
Strain, strain background (Mus musculus)C57BL/6 JCharles RiversRRID:IMSR_JAX:000664
Chemical compound, drugDAPIThermo Fisher ScientificCat # D13061 µg/ml
AntibodyAnti-CD4 (rat, monoclonal)Thermo Fisher Scientific (eBiosciences)RRID:AB_494000Cell surface staining (1:200)
AntibodyAnti-CD8a (rat, monoclonal)BioLegendRRID:AB_2562558Cell surface staining (1:400)
AntibodyAnti-CD8a (rat, monoclonal)BioLegendRRID:AB_312746Immunofluorescence (1:100)
AntibodyAnti-CD8b (rat, monoclonal)eBioscienceRRID:AB_657764RRID:AB_1121888Cell surface staining (1:400)
AntibodyAnti-CD38 (rat, monoclonal)BioLegendRRID:AB_312928RRID:AB_312929Cell surface staining (1:200)
AntibodyAnti-CD39 (rat, monoclonal)BioLegendRRID:AB_2563395Cell surface staining (1:200)
AntibodyAnti-CD44 (rat, monoclonal)BD BiosciencesRRID:AB_1272244Cell surface staining (1:200)
AntibodyAnti-CD62L (rat, monoclonal)Thermo Fisher ScientificRRID:AB_469632Cell surface staining (1:200)
AntibodyAnti-CD73 (rat, monoclonal)BioLegendRRID:AB_11219608Cell surface staining (1:200)
AntibodyAnti-CD96 (rat, monoclonal)BioLegendRRID:AB_1279389Cell surface staining (1:200)
AntibodyAnti-CD103 (Armenian hamster, monoclonal)BioLegendRRID:AB_2563691Cell surface staining (1:200)
AntibodyAnti-CD160 (rat, monoclonal)BioLegendRRID:AB_10960740RRID:AB_10960743Cell surface staining (1:200)
AntibodyAnti-CD244 (rat, monoclonal)eBioscienceRRID:AB_657872Cell surface staining (1:200)
AntibodyAnti-E-cadherin (mouse monoclonal)BD BiosciencesRRID:AB_397581Immunofluorescence (1:100)
AntibodyAnti-LAG-3 (rat, monoclonal)eBioscienceRRID:AB_2573427Cell surface staining (1:100)
AntibodyAnti-P2 × 7 R (rat, monoclonal)BioLegendRRID:AB_2650951Cell surface staining (1:200)
AntibodyAnti-TCRb (Armenian hamster, monoclonal)BioLegendRRID:AB_2629696Cell surface staining (1:100)
AntibodyAnti-EpCam (rat, monoclonal)eBioscienceRRID:AB_953617Cell surface staining (1:200)
AntibodyAnti-E-cadherin (rat, monoclonal)eBioscienceRRID:AB_1834417Cell surface staining (1:100)
AntibodyAnti-TCRγδ (Armenian hamster, monoclonal)BioLegendRRID:AB_2563356Cell surface staining (1:200)
AntibodyAnti-phospho S6 (S235/236) (rabbit, monoclonal)Cell Signaling TechnologyRRID:AB_916156Intracellular staining (1:25)
AntibodyAnti-phospho ERK1/2 (T202/Y204) (rabbit, monoclonal)Cell Signaling TechnologyRRID:AB_331775Intracellular staining (1:200)
AntibodyAnti-ZO-2 (rabbit polyclonal)Cell Signaling TechnologyRRID:AB_2203575Immunofluorescence (1:50)
AntibodyAnti-CD3e (Armenian hamster, monoclonal)BioLegendRRID:AB_312667TCR stimulation (30 µg/ml)
Chemical compound, drugPP2Merck (Calbiochem)Cat # 529,573TCR stimulation, Src inhibitor
Chemical compound, drugO-Propargyl-puromycinJenaBioscienceNU-931–05Protein synthesis measurements
Commercial assay or kitEasySep CD8+ T cell isolation kitSTEMCELL Technologies, UKCat # 19,853For isolating CD8+ T cells from LN
Commercial assay or kitEasySep Mouse CD8a-positive selection kit IISTEMCELL Technologies, UKCat # 18,953For enriching CD8α+ IEL
Commercial assay or kitEasySep Dead Cell Removal (Annexin V) KitSTEMCELL Technologies, UKCat # 17,899For removing dead epithelial cells and enriching IEL
Commercial assay or kitAmplex Red cholesterol Assay KitInvitrogenCat # A12216Cholesterol assay
Commercial assay or kitEZQ protein quantification kitThermo Fisher ScientificCat # R33200For accurate protein quantification for proteomics
Software, algorithmMaxQuanthttps://www.maxquant.org/RRID:SCR_014485Version 1.6.3.3
Software, algorithmLimmaRitchie et al., 2015RRID:SCR_010943Version 3.7
Software, algorithmqvalueBioconductorRRID:SCR_001073Version 2.10
Software, algorithmFlowJoTreeStarVersion 10
Software, algorithmOMERO.figurehttps://pypi.org/project/omero-figure/Version 4.4.0
OtherRPMI 1640Thermo Fisher Scientific/GIBCO21875–034Media to culture cells

Mice

All mice were bred and maintained with approval by the University of Dundee ethical review committee in compliance with UK Home Office Animals (Scientific Procedures) Act 1986 guidelines. C57BL/6 J mice were purchased from Charles Rivers and acclimatised for a minimum of 10 days prior to use in experiments. Mice were maintained in a standard barrier facility on a 12 hr light/dark cycle at 21°C, 45–65% relative humidity, in individually ventilated cages with corn cob and sizzler-nest material and fed an R&M3 diet (Special Diet Services, UK) and filtered water ad libitum. Cages were changed at least every 2 weeks. For all experiments, mice were used between 8 and 12 weeks of age, and for proteomics, male mice aged between 8 and 9 weeks were used.

T-IEL and LN CD8 T cell isolation

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T-IEL were isolated for sorting from mice and as described in James et al., 2020. Briefly, small intestines were extracted and flushed. Small intestines were longitudinally opened, then transversely cut into ~5 mm pieces and put into warm media containing 1 mM DTT. Small intestine pieces were shaken for 40 min, centrifuged, vortexed, and passed through a 100 μm sieve. The flow-through was centrifuged on a 36%/67% Percoll density gradient at 700 g for 30 min. The T-ΙEL were isolated from the interface between 36% and 67% Percoll. In some experiments, isolated T-IEL were further enriched using an EasySep Mouse CD8α-positive selection kit (STEMCELL Technologies) as per the manufacturer’s instructions. Isolation and sorting details for the LN and effector populations used for proteomics can be found at http://www.immpres.co.uk/ under the ‘Protocols & publications’ tab.

Proteomics sample preparation and peptide fractionation

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Sample preparation was done as in Howden et al., 2019. Briefly, cell pellets were lysed, boiled, and sonicated, and proteins purified using the SP3 method (Hughes et al., 2014). Proteins were digested with LysC and trypsin and TMT labelling and peptide clean-up performed according to the SP3 protocol. The TMT labelling set-up is available in Supplementary file 6. The TMT samples were fractionated using off-line high-pH reversed-phase chromatography: samples were loaded onto a 4.6 mm × 250 mm Xbridge BEH130 C18 column with 3.5μm particles (Waters). Using a Dionex BioRS system, the samples were separated using a 25 min multistep gradient of solvents A (10 mM formate at pH 9 in 2% acetonitrile) and B (10 mM ammonium formate at pH 9 in 80% acetonitrile), at a flow rate of 1 ml/min. Peptides were separated into 48 fractions, which were consolidated into 24 fractions. The fractions were subsequently dried, and the peptides were dissolved in 5% formic acid and analysed by liquid chromatography–mass spectrometry.

Liquid chromatography electrospray–tandem MS analysis

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For each fraction, 1 μg was analysed using an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) equipped with a Dionex ultra-high-pressure liquid chromatography system (RSLCnano). Reversed-phase liquid chromatography was performed using a Dionex RSLCnano high-performance liquid chromatography system (Thermo Fisher Scientific). Peptides were injected onto a 75 μm × 2 cm PepMap-C18 pre-column and resolved on a 75 μm × 50 cm RP C18 EASY-Spray temperature-controlled integrated column emitter (Thermo Fisher Scientific) using a 4 hr multistep gradient from 5 B to 35% B with a constant flow of 200 nl/min. The mobile phases were: 2% acetonitrile incorporating 0.1% formic acid (solvent A) and 80% acetonitrile incorporating 0.1% formic acid (solvent B). The spray was initiated by applying 2.5 kV to the EASY-Spray emitter, and the data were acquired under the control of Xcalibur software in a data-dependent mode using the top speed and 4 s duration per cycle. The survey scan was acquired in the Orbitrap covering the m/z range from 400 to 1400 Thomson units (Th), with a mass resolution of 120,000 and an automatic gain control (AGC) target of 2.0 × 105 ions. The most intense ions were selected for fragmentation using collision-induced dissociation in the ion trap with 30% collision-induced dissociation energy and an isolation window of 1.6 Th. The AGC target was set to 1.0 × 104, with a maximum injection time of 70 ms and a dynamic exclusion of 80 s. During the MS3 analysis for more accurate TMT quantifications, 10 fragment ions were co-isolated using SPS, a window of 2 Th and further fragmented using a higher-energy collisional dissociation energy of 55%. The fragments were then analysed in the Orbitrap with a resolution of 60,000. The AGC target was set to 1.0 × 105 and the maximum injection time was set to 300 ms.

MaxQuant processing

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The raw proteomics data were analysed with MaxQuant (version 1.6.3.3) (Cox and Mann, 2008; Tyanova et al., 2016) and searched against a hybrid database. The database contained all murine SwissProt entries, along with TrEMBL entries with a human paralog annotated within human SwissProt and with protein-level evidence. The data was searched with the following modifications: carbamidomethylation of cysteine, as well as TMT modification on peptide amino termini and lysine side chains as fixed modifications; methionine oxidation and acetylation of amino termini of proteins were variable modifications. The FDR was set to 1% at the protein and PSM level.

Protein and BioReplicate filtering

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Proteins groups marked as ‘Contaminants’, ‘Reverse’, or ‘Only identified by site’ were filtered out. Additionally, proteins detected with less than two unique and razor peptides were also filtered out.

Within both the TCRαβ CD8αα and TCRαβ CD8αβ T-IEL, one replicate (replicate 4) was filtered out from the downstream analysis due to protein content discrepancies. This biorep displayed a 15% reduction in protein content compared to the other three replicates within the TCRγδ CD8αα and an increase in protein content of 32% when compared to the remaining three replicates within the TCRαβ CD8αβ.

Protein copy numbers and protein content

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Protein copy numbers were estimated from the MS data using the proteomic ruler (Wisniewski et al., 2014) after allocating the summed MS1 intensities to the different experimental conditions according to their fractional MS3 reporter intensities. The protein content was calculated based on copy numbers. The molecular weight (in Da) of each protein was multiplied by the number of copies for the corresponding protein and then divided by NA (Avogadro’s constant) to yield the individual protein mass in g/cell. The individual masses were converted into picograms and then summed for all proteins to calculate the protein content.

Differential expression and overrepresentation analyses

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All fold changes and p-values for the individual proteins were calculated in R utilising the bioconductor package LIMMA version 3.7. The Q-values provided were generated in R using the ‘qvalue’ package version 2.10.0. All other p-values were calculated using Welch’s t-test. For all ORA, the background was set to the subset of proteins which were identified in either TCRαβ CD8αβ T-IEL or LN TCRαβ CD8αβ T cells. The GO ORAs were done using DAVID (Jiao et al., 2012) and PANTHER (Mi et al., 2019). Two distinct analyses were performed, one for proteins with a p-value < 0.001 and fold change greater than or equal to the median+1.5 standard deviations and a second one for proteins with a p-value < 0.001 and fold change smaller than or equal to the median−1.5 standard deviations. The exhaustion ORA was done using WebGestalt (Wang et al., 2017) using the exhaustion markers provided reported within the literature (Khan et al., 2019) as a functional database for the analysis.

Statistical significance thresholds

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For the bar and box plots, symbols on bars represent independent biological replicates. For the MS derived bar plots and heatmaps; ** = p-value < 0.001 and fold change greater than or equal to the median+1 standard deviation, *** = p-value < 0.0001 and fold change greater than or equal to the median±1.5 standard deviations, based on the differential expression analyses described above. For all plots with non-MS based data, statistical analyses were carried out using R and GraphPad Prism version 8. The exact tests used are described in the figure legends, and p-values < 0.05 were considered significant.

Flow cytometry

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Cells were stained with titrated concentrations of the following murine monoclonal antibodies: TCRβ (clone H57–597 [BioLegend]), TCRγδ (clone GL3 [BioLegend or eBioscience]), CD4 (clone RM4-5 [BioLegend]), CD8α (clone 53–6.7 [BioLegend]), CD8β (clone H35–17.2 [eBioscience]), CD103 (clone 2E7 [BioLegend]), CD39 (clone Duha59 [BioLegend]), CD73 (clone Ty/11.8 [BioLegend]), CD38 (clone 90 [BioLegend]), P2 × 7 R (clone 1F11 [BioLegend]), CD244 (clone eBio244F4 [eBioscience]), LAG-3 (clone eBioC9B7W), CD160 (clone 7H1 [BioLegend]), CD96 (clone 3.3 [BioLegend]), EpCAM (clone G8.8 [eBioscience]), E-cadherin (clone DECMA-1 [eBioscience]), TOX (clone TXRX10 [eBioscience]). All data was acquired on an LSR Fortessa flow cytometer with DIVA software (BD Biosciences). Data were analysed using FlowJo software version 10 (TreeStar). For TCR stimulation, T-IEL and LN T cells were isolated and enriched for CD8+ as described above. T-IEL were stained with Live/Dead fixable Near-IR (ThermoFisher) (1:250) for 10 min prior to stimulation, then combined with LN T cells at a 1:1 ratio and resuspended at a concentration of 106 cells/ml in RPMI containing 1% FBS, L-glutamine and penicillin/streptomycin. Cells were warmed at 37°C before being stimulated with 30 μg/ml of anti-CD3 antibody (clone 145–2 C11 [BioLegend]) and 5 μg/ml of polyclonal anti-hamster cross-linking antibody (Jackson ImmunoResearch) for 5 min at 37°C. For some samples as indicated, PP2 was added at a concentration of 20 μM for 1 hr prior to stimulation. After stimulation, cells were directly fixed in 2% paraformaldehyde (PFA) 10 min at 37°C before permeabilisation with 90% ice-cold methanol. Samples were then fluorescently barcoded with different concentrations (0, 11.1, 33.3, or 100 μg/ml) of the Pacific Blue Dye (ThermoFisher) for 40 min, on ice before quenching with PBS + 0.5% bovine serum albumin (BSA) (v:v). Barcoded samples were then pooled and stained for intracellular phospho-proteins, phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (Clone 197G2 [Cell Signaling Technology]) and phospho-S6 ribosomal protein (Ser235/236) (Clone D57.2.2E [Cell Signaling Technology]) for 30 min at room temperature, protected from light, followed by secondary DyLight649 antibody (BioLegend). Cells were then stained for surface markers. Data were acquired using CytoFlex flow cytometer and analysed using FlowJo software (version 10). Data were analysed using the ‘Forward deconvolution method’ described in Krutzik and Nolan, 2006. Briefly, samples were differentiated based in the fluorescence intensities of each dye and then individual samples were analysed for their respective phospho-protein expression.

Protein synthesis measurements

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For comparing rates of protein synthesis, T-IEL and LN single-cell suspensions were cultured with 20 µM OPP (JenaBioscience) for 15 min. As a negative control, cells were pre-treated with 0.1 mg/ml CHX for 15 min before adding the OPP for 15 min (30 m total CHX exposure). Cells were then harvested, fixed with 4% PFA, and permeabilised with 0.5% Triton X-100 before undergoing a copper catalysed click chemistry reaction with Alexa 647-azide (Sigma). Following surface marker staining, cells were resuspended in PBS + 1% BSA and analysed by flow cytometry to determine the degree of incorporation of OPP. All samples were acquired on an LSR Fortessa flow cytometer with DIVA software (BD Biosciences). Data were analysed using FlowJo software.

Cellular cholesterol measurements

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Cholesterol content was measured using the Amplex Red cholesterol Assay Kit (Invitrogen). T-IEL were sorted into TCRβ+ CD8αα+, TCRβ+ CD8αβ+, and TCRγδ+ CD8αα+ populations and LN cells were sorted for TCRβ+ CD8αβ+ CD44-lo CD62L-hi cells using the BD Influx Cell Sorter (BD Biosciences). Each population was lysed at a concentration of 10 × 106 cells/ml in 1× Amplex Red reaction buffer (Invitrogen) for 10 min at 4°C then spun at 13,200 rpm for 12 min. Lysate was removed and diluted in 1× Amplex Red reaction buffer; 50 μl of 300 μM Amplex Red reagent (containing 2 U/ml horseradish peroxidase, 2 U/ml cholesterol oxidase, and 0.2 U/ml cholesterol esterase) was added to 50 μl of diluted lysate. The reaction was incubated for 30 min at 37°C in the dark before reading on the Clariostar microplate reader (BMG Labtech) at excitation wavelength of 530 nm and emission wavelength of 590 nm. Cholesterol content in the lysates was calculated with reference to cholesterol standards.

Immunofluorescence and imaging

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T-IEL and LN T cells were isolated and enriched for CD8+ as described above. T-IEL were then washed twice in RPMI/10%FBS-containing media and further depleted of contaminating dead cells using EasySep Dead Cell Removal (Annexin V) Kit (STEMCELL Technologies) as per manufacturer’s instructions, for negative enrichment of T-IEL. The purity and viability were checked by flow cytometry with CD8α-APC antibody and DAPI. LN CD8 T cells were at >98% purity, and the IEL were enriched to 60% purity.

1 × 106 lymphocytes in PBS were gravity-sedimented onto each 18 mm round coverslip (N1.5) as described (Tsang et al., 2017). Cell were fixed in 3.7% PFA in PBS, pH = 7.4, for 10 min at room temperature, washed with PBS, permeabilised with 0.5% Triton X-100 in PBS for 15 min, washed with PBS, and blocked with 2% BSA and 0.1% Triton X-100 in PBS for 45 min prior to staining with primary antibodies diluted as indicated below in blocking solution, or blocking solution alone, for 1 hr 45 min at room temperature. Antibodies used were polyclonal rabbit anti-ZO-2 (Cell Signaling Technology) used at 1:50 dilution, mouse monoclonal anti-E-cadherin (BD Biosciences), used at 1:100 dilution, rat monoclonal anti-CD8α-FITC (clone 53–6.7 [Biolegend], used at 1:100). After three washes with PBS, cells were further incubated with appropriate secondary antibodies diluted 1:500 in blocking solution for 1 hr. Secondary antibodies used were goat anti-rat-AlexaFluor488 (Invitrogen); goat anti-mouse-AlexaFluor568 (Invitrogen); donkey anti-rabbit-AlexaFluor647 (Jackson ImmunoResearch). After additional three washes with PBS, cells were stained with 1 µg/ml DAPI in PBS for 10–15 min, washed with PBS, and mounted onto glass slides using ProLong gold antifade reagent (Thermo Fisher Scientific) as a mounting media. Stained cells were imaged using LSM 710 confocal microscope operated by Zen software (Zeiss) with a 63×/1.4NA oil immersion objective. For each image, 7–12 optical sections spanning the entire thickness of the cells were collected. The maximal intensity projections were generated and intensities adjusted in identical manner for all images in OMERO using the OMERO.figure app (Allan et al., 2012).

Data availability

The raw and processed mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al., 2019) with the dataset identifier PXD023140 (https://www.ebi.ac.uk/pride/archive/projects/PXD023140/). All other data generated in this study are included within the manuscript and supporting files.

The following data sets were generated
    1. Brenes AJ
    2. Vandereyken M
    3. James OJ
    4. Spinelli L
    5. Hukelmann J
    6. Lamond AI
    7. Swamy M
    (2021) PRIDE
    ID PXD023140. Tissue adaptation is the dominant driver of the proteomic landscape of intestinal intraepithelial lymphocytes.

References

    1. Pan X
    2. Hussain MM
    (2012) Gut triglyceride production
    Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 1821:727–735.
    https://doi.org/10.1016/j.bbalip.2011.09.013

Decision letter

  1. Carla V Rothlin
    Senior and Reviewing Editor; Yale School of Medicine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

[Editors' note: this paper was reviewed by Review Commons.]

Acceptance summary:

In this manuscript, Brenes and colleagues make use of quantitative mass spectrometry to compare the proteomes of induced tissue-resident intestinal intraepithelial T lymphocytes (T-IEL) and natural T-IEL subsets with naive CD8 T cells. Their findings reveal a dominant effect of the intestinal environment over the ontogeny on the T-IEL phenotypes. The results from this study should be a good resource to scientists interested in IELs specifically as well as those interested in the plasticity and adaptation of immune cells to the environments they reside in general.

https://doi.org/10.7554/eLife.70055.sa1

Author response

We would like to thank the reviewers for their interest in our work and for their constructive comments and suggestions, which have strengthened our findings and improved our manuscript. A point-by-point response addressing each of their comments, and how our revised manuscript addresses the issues raised, is detailed below.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Intestinal intraepithelial T lymphocytes (T-IEL) are a hetergenous T cell population with distinct developmental pathways. In the submitted manuscript, the authors applied high-resolution, quantitative proteomics to characterize subsets of T-IELs from mice. By comparing the proteomes of different T-IEL subsets as well as with naive CD8+ T cells from lymph nodes, they found more similarities between the T-IEL subsets than peripheral T cells, suggesting tissue microenvironment may play essential role to define intestinal T-IEL's identity. They further identified reduced protein synthesis, distinct metabolic profile and changes in T cell antigen receptor signalling pathways in T-IEL compared to peripheral T cells from LN. They also reported that T-IEL share some similarities with exhausted T cells. High quality proteomics data have been generated in this study, which can be served as resource for further studies for functional characterization of critical proteins to maintain intestine homeostasis or immunity. However, there are certain issues raised and need to be addressed before publication.

We thank the reviewer for recognising the quality of our data in this manuscript, and its value as a resource.

Major concerns:

1. Lack of functional assays to characterize the proposed effects. All the claimed conclusions in this study are mostly based on proteomics data. Although OPP incorporation assay was performed to confirmed the reduced protein synthesis, but no statistical analysis was presented in the shown data (Figure 3F). Additionally, statistical analysis is missing in several figures, such as Figure 3, Figure 4B, 4D, Figure 6D, 6E. For Figure 7, whether all the presented selected proteins are statistically significantly changed? Therefore, it seems most of the conclusions are over stated. 

We apologise for the omission of statistical information. We have now added the requested p-values to all figures, including the OPP incorporation assay, which was indeed significantly different between T-IEL and naïve LN T cells.

Besides the OPP assay, validation of surface receptor expression of CD73, P2X7R, CD39, CD38, CD244, LAG3, CD160 and CD96 were also shown in Figure 6D, E (now Figure 7a-d), for cell surface receptor expression. To make it clearer that these figures were based on flow cytometry, we have included the flow plots of the different subsets.

We have now provided further functional validation of the proteome, including measurement of cholesterol levels in T-IEL compared to LN T cells (Figure 5c). These data show that all 3 T-IEL subsets contain up to 4-fold more cholesterol than LN T cells, supporting our finding that the cholesterol biosynthetic pathway is highly overexpressed in T-IEL. Further, immunofluorescence imaging of so-called epithelial proteins (ZO-2, E-Cadherin), and flow cytometry (EpCAM, E-Cadherin, TOX), show that T-IEL do indeed express epithelial proteins and the exhaustion-driving transcription factor TOX. These data are included in Figures 6, 7 and figure 6-supplement 1. Finally we have added new data to Figure 8, showing that both the natural T-IEL subsets are unresponsive to TCR stimulation, but the induced TCRab CD8ab population are highly responsive.

We have also included statistical information on all the heatmaps to make it clearer which proteins are differentially expressed in T-IEL by at least 1 standard deviation (~2.5 fold) significantly compared to LN T cells. With this information and the new validation, we hope the reviewer agrees that our data support our conclusions.

2. Purity of FACS sorted cells is only "about 50% to greater than 95%" (page 4). This shows big variation of cell purity, it's hard to accept 50% purity of FACS sorted cells. The purity should be improved. 

We apologise for this misunderstanding – what we meant was that before cell sorting the purity was 50%, and after cell sorting the purity was >95%. We have modified this sentence to read “CD8+ T-IEL subsets were purified from wild type (WT) murine small intestinal epithelial preparations to greater than 95% purity by cell sorting.”

3. What is the retionale to use naïve CD8+ T cells from P14 transgenic mice to compare with T-IEL subsets? 

We used P14 LN T cells mainly as a comparator for naïve LN T cells from WT mice- that is, as P14 are genetically and developmentally distinct, due to the expression of a transgenic TCR, they could have been completely different from polyclonal naïve CD8+ LN T cells. Yet WT naïve LN T cells share more similarity to P14 T cells than to WT IEL from the same genetic background. These findings are shown in Figure 1e and 1i. We have added new text to the first Results section to make this clearer.

4. It's a bit surprise to see less protein synthesis in T-IEL than naïve CD8+ T cells from LN. It'd be good to show the proportions of naïve and actived T cells in each subsets of T-IELs. This will help to understand. 

We agree with the reviewer that it is indeed surprising that T-IEL have lower protein synthesis rates than even naïve T cells. T-IEL bear many hallmarks of activated/memory T cells, including expression of CD44 and CD69 (Author response image 1). We speculate that the low metabolic activity/protein synthesis in these cells serves as an added barrier to spurious activation of these cells, however it would be interesting in the future to investigate how these levels are regulated.

Author response image 1
Representative flow cytometric dot plots showing the expression of activation markers CD69 and CD44 in all 3 populations of T-IEL.

5. Text flow was not very well organized, e.g. cytoskeleton proteins have been described in 2 sections, in page 5, the 3rd paragraph (Figure 2) and page 8, the 4th paragraph (Figure 6). It'd be good to extract protein expression data from cytoskeleton proteins and present them together in one separate section. 

We thank the reviewer for pointing this out. Based on your suggestions we have streamlined the text to minimise repetition. We do still mention cytoskeletal proteins in Figure 2 as part of the global analysis and Gene ontology enrichment, however all detailed analyses of cytoskeletal proteins and names are collected into the section exploring cell surface receptors.

Minor concerns:

1. Some texts in Results section should be moved to discussion, such as in page 6 about ARG2, instead of presenting data, there are too much speculations. 

We thank the reviewer for this recommendation, and have moved any speculative text to the discussion.

2. In page 7, it is claimed "T-IEL express the fatty acid transport proteins (FATP2(Slc27a2) and FATP4 (Slc27a4))". However, FATP2 is not presented in Figure 5C. It is also not clear whether FATP1, 3 and 4 are expressed significantly higher than in T-IEL? 

We thank the reviewer for catching this mistake. It was supposed to be FATP2 in the figure, not FATP3. We have changed this, and also added statistical information to the figure.

3. In page 6, "T-IEL express low levels (<2000 copies per cell) of 3 key amino acid transporters, i.e., SLC1A5, SLC7A5 and SLC38A2 (Figure 3C)". Based on Figure 3C, isn't SLC1A5 really expressed lower in T-IEL? Again, please show the statistical analysis results. 

We did not want to make a point that SLC1A5 was lower in T-IEL as compared to in LN T cells, only that the levels of these 3 amino acid transporters that are important for T cell activation are low. We have added the significance information to the graphs.

Reviewer #1 (Significance (Required)):

Significance

The authors applied high-resolution, quantitative proteomics to characterize subsets of T-IELs from mice. The proteomics data presented are of high quality and can be served as a resource for further studies for functional characterization of critical proteins to maintain interest in homeostasis or immunity. However, this is more for basic immunological research, does not seem to have immediate translational potential. The authors also did not discuss such a potential. Given the data are generated from mice and conclusions made in this study are mostly just based on proteomics data, further functional characterization of the findings are largely missing. 

The reviewer questions the significance of our data since we explore murine T-IEL and not human T-IEL, therefore limiting translational potential. However, the intention of this manuscript is to trigger new research to explore the functional consequences of the differences between T-IEL and LN T cells, and to gain a deeper understanding into the regulation of these unique T lymphocytes. We hope that these future functional studies will have translational potential, as we did in our recent study (James OJ et al. (2021), Nature Communications, 12:4290) that identified PIM kinases are essential for IL-15 responses in murine T-IEL, and then found a similar upregulation of the PIM kinases in human Celiac disease patient samples, demonstrating the translational potential of murine T-IEL studies.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The research article titled "Tissue environment, not ontogeny, defines intestinal intraepithelial T lymphocytes" describes how various environmental signals shape the proteomes of three major tissue-resident intestinal intraepithelial T lymphocytes (T-IELs). The authors present a comprehensive proteomic approach to compare induced and natural T-IELs, as well as peripheral naïve CD8+ T cells derived from the lymph nodes (LNs).With regard to a localization of T-IELs (the interface between an antigen-rich lumen and gut tissue), it is not surprising that these cells are more defined by tissue environment than ontogeny. Although the observations raised by this manuscript are very interesting and some aspects are unexpected, there are several issues that should be addressed, without which clear conclusions cannot be made. 

We thank the reviewer for appreciating that our manuscript reveals novel and interesting aspects about T-IEL.

Major comments:

There are some major concerns that the authors need to address before the manuscript can be further considered. Particularly, the expression of molecules in IELs that are normally produced by epithelial cells should be examined by other methods (e g. fluorescence microscopy etc.). The molecules such as Villin-1 and ZO-2 might originate from the contamination (intestinal epithelial cells?). Therefore, the authors have to explore these effects more thoroughly. 

We appreciate these concerns and have attempted to address them. We have now included immunofluorescence images showing that T-IEL do indeed express ZO-2 and E-Cad (Figure 6c), and flow cytometry data confirming the expression of E-Cadherin and EPCAM on T-IEL (Figure 6—figure supplement 1). Interestingly, analyses of microarray-based gene expression data from the ImmGen database (www.immgen.org), show that T-IEL express proportionately higher levels of mRNA for selected epithelial proteins, such as E-cadherin and ZO-2 (Figure 6—figure supplement 1). The fact that T-IEL have the mRNA for epithelial proteins supports our findings that these proteins are cell-intrinsic, and not contaminants identified by mass spectrometry.

We are gaining more insight into the T-IEL biology, but there is still a risk of over-interpretation of proteome-based data. Therefore, the core findings/most unexpected data should be further examined by functional approaches. E.g., the role of SREBP2 or LAT2 in T-IELs should be investigated by genetic deletion or knockdown of these molecules. Otherwise, these nice and unexpected findings would stay more descriptive. 

We thank the reviewer for these suggestions, and agree that further functional characterisation would be very insightful. However we feel that these are beyond the scope of this manuscript. Obtaining mice with genetic deletion of SREBP2 or LAT2 would take 6 months or longer, and then a further 6-8 months to obtain functional information. We believe rather that this is the strength and purpose of our manuscript, to encourage ideas such as these that open new avenues of research.

Minor comments:

 Although the figures are nicely presented, they look like figures from a review article. E.g., the Figure 5 and Figure 7 recapitulate our knowledge on lipid metabolism and T cell receptor signaling, respectively. They are more a schematic overview than figures originating from a research article. 

Here, we respectfully disagree with the reviewer, as we find it helpful to visualise the whole pathway and to see the protein expression data overlaid on the pathway. For example, in Figure 5a, depiction of the entire pathway makes it easier to see that every step of the cholesterol biosynthetic pathway is overexpressed in T-IEL. We have however modified the figures to make the proteomic data more prominent and have included p-values on them as well.

The authors should explicitly state that all conclusion made in the article are statements without functional data. 

We state at multiple points throughout the manuscript, including in the abstract, that our conclusions are based on analyses of proteomic data. We are also careful to only suggest functional consequences of our findings in the discussion, rather than assigning specific functions, and we end our manuscript with the statement that our data “suggest new ways to investigate how the different T-IEL subsets contribute to tissue and organismal homeostasis.” Therefore we feel we have sufficiently highlighted that our conclusions are based on proteomic data.

Reviewer #2 (Significance (Required)):

There are several interesting and novel aspects in this study (a high significance in the research area of mucosal immunology). However, novel data should be performed by using functional approaches. Otherwise, there is a "danger" of over-interpretation of current results. The current version of the manuscript is rather a descriptive one. 

We believe the value of this manuscript is our in-depth analyses of the proteomes of T-IEL subsets, exploring how they differ from conventional T lymphocytes. These data and the ideas suggested by our analyses are to serve as a resource for the community, and to be thought-provoking for future research.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Brenes et al., provide an exhaustive proteomic characterization of three different IEL populations: TCRabCD8aa, TCRabCD8ab, and TCRgdCD8aa cells. These cells are also compared to naïve CD8+ T cells derived from lymph nodes. These results indicate that these IEL populations, despite having different developmental pathways, they share many proteomic pathways. In addition, although these IEL populations are more similar to naïve peripheral T cells than to activated non-IEL, the different subpopulations of T-IEL resemble each other. The only major concern related to their results is the presence of proteins that are mostly associated with intestinal epithelial cells, such as villin. The authors indicate that the purity of their flow cytometric enrichment was between "50% to greater than 95%," which may account for the detection of proteins associated with epithelial cells. If that is the case, how is it possible for their overall data to be reliable? Can the authors elaborate on this point?

Other than that concern, the manuscript is well written, follows a logical sequence and the data are interesting and compelling. 

We apologise for the misunderstanding– what we meant was that before cell sorting the purity was 50%, and after cell sorting the purity was >95%. We have modified this sentence to read “CD8+ T-IEL subsets were purified from wild type (WT) murine small intestinal epithelial preparations to greater than 95% purity by cell sorting.”

Given the high purity, we think that the epithelial proteins identified in T-IEL were not contaminants, but actually expressed in T-IEL. These data are now corroborated in the manuscript by inclusion of IF and flow cytometry data showing ZO-2, E-cadherin, EpCAM expression in T-IEL, and by mRNA expression data from ImmGen (see also response to reviewer 2) (Figure 6c and Figure 6—figure supplement 1). With these validations, our suggestion that T-IEL express multiple epithelial proteins as a means of communicating with epithelial cells is also better supported.

Reviewer #3 (Significance (Required)):

This is the first report to my knowledge that describes in detail the proteomic profile of three different TCR+ IEL populations, which enhances the significance of this report. I believe, if the results are correct, this manuscript will be well received in the community of groups that study IEL biology. As an investigator that works with IEL, I believe this is an important manuscript with relevant data that will move the field forward. If my major concerns can be clarified, then I think this manuscript should be published.

We thank the reviewer for recognising the significance of our manuscript, and for their positive comments. We hope that they now find the revised manuscript suitable for publication.

https://doi.org/10.7554/eLife.70055.sa2

Article and author information

Author details

  1. Alejandro J Brenes

    1. Centre for Gene Regulation and Expression, University of Dundee, Dundee, United Kingdom
    2. Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft
    Contributed equally with
    Maud Vandereyken
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8298-2463
  2. Maud Vandereyken

    MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, United Kingdom
    Contribution
    Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft
    Contributed equally with
    Alejandro J Brenes
    Competing interests
    none
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6169-7396
  3. Olivia J James

    MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, United Kingdom
    Contribution
    Investigation, Validation, Writing – review and editing
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4294-7855
  4. Harriet Watt

    MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, United Kingdom
    Contribution
    Investigation, Validation
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4239-9051
  5. Jens Hukelmann

    Centre for Gene Regulation and Expression, University of Dundee, Dundee, United Kingdom
    Contribution
    Formal analysis, Methodology
    Competing interests
    None
  6. Laura Spinelli

    Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Writing – review and editing
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5801-6297
  7. Dina Dikovskaya

    MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, United Kingdom
    Contribution
    Validation
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3161-3072
  8. Angus I Lamond

    Centre for Gene Regulation and Expression, University of Dundee, Dundee, United Kingdom
    Contribution
    Funding acquisition, Methodology, Project administration, Resources, Writing – review and editing
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6204-6045
  9. Mahima Swamy

    1. Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom
    2. MRC Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Supervision, Writing – original draft
    For correspondence
    m.swamy@dundee.ac.uk
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3977-3425

Funding

Wellcome Trust (105024/Z/14/Z)

  • Angus I Lamond

Wellcome Trust (206246/Z/17/Z)

  • Mahima Swamy

Wellcome Trust (215309/Z/19/Z)

  • Olivia J James

Wellcome Trust (222320/Z/21/Z)

  • Harriet Watt

Wellcome Trust (073980/Z/03/Z)

  • Angus I Lamond

Wellcome Trust (098503/E/12/Z)

  • Angus I Lamond

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors would like to thank Doreen Cantrell for her critical reading of the manuscript, support, and advice. We also acknowledge the support provided by A Whigham and R Clarke from the Flow Cytometry Facility for cell sorting and flow cytometry, and by the Biological Resources Unit at the University of Dundee. The contribution of the UK Research Partnership Infrastructure Fund award to the Centre for Translational and Interdisciplinary Research for the purchase of the mass spectrometers is gratefully acknowledged.

Ethics

All mice were bred and maintained with approval by the University of Dundee ethical review committee in compliance with U.K. Home Office Animals (Scientific Procedures) Act 1986 guidelines. C57BL/6J mice were purchased from Charles Rivers and acclimatised for a minimum of 10 days prior to use in experiments. Mice were maintained in a standard barrier facility on a 12hour light/dark cycle at 21ºC, 45-65% relative humidity, in individually ventilated cages with corn cob and sizzler-nest material and fed an R&M3 diet (Special Diet Services, UK) and filtered water ad libitum. Cages were changed at least every two weeks. For all experiments, mice were used between 8-12 weeks of age, and for proteomics, male mice aged 8-9 weeks were used.

Senior and Reviewing Editor

  1. Carla V Rothlin, Yale School of Medicine, United States

Publication history

  1. Preprint posted: March 16, 2021 (view preprint)
  2. Received: May 11, 2021
  3. Accepted: September 1, 2021
  4. Accepted Manuscript published: September 2, 2021 (version 1)
  5. Accepted Manuscript updated: September 3, 2021 (version 2)
  6. Accepted Manuscript updated: September 8, 2021 (version 3)
  7. Version of Record published: September 24, 2021 (version 4)

Copyright

© 2021, Brenes et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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