Image-Based Cell Profiling Enables Quantitative Tissue Microscopy in Gastroenterology
| Autor: | Claire M. Barnes, John W. Wills, Michelle Miniter, Paul Rees, John Robertson, Johan D. Söderholm, Huw D. Summers, Åsa V. Keita, Rachel E. Hewitt, Jonathan J. Powell |
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| Přispěvatelé: | Wills, John W [0000-0002-4347-5394], Hewitt, Rachel E [0000-0002-2367-1822], Apollo - University of Cambridge Repository |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
intestinal tissue cell segmentation machine learning immunofluorescence confocal microscopy processing tilescans in CellProfiler | Getis-Ord spatial statistics medicine.medical_specialty Histology Cellbiologi Confocal Biology Immunofluorescence Gastroenterology Pathology and Forensic Medicine law.invention 03 medical and health sciences Mice Peyer's Patches 0302 clinical medicine Confocal microscopy law Internal medicine Microscopy medicine Animals Humans Ussing chamber medicine.diagnostic_test Reproducibility of Results Cell Biology Flow Cytometry Epithelium Rats 030104 developmental biology medicine.anatomical_structure 030220 oncology & carcinogenesis Intraepithelial lymphocyte Cytometry |
| Popis: | 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 Peyers 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. (c) 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry. Funding Agencies|UK Medical Research CouncilMedical Research Council UK (MRC) [MR/R005699/1]; UK Engineering and Physical Sciences Research CouncilEngineering & Physical Sciences Research Council (EPSRC) [EP/H008683/1]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/P026818/1]; Girton College; University of Cambridge Herchel-Smith Fund |
| Databáze: | OpenAIRE |
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