Biology subjects

Stamou, M.

Publications and source records attributed to Stamou, M..

5 recordsLinked to original sources

bioRxiv2026

SMEW: An interactive multi-scale toolkit for cross-condition and network-based analysis of spatial metabolomics data

Spatial metabolomics, measured through mass spectrometry imaging (MSI), provides high-throughput, spatially resolved information on metabolite distributions within tissues, including endogenous metabolites and exogenous compounds. This offers a direct readout of cellular biochemical activity and phenotypes, not fully captured by transcriptomics or proteomic profiling. However, inferring biologically meaningful patterns from noisy, high-dimensional MSI data, particularly across multiple samples and complex experimental designs, remains challenging, and often requires substantial programming expertise. Here we introduce SMEW (Spatial Metabolomics Enhanced Workflow), a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data. SMEW provides a unified environment for hierarchical analysis across bulk-, region- and pixel-level resolutions, allowing comparisons between experimental conditions like disease or treatment groups while highlighting coherent metabolic patterns and linking these patterns to biological pathways. The workflow leverages local spatial covariation to robustly summarise MSI data through dimensionality reduction, clustering and identification of spatially variable metabolites. In addition, metabolite co-localisation and covariation network analysis, together with spatially resolved pathway enrichment facilitate the biological interpretation of cross-condition datasets within a single integrated interface. SMEW is applicable across MSI technologies and mass resolutions, as illustrated through case studies on DESI and MALDI-ToF datasets from lung, liver, and kidney. By complementing existing MSI processing and visualisation tools with an accessible, multi-sample, and biologically interpretable analysis framework, SMEW enables functional, flexible, rigorous and intuitive exploration of spatial metabolomics datasets. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/721059v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1e2abaeorg.highwire.dtl.DTLVardef@753ee9org.highwire.dtl.DTLVardef@1756fc1org.highwire.dtl.DTLVardef@fbedc7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LISMEW provides a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data C_LIO_LIThe SMEW framework enables hierarchical analysis at bulk-, region- and pixel levels within a unified framework without relying on extensive programming expertise C_LIO_LIThe pipeline integrates spatially aware clustering, pathway analysis and identification of metabolite co-localisation modules C_LIO_LIThe workflow facilitates flexible comparison of multi-sample experimental conditions through multivariate modelling, differential testing and covariation networks to study treatment- and disease-associated metabolite dynamics C_LIO_LISMEW has been applied to interrogate diverse biological questions, including characterising disease-associated remodelling in a mouse bleomycin model of pulmonary fibrosis, exploring the therapeutic index of antisense oligonucleotides in the liver and assessing metabolic heterogeneity in a small molecule-treated mouse renal tumour model C_LI

bioinformatics↗

bioRxiv2025

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

Recent developments in spatially resolved -omics have enabled studies linking gene expression and metabolite levels to tissue morphology, offering new insights into biological pathways. By capturing multiple modalities on matched tissue sections, one can better probe how different biological entities interact in a spatially coordinated manner. However, such cross-modality integration presents experimental and computational challenges. To align multimodal datasets into a shared coordinate system and facilitate enhanced integration and analysis, we propose MAGPIE (Multi-modal Alignment of Genes and Peaks for Integrated Exploration), a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We illustrate the generalisability and scalability of MAGPIE on spatial multi-omics data from multiple tissues, combining Visium with both MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to newly generated multimodal datasets created using specialised experimental sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis, to showcase the linking of small-molecule co-detection with endogenous responses in lung tissue. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in pharmacological contexts, and offers a modular, accessible computational workflow for data integration.

bioinformatics↗

bioRxiv2023

Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. To address these limitations, we generated spatially resolved transcriptome maps of human IPF and bleomycin-induced mouse lung fibrosis. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by TGF{beta} signaling alongside factors such as p53 and ApoE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches, and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

molecular biology↗

bioRxiv2022

POU6F2 mutation identified in humans with pubertal failure shifts isoform formation and alters GnRH transcript expression

Idiopathic hypogonadotropic hypogonadism (IHH) is characterized by absent pubertal development and infertility, often due to gonadotropin-releasing hormone (GnRH) deficits. Exome sequencing of two independent cohorts of IHH patients identified 12 rare missense variants in POU6F2. POU6F2 encodes two distinct isoforms. In mouse, pituitary and gonads expressed both isoforms, but only isoform1 was detected in GnRH cells. Although the function of isoform2 is well known, using bioinformatics and cells assays on a human-derived GnRH cell line, we demonstrate isoform1 can also act as a transcriptional regulator, decreasing GNRH1 expression. The impact of two POU6F2 variants (MT1 and MT2) was then examined. MT1, but not MT2, reduced transcriptional activity of either isoform, preventing Hes5 promoter activation by isoform2 and repression of GnRH transcripts by isoform1. GnRH transcription increases as the cells migrate into the brain. Augmentation earlier can disrupt normal GnRH cell migration, consistent with POU6F2 variants contributing to IHH pathogenesis.

genetics↗

bioRxiv2022

Chemical screening of food-related chemicals for human fatty liver risk: Combining high content imaging of cellular responses with in vitro to in vivo extrapolation

1Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent human disease with accumulating evidence linking its pathophysiology and co-morbidities to chemical exposures. The complex pathophysiology of NAFLD has limited the elucidation of potential chemical etiologies. In this study we generated a high-content imaging analysis method for the simultaneous quantification of sentinel steatosis cellular markers in chemically exposed human liver cells in vitro combined with a computational model for the extrapolation of human oral equivalent doses (OED). First, the in vitro test method was generated using 14 reference chemicals with known capacities to induce cellular alterations in nuclear morphology, lipid accumulation, mitochondrial membrane potential and oxidative stress. These effects were quantified on a single cell- and population-level, and then, using physiologically based pharmacokinetic modelling and reverse dosimetry, OEDs were extrapolated from these in vitro data. The extrapolated OEDs were confirmed to be within biologically relevant exposure ranges for the reference chemicals. Next, we tested 14 chemicals found in food, selected from thousands of putative chemicals on the basis of structure-based prediction for nuclear receptor activation. Amongst these, orotic acid had an extrapolated OED overlapping with realistic exposure ranges. By the strategy developed in this study, we were able to characterize known NAFLD-inducing chemicals and translate to data scarce food-related chemicals, amongst which we identified orotic acid to induce steatosis. This strategy addresses needs of next generation risk assessment, and can be used as a first chemical prioritization hazard screening step in a tiered approach to identify chemical risk factors for NAFLD.

pharmacology and toxicology↗