Single-cell RNA sequencing dissects the immunosuppressive signatures in Helicobacter pylori-infected human gastric ecosystem

Hu, Wei; Chen, Ze Min; Wang, Ying; Yang, Chao; Wu, Zi Ying; You, Li Juan; Zhai, Zhi Yong; Huang, Zhao Yu; Zhou, Ping; Huang, Si Lin; Li, Xia Xi; Yang, Gen Hua; Bao, Chong Ju; Cui, Xiao Bing; Xia, Gui Li; Ou Yang, Mei Ping; Zhang, Lin; Wu, William Ka Kei; Li, Long Fei; Tan, Li Kai; Zhang, Yu Xuan; Gong, Wei

doi:10.1038/s41467-025-59339-4

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Published: 25 April 2025

Single-cell RNA sequencing dissects the immunosuppressive signatures in Helicobacter pylori-infected human gastric ecosystem

Wei Hu ORCID: orcid.org/0000-0003-0209-8032^1,2^na1,
Ze Min Chen ORCID: orcid.org/0009-0005-9724-6668³^na1,
Ying Wang ORCID: orcid.org/0009-0007-8578-4199^1,2,
Chao Yang^1,2,
Zi Ying Wu^1,2,
Li Juan You¹,
Zhi Yong Zhai^1,2,
Zhao Yu Huang^1,2,
Ping Zhou^1,2,
Si Lin Huang⁴,
Xia Xi Li¹,
Gen Hua Yang¹,
Chong Ju Bao¹,
Xiao Bing Cui¹,
Gui Li Xia¹,
Mei Ping Ou Yang¹,
Lin Zhang ORCID: orcid.org/0000-0003-1634-3780⁵,
William Ka Kei Wu ORCID: orcid.org/0000-0002-5662-5240³,
Long Fei Li⁶,
Li Kai Tan³,
Yu Xuan Zhang⁷ &
…
Wei Gong ORCID: orcid.org/0000-0002-8568-0599^1,2

Nature Communications volume 16, Article number: 3903 (2025) Cite this article

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Abstract

Helicobacter pylori (H. pylori) manipulates the host immune system to establish a persistent colonization, posing a serious threat to human health, but the mechanisms remain poorly understood. Here we integrate single-cell RNA sequencing and TCR profiling for analyzing 187,192 cells from 11 H. pylori-negative and 12 H. pylori-positive individuals to describe the human gastric ecosystem reprogrammed by H. pylori infection, as manifested by impaired antigen presentation and phagocytosis function. We further delineate a monocyte-to-C1QC⁺ macrophage differentiation trajectory driven by H. pylori infection, while T cell responses exhibit broad functional impairment and hyporesponsiveness with restricted clonal expansion capacity. We also identify an HLA-DRs- and CTLA4-expressing T cell population residing in H. pylori-inhabited stomach that potentially contribute to immune evasion. Together, our findings provide single-cell resolution information into the immunosuppressive microenvironment shaped by H. pylori infection, offering critical insights for developing novel therapeutic approaches to eliminate this globally prevalent pathogen.

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Introduction

Helicobacter pylori (H. pylori), a gram-negative and transmissible pathogen that colonizes the stomachs of nearly half of the global population, poses a severe threat to human health¹. Infection is typically acquired in childhood and persists lifelong unless treated. Individuals infected with this pathogen always develop a sustained immune infiltrate (chronic gastritis), which may progress through atrophic gastritis and intestinal metaplasia (IM) to gastric adenocarcinoma, as outlined by the Correa cascade². Designated by the World Health Organization (WHO) as a “high-priority” pathogen³, H. pylori eradication is universally recommended by the 2015 Kyoto consensus for all infected individuals unless there are compelling counterindications⁴.

H. pylori infection triggers a robust host immune response characterized by leukocyte infiltration in the gastric mucosa, including lymphocytes (T/B cells), monocytes, macrophages, neutrophils, mast cells, and dendritic cells. Infected individuals exhibit H. pylori-specific CD4⁺ T cells in both gastric tissue and peripheral blood, accompanied by a T helper cell (Th)1-polarized response and Th17-mediated IL-17A upregulation, which induces pro-inflammatory cytokines (e.g., TNF-α, IL-1β, IL-6) and chemokines (CXCL1, CCL2, CCL7) release, recruiting neutrophils and macrophages^5,6. Recent studies by Maximilian et al. further identified CD8⁺ T cell-mediated immunity contributing to early bacterial control⁷. Despite these responses, H. pylori successfully establishes a persistent infection leading to chronic inflammation.

Multiple lines of evidence suggest that H. pylori has evolved strategies to orchestrate immune suppression by remodeling its bioactive molecules and manipulating host immunity to reduce bacterial clearance⁸. Compared with other gram-negative pathogens, its lipopolysaccharides and flagellins exhibit structural discrepancies that evade recognition by TLR4 and TLR5^9,10. Additionally, H. pylori employs multiple strategies to subvert adaptive immunity: the virulence factor outer inflammatory protein A (OipA) induces tolerogenic programming in dendritic cells (DCs) by inhibiting their maturation¹¹, while vacuolating cytotoxin (VacA) suppresses DC co-stimulatory molecules (CD40, CD80, CD86), thereby dampening T cell activation¹². We recently demonstrated that H. pylori subverts autophagosomes to create a pro-survival niche by disrupting lysosomal acidification and maturation^13,14. However, these evidence was mostly obtained from in vitro and murine studies. Systematic characterization of clinical samples is needed to elucidate H. pylori’s ability to manipulate host innate and adaptive immune responses to avoid eradication. Single-cell RNA sequencing (scRNA-seq) has enabled host–pathogen interactions to be examined at an unprecedented resolution¹⁵.

In this study, we investigate the gastric ecosystem reprogramming in H. pylori-colonized human stomach by integrating single-cell RNA and T cell receptor (TCR) sequencing on 187,192 cells obtained from 11 H. pylori-negative and 12 H. pylori-positive donors with chronic gastritis. We further perform in vitro and in vivo experiments to validate key findings in the discovery cohort. Using this approach, we reveal a H. pylori-driven immunosuppressive microenvironment, and delineate unique alterations in immune responses that facilitate H. pylori persistence, providing actionable insights for developing therapeutic strategies to eradicate this pathogen.

Results

Cohort characteristics and single cell profiling of H. pylori-reprogrammed human gastric ecosystem

Using strict inclusion and exclusion criteria (See “Methods”), we recruited 12 patients with newly diagnosed H. pylori-associated chronic gastritis and 11 individuals with H. pylori-negative gastritis to systematically survey gastric ecosystem reprogramming (Supplementary Fig. 1a). All subjects were younger than 35. Serologic testing revealed that all H. pylori strains were genotypically CagA-, VacA-, UreA-, and UreB-positive. We collected 22 gastric specimens and 17 paired peripheral blood mononuclear cell (PBMC) samples from these subjects. H. pylori-positive gastric biopsy and paired PBMC samples are annotated as PT and PP; corresponding samples from H. pylori-negative participants are annotated as NT and NP, respectively (Fig. 1a). Quantitative PCR (qPCR) analysis demonstrated significantly elevated OipA expression levels in H. pylori-positive gastric specimens compared to their H. pylori-negative counterparts (Supplementary Fig. 1b, Supplementary Table 1). Detailed clinical and pathological information are provided in Supplementary Table 2.

**Fig. 1: Cohort characteristics and single cell profiling of human gastric ecosystem reprogrammed by *H. pylori* infection.**

For scRNA-seq, we used microbead separation to enrich samples for immune (CD45⁺) cells, and subjected live, dissociated cells to single-cell transcriptome sequencing. After strict data pre-processing and doublet removal, the dataset contained 187,192 high-quality cell measurements. On average, 1195 genes and 3090 unique molecular identifiers (UMI) per cell were detected in each sample, with no obvious batch effects across subjects (Supplementary Fig. 1c). P1T sample data were excluded due to a library exception (average: >7000 genes per cell).

Using differential expression of canonical gene markers of the main expected cell types, we manually annotated unsupervised graph-based clusters, revealing 98,339 T and natural killer (NK) cells (52.5%, CD3D, NKG7); 32,905 B and plasma cells (17.6%, CD19, CD79A); and 27,310 myeloid lineage cells (e.g., monocytes, macrophages, 14.6%, LYZ, CD14). Due to the limited efficiency of microbeads separation, we also captured 28,638 (15.3%) non-immune (CD45^-) cells, designated as gastric tissue and proliferating cells (high ENG, EPCAM, MKI67 expression) (Figs. 1b–e, and Supplementary Fig. 1d-f, Supplementary Data 1). The lack of neutrophil and eosinophil acquisition was attributable to well-known technical limitations¹⁶. The identified cell types were shared among individuals and between H. pylori-negative and -positive samples (Supplementary Figs. 1d–e). We next assessed the enrichment of each cell type in both tissue and PBMC samples by calculating their relative abundances within individual sample and performing the ratio of observed to expected cell numbers (Ro/e) analysis. Comparative analysis revealed striking compositional difference between cell clusters from H. pylori-positive versus negative gastric biopsies, exemplified by increased proportions of B and plasma cells coupled with decreased frequencies of gastric tissue and proliferating cells in H. pylori-positive gastric samples (Fig. 1f, and Supplementary Fig. 1g). These findings suggest a reprogramming of the gastric ecosystem driven H. pylori colonization.

Repressed antigen processing and presentation capacities of H. pylori-infected APCs

Unsupervised clustering of myeloid cells yielded 16 subtypes, including eight monocyte clusters, four DC clusters, two macrophage clusters, and one cluster each of mast cells and megakaryocytes (Figs. 2a–d, and Supplementary Figs. 2a, b). Mast cells (Cluster 5) were mostly recruited from gastric biopsies (Supplementary Fig. 2c) and distinguished by markedly elevated expression of canonical mast cell markers such as TPSAB1, CPA3, KIT¹⁷. A higher proportion of these cells was detected in PT than in NT samples (Fig. 2e). Megakaryocytes (Cluster 11) were primarily enriched in PBMCs (Fig. 2e, Supplementary Fig. 2c), and characterized by expression of PF4, PPBP and GP9 (Fig. 2d)¹⁸.

**Fig. 2: Repressed antigen processing and presentation capacities of *H. pylori*-infected antigen-presenting cells (APCs).**

We captured four classical monocyte subsets (Clusters 0, 2, 7, 9), characterized by CD14, FCN1, and VCAN expression and lack of CD16 (FCGR3A) and CX3CR1 expression (Figs. 2a–d, and Supplementary Figs. 2a, b)¹⁹. We then investigated the proportion of each of the four subsets in patients and found that these classical monocytes were mostly derived from PBMC samples (Fig. 2e, and Supplementary Fig. 2c). In-depth inspection of signature genes in CD14⁺⁺CD16^- monocyte populations revealed elevated expression of multiple genes related to macrophage recruitment, activation and inflammasome maturation, including CCL3, CXCL8, IL1B and NLRP3 in Cluster 7 compared to the other three subsets (Fig. 2f)^20,21. Correspondingly, pathways associated with chemokine production and macrophage activation were selectively enriched in this cluster (Fig. 2g, and Supplementary Fig. 2d). By examining regulatory networks in NP- or PP-derived Cluster 7 (Fig. 2h), we observed marked activation of FOSB and JUNB, which encode transcription factors (TFs) essential for monocyte–macrophage differentiation and macrophage activation, after H. pylori infection^22,23. FLI1, encoding critical regulators of monocyte differentiation and development, was also strongly expressed in Cluster 7 post-infection^24,25. Taken together, these results suggest a potential involvement of Cluster 7 in macrophage differentiation and maturation in response to H. pylori challenge.

Major histocompatibility complex (MHC) class II molecules are essential for processing and presenting exogenous pathogen antigens for CD4⁺ T cell activation²⁶. H. pylori is predominantly considered a non-invasive pathogen that colonizes the extracellular mucosa of the human stomach. To investigate its impact on monocyte function, we calculated the MHC-II scores in these CD14⁺⁺CD16^– monocytes (Supplementary Table 3). In H. pylori-infected PP samples, Clusters 0, 2 and 9 showed significant reductions in MHC-II scores (Supplementary Fig. 2e). Cluster 7 exhibited consistently attenuated MHC-II levels in both PP and PT samples (Supplementary Fig. 2f). Furthermore, by aggregating data from PBMC-derived CD14⁺⁺CD16^– monocytes, we identified a marked decline in MHC-II expression following H. pylori infection (Fig. 2i). These results indicate that H. pylori infection suppresses antigen processing and presentation capacities in these monocytes, which may promote an immunosuppressive microenvironment post-infection.

We also captured a subset of non-classical monocytes (Cluster 13, Mono-CD16⁺⁺CD14^-), primarily enriched in PBMCs, characterized by high expression of FCGR3A (CD16), CX3CR1, RHOC, MS4A7, and SERPINA1, but lacking CD14 expression (Fig. 2a–e, and Supplementary Fig. 2a–c). Additionally, we identified a subset of intermediate monocytes (Cluster 1, Mono-CD14⁺CD16⁺) enriched in PBMCs, showing concurrent modest expression of CD14 and CD16 alongside conventional monocyte markers such as VCAN, S100A8, S100A9 and FCN1 (Figs. 2a–e, and Supplementary Fig. 2a–c). Similarly, a monocyte population (Cluster 15, Mono3), enriched in the stomach, exhibited elevated expression of MXD1, CXCR2, CSF3R and NEAT1 (Fig. 2a–e, Supplementary Fig. 2a–c). Another population (Cluster 3, Mono4), enriched in PBMCs, displayed prominent expression of cytotoxic genes (e.g., GNLY, PRF1, CTSW) (Fig. 2a–e, Supplementary Fig. 2a–c), align with previously reported Mono3 and Mono4 cells¹⁹. Similar to CD14⁺⁺CD16^– monocytes, we observed decreased MHC-II levels in PBMC-enriched Mono-CD14⁺CD16⁺ and Mono4 subsets, as well as in gastric-infiltrating Mono3 following H. pylori challenge (Fig. 2j–k). These findings suggest suppressed antigen presentation activities across these monocyte populations.

To validate suppressed antigen presentation in peripheral monocytes following H. pylori colonization, we infected C57BL/6 J mice with the pathogenic, CagA-proficient H. pylori strain PMSS1 for two months (Supplementary Fig. 2g–h) and analyzed single-cell PBMC suspensions by flow cytometry (Supplementary Fig. 3a). While the proportion of CD11b⁺CD172a⁺ monocytes remained unchanged post-infection (Supplementary Fig. 2i), we detected a substantial reduction in the expression of the MHC-II molecules I-A/I-E within this population (Fig. 2l).

We identified two conventional DCs (cDC) subsets: the cDC-CD1C⁺ (Cluster 8) characterized by high expression of CD1C, CD1D, FCER1A and CLEC10A, and cDC-CLEC9A⁺ (Cluster 14) predominantly derived from gastric tissues, defined by elevated expression of CLEC9A, C1orf54 and IDO1 (Fig. 2a–e, and Supplementary Fig. 2a–c). Additionally, we identified a DC population (Cluster 4) strongly expressing FCGR3A, FTL, SERPINA1, LST1 and AIF1, but lacking CD1C and THBD (CD141), like the previously reported DC4¹⁹. Thus, we designated this cluster as DC-CD1C^-CD141^- (Fig. 2a–e, and Supplementary Fig. 2a–c). We also captured a plasmacytoid DCs (pDC, Cluster 12) population expressing canonical markers such as TCF4, IL3RA, LILRA4, IRF7, PACSIN1, and CLEC4C (Fig. 2a–e, and Supplementary Fig. 2a–c). Notably, similar to the phenomenon observed in monocytes, we detected significantly reduced MHC-II scores in CD1C⁺ DCs from PP and PT samples, as well as in PBMC-enriched CLEC9A⁺ and CD1C^-CD141^- DCs following H. pylori infection (Fig. 2m–n). These findings indicate impaired antigen presentation functionality in these cDC populations post-H. pylori infection.

To validate whether H. pylori infection impairs DC antigen presentation, we established an in vitro model by infecting the immortalized murine DC cell line DC2.4 with two H. pylori strains, TN2GF4 and NCTC 11637 (Supplementary Fig. 3b)²⁷. As anticipated, infected DCs exhibited reduced expression of I-A/I-E compared to uninfected controls (Fig. 2o). We further assessed I-A/I-E expression in peripheral CD11b⁺CD11c⁺ DCs from H. pylori-infected mice (Supplementary Fig. 3c). Consistent with observations in murine monocytes, the proportion of CD11b⁺CD11c⁺ DCs remained unchanged post-infection (Supplementary Fig. 2j). However, I-A/I-E levels were significantly down-regulated in these peripheral DCs following H. pylori challenge (Fig. 2p). Due to technical limitations in isolating leukocytes from murine gastric tissue, we instead analyzed gastric immune cells from 12 H. pylori-positive and 13 H. pylori-negative gastritis patients (Supplementary Table 4). CD45⁺ cells were enriched for flow cytometry (Supplementary Fig. 3d). Compared to H. pylori-negative controls, infected patients showed increased proportions of gastric CD11b⁺CD11c⁺ DCs (Supplementary Fig. 2k). Notably, HLA-DR expression was markedly reduced in gastric DCs from H. pylori-positive subjects (Fig. 2q), confirming impaired antigen-presenting capacity in DCs exposed to H. pylori.

Macrophages develop from monocyte-like precursors exhibiting an immunosuppressive phenotype following H. pylori infection

We identified two macrophage populations in our cohort (Fig. 2a–e, and Supplementary Fig. 2a–c): Cluster 6, predominantly derived from gastric tissues, was characterized by high expression of CD68, CD163, MRC1 (CD206) and CSF1R. A lower proportion of these cells was observed in PT than in NT samples. Cluster 10, derived from both PBMCs and gastric specimens, exhibited modest expression of CD68, CD163, and multiple MHC-II genes such as HLA-DRA, HLA-DRA1, HLA-DRB1 and HLA-DRB5. Comparative transcriptomic analysis of these two populations revealed elevated expression of complement C1Q-encoding genes (C1QC, C1QA, C1QB), APOE, CD163 and SLC40A1 in Cluster 6 (Fig. 3a), which we designated Macro-C1QC. Up-regulated expression of MRC1, a type I membrane glycoprotein receptor critical for phagocytosis and endocytosis^28,29, suggested enhanced phagocytotic capacity. NR4A3, a tissue-resident marker³⁰, was also highly expressed in Macro-C1QC. In contrast, Cluster 10 expressed a set of interferon-related genes (e.g., IFIT1, IFIT2, IFIT5, GBP1, GBP5) and monocyte-associated markers (PTX3, IL27, CCL7 and FN1), indicating a transitional activation state. We therefore annotated this cluster as Macro-IFIT1. Gene Set Variation Analysis (GSVA) further demonstrated remarkable enrichment of complement activation, antigen processing and presentation, and cell migration pathways in Macro-C1QC, whereas interferon signaling and leukocyte transendothelial migration pathways were enriched in Macro-IFIT1 (Fig. 3b).

**Fig. 3: Macrophages developed from monocyte-like precursors exhibiting an immunosuppressive phenotype following *H. pylori* colonization.**

To investigate the functional phenotypes of these macrophages, we calculated M1 and M2 polarization scores (Supplementary Fig. 4a). Both macrophage populations exhibited co-expression of M1- and M2-associated gene signatures, consistent with previous studies^17,31. As described earlier, we identified a monocyte cluster (Cluster 7) that appeared to migrate and differentiate into macrophage. To assess the differentiation states of these subsets, we selected cells from Cluster 7 and the two macrophage populations in H. pylori-positive samples and reconstructed the developmental trajectory using Monocle 3³². Pseudotime analysis revealed a clear differentiation trajectory originating from Cluster 7 monocytes, progressing through intermediate Macro-IFIT1, and culminating in Macro-C1QC (Fig. 3c, Supplementary Fig. 4b). This confirmed that Cluster 7 monocytes serve as cellular precursors to C1QC⁺ macrophages following H. pylori infection.

We next evaluated the antigen presentation and phagocytic capacities of Macro-C1QC and Macro-IFIT1 using MHC-II and phagocytosis-related gene signatures, and observed reduced MHC-II and phagocytic scores in Macro-IFIT1 compared to Macro-C1QC (Supplementary Fig. 4c, d). Notably, compared to control counterparts, both macrophage populations, whether derived from PBMCs or gastric tissues, exhibited lower MHC-II scores following H. pylori infection (Fig. 3d–e). Similarly, phagocytic scores were diminished in stomach-resident macrophages after H. pylori exposure (Fig. 3f). These findings indicate that H. pylori infection restricts both antigen presentation and phagocytic functionality in these macrophage subsets.

To determine whether H. pylori infection impairs macrophage antigen presentation and phagocytosis, we infected mouse bone marrow-derived macrophages (BMDMs) with H. pylori strains TN2GF4 and NCTC 11637. Flow cytometry analysis revealed reduced expression of the MHC-II marker I-A/I-E in infected BMDMs (Fig. 3g, and Supplementary Fig. 3e). Furthermore, we observed an increased proportion of CD11b⁺CD11c^- macrophages in gastric immune cells from H. pylori-positive patients compared to negative controls (Supplementary Figs. 3d, 4e). Consistent with the BMDM findings, gastric CD11b⁺CD11c^- macrophages from infected patients exhibited decreased expression of the human MHC-II marker HLA-DR (Fig. 3h). Finally, using an in vitro phagocytosis model, we confirmed that H. pylori infection impaired macrophage phagocytic capacity (Fig. 3i).

These results delineate a H. pylori-propelled cell transition trajectory from Cluster 7 monocytes, progressing through intermediate Macro-IFIT1, to Macro-C1QC. Additionally, they reveal diminished antigen processing and presentation functions in both peripheral and gastric APC lineages following H. pylori infection. This functional impairment may compromise CD4⁺ T cell recruitment and the initiation of adaptive immunity, thereby fostering an immunosuppressive gastric microenvironment that favors persistent H. pylori colonization.

CD8⁺HLA-DR⁺CTLA4⁺ T cells exhibited immunosuppressive phenotypes upon H. pylori colonization

By performing unsupervised clustering analyzes of 98,339 T/NK cells from our dataset, we identified four major cell populations: CD4⁺ T (33,588 cells, 34.2%), CD8⁺ T (41,610 cells, 42.3%), NK (16,847 cells, 17.1%), and γδT (6294 cells, 6.4%) cells (Supplementary Figs. 5a–c). NK cells were predominantly derived from peripheral blood and exhibited strong expression of canonical markers, including NCAM1 (CD56), FCGR3A (CD16) and cytotoxic genes such as PRF1, GNLY, GZMB and NKG7. γδT cells, also enriched primarily in PBMCs, showed characteristic high expression of TRDV2, TRDV9, TRGC1 and CST7. Comparative analysis of cell type enrichment in PBMCs and gastric specimens revealed a significantly higher proportion of CD4⁺ T cells but a lower proportion of CD8⁺ T cells in PT samples than in NT samples (Supplementary Fig. 5c).

Further sub-clustering analysis revealed 19 subsets of CD8⁺ T cells, categorized into six naïve clusters (Cluster 0, 5, 6, 7, 13 and 18), six cytotoxic clusters (Cluster 3, 8, 9, 11, 14, 17), two effector memory clusters (Cluster 2 and 15), one mucosal-associated invariant T (MAIT) cell cluster (Cluster 10), and four dysfunctional clusters (Cluster 1, 4, 12, 16) (Fig. 4a–c, Supplementary Fig. 5d–f). Naïve CD8⁺ T cells were identified by elevated expression of naïve-associated gene signatures including SELL, IL7R, CCR7, GPR183 and TCF7³³. Cytotoxic CD8⁺ T cells displayed robust expression of cytotoxicity-related genes such as GZMA, NKG7, GZMH, CX3CR1, GNLY and PRF1³⁴. The MAIT cell cluster (CD8-10-CCR6) exhibited defining features of CCR6, SLC4A10, and RORC expression, consistent the previously described CD8-SLC4A10 cells³⁵. We further validated them by predominantly bearing uniquely rearranged TCR (TRAV1-2/TRAJ33, TRAV1-2/TRAJ20, or TRAV1-2/TRAJ12) (Supplementary Fig. 5g). The two effector memory clusters displayed high expression of CXCR3, CCR5, TNF, CXCR6, but lacking the naïve markers SELL and CCR7 expression³⁴. Dysfunctional CD8⁺ T cell populations were distinguished by elevated expression of GZMK, PDCD1, LAYN, TIGIT, ITGAE, CTLA4, EOMES and ID2^33,36.

**Fig. 4: CD8⁺HLA-DR⁺CTLA4⁺ T cells exhibited immunosuppressive phenotypes upon *H. pylori* colonization.**

By quantifying the enrichment of cell populations in PBMC and gastric tissues, we identified four subsets (Cluster 2, 4, 12, 15) that were predominantly enriched in gastric specimens (Supplementary Fig. 5f). Compared to NT samples, PT samples exhibited lower proportions of Cluster 2 and 15, but a higher proportion of Cluster 12 (Fig. 4c). Notably, Cluster 12 showed marked upregulation of inhibitory markers (CTLA4, LAG3, PDCD1, TIGIT, HAVCR2) alongside elevated expression of naïve genes (e.g., CCR7, TCF7, LEF1 and SELL), co-stimulatory markers (TNFRSF14, CD28, TNFRSF9), and multiple MHC-II genes (e.g., HLA-DRA, HLA-DQA1) relative to the other clusters (Fig. 4d). Thus, we annotated this population as CD8-12-HLA-DR, a subset resembling a plastic exhaustion state in CD8⁺ T cells recently reported in gastric adenocarcinoma³⁷.

To investigate infection-associated inhibitory reprogramming in this subset, we quantified the proportion of CD8A⁺HLA-DRA⁺CTLA4⁺ cells and observed a significant increase in PT samples (Fig. 4e). Differentially expressed genes (DEGs) analysis further revealed distinct signatures between PT and NT samples (Fig. 4f): NT samples exhibited upregulation of the tissue resident gene ZNF683 and cytotoxic genes (KLRC1, KLRC2), while PT samples showed elevated expression of inhibitory genes (CTLA4, HAVCR2, TOX2), the predysfunctional marker GZMK, and MHC-II genes (HLA-DRB1, HLA-DQB1, HLA-DRA, HLA-DOA, HLA-DQB2). These findings suggest that H. pylori infection drives reduced cytotoxic activity and enhances inhibitory and MHC-II-related signatures in this CD8⁺ T cell subset.

Next, we determined the differentiation trajectories of CD8⁺ T cells in H. pylori-positive gastric samples. Both RNA velocity and Monocle 3 analyzes revealed a developmental pathway originating from CD8-12-HLA-DR cells, progressing through CD8-2-CXCR3 cells, and terminating at CD8-4-ID2 cells. These analyzes also identified an alternative differentiation route from CD8-12-HLA-DR cells directly to CD8-4-ID2 cells, bypassing the intermediate CXCR3-expressing state (Fig. 4g, h, Supplementary Fig. 5h).

TCR is the gold-standard marker for identifying T cell ancestry³⁸. By leveraging TCR-seq data to trace clonal trajectories of CD8⁺ T cells, we identified 310,268 and 198,192 clonal CD8⁺ T cells (frequency ≥ 2) in H. pylori-negative and -positive individuals, respectively (Supplementary Figs. 5i, j, and Supplementary Data 2). We then examined shared and biased clonotypes across gastric CD8⁺ T subsets in each individual and observed remarkable TCR clonality sharing among the CD8-12-HLA-DR, CD8-2-CXCR3 and CD8-4-ID2 subpopulations (Fig. 4i), aligning with the differentiation trajectory and transitional relationships inferred earlier. Strikingly, while clonotype diversity (measured by the Gini–Simpson index) increased post-infection, mucosal-infiltrating CD8⁺ T cells exhibited markedly restricted clonal expansion (Fig. 4j). This suggests that H. pylori may evade immunity by impairing the clonal expansion capacity of CD8⁺ T cells.

Together, these results define differentiation trajectories of gastric CD8⁺ T cells and demonstrate H. pylori-driven suppression of their clonal expansion potential.

Interaction between HLA-DR⁺CTLA4⁺ T cells and macrophages via the CTLA4/CD86 axis upon H. pylori infection

Re-clustering of CD4⁺ T cells identified 16 distinct subpopulations (Fig. 5a–c, and Supplementary Fig. 6a–c), including seven naïve clusters (Clusters 0, 6, 7, 9, 10, 13, 15), two follicular helper clusters (Tfh; Clusters 1, 12), two regulatory clusters (Treg; Clusters 5, 11), one type 1 helper cluster (Th1; Cluster 4), one Th2 cluster (Cluster 3), one Th17 cluster (Cluster 2), one tissue-resident memory cluster (TRM; Cluster 8), and one interferon-response cluster (Cluster 14). Cluster 14 exhibited elevated expression of IFIT1, IFIT3, STAT1, ISG15 and MX1, consistent with formerly described CD4⁺ T_ISG cells³⁶. Naïve CD4⁺ T clusters displayed characteristic markers LEF1, TXK, TCF7, IL7R, SELL, CCR7 and MAL³³. Tfh clusters were defined by strong expression of CXCR5, IL21, ICOS, PDCD1 and CXCL13. The Th1 cluster showed enrichment of TBX21 (T-bet), CXCR3, IFNG, CCR1 and CCR5. While the Th2 cluster was marked by GATA3, IL5, PTGDR2, CCR8 and IL17RB. Th17 identity was confirmed through CCR6, IL23R, RORC and RORA expression. The TRM cluster exhibited elevated CD69, ITGAE (CD103), IL2 and TGFB1. Treg clusters demonstrated canonical FOXP3, TGFB, IL2RA, ENTPD1 and CTLA4 expression.

Intriguingly, similar to CD8-12-HLA-DR cells, Cluster 5 also exhibited robust co-expression of MHC-II genes (HLA-DRA, HLA-DQA1, HLA-DQB1), inhibitory markers (CTLA4, LAG3), and co-stimulatory genes (TNFRSF14, CD28) (Fig. 5d), leading to its designation as CD4-5-HLA-DR. This population was predominantly derived from gastric specimens and showed significantly higher enrichment in PT samples compared to NT samples (Fig. 5c, and Supplementary Fig. 6c). Notably, Cluster 5 displayed markedly higher TNFRSF9 expression relative to Cluster 11 (Fig. 5d), indicative of an activated Treg phenotype³³. Subsequent quantification of CD4⁺HLA-DRA⁺CTLA4⁺ cells within this population revealed a pronounced increase in PT samples (Fig. 5e). Comparative DEG analysis further highlighted distinct functional signatures: NT samples showed significant enrichment of inflammatory mediators (S100A6, S100A10, S100A11, S110A9, IL1A), cytotoxic effector molecules (XCL1, XCL2, GZMB, PRF1, GNLY, GZMA), and tissue-residency markers (RUNX3, NR4A1). In contrast, PT samples were dominated by immunosuppressive markers (LAG3, CTLA4, TOX) and MHC-II components (HLA-DRA, HLA-DOB) (Fig. 5f). These findings suggest H. pylori infection attenuates cytotoxic potential and tissue-resident functionality while driving an inhibitory phenotype.

As discussed earlier, our data demonstrate constrained antigen presentation capacities in both peripheral and gastric APC populations following H. pylori infection, which might impair subsequent CD4⁺ T cell recruitment. To investigate this relationship, we calculated the correlations between the MHC-II scores and CD4⁺ T cell proportions across experimental groups. Consistent with our hypothesis, MHC-II scores showed positive correlations with CD4⁺ T cell percentages in both PP and PT samples (Supplementary Fig. 6d).

Analysis of TCR-seq data revealed increased clonotype diversity but reduced clonal expansion capacity in CD4⁺ T cells from PT samples compared to NT samples (Fig. 5g, and Supplementary Fig. 6e, Supplementary Data 3), mirroring the trend observed in CD8⁺ T cells. These findings support the conclusion that H. pylori infection impairs T cell clonal expansion.

We identified two T cell subsets predominantly enriched in PT samples: a CD4⁺ population (CD4-5-HLA-DR) and a CD8⁺ population (CD8-12-HLA-DR), both exhibiting strong expression of MHC-II-associated genes (HLA-DRA, HLA-DQA1, HLA-DQB1) and elevated CTLA4 expression. To validate these populations in the H. pylori-colonized human stomach, we enriched gastric immune cells from H. pylori-negative and H. pylori-positive patients prior to flow cytometry analysis. Consistent with our scRNA-seq data, the proportions of CD4⁺HLA-DRA⁺CTLA4⁺ and CD8A⁺HLA-DRA⁺CTLA4⁺ T cells were significantly increased in H. pylori-positive individuals compared to their H. pylori-negative counterparts (Supplementary Fig. 3f, 6f, g, Supplementary Table 4).

To investigate whether these HLA-DR⁺CTLA4⁺-expressing T cells might suppress anti-bacterial immunity, we subjected all T cells and myeloid clusters to ligand–receptor (L-R) analysis and identified selective crosstalk between HLA-DR^hi T cells and macrophages through the CTLA4/CD86 axis in PT samples, but not in NT samples (Fig. 5h). Given CD86’s higher affinity for CTLA4 than for CD28³⁹, these HLA-DR^hiCTLA4⁺ T cells may attenuate the efficiency of antigen presentation by macrophages and subsequent T cell activation, similar to the interaction between malignant cells and DCs via CD274/CD80 and CTLA4/CD80 in early-relapse hepatocellular carcinoma⁴⁰. Further multiplexed immunofluorescent staining verified that the CD68⁺CD86⁺ macrophages physically resided near the CD4⁺HLA-DRA⁺CTLA4⁺ and CD8⁺HLA-DRA⁺CTLA4⁺ T cells (Fig. 5i), corroborating their functional interaction.

In summary, these data demonstrate that H. pylori colonization compromises clonal expansion in both CD4⁺ and CD8⁺ T cells. Furthermore, CD68⁺ macrophages engage HLA-DR^hi T cells through the CD86/CTLA4 axis, dampening macrophage antigen-presenting capacity and subsequent T cell activation—a mechanism that may facilitate bacterial persistence.

Discussion

As the most ubiquitous human pathogen, a hallmark of H. pylori is that it successfully and stealthily evades host immune defenses. However, our understanding of how H. pylori manipulates host innate and adaptive immunity to favor its own persistence remains limited. Using scRNA-seq technology, we systematically characterized the reprogrammed immunosuppressive ecosystem within the H. pylori-infected human stomach. We further performed additional in vitro and in vivo experiments to orthogonally validate the findings in the discovery cohort.

By dissecting immunosuppressive signatures, we revealed that both peripheral and mucosal-infiltrating APCs exhibit reduced antigen presentation capacities after H. pylori infection, consistent with current knowledge. For example, VacA was shown to negatively regulate DC maturation by restoring E2F1 expression, and to impair antigen presentation by inhibiting the export of MHC-II molecules to the DC surface^12,41. Moreover, we revealed a transcriptional trajectory from monocyte precursors to tissue-resident C1QC⁺ macrophages upon H. pylori infection, reminiscent of cell transitions described in colon cancer³¹. This may represent a pattern of development from monocytes to tissue-resident macrophages that is conserved across different settings. In macrophages, compromised phagocytosis was identified post-infection, representing another mechanism by which H. pylori promotes its survival in the human stomach. Our results further revealed a significant increase in mast cell infiltration within H. pylori-infected gastric tissues, aligning with their established role as early responders in gastric inflammation^42,43. Activated mast cells drive pathogenic cascades through TNF-α-mediated epithelial barrier disruption⁴⁴, yet simultaneously orchestrate antimicrobial responses via CXCL8-dependent neutrophil recruitment and cathelicidin secretion⁴⁵. This functional duality suggests mast cells operate at the crux of H. pylori clearance and immunopathology. However, whether H. pylori exploits mast cell-derived signaling pathways (e.g., histamine or cytokine cascades) to subvert host immunity and establish chronic persistence still remains to be investigated.

T cells are the major effector cells in immune responses to H. pylori^5,46. Maximilian et al. recently reported that CD8⁺ T cells defend against H. pylori infection⁷. In this study, we observed generally impaired T cell responses during H. pylori infection, as evidenced by the restricted clonal expansion capacities of CD4⁺ and CD8⁺ T cells. Notably, we profiled a population of T cells with reduced cytotoxicity that was predominantly enriched in H. pylori-infected gastric samples and strongly expressed both MHC-II-related genes and inhibitory-related genes (e.g., CTLA4). A similar cell subset was recently identified in gastric adenocarcinoma samples³⁷. By integrating pseudotemporal and TCR clonality information, we confirmed that this population could transdifferentiate into CD8-2-CXCR3 or CD8-4-ID2 T cells. T cell responsiveness is an important issue in vaccine design. To address the mechanisms that impair responsiveness, we performed L-R-based interaction analysis and detected crosstalk between macrophages and HLA-DRA⁺CTLA4⁺-expressing T cells and verified the physical proximity of these cell populations via multiplexed immunofluorescent staining. Thus, a reduction in CD86/CD28 interactions via competition between CTLA4 and CD86 would prevent functional T cell activation³⁹; it thus may represent a potential immune evasion mechanism of H. pylori.

In conclusion, our study provides evidence for an immunosuppressive gastric ecosystem in response to H. pylori infection, typified by reduced antigen presentation capacities of APCs and restricted clonal expansion capacities among mucosal-infiltrating T cells. These findings may be essential for understanding how H. pylori persists via host immune system manipulation. Our data may help researchers develop novel therapeutic strategies to eliminate this highly prevalent and detrimental pathogen.

Methods

Antibodies and pharmacological agents

Primary antibodies we used for multiplexed immunofluorescence staining were anti-HLA-DR/DP/DQ (ThermoFisher scientific, MA1-80678), anti-CD86 (Abcam ab220188), anti-CD4 (Abcam, ab133616), anti-CD68 (ZSGB-bio, ZM-0060), anti-CTLA4 (Abcam, ab237712), anti-CD8 (ThermoFisher scientific, MA1-80231). Primary antibodies and reagents used for flow cytometry were Fixable Viability Stain 450 (FVS450) (BD, 562247), Zombie NIR™ Fixable Viability Kit (Biolegend, 423105), CD45-AF532 (Thermo Fisher, 58-0451-82), CD11b-PE-Cy7 (BD, 552850), CD172a-APC (Biolegend, 144013), Ly-6G -BV421 (Biolegend, 127627), I-A I-E-PerCP-Cy5.5 (BD, 562363), F4/80-BV605 (BD, 743281), CD11c-APC (Biolegend, 117309), Alexa Fluor 532 anti-human CD3 (eBioscience, 58-0038-42), PerCP/Cyanine5.5 anti-human CD4 (Biolegend, 300529), PE anti-human CD8 (Biolegend, 344705), APC anti-human CD152 (CTLA-4) (Biolegend, 349907), Brilliant Violet 605 anti-human HLA-DR (Biolegend, 307640), PE anti-human CD11c (Biolegend, 371503), Alexa Fluor 700 anti-human CD11b (Biolegend, 301355), Brilliant Violet 421 anti-human CD1c (Biolegend, 331525), FITC anti-human CD19 (Biolegend, 302205), Human TruStain FcX (Biolegend, 422301), and APC Mouse IgG1, κ Isotype Ctrl (FC) (Biolegend, 400121).

Ethics approval and consent to participate

This study was approved by the Ethic Committee of Southern Medical University (NYSZYYEC20190017), and the Ethics Committee of SHSMU on Laboratory Animal Care (No. 2022-157). All patients provided written informed consent for collecting tissue and blood samples for research and publishing relevant clinical information. This study was performed in accordance with the Declaration of Helsinki.

Study design

The study was carried out from September 2020 to Novermber 2024 by enrolling patients at the Department of Gastroenterology, Shenzhen Hospital, Southern Medical University (Guangdong, China). All recruited subjects should be recorded for their clinical baseline characteristics, including gender, BMI, the delta over baseline (DOB) value from ¹³C-urea breath test (C13-UBT), pathological and endoscopic diagnosis reports. The endoscopic performance of stomach tissues was determined by two experienced gastrointestinal (GI) endoscopists. To ensure individuals that were initially diagnosed with chronical gastritis, all enrolled participants should follow the inclusion and exclusion criteria.

Patient inclusion criteria

1.
18 ≤ Age ≤ 35;
2.
H. pylori infection was diagnosed by the ¹³C-urea breath test (C13-UBT), upper endoscopy examination and bacterial culture. A delta over baseline (DOB) value > 4.4 combined with Kyoto classification score ≥ 2 and culture of H. pylori from gastric biopsies led to a definitive diagnosis of infection.
3.
H. pylori-negative donors were defined by DOB value < 1.0, Kyoto classification score = 0, and gastric biopsies culture not detecting H. pylori.
4.
Written informed consent obtained.

Exclusion criteria of H. pylori-positive patients

1.
Duodenogastric reflux disease.
2.
Autoimmune diseases.
3.
Diabetes, hypertension, hyperlipidemia, chronic hepatitis B.
4.
Pregnancy.
5.
Long-term administration of non-steroid anti-inflammatory drugs (NSAIDS) such as aspirin, salicylic acid, and hormone.
6.
Chronic consumption of coffee, strong tea, tobacco, spicy food, or alcohol.
7.
Taken proton pump inhibitors (PPI) or antibiotics within one year.
8.
Taken first-line antibiotics for H. pylori treatment, including clarithromycin, metronidazole, or amoxicillin within three year.
9.
Diagnosed as gastrointestinal diseases other than gastritis within one year.

Exclusion criteria of H. pylori-negative patients

1.
Diagnosed as H. pylori positive.
2.
Pregnancy.
3.
Taken proton pump inhibitors (PPI) or antibiotics within one year.
4.
Taken first-line antibiotics for H. pylori treatment, including clarithromycin, metronidazole, or amoxicillin within three year.
5.
Have symptoms such as epigastric pain, abdominal distension, nausea, vomiting, belching or loss of appetite within one year.

Single-cell dissociation and separation

Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll solution (Solarbio, P8610) according to the manufacturer’s instructions. Briefly, 2 ml of fresh peripheral blood collected prior to endoscopy examination in EDTA anti-coagulant tubes was diluted with equal volumes of phosphate-buffered saline (PBS) and subsequently layered onto separating medium. After centrifugation (650 g, 20 min), PBMCs remained at the lymphocyte interface were carefully transferred to a new tube and washes twice with PBS (650 g, 5 min), then resuspended in autoMACS Rinsing solution (Miltenyi Biotec, 130-091-222).

Fresh gastric biopsies were endoscopically acquired from H. pylori-positive (PT group) or -negative (NT group) mucosae or in either antrum or corpus, then washed with PBS and cut into approximately 1-mm³ pieces. Tissue digestion was performed in digestive enzyme mixture containing 2 ml pre-warmed RPMI-1640 (ThermoFisher scientific), 2 mg/ml dispase II (Solarbio, D6430), 1 mg/ml type IV collagenase (Solarbio, 17104019) and 2 mg/ml DNase I (Solarbio, D8070) for 30 min at 37 °C, then deactivated with Fetal bovine Serum (FBS, Gibco, 16140071). Digested tissues were gently triturated with a 20 ml syringe plunger on a 70 μm cell-strainer (BD, 352350) until uniform cell suspensions were obtained. The suspended cells were centrifuged at 400 g for 6 min at 4 °C, and washed with PBS. Cell pellets were re-suspended in autoMACS Rinsing solution.

Single living immune cells (CD45⁺) were obtained using human CD45 microbeads (Miltenyi Biotec, 130-045-801) according to the manufacturer’s instructions. Briefly, single-cell pellets from PBMCs and gastric biopsies were resuspended in autoMACS Rinsing solution containing 0.5% bovine serum albumin (BSA) and 2 mM EDTA. 20 μl of CD45 Microbeads per 10⁷ cells were added and incubated for 15 min at 4 °C. Cells were washed and centrifuged at 300 g for 10 min, then re-suspended in autoMACS Rinsing solution. CD45⁺ cells already magnetically labeled with microbeads were loaded onto a MACS Column, and enriched by a MACS Separator.

Single cell cDNA amplification, library construction and sequencing

Cellular suspensions were loaded on a 10× Genomics GemCode Single-cell instrument that generates single-cell Gel Bead-In-EMlusion (GEMs). Libraries were generated and sequenced from the cDNAs with Chromium Next GEM Single Cell 3’ Reagent Kits v3.1. Upon dissolution of the Gel Bead in a GEM, primers containing (i) an Illumina R1 sequence (read 1 sequencing primer), (ii) a 16 nt 10x Barcode, (iii) a 10 nt Unique Molecular Identifier (UMI), and (iv) a poly-dT primer sequence were released and mixed with cell lysate and Master Mix. Barcoded, full-length cDNAs were then reverse-transcribed from poly-adenylated mRNA. Silane magnetic beads were used to remove leftover biochemical reagents and primers from the post GEM reaction mixture. Full-length, barcoded cDNAs were then amplified by PCR to generate sufficient mass for library construction. R1 (read 1 primer sequence) were added to the molecules during GEM incubation. P5, P7, a sample index, and R2 (read 2 primer sequence) were added during library construction via End Repair, A- tailing, Adapter Ligation, and PCR. The final libraries contained the P5 and P7 primers used in Illumina bridge amplification. The Single Cell 3’ Protocol produced Illumina-ready sequencing libraries. A Single Cell 3’ Library comprised standard Illumina paired-end constructs which begin and end with P5 and P7. The Single Cell 3’ 16 bp 10x Barcode and 10 bp UMI were encoded in Read 1, while Read 2 was used to sequence the cDNA fragment. Sample index sequences were incorporated as the i7 index read. Read 1 and Read 2 were standard Illumina sequencing primer sites used in paired-end sequencing.

Preprocessing of single cell RNA-seq data

10× Genomics Cell Ranger software (version 3.1.0) was used to convert raw BCL files to FASTQ files, alignment and counts quantification. Briefly, reads with low-quality barcodes and UMIs were filtered out and then mapped to the reference genome. Reads uniquely mapped to the transcriptome and intersecting an exon at least 50% were considered for UMI counting. Before quantification, the UMI sequences would be corrected for sequencing errors, and valid barcodes were identified based on the EmptyDrops method⁴⁷. The cell by gene matrices were produced via UMI counting and cell barcodes calling.

Cell clustering

The cell by gene matrices for each sample were individually imported to Seurat version 3.1.1 for downstream analysis⁴⁸. Cells with unusually high number of UMIs (≥8000) or mitochondrial gene percent (≥ 10%) were filtered out. We also excluded cells with less than 500 or more than 4000 genes detected. After removing unwanted cells from the dataset, we employed a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the results. The formula is shown as follows:

$${{\rm{A}}}\; {{\rm{gene}}}\; {{\rm{expression}}}\; {{\rm{level}}}={{\rm{log}}} \left(1+\frac{{{UMI}}^{A}}{{{UMI}}^{{Total}}} \times 10000 \right)$$

To minimize the effects of batch effect and behavioral conditions on clustering, we used Seurat, which utilized canonical correlation analysis and mutual nearest neighbor analysis, to aggregate all samples⁴⁹. 2000 highly variable genes were selected in each sample based on a variance stabilizing transformation. Anchors between individual data were identified and correction vectors were calculated to generate an integrated expression matrix, which was used for subsequent clustering. Integrated expression matrix is then scaled and performed on principal component analysis for dimensional reduction. Then we implemented a resampling test inspired by the jackStraw procedure. We randomly permuted a subset of the data (1% by default) and rerun PCA, constructing a ‘null distribution’ of gene scores, and repeated this procedure. We identified ‘significant’ PCs as those who have a strong enrichment of low p-value genes for downstream clustering and dimensional reduction⁵⁰. Seurat implements a graph-based clustering approach. Distances between the cells were calculated based on previously identified PCs. Briefly, Seurat embed cells in a shared-nearest neighbor (SNN) graph, with edges drawn between cells via similar gene expression patterns. To partition this graph into highly interconnected quasi-cliques or communities, we first constructed the SNN graph based on the Euclidean distance in PCA space and refined the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard distance). We then applied modularity optimization techniques – SLM⁵¹, to iteratively group cells together, with the goal of optimizing the standard modularity function. For visualization of clusters, Uniform Manifold Approximation and Projection (UMAP) were generated using the same PCs⁵².

TCR analysis

Cell Ranger was used to assemble, quantify, and annotate paired V(D)J transcript sequences for each T cells. Firstly, non-productive, and incomplete length TCR chains were excluded. All single T cells with at least one productive TCRα or β chains were kept for subsequent analysis. Those cells with more than one productive TCRα or β chains would be further filtered. For example, if one single T cell owned two productive TCRα chains at the same time, we would compare their UMIs and discard the lower-expressed one. T cells shared identical paired TCRα and β chains were classified into the same clonotype. Besides, those cells with only one productive TCRα or β chain were merged into clonotypes in which owned complete paired chains and they must share at least one TCR chain with the paired one. The TCR sequencing data of all samples were then combined with the results of 10× single-cell RNA sequencing, we can therefore process the analyzes of cell phenotypes and their TCR clonotypes. T cells that shared the same TCR clones with more than one T cell in the same patient were defined as clonal T cells, and their increase was indicative of clonal expansion.

Doublet removal

Doublet removal was achieved by using the tool DoubletFinder (v2.0.3) by the generation of artificial doublets, using the PC distance to find each cell’s proportion of artificial k nearest neighbors (pANN) and ranking them according to the expected number of doublets⁵³.

Differentially expressed genes analysis

Expression value of each gene in given cluster were compared against the rest of cells using Wilcoxon rank sum test⁵⁴. Significant upregulated genes were identified using a number of criteria. First, genes had to be at least 1.28-fold overexpressed in the target cluster. Second, genes had to be expressed in more than 25% of the cells belonging to the target cluster. Third, p value is less than 0.05.

Gene set variation analysis (GSVA)

We performed Gene Set Variation Analysis (GSVA) using a collection of gene sets from MsigDB to identify pathways and cellular processes enriched in different clusters⁵⁵. GSVA was performed as implemented in the GSVA R package version 1.26 based on the cluster-averaged log-transformed expression matrix⁵⁶.

RNA velocity analyzes

The RNA velocity analysis was performed using the package velocyto with the default parameter, and the BAM files were used as inputs. For visualization, velocity vectors were plotted as locally average vector fields on the tSNE embeddings of our high-quality cells from the previous step⁵⁷.

Ro/e analysis

Tissue preference for each cluster was quantified using the Ro/e analysis. The expected cell numbers for each combination of cell clusters and tissues were obtained from the chi-square test. One cluster was identified as being enriched in a specific tissue if Ro/e > 1.

SCENIC analysis

To carry out transcription factor network inference, analysis was performed on the SCENIC R package⁵⁸. In brief, log-normalized expression matrix generated using Seurat was used as input, and the pipeline was implanted in three steps. First, we identified gene co-expression network via GENIE3. Second, we pruned each module based on a regulatory motif near a transcription start site via RcisTarget. Precisely, networks were retained if the TF-binding motif was enriched among its targets, while target genes without direct TF-binding motifs were removed. The retained networks were called regulons. Third, we scored the activity of each regulon for each single cell via the AUC scores using AUCell R package.

Bacterial and cell culture

The H. pylori strain PMSS1, TN2GF4 and NCTC 11637 strains were obtained from the Department of Microbiology, the Chinese University of Hong Kong. H. pylori was initially grown on horse blood agar plates (Columbia Blood Agar Base with DENT Selective Supplements; Oxoid, SR0147) in an anaerobic jar with a microaerophilic environment for 5 days at 37 °C. Murine dendritic cell line DC2.4 (Millipore, SCC142) was cultured in RPMI-1640 (GibcoTM, Thermo Fisher, 23400021) supplemented with 10% FBS and 10 ng/mL murine GM-CSF at 37 °C in 5% CO₂. THP-1 Cells (ATCC, TIB-202) were cultured in RPMI-1640 (GibcoTM, Thermo Fisher, 23400021) supplemented with 10% FBS at 37 °C in 5% CO₂.

Murine bone marrow-derived macrophages (BMDM) preparation and culture

C57BL/6 J mice were purchased from Shanghai Model Organisms (Shanghai, China). Mice were housed under 12-h light/dark cycles in a specific pathogen-free/SPF room with clean bedding and free access to food and water, and temperature and humidity were kept at 22–26 °C, 55% ± 5%. Cage and bedding changes were performed each week. Eight- to ten-week-old C57/BL6J mice (regardless of gender) were used to generate (BMDM) according to standard procedures⁵⁹. Mice were killed under anesthesia and soaked with 70% ethanol. Holding the foot with a forceps, remove the skin and muscles from the legs down to the hip bone. Isolate the tibia and femur by dislocating them from the hip, and then isolate the hip bone by holding it by the femoral cavity and cutting just beneath. Remove any tissue from the bones. Separate the femur from the tibia by dislocating and cutting through the knee joint, and the tibia from the foot by cutting just below the ankle joint. Cut the epiphyses of the bones and flush the marrow into a 50 mL centrifuge tube using a 5 mL syringe and a 23 G needle. Use 5 mL PBS per bone. Filter the cell suspension with a 70 μm cell strainer in a new 50 mL tube. Centrifuge at 250 × g and 4 °C for 5 min. Discard the supernatant and resuspend the cell pellet in 3 mL of BMDM medium. Count the cells under a microscope. Bone marrow cells were cultured in high glucose DMEM supplemented with 10% FBS, 20% L929-cell conditioned medium, and 100 U/mL penicillin-streptomycin for 7–10 days at 37 °C with 5% CO₂.

Murine H. pylori infection

H. pylori were grown in brain heart infusion (BHI; Oxoid, CM1135) broth supplemented with 15% FBS for 20–30 h at 37 °C under microaerobic conditions and saturated humidity, with shaking at 200 rpm. Six- to seven-week-old C57BL/6 J mice were orally gavaged with 2 × 10⁸ CFUs of H. pylori PMSS1 or sterile BHI broth (as a control) using gastric intubation on alternate days for a total of 3 doses. BHI or H. pylori-treated mice were maintained separately in different cages. At the end of the study, mice were sacrificed under anesthesia.

Flow cytometry

For live/dead-discrimination, BD Horizon Fixable Viability Stain 450 (FVS450, BD, 562247) or Zombie NIR Fixable Viability Kit (Biolegend, 423105) were used to stain cells for 12 mins at RT in the dark. Cells were washed with FACS buffer (PBS + 0.5% BSA) and then stained with panels of fluorophore-conjugated antibodies for 30 minutes at 4 °C. After washing, cells were resuspended in FACS buffer and analyzed using a 3-laser Sony SA3800 Spectral Analyzer. All antibodies and reagents used for FACS staining are listed in Supplementary Table 5.

Multiplexed immunofluorescence staining

Multiplex staining of FFPE samples were performed using the Leica BOND RX platform according to the manufacture’s instruction. The primary antibodies for the validation of T cells interacting with macrophage, including HLA-DR/DP/DQ (ThermoFisher, MA1-80678, 1:800), CD86 (Abcam, ab220188, 1:800), CD4 (Abcam, ab133616, 1:400), CD68 (ZSGB-bio, ZM-0060, 1:100), CTLA4 (Abcam, ab237712, 1:250), CD8 (ThermoFisher, MA1-80231, 1:100) were sequentially applied. Then, slides were incubated with horseradish peroxidase-conjugated secondary antibody. DAPI was used to stain nuclei after all antigens have been labeled. For each slides, ≥5 fields of immune cell enriched areas were selected for image capture. The selected areas were scanned by Vectra Polaris Automated Quantitative Pathology Imaging System (AKOYA) to obtain multispectral images. Spatial distance between target cells were measured by HALO-link platform. Distance <30 μm was considered as physical juxtaposition.

Statistics

All statistical analysis were performed using GraphPad Prism (version 9.0) or SPSS (version 27) software. All quantitative data were shown as means ± standard deviation (SD). Statistical tests used in figures were shown in figure legends and statistical significance was set at p < 0.05. Exact value of n was shown in the figures, figure legends and source data. What n represents was shown in the figure legends.

Reporting summary

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

Data availability

The raw scRNA and TCR sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA004050) [https://bigd.big.ac.cn/gsa-human/browse/HRA004050]. All data are included in the Supplementary Information or available from the authors, as are unique reagents used in this Article. The raw numbers for charts and graphs are available in the Source Data file whenever possible. Source data are provided with this paper.

Code availability

The source code employed for data processing and analysis in this study is available in Supplementary Software 1.

References

Malfertheiner, P. et al. Helicobacter pylori infection. Nat. Rev. Dis. Prim. 9, 19 (2023).
Article PubMed Google Scholar
Correa, P. A human model of gastric carcinogenesis. Cancer Res 48, 3554–3560 (1988).
CAS PubMed Google Scholar
Tacconelli, E. et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect. Dis. 18, 318–327 (2018).
Article PubMed Google Scholar
Sugano, K. et al. Kyoto global consensus report on Helicobacter pylori gastritis. Gut 64, 1353–1367 (2015).
Article PubMed Google Scholar
Jafarzadeh, A., Larussa, T., Nemati, M. & Jalapour, S. T cell subsets play an important role in the determination of the clinical outcome of Helicobacter pylori infection. Micro. Pathog. 116, 227–236 (2018).
Article CAS Google Scholar
Larussa, T., Leone, I., Suraci, E., Imeneo, M. & Luzza, F. Helicobacter pylori and T helper cells: mechanisms of immune escape and tolerance. J. Immunol. Res. 2015, 981328 (2015).
Article PubMed PubMed Central Google Scholar
Koch, M. R. A. et al. CagA-specific gastric CD8(+) tissue-resident T cells control Helicobacter pylori during the early infection phase. Gastroenterology 164, 550–566 (2023).
Article CAS PubMed Google Scholar
Salama, N. R., Hartung, M. L. & Muller, A. Life in the human stomach: persistence strategies of the bacterial pathogen Helicobacter pylori. Nat. Rev. Microbiol 11, 385–399 (2013).
Article CAS PubMed PubMed Central Google Scholar
Stead, C. M., Beasley, A., Cotter, R. J. & Trent, M. S. Deciphering the unusual acylation pattern of Helicobacter pylori lipid A. J. Bacteriol. 190, 7012–7021 (2008).
Article CAS PubMed PubMed Central Google Scholar
Gewirtz, A. T. et al. Helicobacter pylori flagellin evades toll-like receptor 5-mediated innate immunity. J. Infect. Dis. 189, 1914–1920 (2004).
Article CAS PubMed Google Scholar
Teymournejad, O., Mobarez, A. M., Hassan, Z. M., Moazzeni, S. M. & Ahmadabad, H. N. In vitro suppression of dendritic cells by Helicobacter pylori OipA. Helicobacter 19, 136–143 (2014).
Article CAS PubMed Google Scholar
Kim, J. M. et al. Stimulation of dendritic cells with Helicobacter pylori vacuolating cytotoxin negatively regulates their maturation via the restoration of E2F1. Clin. Exp. Immunol. 166, 34–45 (2011).
Article CAS PubMed PubMed Central Google Scholar
Zhang, L. et al. Reduced lysosomal clearance of autophagosomes promotes survival and colonization of Helicobacter pylori. J. Pathol. 244, 432–444 (2018).
Article CAS PubMed Google Scholar
Hu W. et al. Vitamin D3 activates the autolysosomal degradation function against Helicobacter pylori through the PDIA3 receptor in gastric epithelial cells. Autophagy, 4, 707–725 (2019).
Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).
Article CAS PubMed Google Scholar
Tietscher, S. et al. A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer. Nat. Commun. 14, 98 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809.e723 (2021).
Article CAS PubMed Google Scholar
Ren, X. et al. COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas. Cell 184, 5838 (2021).
Article CAS PubMed PubMed Central Google Scholar
Villani A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, (2017).
Schaller, T. H., Batich, K. A., Suryadevara, C. M., Desai, R. & Sampson, J. H. Chemokines as adjuvants for immunotherapy: implications for immune activation with CCL3. Expert Rev. Clin. Immunol. 13, 1049–1060 (2017).
Article CAS PubMed PubMed Central Google Scholar
Swanson, K. V., Deng, M. & Ting, J. P. The NLRP3 inflammasome: molecular activation and regulation to therapeutics. Nat. Rev. Immunol. 19, 477–489 (2019).
Article CAS PubMed PubMed Central Google Scholar
Belyaeva, V. et al. Fos regulates macrophage infiltration against surrounding tissue resistance by a cortical actin-based mechanism in Drosophila. PLoS Biol. 20, e3001494 (2022).
Article CAS PubMed PubMed Central Google Scholar
Fontana, M. F. et al. JUNB is a key transcriptional modulator of macrophage activation. J. Immunol. 194, 177–186 (2015).
Article CAS PubMed Google Scholar
Suzuki, E. et al. The transcription factor Fli-1 regulates monocyte, macrophage and dendritic cell development in mice. Immunology 139, 318–327 (2013).
Article CAS PubMed PubMed Central Google Scholar
Zhao, H. et al. KSRP specifies monocytic and granulocytic differentiation through regulating miR-129 biogenesis and RUNX1 expression. Nat. Commun. 8, 1428 (2017).
Article ADS PubMed PubMed Central Google Scholar
Pishesha, N., Harmand, T. J. & Ploegh, H. L. A guide to antigen processing and presentation. Nat. Rev. Immunol. 22, 751–764 (2022).
Article CAS PubMed Google Scholar
Lu, L. et al. Utilizing murine dendritic cell line DC2.4 to evaluate the immunogenicity of subunit vaccines in vitro. Front Immunol. 15, 1298721 (2024).
Article CAS PubMed PubMed Central Google Scholar
Orsenigo, F. et al. Unifying considerations and evidence of macrophage activation mosaicism through human CSF1R and M1/M2 genes. Cell Rep. 43, 114352 (2024).
Article CAS PubMed Google Scholar
AG, N. et al. Phagocytosis imprints heterogeneity in tissue-resident macrophages. J. Exp. Med. 214, 1281–1296 (2017).
Article Google Scholar
Ma, R. Y., Black, A. & Qian, B. Z. Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol. 43, 546–563 (2022).
Article CAS PubMed Google Scholar
Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459.e429 (2020).
Article CAS PubMed Google Scholar
Hao, D. et al. The single-cell immunogenomic landscape of B and plasma cells in early-stage lung adenocarcinoma. Cancer Discov. 12, 2626–2645 (2022).
Article CAS PubMed PubMed Central Google Scholar
Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).
Article PubMed Google Scholar
van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8(+) T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20, 218–232 (2020).
Article PubMed PubMed Central Google Scholar
Zheng, C. et al. Landscape of Infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356. e1316 (2017).
Article CAS PubMed Google Scholar
Chu, Y. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat. Med. 29, 1550–1562 (2023).
Article CAS PubMed PubMed Central Google Scholar
Wang R., et al. Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression. Cancer Cell, 41, 1407–1426 (2023).
Corcoran, M. et al. Archaic humans have contributed to large-scale variation in modern human T cell receptor genes. Immunity 56, 635–652.e636 (2023).
Article CAS PubMed Google Scholar
Schildberg, F. A., Klein, S. R., Freeman, G. J. & Sharpe, A. H. Coinhibitory Pathways in the B7-CD28 Ligand-Receptor Family. Immunity 44, 955–972 (2016).
Article CAS PubMed PubMed Central Google Scholar
Sun, Y. et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 184, 404–421.e416 (2021).
Article CAS PubMed Google Scholar
Wang, Y. H., Gorvel, J. P., Chu, Y. T., Wu, J. J. & Lei, H. Y. Helicobacter pylori impairs murine dendritic cell responses to infection. PLoS One 5, e10844 (2010).
Article ADS PubMed PubMed Central Google Scholar
Mysorekar, V. V., Dandekar, C. P. & Prakash, B. S. Mast cells in Helicobacter pylori associated antral gastritis. Indian J. Pathol. Microbiol 46, 605–609 (2003).
PubMed Google Scholar
Nakajima, S., Bamba, N. & Hattori, T. Histological aspects and role of mast cells in Helicobacter pylori-infected gastritis. Aliment Pharm. Ther. 20, 165–170 (2004).
Article Google Scholar
Lv, Y. P. et al. Helicobacter pylori-induced IL-33 modulates mast cell responses, benefits bacterial growth, and contributes to gastritis. Cell Death Dis. 9, 457 (2018).
Article PubMed PubMed Central Google Scholar
Elieh Ali Komi, D. & Kuebler, W. M. Significance of mast cell formed extracellular traps in microbial defense. Clin. Rev. Allergy Immunol. 62, 160–179 (2022).
Article CAS PubMed Google Scholar
Velin, D., Straubinger, K. & Gerhard, M. Inflammation, immunity, and vaccines for Helicobacter pylori infection. Helicobacter 21, 26–29 (2016).
Article PubMed Google Scholar
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
Article PubMed PubMed Central Google Scholar
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Article CAS PubMed PubMed Central Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
Article CAS PubMed PubMed Central Google Scholar
Chung, N. C. & Storey, J. D. Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics 31, 545–554 (2015).
Article CAS PubMed Google Scholar
Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).
Article ADS Google Scholar
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol, 37, 38–44 (2018).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e324 (2019).
Article CAS PubMed PubMed Central Google Scholar
Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).
Article ADS CAS PubMed Google Scholar
Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Article CAS PubMed PubMed Central Google Scholar
Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinforma. 14, 7 (2013).
Article Google Scholar
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Article ADS PubMed PubMed Central Google Scholar
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Article CAS PubMed PubMed Central Google Scholar
Toda, G., Yamauchi, T., Kadowaki, T. & Ueki, K. Preparation and culture of bone marrow-derived macrophages from mice for functional analysis. STAR Protoc. 2, 100246 (2021).
Article CAS PubMed Google Scholar

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Acknowledgements

The authors would like to thank Professor Peng CHEN for intellectually contributing to the project. We also thank all participants, and their families, who participated in this study, and thank Gene Denovo technology for high-throughput sequencing and technical supports. Professional English language editing support provided by AsiaEdit. This work was supported by Guangdong Basic and Applied Basic Research Foundation (2023A1515011685 to W.G.; 2023A1515030071 to W.H.), Shenzhen Science and Technology Program (JCYJ20210324131010027 and JCYJ20240813145326035 to W.G.; JCYJ20220530154205011, JCYJ20230807142314030 and JCYJ20240813145328037 to W.H.) and Research Foundation of Shenzhen Hospital of Southern Medical University (23H3ATF02 to W.H.).

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These authors contributed equally: Wei Hu, Ze Min Chen.

Authors and Affiliations

Department of Gastroenterology, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China
Wei Hu, Ying Wang, Chao Yang, Zi Ying Wu, Li Juan You, Zhi Yong Zhai, Zhao Yu Huang, Ping Zhou, Xia Xi Li, Gen Hua Yang, Chong Ju Bao, Xiao Bing Cui, Gui Li Xia, Mei Ping Ou Yang & Wei Gong
The Third School of Clinical Medicine, Southern Medical University, Shenzhen, Guangdong, China
Wei Hu, Ying Wang, Chao Yang, Zi Ying Wu, Zhi Yong Zhai, Zhao Yu Huang, Ping Zhou & Wei Gong
Department of Anaesthesia and Intensive Care and Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
Ze Min Chen, William Ka Kei Wu & Li Kai Tan
Department of Gastroenterology, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, Guangdong, China
Si Lin Huang
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
Lin Zhang
Guangdong Engineering Technology Research Center of Reproductive Immunology for Peri-implantation, Shenzhen Key Laboratory of Reproductive Immunology for Peri-implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong, China
Long Fei Li
Department of Pharmacology and Therapeutics, King’s College London, London, UK
Yu Xuan Zhang

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Wei Hu
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Ze Min Chen
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Ying Wang
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Chao Yang
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Zi Ying Wu
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Li Juan You
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Xia Xi Li
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Gen Hua Yang
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Xiao Bing Cui
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Gui Li Xia
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Lin Zhang
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Long Fei Li
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Li Kai Tan
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Yu Xuan Zhang
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Wei Gong
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Contributions

W.G. designed the study, supervised the study progress, and revised the manuscript. W.H. and Z.M.C. conducted the major experiments and bioinformatic analysis. W.H. prepared the manuscript. Y.W., C.Y. and Z.Y.W. helped to conduct the animal experiments and perform the bioinformatic analysis. L.J.Y., Z.Y.Z., Z.Y.H., P.Z., S.L.H., X.X.L., G.H.Y., C.J.B., X.B.C., G.L.X., M.P.O.Y., L.Z., W.K.K.W., L.F.L., L.K.T. and Y.X.Z. offered the technical, material, and intellectual support.

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Correspondence to Wei Gong.

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Hu, W., Chen, Z.M., Wang, Y. et al. Single-cell RNA sequencing dissects the immunosuppressive signatures in Helicobacter pylori-infected human gastric ecosystem. Nat Commun 16, 3903 (2025). https://doi.org/10.1038/s41467-025-59339-4

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Received: 17 July 2024
Accepted: 18 April 2025
Published: 25 April 2025
Version of record: 25 April 2025
DOI: https://doi.org/10.1038/s41467-025-59339-4

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