Biology subjects

Coffey, R. J.

Publications and source records attributed to Coffey, R. J..

22 records · Page 2Linked to original sources

bioRxiv2020

Murine intrarectal instillation of purified recombinant C. difficile toxins enables mechanistic studies of pathogenesis

Clostridioides difficile is linked to nearly 225,000 antibiotic-associated diarrheal infections and almost 13,000 deaths per year in the United States. Pathogenic strains of C. difficile produce toxin A (TcdA) and toxin B (TcdB), which can directly kill cells and induce an inflammatory response in the colonic mucosa. Hirota, et al. first introduced the intrarectal instillation model of intoxication using TcdA and TcdB purified from VPI 10463 and 630 C. difficile strains. Here, we expand this technique by instilling purified, recombinant TcdA and TcdB, which allows for the interrogation of how specifically mutated toxins affect tissue. Mouse colons were processed and stained with hematoxylin and eosin (H&E) for blinded evaluation and scoring by a board-certified gastrointestinal pathologist. The amount of TcdA or TcdB needed to produce damage was lower than previously reported in vivo and ex vivo. Furthermore, TcdB mutants lacking either endosomal pore-formation or glucosyltransferase activity resemble sham negative controls. Immunofluorescent staining revealed how TcdB initially damages colonic tissue by altering the epithelial architecture closest to the lumen. Tissue sections were also immunostained for markers of acute inflammatory infiltration. These staining patterns were compared with slides from a human C. difficile infection (CDI). The intrarectal instillation mouse model with purified recombinant TcdA and/or TcdB provides the flexibility needed to better understand structure/function relationships across different stages of CDI pathogenesis.

microbiology↗

bioRxiv2020

Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms

Microbial influences on host cells depend upon the identities of the microbes, their spatial localization, and the responses they invoke on specific host cell populations. Multi-modal analyses of both microbes and host cells in a spatially-resolved fashion would enable studies into these complex interactions in native tissue environments, potentially in clinical specimens. While techniques to preserve each of the microbial and host cell compartments have been used to examine tissues and microbes separately, we endeavored to develop approaches to simultaneously analyze both compartments. Herein, we established an original method for mucus preservation using Poloxamer 407 (also known as Pluronic F-127), a thermoreversible polymer with mucus-adhesive characteristics. We demonstrate that this approach can preserve spatially-defined compartments of the mucus bi-layer in the colon and the bacterial communities within, compared with their marked absence when tissues were processed with traditional formalin-fixed paraffin-embedded (FFPE) pipelines. Additionally, antigens for antibody staining of host cells were preserved and signal intensity for 16S rRNA fluorescence in situ hybridization (FISH) was enhanced in Poloxamer-fixed samples. This in turn enabled us to integrate multi-modal analysis using a modified multiplex immunofluorescence (MxIF) protocol. Importantly, we have formulated Poloxamer 407 to polymerize and crosslink at room temperature for use in clinical workflows. These results suggest that the fixative formulation of Poloxamer 407 can be integrated into biospecimen collection pipelines for simultaneous analysis of microbes and host cells.

cell biology↗

bioRxiv2019

Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images

Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.

bioinformatics↗

bioRxiv2018

Quantitative assessment of cell population diversity in single-cell landscapes

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.

bioinformatics↗