Biology subjects

Ruf-Zamojski, F.

Publications and source records attributed to Ruf-Zamojski, F..

5 recordsLinked to original sources

bioRxiv2026

Single-nucleus multiomic analysis reveals modulation of the gene regulatory circuit landscape in pituitary cell types during mouse estrous cycle.

Summary/AbstractThe estrous cycle transcriptional and chromatin dynamics in pituitary cell types have not been investigated. We report single-nucleus multiomics assays in 18 adult mouse pituitaries (102,069 post quality-control nuclei) across 6 cycle time points. Differential analysis revealed cycle stage-dependent epigenetic and transcriptional remodeling across the major pituitary cell populations. In gonadotropes and lactotropes, we identified temporal patterns of differential gene expression linked to various biological processes, notably neuronal and synaptic-related ontologies. Pseudotime trajectory analysis was consistent with a rapid transition of individual gonadotropes through different cellular states. In gonadotropes and lactotropes, we uncovered gene regulatory circuits whose activity varies between consecutive cycle time points. We experimentally validated a gonadotrope ETS2-driven circuit that differentially regulates Fshb gene expression between 2 and 9 am on estrus. Our data and analyses, available at https://rstudio-connect.hpc.mssm.edu/snpit_estrous_browser/, provide a window into gene regulatory mechanisms underlying estrous cycle stage transitions. HighlightsO_LIPituitary cells show chromatin and transcriptome plasticity across the estrous cycle C_LIO_LIWe identify stage-modulated gene regulatory circuits across pituitary cell types C_LIO_LIGonadotrope DEGs form distinct pathway-annotated temporal trajectory clusters C_LIO_LIWe validate a gonadotrope ETS2-driven circuit regulating the Fshb gene C_LI eTOC blurbZhang et al. conduct a single-nucleus multiomics analysis of mouse pituitaries in vivo across the estrous cycle. They reveal an epigenetic and transcriptomic plasticity in pituitary cell populations. In gonadotropes and lactotropes, they demonstrate that DEGs clustered by temporal trajectories are enriched for distinct biological processes and identify stage-modulated gene regulatory circuits. They experimentally validate a gonadotrope ETS2-driven circuit regulating Fshb expression. Their dynamic molecular atlas of the cycling pituitary captures key cis-regulatory mechanisms underlying estrous cycle stage transitions.

genomics↗

bioRxiv2025

Regulation of murine follicle-stimulating hormone β subunit transcription by newly identified enhancers

Activin-class ligands of the transforming growth factor {beta} family induce follicle-stimulating hormone (FSH) production by pituitary gonadotrope cells in mice via the actions of the transcription factors SMAD3, SMAD4, and FOXL2, which bind to cis-elements in the FSH{beta} subunit (Fshb) promoter. An enhancer region for murine Fshb transcription was identified in vitro. However, deletion of the region using CRISPR-Cas9 did not affect FSH synthesis or secretion in mice. Using single-nucleus ATAC-seq of whole murine pituitaries, we identified three additional open chromatin regions upstream of Fshb exclusively in gonadotropes. These regions, as well as the Fshb gene, were fully or partially closed in gonadotropes of FSH-deficient mice with genetically or pharmacologically inactivated activin type II receptors. The initially characterized enhancer region did not significantly alter basal or activin-stimulated murine Fshb promoter-reporter activity in homologous L{beta}T2 cells. In contrast, the other three open chromatin regions enhanced basal and activin A-stimulated Fshb promoter-reporter activity in L{beta}T2 cells, with the two most distal showing the greatest effects. These two regions were open, exhibited enrichment of the enhancer mark H3K27ac, and were bound by SMAD2/3 and FOXL2 in response to activin A in L{beta}T2 cells. The most distal enhancer exhibited strong FOXL2 and weak SMAD4 binding in gel shift assays. SMAD4, but not FOXL2, directly bound the other distal enhancer. Mutation of defined FOXL2 and SMAD4 cis-elements diminished enhancer activity in reporter assays in L{beta}T2 cells. Collectively, the data indicate that there may be as many as four activin-sensitive enhancers upstream of murine Fshb.

molecular biology↗

bioRxiv2025

Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery and analyses in pituitary gland research

Previous single-cell profiling studies of the pituitary gland have yielded minimally reproducible insights largely due to their low statistical power and methodological inconsistencies. To address this problem, we generated a uniformly pre-processed Consensus Pituitary Atlas (CPA) using all existing mouse pituitary single-cell datasets (267 biological replicates, >1.1 million high-quality cells). The CPA revealed novel cell typing and lineage markers, including low-expression transcripts that previous analyses could not detect. The scale of the CPA enabled the development of machine learning models to automate and standardize cell type annotation and doublet identification for future studies. Leveraging the curated metadata, we identified sex-biased and age-dependent gene expression patterns at cell type resolution. To identify drivers of cell fates, first we determined consensus cell communication patterns. Secondly, we used RNA-sequencing and chromatin accessibility data to identify transcription factors associated with cell fates across modalities. The epitome platform acts as an interface with the CPA, allowing streamlined user-friendly analyses. HighlightsO_LIUniform processing of 267 mouse pituitary single-cell datasets (>1.1M cells) C_LIO_LIThe statistical power enabled cell type, sex- and age-specific marker discovery C_LIO_LIMachine learning models facilitate doublet detection and cell typing in new datasets C_LIO_LIepitome platform provides programming-free data access and visualizations C_LI

bioinformatics↗

bioRxiv2023

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDIs data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

bioinformatics↗

bioRxiv2023

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.

genomics↗