Proteogenomic and V(D)J Analysis of Human Decidual T Cells Highlights Unique Transcriptional Programming and Clonal Distribution

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Tcells are major players of the adaptive immune system, having the capacity to undergo recombination of their Ag-specific receptor. TCR recombination, or V(D)J rearrangement, is the basis behind the specific antigenic response of T cells (1) and involves the combination of random segments of V, D, and J segments on both the α- and β-chains (2). After activation with their cognate Ag, T cells undergo clonal expansion, leading to a specific response to the immunological challenge and subsequently immunological memory. T cell repertoire in tissues is the front line of adaptive immune recognition, yet it is still poorly understood, especially in pregnancy.

Pregnancy requires many maternal adaptations, including the ability to maintain immunological tolerance toward the semiallogeneic fetus yet remain vigilant to pathogens. Tolerance toward the fetus is accomplished with a combination of mechanisms: (1) lack of classical MHC class I molecules, HLA-A and HLA-B, by human fetal trophoblasts (3); (2) limiting traffic of immune cells to/from the mouse, but not human, decidua (4–6); and (3) induction of tolerogenic regulatory T cells (T_regs) in humans and mice (7–10). In fact, many have shown that the decidua maintains a significant proportion of T_regs (11–13), with maternal–fetal HLA-C mismatch leading to expansion and activation of decidual T_regs (14), presumably to maintain fetal tolerance. Moreover, dysregulation of decidual T_regs has been linked to pregnancy pathologies, including pre-eclampsia (15, 16) and preterm birth (17), indicating their importance in maintaining fetal tolerance and a healthy pregnancy.

TCR clonal diversity is the central mechanism of immunological responses against a diverse host of Ags. Differences in clonal repertoire have been reported between peripheral T cells and different tissues, both in human health and disease states (18–20). Similar clonal restriction has been observed in human decidua (15, 21–23), although these studies have been limited by looking at TCR β-chain usage only. Furthermore, no conclusive link has been made between clonal diversity and pregnancy pathologies (22, 24, 25), leaving this important question unanswered.

Using single-cell proteogenomic and V(D)J sequencing, we set out to confirm previous observations regarding decidual T cell composition and address the hypothesis that there are transcriptional and clonal differences between decidual and peripheral T cells. We analyzed single-cell transcription, surface proteins, and CDR3 sequences in matched samples from healthy term pregnancies. Our results show that the decidua maintains a unique T cell population, composed primarily of CD8 T cells with an exhausted/effector memory (EM) phenotype and very few naive T cells, confirming previous studies (26, 27). Gene expression analysis further shows that, globally, decidual T cells maintain a transcriptome geared toward immune tolerance and attenuation. Lastly, clonal analysis of decidual T cells illustrates that the decidua maintains unique T cell clones compared with the periphery. Overall, our data show that decidual T cells are programmed by the local decidual environment and are clonally distinct from peripheral T cells.

Placentas and whole blood were obtained from five healthy women undergoing normal elective cesarean sections (>37 wk gestational age) on day of intake in accordance with the University of Wisconsin (UW) Obstetrical Tissue Bank Institutional Review Board protocol (#2014-1223) and UnityPoint Health–Meriter Institutional Review Board protocol (#2018-10). The decidua basalis was dissected and processed as previously described (28–30). Matched PBMCs were isolated from whole blood via density gradient centrifugation. The PBMC layer was extracted and washed with PBS, counted, and adjusted to an optimal concentration of 4 × 10⁶ cells/ml. Mononuclear cells (MCs) from decidua basalis and whole blood were frozen in FBS/10% DMSO and stored until needed.

MCs from decidua basalis and matched PBMCs were thawed and subsequently enriched for T cells using the Dynabeads Untouched Human T Cells Kit (Invitrogen). Enriched T cells were then labeled with Zombie NIR Fixable Viability Kit (BioLegend), treated with TruStain FcX (BioLegend), then labeled with fluorochrome- and TotalSeq-C oligonucleotide-conjugated Abs (Table I). T cells were sorted into PBS (Ca²⁺/MG²⁺-free, 1% nonacetylated BSA, 1 ml/ml RNase inhibitor) using the BD FACSAria, collecting 100,000 live, CD3⁺ cells per sample. One set of samples (matched decidua and PBMCs) were removed because of low cell number recovery, leaving four pairs of matched samples for sequencing.

Cell suspensions were submitted to the UW Biotechnology Center for processing. Collected cells were concentrated, and cell viability was further validated using the Countess II (Invitrogen). A total of 56,000 cells (7000 for each of eight samples) were targeted using the 10× Genomics V(D)J Single-Cell RNA-Seq pipeline. In brief, Gel Bead-in-Emulsions were prepared using the Single Cell A Chip Kit (10× Genomics). Full-length cDNA cleanup was performed using DynaBeads Myone Silane beads (Invitrogen). Full-length cDNA was then amplified by PCR (13 cycles), post-cDNA amplification cleanup was performed using SPRIselect (Beckman Coulter), and quality control was performed using Agilent HS DNA chips. Feature barcode (FB) PCR amplification was done in nine cycles. Sample indexing was then performed using the Chromium i7 Plate N, Set A indices (10× Genomics). Gene expression, FB, and V(D)J libraries were then prepared, respectively. Libraries meeting all quality-control criteria were then sequenced using the Illumina NovaSeq platform at the UW Gene Expression Center in collaboration with the UW Biotechnology Center DNA Sequencing Facility (Madison, WI). Libraries were sequenced at the following depth (reads/cell): gene expression: 85,000; V(D)J: 11,000; and FB: 6,400, with a total of 53,000 cells submitted for sequencing.

We used the CellRanger v3.1 count pipeline to generate filtered gene count matrices for each sample. This pipeline includes demultiplexing, discriminating between cell and background barcodes, and aligning reads to the human transcriptome (GRCh38 3.0). Next, we applied the CellRanger v3.1 vdj pipeline to generate a list of barcodes with CDR3 sequences for each sample. For each barcode, the pipeline assembles the V(D)J transcripts into contigs, aligns the contigs to the TCR reference sequences (GRCh38 3.0), and determines whether the contigs correspond to a CDR3 sequence by annotating both ends of those sequences with V and J genes.

We next enriched the dataset for high-quality cells by filtering on several quality-control metrics: the fraction of universal molecular identifiers aligning to mitochondrial transcripts, fraction of a list of housekeeping genes detected [list compiled by Tirosh et al. (31)], number of RNA or surface features detected, and number of RNA (FB) universal molecular identifiers. We converted each value to median absolute deviations within each sample and removed cells for which any value was outside of [−3, 3]. Finally, although the cells were selected for CDR3 positivity before sequencing, we implemented two additional quality-control filters to ensure a high-confidence dataset. First, we required cells to have full-length CDR3 sequence data [from single cell V(D)J-sequencing]. Next, we plotted the cells by their CD4 and CD8 protein expression (discriminated with Ab FB) and observed that although most cells almost exclusively expressed one or the other, there was a small cluster of apparent double-positives and another of apparent double-negatives. We defined thresholds on CD4 and CD8 to identify and remove the double-positives and double-negatives, which we suspected to be multiplets or false-positive cells. The cells removed by the CD4/CD8 filter were mostly subsumed within the set of cells without full-length CDR3, providing further evidence that they were likely to be artifacts. At the end of the filtering process, both samples from one of the subjects had lost substantially more cells than the other three subjects. We removed both samples for that subject from further analysis, resulting in three subjects each with PBMC and decidua basalis samples included in final analysis. The remaining cell counts per participant and sample type are shown in Table II.

For RNA values, we log-transformed the depth-normalized counts. For surface proteins profiled by feature barcoding, we used centered log-ratio normalization across all cells for each feature.

We used the Seurat v3 integration pipeline (32) to align the per-sample RNA datasets to reduce subject- and localization-specific variability, using parameter settings recommended in Seurat vignettes (nfeatures = 2000, dims = 1:30). This approach employs “anchor” cells, which are cells that have similar nearest neighborhoods in the datasets being reconciled. The corrected expression value for a gene in a cell is a function of its proximity to the anchor cells and a score for each anchor. We specified the order of pairwise alignments as follows. First, we integrated the PBMC T cells across subjects and separately the decidua basalis T cells across subjects. Then we integrated the PBMCs and decidua basalis cells together. The final batch-corrected RNA matrix was used for uniform manifold approximation and projection (UMAP) plots and clustering, but not for expression visualization or statistical analysis (to prevent bias incurred during alignment).

Using Seurat, we applied Leiden clustering (33) on the shared nearest-neighbor graph with resolution = 0.5. We manually compared the highly expressed surface proteins and marker genes for each cluster to annotations of T cell subset markers in the literature. Clusters were annotated based on both gene and protein expression and following previously published phenotypic classifications (34–42).

To interpret the clusters, we performed rank-sum tests to identify marker genes that distinguished each cluster from cells in all other clusters. Genes were filtered in advance of testing for expression in at least 10% of the cluster’s cells and for absolute average log fold change > 0.25. To account for differences in the number of cells available from each sample, we first tested each gene within each sample separately using rank-sum tests (Wilcoxon) on the log-normalized RNA counts. We combined the p values from the six samples using the sum-log method and adjusted the combined p values using Bonferroni correction (n = 33,548 genes). Final marker genes were selected using a threshold of adjusted p < 0.05.

We used a similar method to test for differential expression between decidua and peripheral T cells within each cluster. We performed the tests first between the matched samples for each participant, resulting in three p values for each gene per cluster. We called differentially expressed genes using the threshold of combined, adjusted p < 0.05.

We separated the CDR3 annotations for each sample. Then for every unique combination of TRA and TRB sequences, we counted the number of cells annotated with that combination in each cluster and calculated the Shannon diversity index (43). We examined the patterns of the unique combinations of TRA and TRB sequences by using the UpSetR R package (44) (v1.4.1). This visualization summarizes the combinatorial occurrences across a list of binary variables. For that purpose, we constructed matrices having unique TRA and TRB combinations for a subset of cells as rows and binary features as columns.

We quantified the overlap between the number of clonotypes in PBMCs and decidua cells using the Morisita-Horn index (MHI) (47) using divo (48) (v1.01). The MHI is zero when there is not overlap between two sets and one when the overlap is complete.

χ² and one-sided hypergeometric tests were used to compare the number of unique clonotypes in PBMCs and decidua cells and whether the number of unique clonotypes in Decidua was enriched in any of clusters 1, 6, 9, or 11.

Visualizations of single-cell expression and surface feature data were generated using the R packages Seurat (v3.1.5, v4.0), ggplot2 (49) (v3.3.2), and viridis (50) (v0.5.1). We constructed the chord diagrams in Fig. 4B using the circlize R package (51) (v0.4.12).

Our understanding of clonal and phenotypic T cell diversity at the maternal–fetal interface is critical to deciphering how decidual T cells contribute to a normal pregnancy. We previously reported on the unique distribution of T cell subsets specific to the term human decidua by flow cytometry (30). To further investigate the specificity of decidual T cells, we used emerging single-cell RNA sequencing (scRNA-seq) technologies [Cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) and scV(D)J-seq] to garner gene, protein expression, and receptor repertoire at the single-cell level, simultaneously from decidual and matched maternal peripheral T cells (Fig. 1A, Tables I, II). Unsupervised clustering analysis of the transcriptomic component of the resulting dataset identified 15 unique T cell clusters, with overlapping regions between decidual and peripheral T cells and areas of clear segregation of tissues (Fig. 1B, 1C). The majority of T cells in the decidua were CD8⁺ and included the previously described human decidual CD8⁺ EM subset (cluster 4) (27, 52) (Fig. 1D, top). Interestingly, we found three clusters (1, 6, 9) that were nearly equally distributed between the decidua and periphery (Fig. 1D, bottom). Taken together, this supports our previous observation that the decidua maintains a tissue-specific distribution of T cells compared with the periphery (30).

Proteogenomics has emerged as a powerful tool to assess simultaneous protein and gene expression, allowing for corroboration of expression, or lack thereof, of low-copy transcripts. We used CITE-seq (53) to measure protein expression in both peripheral and decidual T cells (Fig. 1E). Important features noted were the clear demarcation of CD4 and CD8 protein expression, as well as chemokine receptors CD196 (CCR6), CD194 (CCR4), and CD197 (CCR7) (Fig. 1E). Average protein expression levels, in conjunction with gene expression information, were then used to annotate the 15 T cell clusters identified by clustering (Figs. 1C, 2; Supplemental Fig. 1A). Further gene expression analysis across the 15 clusters identified revealed cluster-specific gene expression patterns (Fig. 2). Specifically, we found expression of subset-defining transcription factors GATA3 and RORC in clusters 1 and 7, respectively, confirming their classification as Th-like and Th17 T cells, whereas FOXP3 expression was entirely restricted to cluster 9 (T_regs) (Fig. 2). We found a few cells in cluster 3 expressing the B cell marker CD79A, which has been reported to be expressed by T cells in some instances (54–56). Two clusters (2, 10) were classified as progenitor exhausted CD8 T cells because of moderate protein expression of PD1 (CD279). However, we found that cluster 10 was mostly confined to decidua basalis and additionally expressed CTLA4, TIGIT, and LAG3, whereas cluster 2 contained a mixture of both decidua basalis and peripheral T cells (Fig. 1D).

To illustrate the importance of simultaneous protein and gene expression evaluation and to make recommendations for other researchers who are beginning to analyze multiomics sequence data, we highlight cluster 6 (central memory T cells). Our first step in annotation was to assess average protein expression of the clusters (Supplemental Fig. 1A), which showed high levels of CD4 protein expression, leading us to classify this cluster as CD4⁺ central memory T cells. However, after examination of the data at the level of single cells, we found that cluster 6 was composed of both CD4⁺ and CD8⁺ cells (Fig. 1E). Analysis of RNA expression of CD4 and CD8A/CD8B found that CD4 transcripts were not detected in most cells (Supplemental Fig. 1B, 1C). This prompted us to reassess the reliability of using average protein expression for cluster phenotyping, and we found some notable discrepancies. For example, we saw apparently high levels of TCR-Vɑ24-Jɑ18 average protein expression (Supplemental Fig. 1A), a marker of NKT cells (57, 58). However, these clusters did not express other classic NKT markers, such as CD161 (58, 59). Visualization of RNA expression confirmed that the transcripts for TRAV24 and TRAJ18 were not expressed (Supplemental Fig. 1C) despite the apparent detection of TCR-Vɑ24-Jɑ18 protein when z-scaled (Supplemental Fig. 1D). Notably, this detection was likely an artifact of scaled data visualization of background level of staining, which we confirmed by histogram analysis of absolute (not z-scaled) Vα24-Jα18 expression showing little to no transcripts in any cluster (Supplemental Fig. 1E). We also confirmed the division (nonoverlap) of CD4 and CD8, both at the transcript and protein level, in cluster 6 (Supplemental Fig. 1C, 1D), indicating that despite extremely similar transcriptional programming, this cluster was a mix of CD4 and CD8 cells. These results show how assessing protein expression simultaneously with gene expression, as well as performing multiple complementary visualizations of the data at both single-cell level and in aggregate, is important in the context of assigning phenotypic properties to immune cells.

We further analyzed the data to probe for potential differences in transcriptional programming between decidua basalis and peripheral T cells. Differential expression analysis revealed a total of 872 genes that were differentially expressed in any of the clusters (Fig. 3A). Of these, 152 genes were globally (seven or more clusters) upregulated in decidual T cells (Supplemental Fig. 2). These included genes involved in activation (DUSP, CD69) (24), chemotaxis (CCL4, CXCR4) (60, 61), inflammation (TNFAIP3, NR4A2) (62–64), attenuation/suppression (ZFP36) (65), glucose transport (SLC2A3) (66), trafficking (RGS1) (67), and granule trafficking (SGRN) (68), all of which were heavily enriched in decidual basalis T cells. We then asked whether there were cluster-specific genes that were upregulated or downregulated in decidual T cells in fewer than seven clusters. Of the genes shared across fewer than seven clusters, we examined the top 5 upregulated and top 5 downregulated genes for each cluster. The union of these top genes included 29 upregulated and 46 downregulated genes, with 23 genes unique to one cluster. Some of the top genes were shared between clusters in similar states. These included ARRDC2 and SOX4 (upregulated) and TSPO (downregulated) in both CD4⁺ and CD8⁺ naive T cells. To further illustrate that decidual T cells maintain a distinct transcriptome independent of phenotype, we focused our attention on the top genes identified for T cell clusters that were equally distributed between the decidua and the periphery (clusters 1, 6, and 9; Fig. 3B). We found CST7 to be upregulated in decidual cells, whereas MYC, RASGRP2, AES, and GIMAP4 were downregulated (Fig. 3B). Moreover, specific decidual signatures were found for both clusters 1 and 9, with IFI27 upregulated in cluster 1, and BATF, LINC01943, and TNFRSF18 upregulated in cluster 9. Interestingly, none of the top downregulated genes were exclusive to one cluster (Supplemental Fig. 3). We next turned our attention to the decidual cells in the two clusters labeled progenitor exhausted CD8⁺ T cells (clusters 2 and 10). Comparisons of gene expression between the decidual cluster 2 and 10 cells found that cluster 2 decidual cells differentially expressed genes involved in protein synthesis, whereas decidual cluster 10 differentially expressed genes involved in cytotoxicity and chemotaxis (Fig. 3C), despite both having the same tissue origin and phenotype. Overall, this supports our hypothesis that decidual T cells would maintain a unique transcriptome programming regardless of phenotypic similarities with peripheral T cells.

The level of clonal diversity at the maternal–fetal interface remains an active area of investigation. To increase our understanding of clonal representation in term human decidua, we sequenced V(D)J chains at the single-cell level of both decidual and peripheral T cells. A unique clonotype in this study was defined as a productive full-length TCR α and TCR β pairing with only a single repetition in the entire dataset. As expected based on previous work (15, 21–23), T cells in the decidua overall displayed a lower level of clonal diversity compared with peripheral T cells across all sampled individuals (Fig. 4A). Minor exceptions were found, however, particularly in individuals 83 and 85, where three clusters were found to have higher clonal diversity in the decidua, including CD8⁺ progenitor exhausted and CD8⁺ EM T cells (Fig. 4A). We compared at least one unique clonotype between each subject, tissue, and cluster combination (Supplemental Fig. 4A), and fitting a linear mixed model revealed that the tissue is the main source of variation after testing for the difference between PBMCs and decidua cells (p = 0.009). Furthermore, the subject differences from the reference are not significant (p = 0.982 and p = 0.377, respectively). Taken together, this indicates that tissue localization is a more important factor in clonotype distribution, even after accounting for cellular recovery differences, than intersubject variation. Focusing on the equally distributed clusters, 1, 6, and 9, we found that frequency of chain usage varied across the three clusters, with cluster 1 (CD4⁺ Th-like) being the most restricted in the decidua (Fig. 4B). Surprisingly, both cluster 6 (central memory) and cluster 9 (T_regs) displayed more diverse α and β pairings in the decidua compared with their peripheral counterparts (Fig. 4B). As expected, mucosal-associated invariant T (MAIT) cell α-chain was highly restricted, more so in the decidua, with more diversity observed in the β-chain (Fig. 4B).

Assessing individual clonotype frequency in decidual and peripheral T cells, we found that the decidua had fewer proportions of cells with unique clonotypes (1840) compared with peripheral (3327) T cells (p = 9.99 × 10⁻⁵) when looking at clusters 1, 6, and 9 (Fig. 4C; list of individual clonotypes and abundance per cluster is in Supplemental Table I). Interestingly, although MAIT cells exhibit restricted Vα-segment usage, they maintained a higher number of unique clonotypes in the periphery (182) than in the decidua (78) (Fig. 4C). There was a small number of shared clonotypes between decidual and peripheral T cells, with a total of 146 clonotypes within cluster 1 (CD4⁺ Th-like), cluster 6 (central memory), and MAIT cells, and only 1 shared clonotype with the T_reg cluster (cluster 9), in agreement with previous T_reg clonotype analysis in pregnancy (15) (Fig. 4C); the MHI is 2.67% (99.99% confidence interval: 1.35%–4.19%), suggesting that there is little overlap between both tissues. Coincidentally, clonotype analysis of both CD8 progenitor exhausted T cells (clusters 2 and 10) revealed more shared clonotypes within cluster 2 than cluster 10, suggesting that cluster 2 T cells are allowed to traffic between the periphery and decidua (Supplemental Fig. 4B, C).

The maternal–fetal interface represents an immunological paradox: how to defend against pathogens while maintaining tolerance toward the semiallogeneic fetus? It is understood that this balance is achieved in part by limiting fetal Ag presentation (4, 69) and limiting T cell traffic to the maternal decidua (5, 70). We previously showed that the term human decidua maintains a unique milieu of T cell subsets (30). In this study, we expand on those observations by applying a single-cell multiomics approach to better understand the depth of decidual T cell partitioning. We confirmed that the human decidua maintains a unique distribution of T cell subsets compared with matched peripheral blood. Furthermore, we show that decidual T cells maintain a unique transcriptome even when phenotypically identical to their peripheral counterparts. Clonal analysis further illustrates how decidual T cells are specific to the decidua with little clonal overlap between the decidua and periphery. Overall, our results highlight the uniqueness of decidual T cells, both transcriptionally and clonally.

We first assessed T cell subset diversity in human term decidua and matched PBMCs (Fig. 1) using CITE-seq, a proteogenomic approach that allows for simultaneous measurement of protein and gene expression (53). Having these sets of information allowed us to confidently annotate the T cell clusters identified in our dataset. Notably, we found that one cluster (cluster 6) was mischaracterized based on protein expression and consisted of CD4⁺ and CD8⁺ T cells (Fig. 1; Supplemental Fig. 1). Simultaneous protein assessment has proved important when confirming expression of low-copy transcripts (71), such as those of CD56 (72) and CD4 (71). We determined our mischaracterization of cluster 6 was a result of visualizing protein and transcriptomic expression, with cluster annotation based on median protein expression (Supplemental Fig. 1A). However, in the absence of protein expression data, cluster 6 would have been mischaracterized as CD8⁺ T cells based solely on mRNA detection, because the CD4 transcript was detected in very few cells (Supplemental Fig. 1B, 1C). This serves as an example of why both protein assessment and appropriate data visualization are important to properly assess T cell phenotype in single-cell transcriptomics.

CD8⁺ T cells are the most abundant T cells found in the decidua, with the majority being EM cells (52, 73–75). Although we did identify CD8⁺ EM T cells, they were not the majority in the decidua, with the majority being CD8⁺ progenitor exhausted (Fig. 1D, top). Interestingly, when assessing cytotoxic potential of CD8⁺ EM T cells, Tilburgs et al. (27) found lower expression of both perforin and granzyme B in decidua (both labored and unlabored) compared with matched peripheral CD8 EM T cells. Although seemingly contradictory to our observations of higher granzyme B and perforin expression in decidual CD8⁺ EM T cells (Fig. 2; Supplemental Fig. 3), this discrepancy can be explained by translational regulation of cytotoxic granules, such as granzyme B and perforin, which has been well documented (76–78), thus accounting for the differences in protein versus transcript detection. It has been noted that exhausted EM T cells decrease on labor at term (75). Although we did not include matched samples from labored deliveries, we hypothesize that labor would not affect the composition of the TCR repertoire in the decidua, because EM T cells are considered tissue localized (27, 79) and based on previous observations that show no changes in shared clonotypes between peripheral and decidual CD8⁺ T cells under different pregnancy conditions (21).

We focused our attention on clusters 1, 6, and 9 because they were the most evenly distributed clusters between the decidua and periphery (Fig. 1), and we wanted to illustrate that despite a shared phenotype, these cells are transcriptionally and potentially functionally distinct. Our results, indeed, support our hypothesis that decidual T cells maintain a distinct transcriptional profile compared with their peripheral counterparts (Fig. 3) and are in agreement with transcriptional analysis of CD8⁺ EM T cells, from both early and term decidua (26) and T_regs (80, 81). Furthermore, our clonal analysis of T_regs supports the observation that decidual T_regs are fetal specific (7) and are likely acquiring tissue-resident properties once they enter the maternal–fetal interface (80). We acknowledge, however, that gene expression differences in some clusters (Supplemental Fig. 3) could potentially be influenced by imbalanced tissue representation. Nonetheless, comparison of more-or-less balanced clusters suggests that the decidual environment programs local T cells.

Overall, our multiomics approach confirms a specific decidual T cell signature in human term decidua. Deeper analysis showed how phenotypic similarities between decidual and peripheral T cells are only superficial, because matched T cells were transcriptionally different. This extends to clonal distribution, with the decidua maintaining a unique set of clones. This raises the possibility that decidual T cells are tissue resident or enter the decidua as precursors that differentiate locally. It should be noted, however, that this study did not comprehensively evaluate the totality of the repertoire in circulating or decidual sites, so a low proportion of shared clones cannot be directly interpreted to mean that there is no trafficking between the two sites. That said, it has already been suggested that MAIT cells are transiently tissue resident in the endometrium (82), and it is possible that other T cell subsets in the decidua could be similar. Lastly, we show that phenotypic similarities between decidual and peripheral T cells is not an accurate assessment of T cells during pregnancy. This is highlighted not just by distinct clonal diversity but by differential gene expression between decidual and peripheral T cells, illustrating that, regarding decidual T cells, you cannot judge a book by its cover.

All of the coauthors declare that they do not have any relationships that could be construed as resulting in an actual, potential, or perceived conflict of interest with regard to the manuscript being submitted for review.

We thank the UW Flow Cytometry Core for expert assistance. We used the UW-Madison Biotechnology Center’s Gene Expression Center Core Facility (Research Resource Identifier: SCR_017757) for V(D)J single-cell RNA library preparation and the DNA Sequencing Facility (Research Resource Identifier: SCR_017759) for sequencing. Fig. 1A and visual abstract created with BioRender.com.