Biology subjects

Mahurkar, A. A.

Publications and source records attributed to Mahurkar, A. A..

3 recordsLinked to original sources

bioRxiv2026

SENgEAR: an interactive visualization platform for comparative analysis of senescence signatures and cellular communication

Single-cell and spatial transcriptomic datasets provide tremendous potential to identify the cellular states and communication networks associated with senescence. Annotating senescent cells in these datasets is hindered by the lack of universal markers for senescent cells and their context-dependent variability. To overcome this, several groups have developed gene signatures to annotate cells with distinct senotypes. However, comparing these signatures and their biological contexts to determine the specificity of senescence signatures remains complicated by the separate data platforms for signatures and datasets. We present SENgEAR, a no-code web platform for senescent cell annotation, visualization of cell-cell communication networks, and comparison of senescence signatures across studies, tissues, species, and data modalities. SENgEAR provides a unified framework to explore contextual signature specificity, cellular communication pathways, and senotype heterogeneity. We demonstrate the use of SENgEAR for applications spanning aging, fibrosis, and wound healing and its ability to interrelate these states.

bioinformatics↗

bioRxiv2026

Distinct fibroblast and perivascular senotypes define spatial niches that regulate fibrosis

Fibrotic conditions contribute to significant global morbidity and mortality. Yet the underlying processes that orchestrate fibrosis remain poorly understood due to the cellular and spatial complexity of the stromal, immune, and vascular compartments that regulate fibrotic disease progression. Senescent cells (SnCs) have been implicated in fibrosis, but their roles are unclear, as evidence indicates that they serve both pathogenic and reparative functions. Here, we show that fibrosis-associated SnCs contain functionally divergent senotypes that are organized into distinct spatial niches. Using integrated single-cell and spatial transcriptomics analyses and hierarchical factorization in a murine fibrosis model, we identify fibroblast and perivascular SnC subpopulations that upregulate diverse programs related to extracellular matrix (ECM) production, immune signaling, and vascular remodeling. Fibroblast senotypes localize to discrete microenvironments with distinct tissue architectures, including niches associated with fibrotic signaling, immune activity, and cartilage development. Perivascular SnCs occupy interfaces between fibrotic signaling and immune-active niches and upregulate vascular and fibrotic remodeling pathways. Depletion of pericyte-lineage SnCs increases vascular maturation and fibrotic ECM deposition, providing mechanistic validation of the beneficial role these SnCs play in vascular remodeling and fibrosis modulation. In addition, using a new web-based infrastructure to query our senotype gene signatures in public datasets, we demonstrate that these senotypes are conserved across different murine and human fibrotic conditions. These findings establish senescence as a spatially organized regulator of fibrosis and identify perivascular senescence as a link between vascular remodeling and fibrotic outcomes.

bioengineering↗

bioRxiv2022

The Neuroscience Multi-Omic Archive: A BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org). KEY POINTSO_LIThe Neuroscience Multi-Omic Archive serves as the genomics data repository for the BRAIN Initiative. C_LIO_LIGenomic data from >50 million cells span all the major regions of the brains of humans and mice. C_LIO_LIWe provide a searchable web portal, a cloud-computing interface, and a data visualization platform. C_LI

genomics↗