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Abstract Background The intestinal microbiota plays a significant role in maintaining systemic and intestinal homeostasis, but can also influence diseases such as inflammatory bowel disease (IBD) and cancer. Certain bacterial species within the intestinal tract can chronically activate the immune system, leading to low-grade intestinal inflammation. As a result, plasma cells produce high levels of secretory antigen-specific immunoglobulin A (IgA), which coats the immunostimulatory bacteria. This IgA immune response against intestinal bacteria may be associated with the maintenance of homeostasis and health, as well as disease. Unraveling this dichotomy and identifying the immunostimulatory bacteria is crucial for understanding the relationship between the intestinal microbiota and the immune system, and their role in health and disease. IgA-SEQ technology has successfully identified immunostimulatory, IgA-coated bacteria from fecal material. However, the original technology is time-consuming and has limited downstream applications. In this study, we aimed to develop a next-generation, high-throughput, magnet-based sorting approach (ng-IgA-SEQ) to overcome the limitations of the original IgA-SEQ protocol. Results We show, in various settings of complexity ranging from simple bacterial mixtures to human fecal samples, that our magnetic 96-well plate-based ng-IgA-SEQ protocol is highly efficient at sorting and identifying IgA-coated bacteria in a high-throughput and time efficient manner. Furthermore, we performed a comparative analysis between different IgA-SEQ protocols, highlighting that the original FACS-based IgA-SEQ approach overlooks certain nuances of IgA-coated bacteria, due to the low yield of sorted bacteria. Additionally, magnetic-based ng-IgA-SEQ allows for novel downstream applications. Firstly, as a proof-of-concept, we performed metagenomic shotgun sequencing on 10 human fecal samples to identify IgA-coated bacterial strains and associated pathways and CAZymes. Secondly, we successfully isolated and cultured IgA-coated bacteria by performing the isolation protocol under anaerobic conditions. Conclusions Our magnetic 96-well plate-based high-throughput next-generation IgA-SEQ technology efficiently identifies a great number of IgA-coated bacteria from fecal samples. This paves the way for analyzing large cohorts as well as novel downstream applications, including shotgun metagenomic sequencing, culturomics, and various functional assays. These downstream applications are essential to unravel the role of immunostimulatory bacteria in health and disease. Video Abstract

Abstract Background Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm2 area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art. Results MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods. Conclusion MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.