Publications

Stephan Rogalla, M.D. PhD
Clinical Assistant Professor, Medicine - Gastroenterology & Hepatology

Publications

  • Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis. Research square Rogalla, S., Holman, D., Rubin, S., Ferenc, M., Holman, E., Koron, A., Daniel, R., Boland, B., Nolan, G., Chang, J. 2024

    Abstract

    The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

    View details for DOI 10.21203/rs.3.rs-3965505/v1

    View details for PubMedID 38559236

    View details for PubMedCentralID PMC10980100

  • Looking to Future Applications of Large Language Models. The American journal of gastroenterology Liu, X., Rubin, S. J., Rogalla, S. 2023; 118 (12): 2305

    View details for DOI 10.14309/ajg.0000000000002401

    View details for PubMedID 38033226

  • GPR15 in colon cancer development and anti-tumor immune responses. Frontiers in oncology Namkoong, H., Lee, B., Swaminathan, G., Koh, S. J., Rogalla, S., Paraskevopoulou, M. D., Tang, J., Mikhail, D., Becker, L. S., Habtezion, A. 2023; 13: 1254307

    Abstract

    The chemoattractant receptor, G protein-coupled receptor 15 (GPR15), promotes colon homing of T cells in health and colitis. GPR15 function in colon cancer is largely unexplored, motivating our current studies.In human study, immune cells were isolated from tumor tissues and healthy surgical tumor margins (STM), and their proportions as well as expression of GPR15 was analyzed by flow cytometry. In mouse studies, colon cancer was induced in GPR15-deficient (KO) and GPR15-suficient (Het) mice using azoxymethane (AOM) and dextran sulfate sodium (DSS) solution in drinking water. Serial endoscopy was performed in mice to monitor and visualize the distal region of colon. Mice were euthanized 10 weeks after the initial DSS administration, and the colon length and the number of polyps were recorded. Next, we identified the effects of GPR15L on established tumors in the MC38-colorectal cancer (CRC) mouse model. Immune cells were isolated from the mice colons or tumors and assessed by flow cytometry.Our analysis of human CRC tissue revealed a significant reduction in GPR15+ immune cell frequencies in tumors compared to 'tumor-free' surgical margins. Similarly, our data analysis using The Cancer Genome Atlas (TCGA) indicated that lower GPR15 expression is associated with poor survival in human colon cancer. In the AOM/DSS colitis-associated colon cancer model, we observed increased colonic polyps and lower survival in Gpr15 +-KO compared to Gpr15-Het mice. Analysis of immune cell infiltrates in the colonic polyps showed significantly decreased CD8+ T cells and increased IL-17+ CD4+ and IL-17+ CD8+ T cells in Gpr15-KO than in Het mice. Consistent with a protective role of GPR15, administration of GPR15L to established tumors in the MC38-CRC model increased CD45+ cell infiltration, enhanced TNFa expression on CD4+ and CD8+ T cells at the tumor site and dramatically reduced tumor burden.Our findings highlight an important, unidentified role of the GPR15-GPR15L axis in promoting a tumor-suppressive immune microenvironment and unveils a novel, colon-specific therapeutic target for CRC.

    View details for DOI 10.3389/fonc.2023.1254307

    View details for PubMedID 38074634

    View details for PubMedCentralID PMC10708945

  • A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy SCIENCE ADVANCES Mayer, A. T., Holman, D. R., Sood, A., Tandon, U., Bhate, S. S., Bodapati, S., Barlow, G. L., Chang, J., Black, S., Crenshaw, E. C., Koron, A. N., Streett, S. E., Gambhir, S. S., Sandborn, W. J., Boland, B. S., Hastie, T., Tibshirani, R., Chang, J. T., Nolan, G. P., Schuerch, C. M., Rogalla, S. 2023; 9 (3)

    View details for Web of Science ID 000964550100033

  • A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy. Science advances Mayer, A. T., Holman, D. R., Sood, A., Tandon, U., Bhate, S. S., Bodapati, S., Barlow, G. L., Chang, J., Black, S., Crenshaw, E. C., Koron, A. N., Streett, S. E., Gambhir, S. S., Sandborn, W. J., Boland, B. S., Hastie, T., Tibshirani, R., Chang, J. T., Nolan, G. P., Schürch, C. M., Rogalla, S. 2023; 9 (3): eadd1166

    Abstract

    Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.

    View details for DOI 10.1126/sciadv.add1166

    View details for PubMedID 36662860