Biology subjects

Oshinjo, A.

Publications and source records attributed to Oshinjo, A..

3 recordsLinked to original sources

bioRxiv2026

ECMME: an atlas of selection pressures on the mammalian extracellular matrix reveals contrasting evolutionary dynamics

The extracellular matrix (ECM) is a fundamental metazoan innovation that provides structural support and regulatory cues essential for multicellular life. While core matrisome components are subject to strong functional constraints, their evolutionary dynamics at the molecular level remain incompletely characterized. Here, we present a comprehensive per-residue analysis of selection pressures across 272 human core matrisome proteins using high-quality orthologous sequences from up to 228 placental mammal species. We developed an automated pipeline integrating ortholog identification, codon-aware alignments, and site-specific selection analyses with the MEME and FUBAR methods from the HyPhy suite. Results reveal pervasive strong purifying selection across the matrisome, consistent with its structural and functional indispensability. This is accompanied by episodic positive selection and rarer pervasive positive selection, with collagens exhibiting significantly elevated episodic positive selection compared to glycoproteins and proteoglycans. To facilitate community access, we developed ECMME (ECM Molecular Evolution) browser, an intuitive open-access web resource that visualizes selection metrics plotted directly onto protein topologies. ECMME allows researchers to seamlessly browse and investigate the data, providing a powerful framework for interpreting functional sites. It is available online and requires no local installation or set-up (https://izzilab-ecmme.share.connect.posit.cloud/).

bioinformatics↗

bioRxiv2026

MatriSpace: Identification and visualization of spatially resolved ECM gene expression patterns in health and disease

The extracellular matrix (ECM) is a highly dynamic network of proteins forming the structural organizer of all tissues. Different cell populations contribute to the assembly of the 150+ proteins of a functional ECM. In addition, different ECM subtypes, supporting distinct cellular functions, are found in every organ. Spatial transcriptomics (ST) provides a unique, yet untapped, opportunity to identify which cell populations contribute to ECM production with spatial context. Applied to healthy and diseased samples, this method can identify ECM changes that could be exploited for therapeutic purposes. Here, we introduce MatriSpace, a computational framework to mine ST datasets with a focus on ECM genes. MatriSpace offers two operating modes: researchers can either upload their own ST datasets or explore a large collection of public datasets. Upon analysis, MatriSpace returns spatially resolved maps of matrisome gene expression in relation to cell populations, at multiple levels: from single-gene analysis to tissue niches and functional ECM units. MatriSpace is available as an R package and an online Shiny App (https://matrinet.shinyapps.io/matrispace), making it accessible to all users regardless of their level of expertise. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/720198v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@3f8e28org.highwire.dtl.DTLVardef@8e20beorg.highwire.dtl.DTLVardef@107be22org.highwire.dtl.DTLVardef@153b911_HPS_FORMAT_FIGEXP M_FIG C_FIG KEY POINTSO_LIMatriSpace is a computational framework to interrogate ECM gene expression in spatial transcriptomic datasets. C_LIO_LIResearchers can upload their own spatial transcriptomic datasets for processing by MatriSpace. C_LIO_LIResearchers can interrogate a vast collection of public datasets of healthy and diseased tissues through MatriSpace. C_LIO_LIMatriSpace can identify, quantify, and interpret spatial expression patterns of matrisome genes, gene sets, and niches. C_LIO_LIMatriSpace can uncover regional coordinations of matrisome components and their relationships with non-matrisome genes, such as matrisome receptors. C_LI

systems biology↗

bioRxiv2026

NaVis: a virtual microscopy framework for interactive, high-resolution navigation of spatial transcriptomics data

Despite the widespread adoption of spatial transcriptomics (ST), revealing the alignment between transcriptional layers and tissue morphology remains technically demanding, typically requiring proficiency across multiple computational frameworks and thereby limiting accessibility for a substantial fraction of the biomedical community. Here, we introduce NaVis (https://github.com/Izzilab/NaVis), a point-and-click virtual microscopy framework that redefines ST analysis as an interactive, image-centric experience. NaVis enables rapid high-resolution inference from low-resolution whole-transcriptome platforms, producing microscopy-like visualizations while preserving transcriptome-wide coverage. It further decomposes histological images into quantitative tissue architecture priors - nuclei-rich regions, fibrillar extracellular matrix, and soft tissue - allowing direct integration of gene expression with local morphology. This unified representation supports analyses of compartment enrichment, boundary concordance, spatial cross-correlation, morphological patterning, histology-expression decoupling, and transcriptome-wide spatial similarity. By coupling transcriptomic and image-derived information within an interactive framework, NaVis shifts ST from static computational workflows to an exploratory modality, broadening its accessibility, conceptual reach and potential for biological discoveries.

bioinformatics↗