[HTML][HTML] Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity

ET McKinley, Y Sui, Y Al-Kofahi, BA Millis, MJ Tyska… - JCI insight, 2017 - ncbi.nlm.nih.gov
ET McKinley, Y Sui, Y Al-Kofahi, BA Millis, MJ Tyska, JT Roland, A Santamaria-Pang
JCI insight, 2017ncbi.nlm.nih.gov
Intestinal tuft cells are a rare, poorly understood cell type recently shown to be a critical
mediator of type 2 immune response to helminth infection. Here, we present advances in
segmentation algorithms and analytical tools for multiplex immunofluorescence (MxIF), a
platform that enables iterative staining of over 60 antibodies on a single tissue section.
These refinements have enabled a comprehensive analysis of tuft cell number, distribution,
and protein expression profiles as a function of anatomical location and physiological …
Abstract
Intestinal tuft cells are a rare, poorly understood cell type recently shown to be a critical mediator of type 2 immune response to helminth infection. Here, we present advances in segmentation algorithms and analytical tools for multiplex immunofluorescence (MxIF), a platform that enables iterative staining of over 60 antibodies on a single tissue section. These refinements have enabled a comprehensive analysis of tuft cell number, distribution, and protein expression profiles as a function of anatomical location and physiological perturbations. Based solely on DCLK1 immunoreactivity, tuft cell numbers were similar throughout the mouse small intestine and colon. However, multiple subsets of tuft cells were uncovered when protein coexpression signatures were examined, including two new intestinal tuft cell markers, Hopx and EGFR phosphotyrosine 1068. Furthermore, we identified dynamic changes in tuft cell number, composition, and protein expression associated with fasting and refeeding and after introduction of microbiota to germ-free mice. These studies provide a foundational framework for future studies of intestinal tuft cell regulation and demonstrate the utility of our improved MxIF computational methods and workflow for understanding cellular heterogeneity in complex tissues in normal and disease states.
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