Biology subjects

Ashrafiyan, S.

Publications and source records attributed to Ashrafiyan, S..

4 recordsLinked to original sources

bioRxiv2026

AtlasLens: Metadata-centric exploration and analysis of single-cell atlases

MotivationThe rapid expansion of single-cell RNA sequencing (scRNA-seq) atlases has generated datasets comprising millions of cells annotated with increasingly rich metadata, including tissue, cell type, disease status, sex, age, treatment, and temporal information. Biological questions frequently require simultaneous interrogation of multiple metadata dimensions, such as identifying specific cell populations within defined tissues, disease states, demographic groups, and time points. While existing interactive platforms facilitate visualization and analysis of scRNA-seq data, deep metadata-driven exploration and downstream analysis of atlas-scale datasets remain insufficiently supported. ResultsWe developed AtlasLens, an open-source R/Shiny application for interactive exploration of scRNA-seq datasets and integrated cellular atlases. AtlasLens enables iterative filtering across arbitrary metadata combinations, allowing users to define biologically meaningful cellular subsets and immediately perform downstream analyses. The platform integrates interactive visualization, differential expression analysis, Gene Ontology enrichment with redundancy reduction, temporal expression analysis, and context-dependent gene function profiling through GeneCOCOA. AtlasLens additionally records analysis history and automatically generates corresponding R code to enhance reproducibility. The application is distributed through Docker for simple local deployment, preserving data privacy and eliminating dependency-management challenges. We demonstrate AtlasLens using the Tabula Muris and a time-resolved whole-lung single-cell atlas of bleomycin-induced lung injury and fibrosis, highlighting its ability to support complex metadata-driven biological investigations. AvailabilitySource code is available at https://github.com/SchulzLab/AtlasLens. Contactmarcel.schulz@em.uni-frankfurt.de

bioinformatics↗

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2025

Ischemic stroke induces persistent alteration to brain stromal progenitor cells linked to chronic vascular dysfunction

Fibrotic scar formation after stroke serves a dual role: while essential for providing structural support during post-ischemic recovery, excessive fibrosis in the chronic phase of stroke impairs regenerative processes including axonal regrowth and neovascularization. The temporal dynamics of fibrosis are critical determinants of functional outcomes, as the balance between protective scarring and regenerative capacity differs across distinct stroke phases. Consequently, strategic modulation of fibrotic processes to preserve regenerative potential represents a promising therapeutic approach in stroke recovery. To understand the cellular mechanisms underlying this fibrotic response, we investigated stromal progenitor cell composition in the post-stroke brain. The vast majority of stromal progenitor cells (SPCs) are pericytes, with minorities comprising perivascular fibroblasts (PVFs) and vascular smooth muscle cells. We demonstrate that ischemic stroke drives a long-term shift in this composition, characterized by sustained expansion of the PVF population and excessive laminin deposition in the peri-infarct region, effects that persist for at least six months post-stroke. Single-cell RNA sequencing revealed sustained transcriptional and compositional alterations in the SPC population throughout chronic post-stroke phase, driven by AP-1-mediated signaling via TNF in both PVFs and pericytes. These changes correlate with long-term vasomotor dysfunction and capillary constriction in the peri-infarct region at six weeks post-stroke. Ischemic stroke drives aberrant, persistent PVF accumulation at the capillary bed with implications for post-stroke cerebrovascular dysfunction and recurrent stroke. Taken together, these findings reveal that ischemic stroke drives an aberrant long-term mis-localization of PVFs to the capillary bed that may have clinically-relevant implications for post-stroke cerebrovascular function as well as potential ramifications for recurrent stroke.

neuroscience↗

bioRxiv2025

Harnessing machine learning models for epigenome to transcriptome association studies

Understanding how epigenome variation contributes to gene expression in disease and development is a fundamental challenge. Regulatory regions show cell type-specific epigenome activity and differ in their location, size, and distance to their target genes, complicating discovery and analysis. Recent machine learning models have been proposed to address these problems by learning functions for the prediction of gene expression from epigenomic data. Here, we use the large IHEC EpiATLAS dataset to benchmark state-of-the-art linear and non-linear approaches. Each approach is optimized for over 28,000 human genes, providing a comprehensive regulatory catalog of gene models. In-depth comparison reveals that gene characteristics and the epigenomic complexity of the locus influence the difficulty of predicting the epigenome-to-transcriptome association. The model performance is further evaluated using CRISPRi and eQTL validation data. Based on these models, we conduct histone-acetylation association studies in a systematic way to investigate how epigenomic variation impacts gene expression. The model-based analysis revealed genes and regulatory regions linked to B-cell leukemia in patient data with known disease-related functions. Our work provides a foundation for applications that link epigenome variation to gene expression in human cells, by benchmarking methods on a per-gene basis, illustrating their use in a disease context and making trained models available to the community.

bioinformatics↗