Biology subjects

Ellis, S. R.

Publications and source records attributed to Ellis, S. R..

10 recordsLinked to original sources

bioRxiv2026

STILL-13C: Spatial tracing of isotopically labelled lipids with 13C reveals metabolic heterogeneity in intact tissues

Lipid metabolism is dynamically rewired across tissues in response to developmental, environmental and therapeutic cues. This adaptation drives treatment resistance in a range of human pathologies, but current lipidomic techniques fail to capture the underlying mechanisms, relying on steady-state measurements from homogenised samples that obscure spatial heterogeneity and pathway flux. Here we introduce spatial tracing of isotopically labelled lipids (STILL-13C), a workflow that uses stable isotope tracing and high-resolution mass spectrometry imaging (MSI) to map lipid metabolic flux directly in intact human tissues with unprecedented pathway coverage. STILL-13C overcomes longstanding limitations of bulk and MSI-based analyses by spatially resolving isotopologue labelling of simple and complex lipids, enabling simultaneous tracing of fatty acid synthesis, remodelling and multiple convergent pathways required for phospholipid assembly, while preserving tissue architecture and regional metabolic context. Applied to patient-derived prostate cancer explants cultured ex vivo, STILL-13C revealed heterogeneity in lipid pathway activity between neighbouring epithelial regions and spatially resolved responses to pathway inhibition. This work establishes a broadly applicable platform for investigating spatial heterogeneity in lipid metabolic flux and its perturbation in intact tissues.

molecular biology↗

bioRxiv2026

In vivo deuteration reveals pronounced variation in myelin lipid turnover rates and reduced myelin renewal with ageing

Myelin turnover is essential for its structural and functional integrity, yet how this particularly lipid-rich membrane is renewed and why it deteriorates with ageing remain unresolved. Combining deuterium oxide administration in mice with high resolution lipidomics, we establish that brain lipid turnover rates are highly heterogeneous, differ by brain region, and depend primarily on lipid class. Half-lives of common glycerophospholipids in purified myelin were under 2 months whereas many sphingolipids exhibited half-lives exceeding 8 months, dependent on acyl chain length and saturation. Myelin sphingolipid and cholesterol replacement rates in the corpus callosum decreased markedly between 3 and 12 months of age, while disrupting lipid trafficking through ApoE ablation preferentially impaired cholesterol turnover and incorporation into myelin. Our results establish that myelin renewal occurs through continual replacement of individual lipid constituents in a manner that depends on lipid class, hydrophobicity, and ApoE-dependent trafficking, and that this process slows significantly with ageing.

biochemistry↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2025

Dynamic neutrophil lipidome remodeling during induction of NETosis

Neutrophil Extracellular Trap formation (NETosis) affects a wide variety of clinically relevant human diseases. Although lipid remodeling is essential for neutrophil function and membrane rupture during NETosis, the neutrophil lipidome and its dynamics have not been characterized. Thus, we establish the first quantitative lipidome of human neutrophils comprising 1,039 species across nine orders of magnitude and map its remodeling during NETosis. NET formation caused profound alterations in the phosphatidylinositol, phosphatidic acid, diacylglycerol and lysoglycerophospholipid levels. Calcium- and reactive oxygen species-dependent NETosis pathways displayed distinct lipidomic trajectories, yet converged on the significance of phospholipid lipase networks. Pharmacological inhibition of this networks altered lipid composition and markedly impaired NETosis, while diacylglycerol (DG) treatment revoked the effect. Altogether our findings reveal lipid remodeling as a fundamental determinant of NETosis and identify interconnected and dependent phospholipid lipase networks with downstream DG-dependent signaling as a potential therapeutic target in NET-associated diseases.

molecular biology↗

bioRxiv2025

Subcellular mass spectrometry imaging of lipids and nucleotides using transmission geometry ambient laser desorption and plasma ionisation

Demand for mass spectrometry imaging (MSI) technologies offering subcellular resolution for tissues and cell imaging is rapidly increasing. To accomplish this, efficient analyte ionisation is essential, given the small amounts of material sample in each pixel. Herein, we describe an atmospheric pressure transmission-geometry matrix-assisted laser desorption source equipped with plasma ionisation. By utilising a novel pre-staining method for sample preparation lipid signal intensities were enhanced by an order of magnitude compared to conventional matrix-only methods, while serendipitously enabling imaging of numerous nucleotides. The system enables detection of up to 200 lipids and nucleotides in tissues at 1 {micro}m-pixel size while informative MSI data is still obtained down to 250 nm pixel size. The use of sub-micron pixels is shown to discern subcellular features through combinations with fluorescence microscopy. This method provides a powerful tool for cellular and sub-cellular imaging of small molecules from tissues and cells for spatial biology applications.

cell biology↗

bioRxiv2023

A High-Throughput Data-Independent Acquisition Workflow for Deep Characterisation of the sn-Isomer Lipidome

We report a workflow based on ozone-induced dissociation for untargeted characterization of hundreds of sn-resolved glycerophospholipid isomers from biological extracts in under 20 minutes, coupled with an automated data analysis pipeline. It provides an order of magnitude increase in the number of sn-isomer pairs identified compared to previous reports, reveals that sn-isomer populations are tightly regulated and significantly different between cell lines, and enables identification of rare lipids containing ultra-long chain monounsaturated acyl chains.

cell biology↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

Omics Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue using a Multi-Class Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated the understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focus on a single lipid class. Herein, we overcome limitation of quantitative MSI (Q-MSI) by using a multi-class internal standard lipid mixture that is sprayed homogenously over the tissue surface with analytical concentrations that reflects endogenous brain lipid levels. Using this approach we have performed Q-MSI for 13 lipid classes representing >200 sum-composition lipid species. This was carried out using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalisation of each lipid species signal to the corresponding class-specific IS an approach analogous to that widely used for shotgun lipidomics from biological extracts. This approach allows pixel concentrations of lipids to be reported in pmol/mm2. Q-MSI of lipids covered 3 orders of magnitude in dynamic range and revealed subtle change sin in distribution compared to conventional total-ion-current normalisation approaches. The robustness of the method was evaluated by repeating experiments in two laboratories on biological replicates using both timsTOF and Orbitrap mass spectrometers operated with a ~4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI result obtained using the two approaches with outliers mostly rationalised by isobaric interferences that are only resolved with the Orbitrap system or the higher sensitivity of one instrument for particular lipid species, particularly for lipids detected at low intensity. These data provide insight into how mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and reveal how absolute lipid concentrations vary throughout and between biological tissues.

biochemistry↗

bioRxiv2020

Mass spectrometry imaging of phosphatidylcholine metabolism in lungs administered with therapeutic surfactants and isotopic tracers

Mass spectrometry imaging (MSI) visualises molecular distributions throughout tissues but is blind to dynamic metabolic processes. Here, MSI with high mass resolution together with multiple stable isotope labelling provided spatial analyses of phosphatidylcholine (PC) metabolism in mouse lungs. Dysregulated surfactant metabolism is central to many respiratory diseases. Metabolism and turnover of therapeutic pulmonary surfactants were imaged from distributions of intact and metabolic products of an added tracer, universally 13C-labelled dipalmitoyl PC (U[13C]DPPC). The parenchymal distributions of newly synthesised PC species were also imaged from incorporations of methyl-D9-choline. This dual labelling strategy demonstrated both lack of inhibition of endogenous PC synthesis by exogenous surfactant and location of acyl chain remodelling processes acting on the U[13C]DPPC-labelled surfactant, leading to formation of polyunsaturated PC lipids. This ability to visualise discrete metabolic events will greatly enhance our understanding of lipid metabolism in diverse tissues, and has potential application to both clinical and experimental studies.

biochemistry↗

bioRxiv2020

Metabolic plasticity in cancer activates apocryphal pathways for lipid desaturation

Fatty acid (FA) modifications, such as enzymatic desaturation and elongation, have long been thought to involve sequential and highly specific enzyme-substrate interactions, which result in canonical products that are well-defined in their chain lengths, degree of unsaturation and double bond positions.1 These products act as a supply of building blocks for the synthesis of complex lipids supporting a symphony of lipid signals and membrane macrostructure. Recently, it was brought to light that differences in substrate availability due to enzyme inhibition can activate alternative pathways in a range of cancers, potentially altering the total species repertoire of FA metabolism.2,3 We have used isomer-resolved lipidomics to analyse human prostate tumours and cancer cell lines and reveal, for the first-time, the full extent of metabolic plasticity in cancer. Assigning the double bond position(s) in simple and complex lipids allows mapping of fatty acid desaturation and elongation via hitherto apocryphal metabolic pathways that generate FAs with unusual sites of unsaturation. Downstream utilisation of these FAs is demonstrated by their incorporation into complex structural lipids. The unsaturation profiles of different phospholipids reveal substantive structural variation between classes that will, necessarily, modulate lipid-centred biological processes in cancer cells including membrane fluidity3-5 and signal transduction.6-8

biochemistry↗