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  • Research article
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A human stomach cell type transcriptome atlas

BMC Biology volume 22, Article number: 36 (2024) Cite this article

Abstract

Background

The identification of cell type-specific genes and their modification under different conditions is central to our understanding of human health and disease. The stomach, a hollow organ in the upper gastrointestinal tract, provides an acidic environment that contributes to microbial defence and facilitates the activity of secreted digestive enzymes to process food and nutrients into chyme. In contrast to other sections of the gastrointestinal tract, detailed descriptions of cell type gene enrichment profiles in the stomach are absent from the major single-cell sequencing-based atlases.

Results

Here, we use an integrative correlation analysis method to predict human stomach cell type transcriptome signatures using unfractionated stomach RNAseq data from 359 individuals. We profile parietal, chief, gastric mucous, gastric enteroendocrine, mitotic, endothelial, fibroblast, macrophage, neutrophil, T-cell, and plasma cells, identifying over 1600 cell type-enriched genes.

Conclusions

We uncover the cell type expression profile of several non-coding genes strongly associated with the progression of gastric cancer and, using a sex-based subset analysis, uncover a panel of male-only chief cell-enriched genes. This study provides a roadmap to further understand human stomach biology.

Background

The gastrointestinal (GI) tract is a multiple organ system which can be divided into upper and lower parts, the physical properties and cellular characteristics of which reflect their different roles in digestion, absorption of nutrients, and excretion of waste products [1,2,3]. The stomach, a hollow muscular organ in the upper GI tract, produces an array of acids and gastric enzymes, acting as a reservoir for the mechanical and chemical digestion of ingested food [4]. The constituent cell types of the stomach include parietal cells, chief cells, gastric mucous cells, gastric enteroendocrine cells, mitotic cells, endothelial cells, fibroblasts, and various immune cells [5, 6]. In contrast to lower sections of the GI tract, descriptions of the cellular transcriptional landscape in the stomach are lacking, with this organ absent from large-scale single-cell sequencing (scRNAseq) initiatives, such as Tabula Sapiens [7] and the Human Cell Atlas [8]. Where scRNAseq has been used to profile gene expression in the adult stomach, studies have typically focused on specific cell types, such as the epithelia [9, 10], or in pathological states such as gastric cancer [11,12,13,14]. Whilst scRNAseq studies provide high resolution of individual cell (sub)type gene expression profiles, challenges remain, including artefactual modification of gene expression due to cell removal and processing [15,16,17], compromised read depth, and difficulties with data interpretation [18, 19]. As a limited number of biological replicates are typically analysed, underestimation of biological variance can increase the likelihood of potential false discoveries [20, 21].

Non-coding RNA is emerging as a novel, important class of molecules, involved in the maintenance of healthy stomach tissue and the development and progression of gastric cancer [22, 23], but to date, there is no overall description of stomach cell type-enriched non-coding RNAs.

Here, we analysed 359 bulk RNAseq human stomach samples to identify over 1600 genes with cell type-enriched expression, using our previously developed integrative correlation analysis [24,25,26]. Gastric mucous cells had the highest number of predicted protein-coding and non-coding enriched genes and represented the primary site of expression of genes that were tissue enriched in the stomach over other tissue types. Gastric enteroendocrine cells expressed a panel of non-coding genes that are also selectively expressed in pancreatic and intestinal endocrine cells, indicating a common function in these cell types. Several of the identified cell type-enriched non-coding genes have previously been associated with the progression of gastric cancer, but until now, the cell type site of expression had not been described. Sex subset analysis revealed a high global similarity in cell type transcriptomes between males and females, but a panel of chief cell-enriched Y-linked genes were identified. Data is available through the Human Protein Atlas (HPA) portal (www.proteinatlas.org/humanproteome/tissue+cell+type/stomach).

Results

Identification of cell type transcriptome profiles in stomach

Cell type reference transcripts correlate across unfractionated RNAseq data

To identify stomach cell type-enriched transcriptome profiles, we conducted an analysis based on our previously developed method [24,25,26], using human stomach bulk RNAseq data (N = 359) from the Genotype-Tissue Expression (GTEx) portal V8 [27] (see Additional file 1: Fig. S1 for the method overview). Each sample was unfractionated and thus contained a mix of cell types (Fig. 1A.i), which contribute differing proportions of transcripts subsequently measured by RNAseq (Fig. 1A.ii) (Additional file 1: Fig. S1A). For each major constituent stomach cell type, candidate cell type-specific genes (termed ‘reference transcripts’ [Ref.T.]) were selected based on (i) our in-house proteomic profiling of stomach tissue [5, 6], (ii) older ‘none-omics’ studies [28], (iii) scRNAseq data were available [9, 29], or (iv) databases collated from multiple sources, e.g. Cell Marker [30] and PanglaoDB [31] (Fig. 1B and Additional file 1: Fig. S1B). Three markers were selected for each cell type, based on the following criteria: (i) a high corr. (> 0.85) between Ref.T. within each cell type panel (Fig. 1C and Additional file 2: Table S1, Tab 1), indicating cell type co-expression: parietal cells (PAC) [ATP4B, MFSD4A, ATP4A mean corr. ± STD 0.94 ± 0.013], chief cells (CC) [PGC, LIPF, AZGP1, 0.89 ± 0.013], gastric enteroendocrine cells (GEEC) [ST18, INSM1, ARX, 0.89 ± 0.021], gastric mucous cells (GMC) [LGALS4, VILL, CAPN8, 0.94 ± 0.008], mitotic cells (MTC) [NCAPG, KIFC1, NCAPH, 0.93 ± 0.009], endothelial cells (EC) [PECAM1, CDH5, ERG, 0.89 ± 0.013], fibroblasts (FB) [PCOLCE, CLEC11A, MMP2, 0.87 ± 0.027], macrophages (MC) [C1QB, FCGR3A, ITGB2, 0.86 ± 0.015], neutrophils (NP) [CXCR2, FCGR3B, CXCR1, 0.86 ± 0.009], T-cells (TC) [CD3E, CD2, CD3G, 0.9 ± 0.019], and plasma cells (PC) [IGKC, JCHAIN, IGLC1, 0.97 ± 0.009]; (ii) a low corr. between Ref.T. across the different cell type panels (Fig. 1C) (Additional file 2: Table S1, Tab 1), indicating cell type specificity (mean inter-panel corr. ± STD 0.08 ± 0.14); and (iii) a normal distribution of Ref.T. expression across the samples (Additional file 3: Fig. S2A).

Fig. 1
figure 1

Integrative co-expression analysis can resolve constituent cell type identities from unfractionated human stomach tissue RNAseq data. (A) RNAseq data for 359 unfractionated human stomach samples were retrieved from GTEx V8. Each sample contained (i) mixed cell types, which contributed (ii) differing proportions of sequenced mRNA. (B) To profile cell type-enriched transcriptomes, constituent cell types were identified and candidate marker genes (´reference transcripts´ [Ref.T.]) for virtual tagging of each were selected, based on in house tissue protein profiling and/or existing literature and datasets. (C) Matrix of correlation coefficients between selected Ref.T. across the sample set. (D) Mean correlation coefficients of genes above designated thresholds for classification as cell-type enriched in stomach: (i) parietal cells [PC], (ii) chief cells [CC], (iii) gastric enteroendocrine cells [GEEC], (iv) gastric mucous cells [GMC], (v) mitotic cells [MTC], (vi) endothelial cells [EC], (vii) fibroblasts [FB], (viii) macrophages [MC], (ix) neutrophils [NP], (x) T-cells [TC], (xi) plasma cells [PC] with all Ref.T. panels. (E) Over-represented gene ontology terms among genes predicted to be: (i) endothelial cell, (ii) fibroblast or (iii) T-cell enriched. (F) Principal component analysis of correlation profiles of cell type enriched genes. See also Table S1 Tab 1 and 2 and Figure S1 for method overview

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Using reference transcript analysis to identify cell type-enriched genes

Correlation coefficients (corr.) between each selected Ref.T. and all other sequenced transcripts (> 56,000) were calculated across stomach RNAseq samples (Additional file 1: Fig. S1C). The proportion of cell types represented in each sample varies, due to biological and sampling variability, but ratios should remain consistent between constitutively expressed cell-enriched genes. Thus, a high corr. of a given transcript with all Ref.T. in only one cell type panel is consistent with enrichment in the corresponding cell type. For each cell type, a list of enriched genes was generated (Fig. 1D (i–xi)), with inclusion based on (i) the gene having a mean corr. > 0.50 with the Ref.T. panel representing the cell type (Additional file 1: Fig. S1C.ii) and (ii) a differential correlation between this value and the maximum mean corr. with any other Ref.T. panel > 0.15 (Additional file 1: Fig. S1D-E). This excluded genes that were potentially co-enriched in two or more cell types, as we previously described [26] (all data in Additional file 2: Table S1, Tab 2). For certain cell types, enriched genes were less well separated by corr. value that others, e.g. those most highly correlating with the fibroblast Ref.T. panel (Fig. 1D (vii)) tended to show elevated corr. with the Ref.T. panel for endothelial cells, and vice versa (Fig. 1D (vi)). However, all cell type-enriched genes were well separated when the individual gene differential correlations vs. other Ref.T. panels were plotted (Additional file 3: Fig. S2B), and Gene Ontology (GO) and reactome analysis [32, 33] revealed over-represented terms for these cell types were consistent with known functions e.g. for endothelial cells most significantly enriched terms included vascular development and angiogenesis (Fig. 1E.i), for fibroblasts extracellular matrix organisation and collagen fibril organisation (Fig. 1E.ii), and for T-cells T-cell activation and immune response (Fig. 1E (iii)) (Additional file 2: Table S1, Tab 8, 9 and 12). Principal component analysis of the corr. values of cell type-enriched genes [34] revealed the largest variance was between stomach-specific cell types vs. stromal/vasculature-related ones (Fig. 1F).

Stomach cell type-enriched gene signatures

The majority of stomach cell type-enriched genes are protein coding

A total of 1694 genes were predicted to be cell type-enriched (Fig. 2A and Additional file 2: Table S1, Tab 2). Gastric mucous cells, plasma cells, and fibroblasts had the highest number of predicted enriched genes (n = 517, 214, and 186, respectively) (Fig. 2A (i–iii)). Of the other cell types found in all, or most, tissue types, mitotic cells, and macrophages had the most enriched genes (n = 171 and 158, respectively) (Fig. 1A (iv, v)). Other stomach-specialised cell types, parietal cells, chief cells, and gastric enteroendocrine cells, had significantly fewer enriched genes (n = 123, 103, and 86, respectively) (Fig. 2A (vi, vii, and ix), and T-cells and neutrophils had the fewest overall (n = 24 and 20, respectively) (Fig. 2A (x, xi)). In all cases, the majority of cell type-enriched genes were classified as protein coding [35], with the exception of plasma cells, in which the immunoglobulin (IG) gene was the most common classification (Fig. 2A (ii)). Long non-coding RNA (lncRNA) were the most common type of non-coding cell type-enriched transcript, with the exception of plasma cells, where IG pseudogene was the most common non-coding classification (Fig. 2A.ii).

Fig. 2
figure 2

Integrative co-expression analysis of unfractionated RNAseq reveals enriched genes in human stomach cell types. (A) Total number and proportional representation of class for cell type enriched genes in: (i) gastric mucous cells, (ii) plasma cells, (iii) fibroblasts, (iv) mitotic cells, (v) macrophages, (vi) parietal cells, (vii) chief cells, (viii) endothelial cells, (ix) gastric enteroendocrine cells, (x) T-cells and (xi) neutrophils. (cells, (viii) endothelial cells, (ix) gastric enteroendocrine cells, (x) T-cells and (xi) neutrophils. (B) RNAseq data for) RNAseq data for 359 unfractionated human stomach samples was subject to weighted correlation network analysis (WGCNA). (i) Coloured squares indicate cell type Ref.T. positions on resultant dendrogram. (ii) Coloured bars show distribution of protein coding genes classified as cell type-enriched across dendrogram groups. (C) Human stomach tissue profiling for proteins encoded by genes classified as: (i) gastric enteroendocrine cell, (ii) mitotic cell, (iii) parietal cell, (iv) chief cell or (v) gastric mucous cell enriched. (D) Over-represented gene ontology terms among genes predicted to be (i) gastric enteroendocrine cell, (ii) parietal cell or (iii) gastric mucous cell enriched. See also Table S1 Tab 2, 3, 5 and 6

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Alternative analysis and protein profiling support cell-type classifications

Unsupervised weighted network correlation analysis is consistent with Ref.T. analysis

As our analysis is based on manually selected Ref.T. panels, cell type classification is subject to an input bias. As a comparison, we subjected the same GTEx RNAseq dataset to a weighted network correlation analysis (WGCNA) [36], an unbiased method that does not require any manual input or marker gene selection. WGCNA generates corr. coefficients between all transcripts and subsequently clusters them into related groups, based on expression similarity (Fig. 2B). In general, Ref.T. belonging to the same cell type panel were found in the same WGCNA cluster (Fig. 2B (i), coloured boxes represent the modules in which the Ref.T. appeared), e.g. gastric enteroendocrine cells (cluster 50) or clusters on the same branch, e.g. gastric mucous cells (clusters 25 and 22) and macrophages (clusters 47 and 59) (Fig. 2B (i)). Protein coding genes that we predicted to be cell type enriched were predominantly clustered into the same WGCNA group as the corresponding Ref.T. but were also frequently classified into related modules on the same branch, consistent with our classifications (Fig. 2B (ii)). Most genes in the Ref.T. panels representing parietal and chief cells appeared in the same large group (cluster 3) (Fig. 2B (ii)), as were the genes in the respective predicted enriched gene lists, despite clear separation in our Ref.T-based method (Fig. 1C, D). Despite the lack of separation for the enriched gene signatures for parietal and chief cells by WGCNA, each contained several well-described marker genes for the respective cell type, e.g. GIF, SLC26A7 (parietal) and PGA4, SLC1A2 (chief cell). Indeed, we have previously shown that Ref.T.-based analysis can have a higher sensitivity than WGCNA for cell type gene enrichment analysis [25]. Stomach tissue protein profiling revealed staining consistent with expression in the respective cell types for proteins encoded by genes predicted to be gastric enteroendocrine cell (Fig. 2C (i)), mitotic cell (Fig. 2C (ii)), parietal cell (Fig. 2C (iii)), chief cell (Fig. 2C (iv)), or gastric mucous cell (Fig. 2C (v)) enriched. GO and reactome analysis [32, 33] revealed that over-represented terms for predicted stomach specialised cell type-enriched genes were consistent with known cell functions, e.g. for gastric enteroendocrine cells enteroendocrine cell differentiation (Fig. 2D (i)), for parietal cells inorganic ion transport across the plasma membrane and gastric acid secretion (Fig. 2D (ii)), and for gastric mucous cells lipid metabolic processes (Fig. 2D (iii)) (for all cell types see Additional file 2: Table S1, Tab 3–13).

Stomach cell type gene enrichment signatures

Figure 3 highlights 25 examples of enriched protein coding enriched genes for each cell type, ordered by highest corr. with the relevant Ref.T. panel (Fig. 3A (i)–K (i)), with differential corr. values and expression levels in the bulk RNAseq dataset (mean TPM). The mean TPM levels were generally highest for genes predicted to be enriched in parietal cells (Fig. 3A (i(), chief cells (Fig. 3B (i)), gastric mucous cells (Fig. 3D (i)), fibroblasts (Fig. 3G (i)), and plasma cells (Fig. 3K (i)) and lowest for those in mitotic cells (Fig. 3E (i)), neutrophils (Fig. 3I (i)), and T-cells (Fig. 3J (i)). This likely reflects differing numbers of each given cell type with the samples; however, as a range of expression values are observed within each given cell type, there is likely also individual gene variation in factors such as regulation and transcript stability. The highest differential values, and thus relative uniqueness among the profiled cell types, were observed for mitotic cell-enriched genes (Fig. 3E.i), most of which have well-studied roles in the regulation of the cell cycle, such as TOP2A and BUB1B. For all other cell types, top enriched genes included both known cell type-specific genes, together with those that have not been previously reported as such, e.g. PECAM1 and SHE were both predicted to be endothelial cell-enriched (Fig. 3F (i)); PECAM1 is a commonly used marker gene for this cell type, whilst there are no existing reports for the selective expression of SHE in this context. Tissue profiling for proteins encoded by representative cell type-enriched genes showed expression consistent with our classifications (Fig. 3A (ii)–K (ii)).

Fig. 3
figure 3

Protein coding gene signatures of human stomach cell types. Cell type-enriched protein coding genes in: (A) parietal cells, (B) chief cells, (C) gastric enteroendocrine cells, (D) gastric mucous cells, (E) mitotic cells, (F) endothelial cells, (G) fibroblasts, (H) macrophages, (I) neutrophils (J) T-cells and (K) plasma cells, showing: (i) differential correlation score (correlation with cell type Ref.T., panel minus max correlation with any other Ref.T. panel) and mean expression in bulk RNAseq. (ii) Human stomach tissue protein profiling for selected cell type enriched genes. See also Table S1 Tab 2

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Ref.T. analysis can predict the source of stomach-enriched protein-coding genes

Genes with enriched expression in the human stomach vs. other tissue types can be identified by a comparative analysis of unfractionated tissue RNAseq data. We extracted the top 200 human stomach-enriched genes from the HPA [6] and GTEx project [27], through the Harminozome database [37] (Fig. 4). Of the 78 genes classified as stomach-enriched in both datasets, 46/78 (59.0%) were classified as cell type enriched in our analysis, 28/46 (61.0%) in gastric mucous cells, 11/46 (24.0%) in parietal cells, 6/46 (13.0%) in chief cells, and 1/46 (2.2%) in gastric enteroendocrine cells (Fig. 4B (i, ii), respectively, large symbols). Of those not classified as cell type-enriched in our analysis (n = 32), 11/32 (34.4%), only narrowly failed to reach one of the thresholds for classification as either parietal-, chief-, or gastric mucous cell-enriched (Fig. 4B (i, ii), medium symbols). The majority of the remaining genes most highly correlated with Ref.T. panel representing one, or more, of the same cell types: parietal, chief, or gastric mucous, but were excluded from the cell type classifications due to shared enrichment. None of the stomach-enriched genes was predicted to be enriched in any cell type found across multiple tissue types, such as endothelial or immune cells, consistent with the lack of specificity of these cell types to the stomach. Thus, our analysis indicates that most stomach tissue-enriched genes are primarily expressed in gastric mucous, parietal, or chief cells.

Fig. 4
figure 4

Gastric mucous cells, parietal cells and chief cells are the primary source of stomach tissue enriched genes. (A) The top 200 stomach enriched genes (vs. other tissue types) in RNAseq data from the GTEx Portal or Human Protein Atlas (HPA) were compared to identify genes common to both datasets (n=78). For each, the following was plotted: (B) (i) the mean correlation with each cell type Ref.T. panel, and (ii) the differential value vs. the next most highly correlating Ref.T. panel (dotted line indicates threshold for classification as cell type enriched). Enlarged circles represent genes with predicted cell type enrichment

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Cell type-enriched non-coding genes in the stomach

A total of 252 non-coding genes were identified as cell type-enriched in the stomach (Fig. 5A), the greatest number of which were in gastric mucous cells, plasma cells, or fibroblasts (n = 100, 44, and 30, respectively). When the sample set was analysed by WGCNA (Fig. 5B (i)), non-coding genes that we predicted to be cell type enriched predominantly clustered into the same WGCNA group as the corresponding Ref.T., or into adjacent groups on the same branch (Fig. 5B (ii)). Up to 25 examples of non-coding enriched genes in gastric enteroendocrine cells (Fig. 5C (i)), gastric mucous cells (Fig. 5D (i)), endothelial cells (Fig. 5E (i)), parietal cells (Fig. 6A (i)), chief cells (Fig. 6B (i)), plasma cells (Fig. 6C (i)), and fibroblasts (Fig. 6D (i)), ordered by corr. with the relevant Ref.T panel, are displayed with differential corr. values vs. other profiled cell types, expression in the bulk RNAseq data (mean TPM), and transcript type. In all cell types, with the exception of plasma cells, where the most common type of enriched non-coding gene was IG pseudogene (Fig. 6C (i)), long non-coding RNAs made up the majority of the predicted enriched genes. Generally, gastric mucous cell (Fig. 5D (i)) and fibroblast (Fig. 6D (i)) enriched non-coding genes were expressed at the highest levels in the stomach bulk RNAseq. This likely reflects the differing numbers of each given cell type within the samples, but the intra-cell type variation also indicates individual gene regulation.

Fig. 5
figure 5

Non-coding gene signatures of human stomach cell types. (A) Heat map of non-coding genes predicted to be cell type enriched, showing differential score between mean correlation coefficient with the corresponding Ref.T. panel vs. highest mean correlation coefficient amongst the other Ref.T. panels. (B) RNAseq data for 359 unfractionated human stomach samples was subject to weighted correlation network analysis (WGCNA). (i) Coloured squares indicate cell type Ref.T. positions on resultant dendrogram. (ii) Coloured bars show distribution of non-coding genes classified as cell type-enriched across dendrogram groups. Non-coding gene enrichment signatures for: (C) gastric enteroendocrine cells, (D) gastric mucous cells and (E) endothelial cells, detailing: (i) up to 25 examples of cell type enriched non-coding genes, ordered by correlation coefficient with the Ref.T. panel, showing differential correlation scores (correlation with corresponding cell type Ref.T., panel minus max correlation with any other Ref.T. panel), mean expression in bulk RNAseq and transcript type. (ii and iii) scRNAseq data from analysis of epithelial, endothelial, immune or stromal cell compartments across 24 human tissues was sourced from Tabula Sapiens (Tabula Sapiens et al., [7]), and used to generate UMAP plots showing the expression profiles of example cell type enriched non-coding genes. The largest plot shows the compartment with the highest expression. See also Table S1 Tab 2 and Figure S3 (for all UMAP plot annotations)

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Fig. 6
figure 6

Core non-coding gene signatures of human stomach cell types and tissue distribution patterns. Non-coding gene enrichment signatures for: (A) parietal cells, (B) chief cells, (C) plasma cells and (D) endothelial cells, detailing (i) up to 25 examples of cell type enriched non-coding genes, ordered by correlation coefficient with the Ref.T. panel, showing differential correlation scores (correlation with corresponding cell type Ref.T., panel minus max correlation with any other Ref.T. panel), mean expression in bulk RNAseq and gene type. (ii and iii) scRNAseq data from analysis of epithelial, endothelial, immune, or stromal cell compartments across 24 human tissues was sourced from Tabula Sapiens (Tabula Sapiens et al., 2022), and used to generate UMAP plots showing the expression profiles of example cell type enriched non-coding genes. The largest plot shows the compartment with the highest expression. (E) The most highly expressed cell type enriched non‐coding genes in stomach bulk RNAseq. (F) Expression of genes classified as enriched in parietal cells: (i) LINC00982 and (ii) PP7080, plasma cells: (iii) IGLC6, gastric mucous cells: (vi) FER1L4 and (v) RP11-363E7.4, fibroblasts: (vi) HSPA7 and chief cells: (vii) C9orf147, in bulk RNAseq of different human organs. Mean TMP expression is annotated for selected organs on each plot. See also Table S1 Tab 2 and Figure S2 (for all UMAP plot annotations)

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There is currently no existing dataset of non-coding enriched genes in stomach cell types that could be used to validate our predictions. However, we sourced scRNAseq data from the analysis of 24 tissue types in Tabula Sapiens [7] (data for the stomach was not available) that had been classified into endothelial, epithelial, immune, and stromal cell functional compartments (for Tabula Sapiens UMAP cell type classifications, see Additional file 4: Fig. S3 A-D). We generated UMAP plots for each of these compartments to determine expression profiles for selected non-coding genes that we predicted to be cell type enriched. The predicted gastric enteroendocrine enriched genes MIR7-3HG and RP5-984P4.6 were expressed only in the epithelial cell compartment, specifically in the clusters annotated as intestinal enteroendocrine and pancreatic alpha and beta cells (Fig. 5C (ii, iii)), consistent with a specialised role in endocrine cells, not only in the stomach, but also in the pancreas and other parts of the GI tract. The predicted gastric mucous cell-enriched genes CTD-2396E7.11 and RP11-27G14.4 were widely expressed in the epithelial compartment but not in the endothelial, immune, or stromal cell compartments (Fig. 5D (ii, iii)). The predicted endothelial cell-enriched genes GATA2-AS1 and AC007743.1 were expressed predominantly in the endothelial cell compartment (Fig. 5E (ii, iii)), also consistent with our classifications. Genes predicted to be parietal cell enriched, LINC00671 and AC008268.1 (Fig. 6A (ii, iii)), and chief cell enriched, RP11-526I8.2 and AZGP1P1 (Fig. 6B (ii, iii)), were predominantly expressed in the epithelial compartment. The type of epithelial cell in which the genes were expressed varied, e.g. the chief cell enriched gene AZGP1P1 (Fig. 6B (ii)) was expressed predominantly in luminal cells of the prostate and hepatocytes; one could speculate that this gene indicates a shared secretory function between these specific cell types, whilst RP11-526I8.2 was more generally expressed in the epithelial compartment (Fig. 6B (iii)) perhaps indicating a more general role. The predicted plasma cell-enriched genes IGLV2-5 and IGLVI-70 were expressed only in the immune cell compartment (Fig. 6C (ii, iii)) in clusters annotated as either plasma cells or B-cells. The predicted fibroblast-enriched genes LINC01140 and AC006007.1 were expressed predominantly in the stromal cell compartment (Fig. 6D (ii, iii)), also consistent with our classifications. Thus, the Tabula Sapiens scRNAseq data provides supportive evidence for our cell type classifications, despite the lack of stomach cell type analysis in this dataset.

Of those non-coding genes that we classified as cell type enriched, 17 had relatively high expression in the bulk RNAseq stomach samples (mean TPM > 10) and were most frequently predicted to be gastric mucous cell enriched (Fig. 6E). To determine the expression profile of these genes in different organ types, we sourced data from bulk RNAseq of other tissues in GTEx (Fig. 6F). The most highly expressed parietal cell enriched non-coding genes, LINC00982 and PP7080 (mean TPM 99 and 49, respectively), both had high relative expression in stomach tissue (Fig. 6F (i, ii)), consistent with a specialised function in this organ. IGLC6, the most highly expressed non-coding transcript we predicted to be enriched in plasma cells, was highly expressed in the spleen and salivary gland: tissues that contain high numbers of plasma cells (Fig. 6F (iii)). The most highly expressed non-coding genes we predicted to be enriched in gastric mucous cells, FER1L4 and RP11-363E7.4, both had high relative expression in the stomach and bladder (Fig. 6F (iv, v)); one could speculate these genes have specific functions in the mucous cells found in these tissue types. HSPA7, the most highly expressed predicted fibroblast-enriched gene had variable expression across tissue types (Fig. 6F (vi)), consistent with the ubiquitous presence of this cell type across organs, whilst the chief cell enriched transcript, C9orf147, had high relative expression only in stomach tissue (Fig. 6F (vii)). Thus, the most highly expressed non-coding genes predicted to be enriched in the stomach specialised cell types were detected at relatively high levels in stomach tissue (and in relatively few other tissue types), consistent with a specialised function here. Conversely, those predicted to be enriched in less specialised cell types, such as plasma cells, were more broadly expressed across tissue types, consistent with a common cell type function in multiple organs. All data for non-coding genes can be searched via the web portal https://cell-enrichment.shinyapps.io/noncoding_stomach/.

Stomach tissue scRNAseq supports Ref.T. analysis

To our knowledge, there is no existing comprehensive scRNAseq dataset where all healthy stomach cell types have been analysed and compared, to use for further confirmation of our findings. However, we performed a comparison between our results and two stomach RNAseq studies, one with a focus on the analysis of stomach epithelial cell types [9] and another where cell types from gastric mucosa samples in premalignant and early-malignant lesions were characterized [11] (Additional file 2: Table S1, Tab 14). Neither study contained all cell types we profiled and for some cell types, such as gastric enteroendocrine cells, classification and/or terminology varied, as is typical [38]. Therefore, we made comparisons between closely related cells or cell sub-types across studies, e.g. those annotated as D cells, G cells, X cells, antral enterochromaffin cells, and oxyntic enterochromaffin-like cells in Busslinger et. al. [9] were considered together as a single group and compared to data for ‘enteroendocrine cells’ in the current study, and that by Zhang et. al. [11]. Where data was available, we also compared our results to those from our HPA single cell section [29] and to cell type marker genes defined by the analysis of multiple human and murine datasets [31] (Additional file 2: Table S1, Tab 14 [row 2 states source and cell type annotation]). Independent verification of genes we classified as cell type enriched (i.e. those that had a comparable classification in at least one other independent dataset) varied between cell types (Additional file 2: Table S1, Tab 14). Macrophages and T-cells had the highest agreement with one or more of the independent studies (71 and 96%, respectively). As expected, cell types with a higher proportion of non-coding and/or lowly expressed predicted enriched genes, such as gastric mucous cells, fibroblasts, and parietal cells (the latter of which were also only represented in one study) tended to have a lower level of independent validation (40%, 38%, and 30%, respectively). However, Gene Ontology and reactome analysis of genes that were not validated in the other datasets were significantly enriched for terms linked to the predicted cell type, e.g. for gastric mucous cells lipid metabolic processes (adjusted FDR 1.9 × 10−03), for fibroblasts extracellular structure organisation (adjusted FDR 3.8 × 10−02), and for parietal cells monoatomic ion transport (adjusted FDR 2.4 × 10−03) and gastric acid secretion (adjusted FDR 1.1 × 10−02), consistent with these genes having specialised roles in these cell types.

To compare global cell profiles from the stomach RNAseq datasets [9, 11] with our study and each other, we calculated the significance of the overlap between cell type-enriched genes, using a hypergeometric test (Additional file 4: Fig. S3 E). Genes predicted to be cell type enriched in our study were significantly overrepresented in enriched genes in the corresponding cell types in both scRNAseq studies (Additional file 4: Fig. S3 E). This overlap was comparable to, or more significant than, that between the two scRNAseq studies themselves. Whilst such comparisons can be helpful, the variability between these studies (i.e. general vs. specific cell type focus, healthy vs. diseased tissue) means the data can only be used as supported evidence, as opposed to a definitive validation.

Comparison of predicted sex-specific stomach cell type-enriched genes

We performed a subset analysis of the stomach RNAseq dataset (male n = 227, female n = 132,), to identify sex-specific cell type-enriched genes. Similar to the full dataset, intra-panel cell type Ref.T. correlated well in single-sex sample subsets (all > 0.84) (Additional file 5: Table S2, Tab 1, Table A and B). Cell type-enriched genes were calculated for the whole dataset. To compare gene enrichment profiles in males and females, the following was calculated for any gene that was classified as cell type enriched in either subset: (i) the differential correlation score, defined as the difference between the mean corr. coefficient with the cell type Ref.T, in the male and female sample subsets (to highlight potential differences in enrichment between the sexes) and (ii) the enrichment score, based on the mean corr. value with the Ref.T. panel (highest score = highest corr.) (to give an overview of the relative degree of enrichment of highlighted genes). Cell profiles were mainly comparable between sexes, for both stomach-specialised cell types (Fig. 7A (i–iv)) and others (Additional file 6: Fig. S4 A-G) (genes enriched in both males and females represented by square symbols). For those genes classified as enriched only in males or females (represented by differently coloured triangle and circle symbols, respectively), most had differential corr. scores close to 0; indicating that they fell marginally below the designated threshold for classification as enriched in the other sex. A small number of distinct male-only enriched genes were identified in chief cells: ARSFP1, TBL1Y, and RP11-115H13.1 (Fig. 7A (iv)), all of which were Y-linked, with expression levels above background level only in male samples (Fig. 7B (i–iii)). As described above, we sourced scRNAseq data from Tabula Sapiens [7] for cells classified as endothelial, epithelial, immune, or stromal (Additional file 4: Fig. S3 A-D). We generated UMAP plots (using cell data from male donors only) to show the expression profiles of the male-only chief cell-enriched genes. ARSFP1 was detected only at low levels in the epithelial compartment (Fig. 7C (i)), whilst TBL1Y (Fig. 7C (ii)) and RP11-115H13.1 (Fig. 7C (iii)) had strikingly similar expression profiles, with the highest levels in both cases detected in prostate epithelial cells. All three male-only chief cell-enrichened genes had low/no expression in the endothelial, immune, or stromal compartments (Fig. 7C (i–iii)). To determine the broad expression profile of the most highly expressed non-coding enriched genes across organs (from male donors), we sourced data from GTEx (Fig. 7D). ARSFP1 had enhanced expression only in the stomach and oesophagus (Fig. 7D (i)); both of which are tissue types not included in the Tabula Sapiens dataset, consistent with the low detection observed there. TBL1Y and RP11-115H13.1 had similar expression profiles across tissue types, with enhanced expression in the thyroid (which was also absent from the Tabula Sapiens dataset) followed by the prostate, in keeping with the high expression observed in prostate epithelial cells in the scRNAseq (Fig. 7D (ii, iii)). Thus, one could speculate that male-only chief cell-enriched gene ARSFP1 has a stomach-specific function, whilst TBL1Y and RP11-115H13.1 appear to be co-expressed also in cell types outside the stomach, suggesting a broader function in multiple cell types.

Fig. 7 
figure 7

Identification of sex-specific cell-enriched genes in human stomach tissue. (A) Human stomach RNAseq data (n=359 individuals) was retrieved from GTEx V8 and divided into female (n=132) and male (n=227) subgroups before classification of cell type-enriched genes. For genes classified as: (i) parietal, (ii) gastric mucous, (iii) gastric enteroendocrine or (vi) chief cell enriched in either sex, the ´sex differential corr. score’ (difference between mean corr. with the Ref.T. panel in females vs. males) was plotted vs. ‘enrichment score´ (position in each respective enriched list, highest score = highest corr.). On each plot, genes enriched in both females and males are represented by common-coloured square symbols, and genes classified as enriched only in females or males are represented by differently coloured circle and triangle symbols, respectively. (B) Expression in female or male samples for genes classified as male-only enriched in chief cells: (i) ARSFP1, (iii) TBL1Y and (iii) RP11-115H13.1. (C) scRNAseq data from analysis of epithelial, endothelial, immune or stromal cell compartments across human tissues from male donors was sourced from Tabula Sapiens (Tabula Sapiens et al., [7]), and used to generate UMAP plots showing the expression profiles of: (i) ARSFP1, (iii) TBL1Y and (iii) RP11-115H13.1. (D) Expression of: (i) ARSFP1, (iii) TBL1Y and (iii) RP11-115H13.1 in bulk RNAseq of different human organs from male donors. The largest plot shows the compartment with the highest expression. Mean expression is annotated for selected organs on each plot. See also Table S2 Tab 1, Figure S2 (for all UMAP plot annotations) and Figure S3

Full size image

Discussion

Here, we present a genome-wide cell type-enriched transcriptome atlas for the human stomach, using our previously described method to resolve unfractionated tissue RNAseq data to the cell type level [24,25,26]. Our method circumvents some challenges associated with scRNAseq analysis, including issues associated with cell isolation, material amplification [18, 39, 40], and induction of expression artefacts, due to loss of tissue-specific cues or processing [15]. Our analysis incorporates a high number of biological replicates, reducing the impact of individual variation and allowing for well-powered subgroup comparisons, e.g. female vs. male. As data for gene enrichment signatures of stomach cell types are lacking in the existing literature, with this organ absent from large-scale scRNAseq initiatives, such as Tabula Sapiens [7] and the Human Cell Atlas [8], our study provides a useful resource, which can be searched on a gene-by-gene basis on the HPA (www.proteinatlas.org/humanproteome/tissue+cell+type/stomach) or https://cell-enrichment.shinyapps.io/noncoding_stomach/, for protein coding and non-coding genes, respectively.

Of the 11 cell types we profiled in the stomach, gastric mucous cells had the highest number of predicted enriched genes, which included those encoding for proteins with known cell type-specific functions, such as in mucosal defence, e.g. CAPN8, CAPN9 [41], GKN1 [42], MUC13 [43], TFF1, and TFF2 [44], and lipid metabolism, e.g. PLPP2 [45], PPARG [46], and PLA2G10 [47]. In addition, several genes we identified have no reported role in this cell type, including FAM83E, CYP2S1, and PLAC8. It was not possible to identify discrete expression signatures for gastric mucous cell subsets, i.e. MUC6 + gland mucous cells vs. MUC5AC + pit mucous cells. As MUC5AC was classified as strongly gastric mucous cell enriched in our analysis, one could speculate that the profiled population is primarily composed of pit mucous cells, which is consistent with the higher prevalence of this sub-type in the region of the stomach (body) that was analysed [48].

Predicted gastric enteroendocrine-enriched genes also included those with known cell type function, such as CAMK2B, which is involved in intracellular calcium signalling [49], and the neuroendocrine secretory protein CHGA [50]. Other predicted gastric enteroendocrine enriched genes had not been described in gastric enteroendocrine cells previously, such as LHX5, SERPINA10, and KCNH6. LHX5 has mainly been studied in the context of neuronal development [51, 52], but in the GTEx database, the only tissue type, outside the brain, where LHX5 had elevated expression compared to others was the stomach [27]; thus, one could speculate that this gene also has a specific functional role here. SERPINA10 was previously identified as a biomarker for gastrointestinal neuroendocrine carcinoma [53], and KCNH6 has a role in the regulation of insulin secretion in the pancreas [54]; both were consistent with our prediction that these genes have an endocrine cell enriched profile.

Many genes we predicted to be parietal cell enriched were well-known markers of this cell type, such as GIF [55] and SLC26A7 [56]. However, others had no reported cell type-specific expression or function, such as ACSS1, a mitochondrial matrix protein functioning as a catalyst of acetyl-CoA synthesis [57] and MFSD4, a marker for hepatic metastasis in gastric cancer [58]. Our classifications were supported by a scRNAseq study that showed elevated expression of ACSS1 and MFSD4 in parietal cells vs. other stomach epithelial cells [9]. Other predicted enriched genes for which a function in parietal cells has not yet been described included SLC12A3, ETNPPL, FNDC10, TUBA3C, TRIM73, TRIM74, and CLCNKA. Chief cell-enriched genes included BHLHA15, a known chief cell marker [59], and KIAA1324, which is required for chief cell secretory granule maturation [60]. Novel predicted chief cell-enriched genes included the orphan receptor GPR150, a G-protein coupled receptor in which aberrant methylation has been linked to ovarian cancer [61]; MOGAT1, a monoacylglycerol acyltransferase that functions in the absorption of dietary fat in the intestine [62]; and LIPK, previously identified in the epidermis with a function in lipid metabolism [63].

Whilst there is no existing database of non-coding gene enrichment profiles in the cell types of the stomach, and a lack of information regarding the function of any such genes in normal tissue, increasing evidence of the involvement of non-coding genes in the development of gastric cancer [22, 64, 65] and associated drug resistance [66] indicates that this transcript class has important functions in this tissue type. Of the stomach-specialised cell types we profiled, gastric mucous cells had the highest number of predicted enriched non-coding genes, which included several antisense transcripts to corresponding gastric mucous cell-enriched protein-coding genes, such as SOX21-AS1 and TRIM31-AS1, suggesting a local regulation of gene transcription. Many gastric mucous cell enriched non-coding genes were expressed at relatively high levels, compared to other non-coding genes in the same or other cell types, including LINC01133, FER1L4, RP11-363E7.4, and CTD-2396E7.11. LINC01133 and the pseudogene FER1L4 are inhibitors of gastric cancer progression, with reduced expression associated with a more aggressive tumour phenotype [67, 68]. To date, there is a single publication on RP11-363E7.4, where a genome-wide screen of gastric cancer samples identified it as a key regulator of disease progression, with higher expression associated with overall survival [69]. All the aforementioned studies were based on analysis of bulk RNAseq cancer samples, and the cell type in which these genes primarily function in healthy tissue is not reported; our data strongly indicates that this site is the mucous cell compartment. CTD-2396E7.11 has not been described in the context of gastric cancer, but it was identified as one of four hub lncRNAs associated with reduced colon adenocarcinoma progression [70]. As this tumour type also arises from the mucosa, one could speculate CTD-2396E7.11 has a similar expression profile in healthy colon tissue. LIN00982, the highest expressed of all classified non-coding genes, was enriched in parietal cells and had, similar to those discussed above been shown to have a role in the inhibition of gastric cancer progression [71].

Examples of non-coding genes we predicted to have gastric enteroendocrine cell-enriched expression included MIR7-3HG and RP5-984P4.6. The selective expression of these genes in pancreatic and intestinal endocrine cells [7] is consistent with them having a conserved endocrine function. MIR7-3HG can act as an autophagy inhibitor [72], but there are no reports of its function in an endocrine context. RP5-984P4.6 is currently completely uncharacterised. Other gastric enteroendocrine cell enriched non-coding genes included LHX5-AS1, an antisense transcript to the gastric enteroendocrine cell enriched corresponding protein-coding gene.

Despite reported differences in stomach function between males and females, such as in speed of gastric emptying [73], gastrointestinal motility [74], incidence of gastric cancer [75], and gastric cancer survival [76], there are no studies of sex differences between stomach cell-type gene enrichment profiles. We found that global cell type gene enrichment signatures were similar between sexes, but we did identify 3 male-only chief cell-enriched genes—ARSFP1, RP11-115H13.1, and TBL1Y, all of which were Y-linked [77, 78]. In the GTEx database, the pseudogene ARSFP1 was most highly expressed in male stomach samples, compared to the other 53 tissue types profiled from males [27], supportive of a currently unknown sex and tissue-specific role, and consistent with our predicted enrichment in a stomach-specific cell type in males. Although it is often assumed that pseudogenes lack function, recent studies have shown that they can have key roles, functioning as antisense, interference or competing endogenous transcripts [79,80,81]. RP11-115H13.1 was one of only eight lncRNAs identified as associated with a high risk of gastric cancer [82], but the dataset analysed in this study contained both male and female samples, meaning the prognostic value of RP11-115H13.1 in male patients was likely underestimated. To our knowledge, there are no existing reports of the potential cellular function of RP11-115H13.1 or ARSFP1. TBL1Y has been reported as involved in syndromic hearing loss [83] and cardiac differentiation [84], but studies of its function in the stomach are lacking.

There are limitations in our study. The RNAseq data we analysed is generated from samples taken from the corpus (body) of the stomach, so specialised cell profiles found in other regions of the stomach may not be represented in our dataset. We do not profile cell subtypes, such as those included under the umbrella term of ‘gastric enteroendocrine cells’ including D-cells and G-cells, for which it was not possible to identify Ref.T. that fulfilled the required criteria. Our observations are consistent with these sub-cell types being typically defined by the expression of a limited number of specialised proteins [85,86,87], rather than large distinct gene signature panels. Gene expression in the stomach can be modified by genetic or environmental factors, such as the individual variation in the gastrointestinal microbiome [88]. Strongly regulated genes may therefore not correlate with the more constitutively expressed Ref.T. selected to represent the cell type in which they are primarily expressed, as variation across samples could be independent of cell type proportions. Thus, such genes could be false negatives in our analysis. Furthermore, we have used high thresholds for the classification of genes as cell type-enriched, which could lead to incorrect exclusion. For example, tissue profiling showed that proteins encoded by MUC4 and MUC5B are selectively expressed in gastric mucous cells [89], but they fall just below the threshold for classification as such in our analysis. In addition, the exclusion of lowly expressed genes from the analysis may also result in false-negative classifications for rarer cell types, for example, PAX6, which controls endocrine cell differentiation [90], and proglucagon [91] and gastric inhibitory polypeptide [92] production, was excluded from classification as a gastric enteroendocrine enriched gene only due to expression level below the designated cut off. However, in all cases the individual enrichment scores clearly indicate a cell-type enriched expression; thus, our classifications should be regarded as a guide, and the data should be considered on a gene-by-gene basis.

Conclusions

Here, we present a genome-wide cell type-enriched transcriptome atlas for the human stomach and provide an open access database for the research community.

Methods

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Dr. Lynn Marie Butler (email: Lynn.butler@ki.se).

Experimental model and subject details

Bulk RNAseq data analysed in this study was obtained from the Genotype-Tissue Expression (GTEx) Project (gtexportal.org) [27] accessed on 2021/04/26 (dbGaP Accession phs000424.v8.p2). Transcript types were categorised according to Biotype definitions in ENSEMBL release 102 [35]. Human tissue protein profiling was performed in-house as part of the HPA project [6, 93, 94] (www.proteinatlas.org). Human stomach tissue samples were obtained from the Department of Pathology, Uppsala University Hospital, Uppsala, Sweden, as part of the Uppsala Biobank. Samples were handled in accordance with Swedish laws and regulations, with approval from the Uppsala Ethical Review Board [6].

Method details

Tissue profiling: human tissue sections

Stomach tissue sections were stained, as previously described [6, 93]. Briefly, formalin-fixed and paraffin-embedded tissue samples were sectioned, de-paraffinised in xylene, hydrated in graded alcohols, and blocked for endogenous peroxidase in 0.3% hydrogen peroxide diluted in 95% ethanol. For antigen retrieval, a Decloaking chamber® (Biocare Medical, CA) was used. Slides were boiled in Citrate buffer®, pH6 (Lab Vision, CA). Primary antibodies and a dextran polymer visualisation system (UltraVision LP HRP polymer®, Lab Vision) were incubated for 30 min each at room temperature, and slides were developed for 10 min using Diaminobenzidine (Lab Vision) as the chromogen. Slides were counterstained in Mayers haematoxylin (Histolab) and scanned using Scanscope XT (Aperio). Primary antibodies, source, target, and identifier are as follows: atlas antibodies: ACSS1 (Cat#HPA043228, RRID:AB_2678372), ATP4A (Cat#HPA076684, RRID:AB_10672772), ATP4B (Cat#HPA045400, RRID:AB_2679314), MFSD4A (Cat#055407), SH3GL2 (Cat#HPA026685, RRID:AB_1856817), SLC9A3 (Cat#HPA036493, RRID:AB_10673353), TPCN2 (Cat#HPA027080, RRID:AB_10600917), CEBPA (Cat#HPA065037, RRID:AB_2685410), LIPF (Cat#HPA045930, RRID:AB_10959518), SPTBN2 (Cat#HPA043529, RRID:AB_2678531), BHLHA15 (Cat#HPA047834, RRID:AB_2680172), KIAA1324 (Cat#HPA029869, RRID:AB_10794320), PGC (Cat#HPA031717, RRID:AB_10670130), CAMK2B (Cat#HPA053973, RRID:AB_2682328), SLC18A1 (Cat#HPA063797, RRID:AB_2685125), MS4A8 (Cat#HPA007319, RRID:AB_1854138), NKX2-2 (Cat#HPA003468, RRID:AB_1079490), TFF2 (Cat#HPA036705, RRID:AB_2675263), VILL (Cat#HPA035675, RRID:AB_10671223), CTSE (Cat#HPA012940, RRID:AB_2668773), FER1L6 (Cat#HPA054117, RRID:AB_2682387), LGALS4 (Cat#HPA031186, RRID:AB_2673778), PLAC8 (Cat#HPA040465, RRID:AB_10794875), CCNB1 (Cat#HPA061448, RRID:AB_2684522), DLGAP5 (Cat#HPA005546, RRID:AB_1078677), TPX2 (Cat#HPA005487, RRID:AB_1858223), PECAM1 (Cat#HPA004690, RRID:AB_1078462), CD93 (Cat#HPA009300, RRID:AB_1846342), MFAP2 (Cat#HPA007354, RRID:AB_1079365), MFAP4 (Cat#HPA054097, RRID:AB_2682378) EMILIN1 (Cat#HPA002822, RRID:AB_1078738), AIF1 (Cat#HPA049234, RRID:AB_2680685), ITGB2 (Cat#HPA016894, RRID:AB_1846257), CXCR2 (Cat#HPA032017, RRID:AB_2674112), PADI4 (Cat#HPA017007, RRID:AB_1854921), S100A12 (Cat#HPA002881, RRID:AB_1848175), CD2 (Cat#HPA003883, RRID:AB_1846263), CD3E (Cat#HPA043955, RRID:AB_2678747), IGHA1 (Cat#HPA001217, RRID:AB_1079120), JCHAIN (Cat#HPA044132, RRID:AB_2678826) and MZB1 (Cat#HPA043745, RRID:AB_10960359) SCG5 (Cat#HPA013136, RRID:AB_1856657), DPEP1 (Cat#HPA01278, RRID:AB_1847842), VWA5B2 (atlas antibodies Cat#HPA036823, RRID:AB_10672269), from Santa Cruz Biotechnology: AZGP1 (Cat#sc-13585, RRID:AB_667849), BIRC5 (Cat#sc-17779, RRID:AB_628302), CDC20 (Cat#sc-13162, RRID:AB_628089), S1PR1 (Cat#sc-48356, RRID:AB_2238920), FCGR3A (Cat#sc-20052, RRID:AB_626925) from Agilent: CD8A (Cat#M7103, RRID:AB_2075537) from Leica Biosystems: TOP2A (Cat#NCL-TOPOIIA, RRID:AB_564035), TFF1 (Cat#NCL-pS2, RRID:AB_563985) from Epitomics an AbCam company: CDK1 (Cat#1161–1, RRID:AB_344898) and from Roche: CHGA (Product name: 1199 021).

Quantification and statistical analysis

Reference transcript-based correlation analysis and criteria for cell type enrichment

This method was adapted and expanded from that previously developed to determine the cross-tissue pan-EC-enriched transcriptome [24] and human brain and adipose tissue cell-enriched genes [25, 26].

Human stomach bulk RNAseq data (N = 359) was downloaded from the Genotype-Tissue Expression (GTEx) portal V8 (https://gtexportal.org). Analysed samples were collected from the corpus (body) of the stomach, and donor age groups were represented as follows: 2029 years n = 44, 30–39 years n = 39, 40–49 years n = 64, 50–59 years n = 128, and 60–70 years n = 84. Pairwise Spearman correlation coefficients were calculated between reference transcripts selected as proxy markers (‘Ref.T. panels’) for parietal cells [ATP4B, MFSD4A, ATP4A], chief cells [PGC, LIPF, AZGP1], gastric enteroendocrine cells [ST18, INSM1, ARX], gastric mucous cells [LGALS4, VILL, CAPN8], mitotic cells [NCAPG, KIFC1, NCAPH], endothelial cells [PECAM1, CDH5, ERG], fibroblasts [PCOLCE, CLEC11A, MMP2], macrophages [C1QB, FCGR3A, ITGB2], neutrophils [CXCR2, FCGR3B, CXCR1], T-cells [CD3E, CD2, CD3G], and plasma cells [IGKC, JCHAIN, IGLC1] and all other sequenced transcripts. Correlation coefficients were calculated in R using the corr.test function from the psych package (v 1.8.4) and false discovery rate (FDR) adjusted p-values (using Bonferroni correction), and raw p-values were calculated. Genes were classified as cell type enriched when the following criteria were fulfilled: (i) a mean correlation > 0.50 (FDR < 0.0001) with the Ref.T. panel representing that cell type, (ii) a minimum ‘differential correlation’ between this value and the next highest mean correlation with any other Ref.T. panel (representing another cell type) > 0.15, and (iii) TPM expression < 0.1 in over 50% of samples. See Additional file 1: Fig. S1 for the method overview.

Weighted correlation network (WGCNA) analysis

The R package WGCNA [36] was used to perform co-expression network analysis for gene clustering, on log2 expression TPM values. Transcripts with a TPM = 0 in > 50% of samples were excluded prior to WGCNA analysis, leaving 28,254 gene transcripts for analysis. The soft threshold power was chosen based on the scale-free topology index and was set at 19; clustering of genes was performed with modules having a minimum size of 15 genes, resulting in 67 separate modules using the selected soft thresholding power. Dendrogram plots were also created using the WGCNA package.

Gene Ontology and reactome analysis

The Gene Ontology Consortium [32] and PANTHER classification resource [95] were used to identify over-represented terms (biological processes) in each set of predicted cell type enriched genes from the GO ontology (release date 2022–10-13) or reactome (version 77, release date 2021–10-01) databases. Dendrogram plots showing over-represented GO terms in selected cell types were created using the R package clusterProfiler [96, 97].

Additional datasets and analysis

Single-cell RNAseq data was downloaded from Tabula Sapiens [7] and analysed using the Seurat package in R [98], which was also used to create the UMAP plots. Information on tissue-enriched gene expression was downloaded from the HPA tissue atlas [6] or GTEx database [27], as collated in the Harminozome database [37].

Stomach cell type classifications from Zhang et al. [11], based on the analysis of gastric mucosae in premalignant and early-malignant lesions, or Busslinger et al. [9] based on the analysis of healthy stomach epithelia were sourced from the respective supplemental material sections (cell type enrichment = log FC gene expression vs. other cell types > 0.58 and > 1.0, respectively [adjusted p-value cut off < 0.01], see Additional file 2: Table S1 Key Tab for further details). The statistical significance of overlap between predicted cell type-enriched genes in this study and these scRNAseq studies was calculated using a hypergeometric test (Additional file 4: Fig. S3 E).

The HPA Single Cell Type Section [29] (www.proteinatlas.org/humanproteome/single+cell+type) and Panglao DB [31] were used to identify cell type marker genes (see Additional file 2: Table S1, Key Tab for further details) for comparisons with cell type enriched gene predictions.

Visualisation

Unless otherwise indicated, plots and graphs were created using GraphPad Prism version 10, GraphPad Software, Boston, MA, USA, www.graphpad.com. Circular graphs were constructed using the R package circlize [99]. The principle component analysis plot was generated using https://biit.cs.ut.ee/clustvis/ [34]. Some figure sections were created with BioRender.com.

Additional resources

Analysed data for all protein-coding genes is provided on the HPA website: (https://www.proteinatlas.org/humanproteome/tissue+cell+type/stomach). Data for non-coding genes is provided at https://cell-enrichment.shinyapps.io/noncoding_stomach/. The published article includes all datasets generated during this study (Tables S1 and S2). 

Availability of data and materials

This study did not generate new unique reagents.

• This paper analyses existing, publicly available data from the Genotype-Tissue Expression (GTEx) Project (https://gtexportal.org) with accession number phs000424.v8.p2  [27] and single-cell RNAseq data from Tabula Sapiens  [7] retrieved on 2022/07/29.

• All original code has been deposited at GitHub and is publicly available as of the date of publication, link: https://github.com/PhilipDusart/cell-enrichment  [100].

• No additional information should be required to reanalyse the data reported in this paper, but any necessary clarifications or queries can be directed towards the lead contact.

Abbreviations

CC:

Chief cell

Corr:

Correlation coefficients

EC:

Endothelial cell

FB:

Fibroblast

FDR:

False discovery rate

GEEC:

Gastric enteroendocrine cell

GI:

Gastrointestinal

GMC:

Gastric mucous cell

GO:

Gene Ontology

GTEx:

Genotype-Tissue Expression

HPA:

Human Protein Atlas

IG:

Immunoglobulin

lncRNA:

Long non-coding RNA

MC:

Macrophage

MTC:

Mitotic cell

NP:

Neutrophil

PAC:

Parietal cell

PC:

Plasma cell

Ref.T.:

Reference transcripts

scRNAseq:

Single-cell RNA sequencing

STD:

Standard deviation

TC:

T-cell

TPM:

Transcripts per million

UMAP:

Uniform Manifold Approximation and Projection

WGCNA:

Weighted network correlation analysis

References

  1. de Santa BP, van den Brink GR, Roberts DJ. Development and differentiation of the intestinal epithelium. Cell Mol Life Sci (CMLS). 2003;60(7):1322–32.

    Article  Google Scholar 

  2. Choi E, Roland JT, Barlow BJ, O’Neal R, Rich AE, Nam KT, et al. Cell lineage distribution atlas of the human stomach reveals heterogeneous gland populations in the gastric antrum. Gut. 2014;63(11):1711–20.

    Article  PubMed  Google Scholar 

  3. Thompson CA, DeLaForest A, Battle MA. Patterning the gastrointestinal epithelium to confer regional-specific functions. Dev Biol. 2018;435(2):97–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kim TH, Shivdasani RA. Stomach development, stem cells and disease. Development. 2016;143(4):554–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gremel G, Wanders A, Cedernaes J, Fagerberg L, Hallström B, Edlund K, et al. The human gastrointestinal tract-specific transcriptome and proteome as defined by RNA sequencing and antibody-based profiling. J Gastroenterol. 2015;50(1):46–57.

    Article  CAS  PubMed  Google Scholar 

  6. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.

    Article  PubMed  Google Scholar 

  7. Tabula Sapiens C, Jones RC, Karkanias J, Krasnow MA, Pisco AO, Quake SR, et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science. 2022;376(6594):eabl4896.

    Article  Google Scholar 

  8. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas gingeras TR, editor. eLife. 2017;6:e27041.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Busslinger GA, Weusten BLA, Bogte A, Begthel H, Brosens LAA, Clevers H. Human gastrointestinal epithelia of the esophagus, stomach, and duodenum resolved at single-cell resolution. Cell Rep. 2021;34(10):108819.

    Article  CAS  PubMed  Google Scholar 

  10. Tsubosaka A, Komura D, Katoh H, Kakiuchi M, Onoyama T, Yamamoto A, et al. Single-cell transcriptome analyses reveal the cell diversity and developmental features of human gastric and metaplastic mucosa [Internet]. bioRxiv; 2022 [cited 2022 Jun 13]. p. 2022.05.22.493006. Available from: https://www.biorxiv.org/content/10.1101/2022.05.22.493006v1

  11. Zhang P, Yang M, Zhang Y, Xiao S, Lai X, Tan A, et al. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep. 2019;27(6):1934-1947.e5.

    Article  CAS  PubMed  Google Scholar 

  12. Sathe A, Grimes SM, Lau BT, Chen J, Suarez C, Huang RJ, et al. Single-cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment. Clin Cancer Res. 2020;26(11):2640–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang R, Dang M, Harada K, Han G, Wang F, Pool Pizzi M, et al. Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma. Nat Med. 2021;27(1):141–51.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kim J, Park C, Kim KH, Kim EH, Kim H, Woo JK, et al. Single-cell analysis of gastric pre-cancerous and cancer lesions reveals cell lineage diversity and intratumoral heterogeneity. NPJ Precis Oncol. 2022;6(1):9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. O’Flanagan CH, Campbell KR, Zhang AW, Kabeer F, Lim JLP, Biele J, et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019;20(1):210.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Denisenko E, Guo BB, Jones M, Hou R, de Kock L, Lassmann T, et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 2020;21(1):130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Massoni-Badosa R, Iacono G, Moutinho C, Kulis M, Palau N, Marchese D, et al. Sampling time-dependent artifacts in single-cell genomics studies. Genome Biol. 2020;21(1):112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet. 2016;17(3):175–88.

    Article  CAS  PubMed  Google Scholar 

  19. Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol. 2022;23(1):31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Squair JW, Gautier M, Kathe C, Anderson MA, James ND, Hutson TH, et al. Confronting false discoveries in single-cell differential expression. Nat Commun. 2021;12(1):5692.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Denninger JK, Walker LA, Chen X, Turkoglu A, Pan A, Tapp Z, et al. Robust transcriptional profiling and identification of differentially expressed genes with low input RNA sequencing of adult hippocampal neural stem and progenitor populations. Front Mol Neurosci. 2022;31(15):810722.

    Article  Google Scholar 

  22. Gao Y, Wang JW, Ren JY, Guo M, Guo CW, Ning SW, et al. Long noncoding RNAs in gastric cancer: from molecular dissection to clinical application. World J Gastroenterol. 2020;26(24):3401–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Razavi H, Katanforosh A. Identification of novel key regulatory lncRNAs in gastric adenocarcinoma. BMC Genomics. 2022;23(1):352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Butler LM, Hallström BM, Fagerberg L, Pontén F, Uhlén M, Renné T, et al. Analysis of body-wide unfractionated tissue data to identify a core human endothelial transcriptome. Cell Syst. 2016;3(3):287-301.e3.

    Article  CAS  PubMed  Google Scholar 

  25. Dusart P, Hallström BM, Renné T, Odeberg J, Uhlén M, Butler LM. A systems-based map of human brain cell-type enriched genes and malignancy-associated endothelial changes. Cell Rep. 2019;29(6):1690-1706.e4.

    Article  CAS  PubMed  Google Scholar 

  26. Norreen-Thorsen M, Struck EC, Öling S, Zwahlen M, Von Feilitzen K, Odeberg J, et al. A human adipose tissue cell-type transcriptome atlas. Cell Rep. 2022;40(2):111046.

    Article  CAS  PubMed  Google Scholar 

  27. Consortium GTe. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60.

    Article  Google Scholar 

  28. Hassan MI, Toor A, Ahmad F. Progastriscin: structure, function, and its role in tumor progression. J Mol Cell Biol. 2010;2(3):118–27.

    Article  CAS  PubMed  Google Scholar 

  29. Karlsson M, Zhang C, Mear L, Zhong W, Digre A, Katona B, et al. A single-cell type transcriptomics map of human tissues. Sci Adv. 2021;7(31). Available from: https://www.ncbi.nlm.nih.gov/pubmed/34321199

  30. Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, et al. Cell Marker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019;47(D1):D721-8.

    Article  CAS  PubMed  Google Scholar 

  31. Franzen O, Gan LM, Bjorkegren JLM. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford) [Internet]. 2019;2019. Available from: https://www.ncbi.nlm.nih.gov/pubmed/30951143

  32. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gene Ontology C. The Gene Ontology resource: enriching a gold mine. Nucleic Acids Res. 2021;49(D1):D325-34.

    Article  Google Scholar 

  34. Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res. 2015;43(Web Server issue):W566-70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48(D1):D682–8.

    CAS  PubMed  Google Scholar 

  36. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford). 2016;2016. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27374120

  38. Wang S, Pisco AO, McGeever A, Brbic M, Zitnik M, Darmanis S, et al. Leveraging the cell ontology to classify unseen cell types. Nat Commun. 2021;12(1):5556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14(9):618–30.

    Article  CAS  PubMed  Google Scholar 

  40. Grün D, van Oudenaarden A. Design and analysis of single-cell sequencing experiments. Cell. 2015;163(4):799–810.

    Article  PubMed  Google Scholar 

  41. Hata S, Abe M, Suzuki H, Kitamura F, Toyama-Sorimachi N, Abe K, et al. Calpain 8/nCL-2 and calpain 9/nCL-4 constitute an active protease complex, G-calpain, involved in gastric mucosal defense. PLoS Genet. 2010;6(7):e1001040.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Choi WS, Seo HS, Song KY, Yoon JH, Kim O, Nam SW, et al. Gastrokine 1 expression in the human gastric mucosa is closely associated with the degree of gastritis and DNA methylation. J Gastric Cancer. 2013;13(4):232–41.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ja G, Yh K, Pm F, S H, Wi K. Mucins in intestinal mucosal defense and inflammation: learning from clinical and experimental studies. Front Immunol. 2020;11. [cited 2022 Oct 20]. Available from: https://pubmed.ncbi.nlm.nih.gov/33013869/

  44. Aihara E, Engevik KA, Montrose MH. Trefoil factor peptides and gastrointestinal function. Annu Rev Physiol. 2017;10(79):357–80.

    Article  Google Scholar 

  45. Hooks SB, Ragan SP, Lynch KR. Identification of a novel human phosphatidic acid phosphatase type 2 isoform. FEBS Lett. 1998;427(2):188–92.

    Article  CAS  PubMed  Google Scholar 

  46. Kang Y, Hengbo S, Jun L, Jun L, Wangsheng Z, Huibin T, et al. PPARG modulated lipid accumulation in dairy GMEC via regulation of ADRP gene. J Cell Biochem. 2015;116(1):192–201.

    Article  CAS  PubMed  Google Scholar 

  47. Hanasaki K, Yamada K, Yamamoto S, Ishimoto Y, Saiga A, Ono T, et al. Potent modification of low density lipoprotein by group x secretory phospholipase A2 is linked to macrophage foam cell formation*. J Biol Chem. 2002;277(32):29116–24.

    Article  CAS  PubMed  Google Scholar 

  48. Ho SB, Takamura K, Anway R, Shekels LL, Toribara NW, Ota H. The adherent gastric mucous layer is composed of alternating layers of MUC5AC and MUC6 mucin proteins. Dig Dis Sci. 2004;49(10):1598–606.

    Article  CAS  PubMed  Google Scholar 

  49. Tsakmaki A, Fonseca Pedro P, Pavlidis P, Hayee B, Bewick GA. ISX-9 manipulates endocrine progenitor fate revealing conserved intestinal lineages in mouse and human organoids. Molecular Metabolism. 2020;1(34):157–73.

    Article  Google Scholar 

  50. Goldspink DA, Reimann F, Gribble FM. Models and tools for studying enteroendocrine cells. Endocrinology. 2018;159(12):3874–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhao Y, Sheng HZ, Amini R, Grinberg A, Lee E, Huang S, et al. Control of hippocampal morphogenesis and neuronal differentiation by the LIM homeobox gene Lhx5. Science. 1999;284(5417):1155–8.

    Article  CAS  PubMed  Google Scholar 

  52. Pillai A, Mansouri A, Behringer R, Westphal H, Goulding M. Lhx1 and Lhx5 maintain the inhibitory-neurotransmitter status of interneurons in the dorsal spinal cord. Development. 2007;134(2):357–66.

    Article  CAS  PubMed  Google Scholar 

  53. Leja J, Essaghir A, Essand M, Wester K, Oberg K, Tötterman TH, et al. Novel markers for enterochromaffin cells and gastrointestinal neuroendocrine carcinomas. Mod Pathol. 2009;22(2):261–72.

    Article  CAS  PubMed  Google Scholar 

  54. Yang JK, Lu J, Yuan SS, Asan, Cao X, Qiu HY, et al. From hyper- to hypoinsulinemia and diabetes: effect of KCNH6 on insulin secretion. Cell Rep. 2018;25(13):3800-3810.e6.

    Article  CAS  PubMed  Google Scholar 

  55. Alpers DH, Russell-Jones G. Gastric intrinsic factor: the gastric and small intestinal stages of cobalamin absorption. A personal journey. Biochimie. 2013;95(5):989–94.

    Article  CAS  PubMed  Google Scholar 

  56. Petrovic S, Ju X, Barone S, Seidler U, Alper SL, Lohi H, et al. Identification of a basolateral Cl−/HCO 3 − exchanger specific to gastric parietal cells. Am J Physiol Gastrointestin Liver Physiol. 2003;284(6):G1093-103.

    Article  CAS  Google Scholar 

  57. Schwer B, Bunkenborg J, Verdin RO, Andersen JS, Verdin E. Reversible lysine acetylation controls the activity of the mitochondrial enzyme acetyl-CoA synthetase 2. Proc Natl Acad Sci. 2006;103(27):10224–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shimizu D, Kanda M, Kodera Y. Emerging evidence of the molecular landscape specific for hematogenous metastasis from gastric cancer. World J Gastrointest Oncol. 2018;10(6):124–36.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lennerz JKM, Kim SH, Oates EL, Huh WJ, Doherty JM, Tian X, et al. The transcription factor MIST1 is a novel human gastric chief cell marker whose expression is lost in metaplasia, dysplasia, and carcinoma. Am J Pathol. 2010;177(3):1514–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cho CJ, Park D, Mills JC. ELAPOR1 is a secretory granule maturation-promoting factor that is lost during paligenosis. Am J Physiol Gastroint Liver Physiol. 2022;322(1):49–65.

    Article  Google Scholar 

  61. Cai LY, Abe M, Izumi SI, Imura M, Yasugi T, Ushijima T. Identification of PRTFDC1 silencing and aberrant promoter methylation of GPR150, ITGA8 and HOXD11 in ovarian cancers. Life Sci. 2007;80(16):1458–65.

    Article  CAS  PubMed  Google Scholar 

  62. Yen CLE, Stone SJ, Cases S, Zhou P, Farese RV. Identification of a gene encoding MGAT1, a monoacylglycerol acyltransferase. Proc Natl Acad Sci U S A. 2002;99(13):8512–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Toulza E, Mattiuzzo NR, Galliano MF, Jonca N, Dossat C, Jacob D, et al. Large-scale identification of human genes implicated in epidermal barrier function. Genome Biol. 2007;8(6):R107.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ghafouri-Fard S, Taheri M. Long non-coding RNA signature in gastric cancer. Exp Mol Pathol. 2020;1(113):104365.

    Article  Google Scholar 

  65. Li PF, Chen SC, Xia T, Jiang XM, Shao YF, Xiao BX, et al. Non-coding RNAs and gastric cancer. World J Gastroenterol. 2014;20(18):5411–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wei L, Sun J, Zhang N, Zheng Y, Wang X, Lv L, et al. Noncoding RNAs in gastric cancer: implications for drug resistance. Mol Cancer. 2020;19(1):62.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Xia T, Chen S, Jiang Z, Shao Y, Jiang X, Li P, et al. Long noncoding RNA FER1L4 suppresses cancer cell growth by acting as a competing endogenous RNA and regulating PTEN expression. Sci Rep. 2015;5(1):13445.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Yang XZ, Cheng TT, He QJ, Lei ZY, Chi J, Tang Z, et al. LINC01133 as ceRNA inhibits gastric cancer progression by sponging miR-106a-3p to regulate APC expression and the Wnt/β-catenin pathway. Mol Cancer. 2018;17(1):126.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wang P, Li J, Zhao W, Shang C, Jiang X, Wang Y, et al. A novel LncRNA-miRNA-mRNA triple network identifies LncRNA RP11–363E7.4 as an important regulator of miRNA and gene expression in gastric cancer. CPB. 2018;47(3):1025–41.

    CAS  Google Scholar 

  70. Jiang S, Tan B, Zhang X. Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis. J Cell Biochem. 2019;120(4):6490–501.

    Article  CAS  PubMed  Google Scholar 

  71. Zheng L, Cao J, Liu L, Xu H, Chen L, Kang L, et al. Long noncoding RNA LINC00982 upregulates CTSF expression to inhibit gastric cancer progression via the transcription factor HEY1. Am J Physiol Gastrointes Liver Physiol. 2021;320(5):G816-28.

    Article  CAS  Google Scholar 

  72. Capizzi M, Strappazzon F, Cianfanelli V, Papaleo E, Cecconi F. MIR7-3HG, a MYC-dependent modulator of cell proliferation, inhibits autophagy by a regulatory loop involving AMBRA1. Autophagy. 2017;13(3):554–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Datz FL, Christian PE, Moore J. Gender-related differences in gastric emptying. J Nucl Med. 1987;28(7):1204–7.

    CAS  PubMed  Google Scholar 

  74. Al-Shboul O. The role of the RhoA/ROCK pathway in gender-dependent differences in gastric smooth muscle contraction. J Physiol Sci. 2016;66(1):85–92.

    Article  CAS  PubMed  Google Scholar 

  75. Lou L, Wang L, Zhang Y, Chen G, Lin L, Jin X, et al. Sex difference in incidence of gastric cancer: an international comparative study based on the Global Burden of Disease Study 2017. BMJ Open. 2020;10(1):e033323.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Li H, Wei Z, Wang C, Chen W, He Y, Zhang C. Gender differences in gastric cancer survival: 99,922 cases based on the SEER database. J Gastrointest Surg. 2020;24(8):1747–57.

    Article  PubMed  Google Scholar 

  77. Kirsch S, Weiss B, Zumbach K, Rappold G. Molecular and evolutionary analysis of the growth-controlling region on the human Y chromosome. Hum Genet. 2004;114(2):173–81.

    Article  CAS  PubMed  Google Scholar 

  78. Yan HT, Shinka T, Kinoshita K, Sato Y, Umeno M, Chen G, et al. Molecular analysis of TBL1Y, a Y-linked homologue of TBL1X related with X-linked late-onset sensorineural deafness. J Hum Genet. 2005;50(4):175–81.

    Article  CAS  PubMed  Google Scholar 

  79. Pink RC, Wicks K, Caley DP, Punch EK, Jacobs L, Carter DRF. Pseudogenes: pseudo-functional or key regulators in health and disease? RNA. 2011;17(5):792–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kovalenko TF, Patrushev LI. Pseudogenes as functionally significant elements of the genome. Biochemistry Moscow. 2018;83(11):1332–49.

    Article  CAS  PubMed  Google Scholar 

  81. Cheetham SW, Faulkner GJ, Dinger ME. Overcoming challenges and dogmas to understand the functions of pseudogenes. Nat Rev Genet. 2020;21(3):191–201.

    Article  CAS  PubMed  Google Scholar 

  82. Zhao X, Wu P, Liu D, Li C, Xue L, Liu Z, et al. An immunity-associated lncRNA signature for predicting prognosis in gastric adenocarcinoma. J Healthcare Eng. 2022;25(2022):e3035073.

    Google Scholar 

  83. Di Stazio M, Collesi C, Vozzi D, Liu W, Myers M, Morgan A, et al. TBL1Y: a new gene involved in syndromic hearing loss. Eur J Hum Genet. 2019;27(3):466–74.

    Article  PubMed  Google Scholar 

  84. Meyfour A, Ansari H, Pahlavan S, Mirshahvaladi S, Rezaei-Tavirani M, Gourabi H, et al. Y chromosome missing protein, TBL1Y, may play an important role in cardiac differentiation. J Proteome Res. 2017;16(12):4391–402.

    Article  CAS  PubMed  Google Scholar 

  85. Sjölund K, Sandén G, Håkanson R, Sundler F. Endocrine cells in human intestine: an immunocytochemical study. Gastroenterology. 1983;85(5):1120–30.

    Article  PubMed  Google Scholar 

  86. Engelstoft MS, Egerod KL, Lund ML, Schwartz TW. Enteroendocrine cell types revisited. Curr Opin Pharmacol. 2013;13(6):912–21.

    Article  CAS  PubMed  Google Scholar 

  87. Gribble FM, Reimann F. Enteroendocrine cells: chemosensors in the intestinal epithelium. Annu Rev Physiol. 2016;78(1):277–99.

    Article  CAS  PubMed  Google Scholar 

  88. Nichols RG, Davenport ER. The relationship between the gut microbiome and host gene expression: a review. Hum Genet. 2021;140(5):747–60.

    Article  CAS  PubMed  Google Scholar 

  89. Uhlen M, Karlsson MJ, Zhong W, Tebani A, Pou C, Mikes J, et al. A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science. 2019;366(6472):eaax9198.

    Article  CAS  PubMed  Google Scholar 

  90. Beucher A, Gjernes E, Collin C, Courtney M, Meunier A, Collombat P, et al. The homeodomain-containing transcription factors Arx and Pax4 control enteroendocrine subtype specification in mice. PLoS ONE. 2012;7(5):e36449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Hill ME, Asa SL, Drucker DJ. Essential requirement for Pax6 in control of enteroendocrine proglucagon gene transcription. Mol Endocrinol. 1999;13(9):1474–86.

    Article  CAS  PubMed  Google Scholar 

  92. Fujita Y, Chui JWY, King DS, Zhang T, Seufert J, Pownall S, et al. Pax6 and Pdx1 are required for production of glucose-dependent insulinotropic polypeptide in proglucagon-expressing L cells. Am J Physiol-Endocrinol Metab. 2008;295(3):E648–57.

    Article  CAS  PubMed  Google Scholar 

  93. Ponten F, Jirstrom K, Uhlen M. The human protein atlas - a tool for pathology. J Pathol. 2008;216(4):387–93.

    Article  CAS  PubMed  Google Scholar 

  94. Uhlen M, Zhang C, Lee S, Sjostedt E, Fagerberg L, Bidkhori G, et al. A pathology atlas of the human cancer transcriptome. Science [Internet]. 2017;357(6352). Available from: https://www.ncbi.nlm.nih.gov/pubmed/28818916

  95. Mi H, Muruganujan A, Casagrande JT, Thomas PD. Large-scale gene function analysis with the PANTHER classification system. Nat Protoc. 2013;8(8):1551–66.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2(3):100141.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Mi H, Poudel S, Muruganujan A, Casagrande JT, Thomas PD. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 2016;44(D1):D336–42.

    Article  CAS  PubMed  Google Scholar 

  98. Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30(19):2811–2.

    Article  CAS  PubMed  Google Scholar 

  100. Dusart P. figshare. figshare; 2023 [cited 2023 Dec 15]. Cell-enrichment. Available from: https://figshare.com/articles/code/cell-enrichment/24827706/1

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Acknowledgements

The Human Protein Atlas is funded by the Knut and Alice Wallenberg Foundation. We used data from the Genotype-Tissue Expression (GTEx) Project (gtexportal.org)  [27] supported by the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.

Funding

Funding was granted to LMB from Hjärt Lungfonden (20170759, 20170537, 20200544) and Swedish Research Council (2019–01493) and to JO from Stockholm County Council (SLL 2017–0842). Open access funding provided by Karolinska Institute.

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Authors and Affiliations

  1. Department of Clinical Medicine, Translational Vascular Research, The Arctic University of Norway, 9019, Tromsø, Norway

    S. Öling, E. Struck, M. Noreen-Thorsen, J. Odeberg & L. M. Butler

  2. Science for Life Laboratory, Department of Protein Science, Royal Institute of Technology (KTH), 171 21, Stockholm, Sweden

    M. Zwahlen, K. von Feilitzen, J. Odeberg, M. Uhlén, P. Dusart & L. M. Butler

  3. The University Hospital of North Norway (UNN), 9019, Tromsø, Norway

    J. Odeberg

  4. Department of Haematology, Coagulation Unit, Karolinska University Hospital, 171 76, Stockholm, Sweden

    J. Odeberg

  5. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 752 37, Uppsala, Sweden

    F. Pontén & C. Lindskog

  6. Clinical Chemistry and Blood Coagulation Research, Department of Molecular Medicine and Surgery, Karolinska Institute, 171 76, Stockholm, Sweden

    P. Dusart & L. M. Butler

  7. Clinical Chemistry, Karolinska University Laboratory, Karolinska University Hospital, 171 76, Stockholm, Sweden

    P. Dusart & L. M. Butler

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  1. S. Öling
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  11. L. M. Butler
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Contributions

Conceptualisation: LMB. Methodology: SÖ, ES, MNT, and PD. Formal analysis: SÖ, PD, and LMB. Investigation SÖ, PD, LMB, and CL. Resources: MU, FP, JO, LB, and CL. Writing—original draft: SÖ and LMB. Writing—review and editing: all. Visualisation: SÖ, LMB, PD, MZ, and KVF. Supervision: LMB and PD. Funding acquisition: LMB and JO. All authors read and approved the final manuscript.

Corresponding author

Correspondence to L. M. Butler.

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Ethics approval and consent to participate

See Genotype-Tissue Expression (GTEx) Project (https://gtexportal.org) [27] and Tabula Sapiens (https://tabula-sapiens-portal.ds.czbiohub.org/) [7].

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

Supplementary Fig. S1.

Additional file 2:

Table S1. Reference transcript selection and analysis criteria. (Tab 1): Correlation coefficient values were calculated between selected Ref.T. to represent constituent stomach cell types. (Tab 2): Correlation coefficient values were calculated between selected Ref.T. and all other sequenced transcripts in GTEx stomach mRNAseq data (Table A) and the mean differential vs. all Ref.T. panels (Table B). Genes classified as enriched in: (Tab 3) parietal cells, (Tab 4) chief cells, (Tab 5) gastric enteroendocrine cells, (Tab 6) gastric mucous cells, (Tab 7) mitotic cells, (Tab 8) endothelial cells, (Tab 9) fibroblasts, (Tab 10) macrophages, (Tab 11) neutrophils, (Tab 12) T-cells and (Tab 13) plasma cells were analysed to identify over-represented terms in the (Table A) gene ontology or (Table B). (Tab 14) Comparison of our cell type enrichment predictions with scRNAseq datasets. See key for column details. Related to all Figures.

Additional file 3: Supplementary Fig. S2

.

Additional file 4: Supplementary Fig. S3

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Additional file 5: Table S2.

Sex stratified subset analysis of cell-enriched genes in human stomach. (Tab 1): Correlation coefficient values were calculated between selected Ref.T. to represent constituent stomach cell types in females (Table A) or males (Table B). (Tab 2) Correlation coefficient values were calculated between selected Ref.T. and all other sequenced transcripts in stomach mRNAseq data (GTEx), subdivided into (Table A) female or (Table B) male only sample sets. See key for column details. Related to Fig. 7 and Fig. S4.

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Öling, S., Struck, E., Noreen-Thorsen, M. et al. A human stomach cell type transcriptome atlas. BMC Biol 22, 36 (2024). https://doi.org/10.1186/s12915-024-01812-5

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  • DOI: https://doi.org/10.1186/s12915-024-01812-5

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BMC Biology

ISSN: 1741-7007

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