Eleanor C. Williams

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 ( S patial M etabolomics E nhanced W orkflow), 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 Key Points: SMEW provides a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data The SMEW framework enables hierarchical analysis at bulk-, region- and pixel levels within a unified framework without relying on extensive programming expertise The pipeline integrates spatially aware clustering, pathway analysis and identification of metabolite co-localisation modules The 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 SMEW 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

Recent developments in spatially resolved -omics have enabled the joint study of gene expression, metabolite levels and tissue morphology, offering greater insights into biological pathways. Integrating these modalities from matched tissue sections to probe spatially-coordinated processes, however, remains challenging. Here we introduce MAGPIE, a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We show MAGPIE’s generalisability and scalability on spatial multi-omics data from multiple tissues, combining Visium with MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to new multi-modal datasets generated with a specialised sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis and to link small-molecule co-detection with endogenous lung responses. MAGPIE demonstrates the refined resolution and enhanced interpretability that spatial multi-modal analyses provide for studying tissue injury especially in pharmacological contexts, and delivers a modular, accessible workflow for data integration.

Comprehensive understanding and early detection of drug-induced lung toxicity remain critical challenges in respiratory drug development. In this study, we propose a multi-omics framework that integrates spatial and temporal tissue-specific transcriptomic signatures with proteomics from minimally invasive biofluids to understand mechanisms and identify safety biomarkers associated with lung toxicity. Using this framework, we identified a panel of candidate biomarkers in bronchoalveolar lavage fluid and plasma, including LCN2/NGAL, RETNLA, SP-D, SPP1/osteopontin, and MMP7, that correlate with histopathological features (e.g., inflammation and epithelial remodeling). We confirmed that these molecular biomarkers were consistently dysregulated across a range of inhaled lung toxicants, human disease (IPF), and environmental exposures (smoke, Alternaria), demonstrating broad applicability across different toxic exposures and translatability. Collectively, this study establishes a robust workflow for mechanism-guided biomarker discovery and proposes a panel of candidates for monitoring drug-induced lung injury in humans.

Hepatoblasts play a key role in liver organogenesis by differentiating into hepatocytes and cholangiocytes, the main functional cell types of the liver. Mouse studies have demonstrated an association of Wnt signalling with proliferation and differentiation of hepatoblasts. However, the exact function of this pathway in hepatic development has not been fully uncovered, especially in human. Here, we use hepatoblast organoids derived from human foetal livers to investigate the importance of Wnt signalling in self-renewal and cell fate decisions during liver development. We first showed that Wnt plays a key role in hepatoblast self-renewal capacity in vitro by maintaining their proliferative state. However, Wnt was not sufficient to block differentiation of hepatoblast organoids into hepatocytes or cholangiocytes, suggesting that other factors are necessary to maintain hepatoblast bipotency. Finally, single-cell transcriptomic analyses revealed that Wnt signalling activity correlates with hepatoblast proliferation in the human foetal liver, suggesting that the role for Wnt could be conserved in vivo. Taken together, our results support a model in which Wnt signalling acts to preserve the proliferative capacity of hepatoblasts without being sufficient to maintain their bipotent state.

Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and have different therapeutic vulnerabilities^1–3. Here we identified three metabolic subtypes: glycolytic, oxidative and a mix of glycolytic and oxidative, using mass spectrometry imaging of rapidly excised tumour sections from two patients with GB who were infused with [U-¹³C]glucose and from spatial transcriptomic analysis of contiguous sections. The phenotypes are not correlated with microenvironmental features, including proliferation rate, immune cell infiltration and vascularization, are retained when patient-derived cells are grown in vitro or as orthotopically implanted xenografts and are robust to changes in oxygen concentration, demonstrating their cell-intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes is large enough to be detected using clinically applicable metabolic imaging techniques. A limitation of the study is that it is based on only two patient tumours, albeit on multiple sections, and therefore represents a proof-of-concept study.

The identification of tumour-specific protein–protein interactions remains a challenge for the development of targeted cancer therapies. In this study we describe our approach for the identification of triple negative breast cancer (TNBC)-specific protein–protein interactions focusing on the oncogene BCL11A. We used a proteomic approach to identify the BCL11A protein networks in TNBC and compared it to its network in B-cells, a cell type in which BCL11A plays crucial roles. This approach identified the chromatin remodeller CHD8 as a TNBC-specific interaction partner of BCL11A. We show that CHD8 also plays a key role in TNBC pathogenesis, with detailed multi-omics analysis revealing that BCL11A and CHD8 co-regulate several targets and synergise to drive tumour development and progression. Using a battery of biophysical assays, we confirm that the BCL11A-CHD8 interaction is direct and identify chemical fragments that disrupt this interaction and affect downstream targets, decreasing proliferation in 3D colony assays. Our study provides a proof-of-principle approach for investigating tumour-specific protein–protein interactions and identifies lead chemical compounds that could be developed into novel therapeutics for TNBC.

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 ( M ulti-modal A lignment of G enes and P eaks for I ntegrated E xploration ), 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.

For many adult human organs, tissue regeneration during chronic disease remains a controversial subject. Regenerative processes are easily observed in animal models, and their underlying mechanisms are becoming well characterized^1–4, but technical challenges and ethical aspects are limiting the validation of these results in humans. We decided to address this difficulty with respect to the liver. This organ displays the remarkable ability to regenerate after acute injury, although liver regeneration in the context of recurring injury remains to be fully demonstrated. Here we performed single-nucleus RNA sequencing (snRNA-seq) on 47 liver biopsies from patients with different stages of metabolic dysfunction-associated steatotic liver disease to establish a cellular map of the liver during disease progression. We then combined these single-cell-level data with advanced 3D imaging to reveal profound changes in the liver architecture. Hepatocytes lose their zonation and considerable reorganization of the biliary tree takes place. More importantly, our study uncovers transdifferentiation events that occur between hepatocytes and cholangiocytes without the presence of adult stem cells or developmental progenitor activation. Detailed analyses and functional validations using cholangiocyte organoids confirm the importance of the PI3K–AKT–mTOR pathway in this process, thereby connecting this acquisition of plasticity to insulin signalling. Together, our data indicate that chronic injury creates an environment that induces cellular plasticity in human organs, and understanding the underlying mechanisms of this process could open new therapeutic avenues in the management of chronic diseases.

Cardiac involvement is common in patients hospitalized with COVID-19 and correlates with an adverse disease trajectory. While cardiac injury has been attributed to direct viral cytotoxicity, serum-induced cardiotoxicity secondary to serological hyperinflammation constitutes a potentially amenable mechanism that remains largely unexplored. To investigate serological drivers of cardiotoxicity in COVID-19 we have established a robust bioassay that assessed the effects of serum from COVID-19 confirmed patients on human embryonic stem cell (hESC)-derived cardiomyocytes. We demonstrate that serum from COVID-19 positive patients significantly reduced cardiomyocyte viability independent of viral transduction, an effect that was also seen in non-COVID-19 acute respiratory distress syndrome (ARDS). Serum from patients with greater disease severity led to worse cardiomyocyte viability and this significantly correlated with levels of key inflammatory cytokines, including IL-6, TNF-α, IL1-β, IL-10, CRP, and neutrophil to lymphocyte ratio with a specific reduction of CD4⁺ and CD8⁺ cells. Combinatorial blockade of IL-6 and TNF-α partly rescued the phenotype and preserved cardiomyocyte viability and function. Bulk RNA sequencing of serum-treated cardiomyocytes elucidated specific pathways involved in the COVID-19 response impacting cardiomyocyte viability, structure, and function. The observed effects of serum-induced cytotoxicity were cell-type selective as serum exposure did not adversely affect microvascular endothelial cell viability but resulted in endothelial activation and a procoagulant state. These results provide direct evidence that inflammatory cytokines are at least in part responsible for the cardiovascular damage seen in COVID-19 and characterise the downstream activated pathways in human cardiomyocytes. The serum signature of patients with severe disease indicates possible targets for therapeutic intervention.

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification of expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks.