Optimizing single-cell RNA sequencing methods for human colon biopsies: droplet-based vs. picowell-based platforms.

Background & Aims: Single-cell RNA sequencing (scRNA) has empowered many insights into gastrointestinal microenvironments. However, profiling human biopsies using droplet-based scRNA (D-scRNA) is challenging since it requires immediate processing to minimize epithelial cell damage. In contrast, picowell-based (P-scRNA) platforms permit short-term frozen storage before sequencing. We compared P- and D-scRNA platforms on cells derived from human colon biopsies. Methods: Endoscopic rectosigmoid mucosal biopsies were obtained from two adults and conducted D-scRNA (10X Chromium) and P-scRNA (Honeycomb HIVE) in parallel using an individual's pool of single cells (> 10,000 cells/participant). Three experiments were performed to evaluate 1) P-scRNA with cells under specific storage conditions (immediately processed [fresh], vs. frozen at -20C vs. -80C [2 weeks]); 2) fresh P-scRNA versus fresh D-scRNA; and 3) P-scRNA stored at -80C with fresh D-scRNA. Results: Significant recovery of loaded cells was achieved for fresh (80.9%) and -80C (48.5%) P-scRNA and D-scRNA (76.6%), but not -20C P-scRNA (3.7%). However, D-scRNA captures more typeable cells among recovered cells (71.5% vs. 15.8% Fresh and 18.4% -80C P-scRNA), and these cells exhibit higher gene coverage at the expense of higher mitochondrial read fractions across most cell types. Cells profiled using D-scRNA demonstrated more consistent gene expression profiles among the same cell type than those profiled using P-scRNA. Significant intra-cell-type differences were observed in profiled gene classes across platforms. Conclusions: Our results highlight non-overlapping advantages of P-scRNA and D-scRNA and underscore the need for innovation to enable high-fidelity capture of colonic epithelial cells. The platform-specific variation highlights the challenges of maintaining rigor and reproducibility across studies that use different platforms.

Select symbionts drive high IgA levels in the mouse intestine.

Immunoglobulin A (IgA) is an important factor in maintaining homeostasis at mucosal surfaces, yet luminal IgA levels vary widely. Total IgA levels are thought to be driven by individual immune responses to specific microbes. Here, we found that the prebiotic, pectin oligosaccharide (pec-oligo), induced high IgA levels in the small intestine in a T cell-dependent manner. Surprisingly, this IgA-high phenotype was retained after cessation of pec-oligo treatment, and microbiome transmission either horizontally or vertically was sufficient to retain high IgA levels in the absence of pec-oligo. Interestingly, the bacterial taxa enriched in the overall pec-oligo bacterial community differed from IgA-coated microbes in this same community. Rather, a group of ethanol-resistant microbes, highly enriched for Lachnospiraceae bacterium A2, drove the IgA-high phenotype. These findings support a model of intestinal adaptive immunity in which a limited number of microbes can promote durable changes in IgA directed to many symbionts.

Reverse translation approach generates a signature of penetrating fibrosis in Crohn's disease that is associated with anti-TNF response.

OBJECTIVE: Fibrosis is a common feature of Crohn's disease (CD) which can involve the mesenteric fat. However, the molecular signature of this process remains unclear. Our goal was to define the transcriptional signature of mesenteric fibrosis in CD subjects and to model mesenteric fibrosis in mice to improve our understanding of CD pathogenesis. DESIGN: We performed histological and transcriptional analysis of fibrosis in CD samples. We modelled a CD-like fibrosis phenotype by performing repeated colonic biopsies in mice and analysed the model by histology, type I collagen-targeted positron emission tomography (PET) and global gene expression. We generated a gene set list of essential features of mesenteric fibrosis and compared it to mucosal biopsy datasets from inflammatory bowel disease patients to identify a refined gene set that correlated with clinical outcomes. RESULTS: Mesenteric fibrosis in CD was interconnected to areas of fibrosis in all layers of the intestine, defined as penetrating fibrosis. We found a transcriptional signature of differentially expressed genes enriched in areas of the mesenteric fat of CD subjects with high levels of fibrosis. Mice subjected to repeated colonic biopsies showed penetrating fibrosis as shown by histology, PET imaging and transcriptional analysis. Finally, we composed a composite 24-gene set list that was linked to inflammatory fibroblasts and correlated with treatment response. CONCLUSION: We linked histopathological and molecular features of CD penetrating fibrosis to a mouse model of repeated biopsy injuries. This experimental system provides an innovative approach for functional investigations of underlying profibrotic mechanisms and therapeutic concepts in CD.

SELfies and CELLfies: Whole Genome Sequencing and Annotation of Five Antibiotic Resistant Bacteria Isolated from the Surfaces of Smartphones, An Inquiry Based Laboratory Exercise in a Genomics Undergraduate Course at the Rochester Institute of Technology.

Are touchscreen devices a public health risk for the transmission of pathogenic bacteria, especially those that are resistant to antibiotics? To investigate this, we embarked on a project aimed at isolating and identifying bacteria that are resistant to antibiotics from the screens of smartphones. Touchscreen devices have become ubiquitous in society, and it is important to evaluate the potential risks they pose towards public health, especially as it pertains to the harboring and transmission of pathogenic bacteria that are resistant to antibiotics. Sixteen bacteria were initially isolated of which five were unique (four Staphylococcus species and one Micrococcus species). The genomes of the five unique isolates were subsequently sequenced and annotated. The genomes were analyzed using in silico tools to predict the synthesis of antibiotics and secondary metabolites using the antibiotics and Secondary Metabolite Analysis SHell (antiSMASH) tool in addition to the presence of gene clusters that denote resistance to antibiotics using the Resistance Gene Identifier (RGI) tool. In vivo analysis was also done to assess resistance/susceptibility to four antibiotics that are commonly used in a research laboratory setting. The data presented in this manuscript is the result of a semester-long inquiry based laboratory exercise in the genomics course (BIOL340) in the Thomas H. Gosnell School of Life Sciences/College of Science at the Rochester Institute of Technology.