Biology subjects

Papas, B.

Publications and source records attributed to Papas, B..

3 recordsLinked to original sources

bioRxiv2025

Single-nucleus multiomics of murine gonadal cells reveals transcriptional regulatory network underlying supporting lineage differentiation

Sex determination of mammalian gonads hinges upon sex-specific differentiation of gonadal supporting cells: Sertoli cells in the testis and granulosa cells in the ovary. To gain insights into how supporting cells acquire their identities, we performed joint single-nucleus transcriptomics and chromatin accessibility assays on murine gonadal cells across sex determination. By contrasting sex-specific gene expression and corresponding chromatin accessibility among progenitor and differentiated cells, we discovered that sex-specific chromatin regions in supporting cells are established shortly after sex determination, accompanied by the acquisition of active histone marks. The presence of potential transcription factor binding motifs in the open chromatin regions revealed regulatory networks underlying ovary-enriched factors LEF1 and MSX1, which promote granulosa fate by inducing granulosa-specific genes such as Foxl2 and Fst. Our results not only identify the gene regulatory framework underlying supporting cell sex differentiation, but also provide invaluable resources for the field. TeaserSex-specific changes in chromatin and gene expression underly the divergent developmental fates of the bipotential gonadal primordium.

developmental biology↗

bioRxiv2024

Toll-Like-Receptor 5 protects against pulmonary fibrosis by reducing lung dysbiosis

Idiopathic pulmonary fibrosis (IPF) is a devastating pulmonary disease with no curative treatment other than lung transplantation. IPF results from maladaptive responses to lung epithelial injury, but the underlying mechanisms remain unclear. Here, we show that deficiency in the innate immune receptor, toll-like receptor 5 (TLR5), is associated with IPF in humans and with increased susceptibility to epithelial injury and experimental fibrosis in mice, while activation of lung epithelial TLR5 through a synthetic flagellin analogue protects from experimental fibrosis. Mechanistically, epithelial TLR5 activation induces antimicrobial gene expression and ameliorates dysbiosis after lung injury. In contrast, TLR5 deficiency in mice and IPF patients is associated with lung dysbiosis. Elimination of the microbiome in mice through antibiotics abolishes the protective effect of TLR5 and reconstitution of the microbiome rescues the observed phenotype. In aggregate, TLR5 deficiency is associated with IPF and dysbiosis in humans and in the murine model of pulmonary fibrosis. Furthermore, TLR5 protects against pulmonary fibrosis in mice and this protection is mediated by effects on the microbiome. One-sentence summaryDeficiency in the innate immune receptor TLR5 is a risk factor for pulmonary fibrosis, because TLR5 prevents microbial dysbiosis after lung injury.

immunology↗

bioRxiv2019

Patterns, Profiles, and Parsimony: dissecting transcriptional signatures from minimal single-cell RNA-seq output with SALSA

Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, start-up costs and data volumes currently required for statistically reproducible insight remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow to address those issues from a data science perspective. SALSA is an integrative and systematic methodology that introduces matrix focusing, a parametric frequentist approach to identify fractions of statistically significant and robust data within single-cell expression matrices. SALSA then transforms the focused matrix into an imputable mix of data-positive and data-missing information, projects it into a latent variable space using generalized linear modelling, and extracts patterns of enrichment. Last, SALSA leverages multivariate analyses, adjusted for rates of library-wise transcript detection and cluster-wise gene representation across latent patterns, to assign individual cells under distinct transcriptional profiles via unsupervised hierarchical clustering. In SALSA, cell type assignment relies exclusively on genes expressed both robustly, relative to sequencing noise, and differentially, among latent patterns, which represent best-candidates for confirmatory validation assays. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard comprising 2,700 single-cell barcodes from human frozen peripheral blood with transcripts aligned to 16,634 genes. SALSA identified at least 7 distinct transcriptional profiles in PBMC 3K based on <500 differentially expressed Profiler genes determined agnostically, which matched expected frequencies of dominant cell types in peripheral blood. We confirmed that each transcriptional profile inferred by SALSA matched known expression signatures of blood cell types based on surveys of 15 landmark genes and other supplemental markers. SALSA was able to resolve transcriptional profiles from only [~]9% of the total count data accrued, spread across <0.5% of the PBMC 3K expression matrix real estate (16,634 genes x 2,700 cells). In conclusion, SALSA amalgamates scRNA-seq data in favor of reproducible findings. Furthermore, by extracting statistical insight at lower experimental costs and computational workloads than previously reported, SALSA represents an alternative bioinformatics strategy to make single-cell technologies affordable and widespread.

bioinformatics↗