Biology subjects

Ghafoor, M.

Publications and source records attributed to Ghafoor, M..

3 recordsLinked to original sources

bioRxiv2025

Cell-ECM Graphs: A Graph-Based Method for Joint Analysis of Cells and the Extracellular Matrix

Spatial proteomics technologies now profile cells and the extracellular matrix (ECM) together in situ. Yet analysis tools remain cell-centric, despite the ECM playing an essential role in health and disease. Here we present Mantpy, a framework that represents the ECM, and its interface with cells, as spatial graphs. Mantpy builds ECM graphs directly from matrix markers and links them with cell graphs for joint cell-ECM analysis, supporting graph statistics, explainable graph deep learning and visualisation. From a single ECM marker to multiplexed panels of ECM and cellular markers, Mantpy recovers layered tissue architecture in human intestine, resolves disease-associated matrix composition and organisation in infected mouse liver, and characterises cell-matrix associations in mouse lung. Released with ECM-inclusive datasets and interoperating with the scverse ecosystem, Mantpy extends the unit of spatial analysis beyond the cell, to the matrix that surrounds it.

bioinformatics↗

bioRxiv2024

Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation.

The extracellular matrix (ECM) forms the scaffold in which cells reside and interact. The composition of this scaffold guides the development of local immune responses and tissue function. With the advent of multiplexed spatial imaging methodologies, investigating the intricacies of cellular spatial organisation are more accessible than ever. However, the relationship between cellular organisation and ECM composition has been broadly overlooked. Using imaging mass cytometry, we investigated the association between cellular niches and their surrounding matrix environment during allergic airway inflammation in two commonly used mouse strains. By first classifying cells according to their canonical intracellular markers and then by developing a novel analysis pipeline to independently characterise a cells ECM environment, we integrated analysis of both intracellular and extracellular data. Applying this methodology to three distinct tissue regions we reveal disparate and restricted responses. Recruited neutrophils were dispersed within the alveolar parenchyma, alongside a loss of alveolar type I cells and an expansion of alveolar type II cells. This activated parenchyma was associated with increased proximity to hyaluronan and chondroitin sulphate. In contrast, infiltrating CD11b+ and MHCII+ cells accumulated in the adventitial cuff and aligned with an expansion of the subepithelial layer. This expanded subepithelial region was enriched for closely interacting stromal and CD11b+ immune cells which overlaid regions enriched for type-I and type-III collagen. The cell-cell and cell-matrix interactions identified here will provide a greater understanding of the mechanisms and regulation of allergic disease progression across different inbred mouse strains and provide specific pathways to target aspects of remodelling during allergic pathology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/623782v2_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@dcaf56org.highwire.dtl.DTLVardef@7b5d7forg.highwire.dtl.DTLVardef@1376d4eorg.highwire.dtl.DTLVardef@1e95801_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

CellPie: a fast spatial transcriptomics topic discovery method via joint factorization of gene expression and imaging data

Spatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here we present CellPie, a fast, unsupervised factor discovery method, based on joint non-negative matrix factorisation of spatial RNA transcripts and histological image features.CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional spatial transcriptomics datasets. We assessed CellPie on two different human cancer types and spatial resolutions, showing an improved performance against published factorisation methods. Additionally, we applied CellPie to a highly resolved Visium HD dataset, demonstrating its high computational efficiency compared to standard non-negative matrix factorisation and other existing methods. Availabilityhttps://github.com/ManchesterBioinference/CellPie

bioinformatics↗