Unraveling IBD Mechanisms with Single-Cell and Spatial Technologies

1. The Target Identification Problem in IBD

The core challenge in Inflammatory Bowel Disease (IBD) research is tissue heterogeneity. Inflammation affects different regions of the intestine and different cell types in distinct ways, making it difficult to interpret gene expression changes. Without knowing which cell type expresses a gene and where it appears in the tissue, it becomes nearly impossible to evaluate whether that gene is truly involved in disease mechanisms.

This lack of spatial and cellular resolution hinders understanding of how inflammation develops at the epithelial barrier and how different cell populations interact within affected regions.

To overcome this limitation, we used a multi-omic workflow in Talk2Data that integrates single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptomics, and examined tissue localization using SpatialX. This combined approach allowed us to pinpoint which cell types express disease-associated genes and assess their spatial relevance within inflamed intestinal tissue.

Figure 1. Overview of inflammatory bowel disease and affected tissue regions. Designed by Freepik

2. BioTuring’s Multi-Omic, Cross-Study Target Evaluation Workflow

The analysis began with a list of IBD-associated genes from the Open Targets database. This platform aggregates evidence from genetics, expression, literature text-mining, and experimental data. The initial list includes well-established IBD-related genes such as NOD2, IL23R, SMAD3, ATG16L1, TNFSF15, and PTGER4, alongside many other candidates supported by varying levels of evidence. Because this list is broad, further refinement is needed by examining how each gene behaves across disease conditions and cell types.

Screening across single-cell datasets

We first screened the gene list across single-cell RNA sequencing studies of the intestine. Datasets were filtered by anatomy, key cell types, and disease state. This allowed us to directly compare expression in epithelial cells, macrophages, T cells, and B cells across normal, ulcerative colitis, and Crohn’s disease. PLCG2 emerged as a strong candidate because it showed a prominent increase in both ulcerative colitis and Crohn’s disease, specifically within epithelial cells.

Figure 2. Single-cell screening of candidate genes across normal, Ulcerative Colitis, and Crohn’s disease conditions.

Validation in bulk RNA and spatial transcriptomics

Bulk RNA datasets confirmed that PLCG2 expression is elevated in diseased samples compared to normal controls. Spatial transcriptomic datasets provided an additional layer of validation by showing increased PLCG2 expression in epithelial regions of ulcerative colitis tissue sections.

Figure 3. Bulk RNA and spatial transcriptomics confirm PLCG2 upregulation in ulcerative colitis and Crohn’s disease.

Analysis tools and AI Sonya

AI Sonya provided a concise summary of PLCG2’s biological functions. PLCG2 hydrolyzes PIP2 to generate IP3 and DAG, which increase intracellular calcium and activate protein kinase C. These pathways have been linked to inflammation and the regulation of the epithelial barrier.

Co-expression analysis identified KYNU and MALAT1 as genes correlated with PLCG2, both of which have documented roles in intestinal inflammation.

Figure 4. AI Sonya summary of PLCG2 function.

Figure 5. Genes correlated with PLCG2 were identified through co-expression analysis.

Custom atlas for cross-study consistency

The custom atlas tool enabled us to generate a disease atlas, for example atlas for ulcerative colitis. Within the atlas, we further explored target gene expression across different cell types and subtypes, as well as different conditions, samples, and any metadata included. In this case, we identified, in the ulcerative colitis condition, that the PLCG2 target gene is highly expressed in colon epithelial cells, suggesting targeting PLCG2 in colon epithelial cells could be a potential approach for targeting ulcerative colitis therapy.

Figure 6. Ulcerative colitis atlas showing that PLCG2 is highly expressed in colon epithelial cells.

Spatial exploration using the spatial database

The spatial database allowed us to search for PLCG2 expression across thousands of spatial slides to have an overview of the expression pattern and search for a particular study of interest. 

Figure 7. Spatial transcriptomic slides showing PLCG2 expression patterns.

3. The Result: A Potential Epithelial Target That Supports Further Investigation

Across single-cell, bulk RNA, and spatial transcriptomic studies, PLCG2 demonstrated a consistent pattern of upregulation in ulcerative colitis and Crohn’s disease. Importantly, its expression was concentrated in epithelial cells. Spatial analysis showed that PLCG2 increased in areas where epithelial organization appeared altered or weakened.

To explore possible functional consequences, we tested the spatial distribution of PLCG2 and TJP1 in normal versus Crohn’s disease tissues. TJP1 is a well-established marker of tight junctions. In Crohn’s disease tissue, PLCG2 is localized in regions where TJP1 signal is diminished (Fig. 8). This inverse pattern suggests a potential relationship between PLCG2 activity and tight junction instability, a common feature of epithelial barrier disruption in IBD.

Figure 8. PLCG2 is less localized with the tight junction marker TJP1 in Crohn’s disease compared to the normal control.

These combined results indicate that PLCG2 may influence epithelial signaling and barrier structure, making it a suitable candidate for further study in IBD models.

PLCG2 can modulate tight junction turnover, actin cytoskeleton, vesicle trafficking, and secretion. We further explored possible mechanisms of how PLCG2 can regulate IBD by a single public spatial transcriptomic dataset using SpatialX.

If you would like to explore this workflow using Talk2Data and SpatialX or apply similar methods to your own dataset, our team can provide a short walkthrough tailored to your research questions. Get a free demo:  https://bioturing.com/spatialx

References:

1. Buniello, A. et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research.