Image-Based Cell Profiling Enables Quantitative Tissue Microscopy in Gastroenterology

Abstract

Immunofluorescence microscopy is an essential tool for tissue-based research, yet data reporting is almost always qualitative. Quantification of images, at the per-cell level, enables "flow cytometry-type" analyses with intact locational data but achieving this is complex. Gastrointestinal tissue, for example, is highly diverse: from mixed-cell epithelial layers through to discrete lymphoid patches. Moreover, different species (e.g., rat, mouse, and humans) and tissue preparations (paraffin/frozen) are all commonly studied. Here, using field-relevant examples, we develop open, user-friendly methodology that can encompass these variables to provide quantitative tissue microscopy for the field. Antibody-independent cell labeling approaches, compatible across preparation types and species, were optimized. Per-cell data were extracted from routine confocal micrographs, with semantic machine learning employed to tackle densely packed lymphoid tissues. Data analysis was achieved by flow cytometry-type analyses alongside visualization and statistical definition of cell locations, interactions and established microenvironments. First, quantification of Escherichia coli passage into human small bowel tissue, following Ussing chamber incubations exemplified objective quantification of rare events in the context of lumen-tissue crosstalk. Second, in rat jejenum, precise histological context revealed distinct populations of intraepithelial lymphocytes between and directly below enterocytes enabling quantification in context of total epithelial cell numbers. Finally, mouse mononuclear phagocyte-T cell interactions, cell expression and significant spatial cell congregations were mapped to shed light on cell-cell communication in lymphoid Peyer's patch. Accessible, quantitative tissue microscopy provides a new window-of-insight to diverse questions in gastroenterology. It can also help combat some of the data reproducibility crisis associated with antibody technologies and over-reliance on qualitative microscopy. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.

Keywords: cell segmentation; confocal microscopy; immunofluorescence; intestinal tissue; machine learning; processing tilescans in CellProfiler | Getis-Ord spatial statistics.