Background
Perianal fistulas are a debilitating complication of Crohn’s disease (CD). Due to unknown reasons, CD-associated fistulas are in general more difficult to treat than cryptoglandular fistulas (non-CD-associated). Understanding the immune cell landscape is a first step towards the development of more effective therapies for CD-associated fistulas. In this work, we characterized the composition and spatial localization of disease-associated immune cells in both types of perianal fistulas by high-dimensional analyses.

Methods
We applied single-cell mass cytometry (scMC), spectral flow cytometry (SFC), and imaging mass cytometry (IMC) to profile the immune compartment in CD-associated perianal fistulas and cryptoglandular fistulas. An exploratory cohort (CD fistula, n = 10; non-CD fistula, n = 5) was analyzed by scMC to unravel disease-associated immune cell types. SFC was performed on a second fistula cohort (CD, n = 10; non-CD, n = 11) to comprehensively phenotype disease-associated T helper (Th) cells. IMC was used on a third cohort (CD, n = 5) to investigate the spatial distribution/interaction of relevant immune cell subsets.

Results
Our analyses revealed that activated HLA-DR+CD38+ effector CD4+ T cells with a Th1/17 phenotype were significantly enriched in CD-associated compared with cryptoglandular fistulas. These cells, displaying features of proliferation, regulation, and differentiation, were also present in blood, and colocalized with other CD4+ T cells, CCR6+ B cells, and macrophages in the fistula tracts.

Conclusions
Overall, proliferating activated HLA-DR+CD38+ effector Th1/17 cells distinguish CD-associated from cryptoglandular perianal fistulas and are a promising biomarker in blood to discriminate between these 2 fistula types. Targeting HLA-DR and CD38-expressing CD4+ T cells may offer a potential new therapeutic strategy for CD-related fistulas.

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.