Biology subjects

Patrick, E.

Publications and source records attributed to Patrick, E..

At least 19 recordsLinked to original sources

bioRxiv2026

PACE, Proximity-Associated Changes in Expression

Cellular transcriptional states are shaped by local tissue context, yet quantifying how cellular gene expression varies with proximity to different cell types remains challenging. Cell-resolved spatial transcriptomics data are typically sparse and susceptible to contamination from neighbouring cells through diffusion, imperfect segmentation and cell overlap, making it difficult to distinguish genuine cell-state changes from technical artefacts. We present PACE (Proximity-Associated Changes in Expression), a hierarchical empirical Bayes framework for quantifying cell-type-resolved proximity effects on gene expression. PACE uses partial pooling to stabilise inference across genes and cell types, separates contamination from biologically meaningful spatial associations, and identifies coordinated transcriptional programs underlying each proximity effect. Applied to Xenium-profiled breast cancer tissue, PACE reveals tumour-associated reprogramming of stromal cells and macrophages at tumour interfaces. In CosMx-profiled melanoma, it identifies fibroblast responses to tumour proximity, including extracellular matrix programs that differ between tumours from patients with progressive and stable disease following immunotherapy. PACE provides a robust and interpretable framework for quantifying how tissue organisation shapes cellular state in spatial molecular data.

bioinformatics↗

bioRxiv2026

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

Single-cell transcriptomics technology offers unprecedented insights into molecular heterogeneity. However, capturing sample-level representations that reflect both systemic and cellular states remains challenging, especially when disease annotations are mostly available as coarse sample-level labels. Here, we introduce Phenoverse, an interpretable deep learning framework that learns sample-level disease state representations through cell type-aware residual encoding, prototype learning, and Perceiver-based aggregation. Applied to independent single-cell transcriptomic cohorts of COVID-19, Alzheimers disease, and systemic lupus erythematosus, totaling over 5 million cells, we demonstrate that learned sample representations enable disease state prediction and encode a continuous spectrum of disease severity on unseen data that correlate with multiple clinical and pathological measures, despite being trained solely on binary phenotype labels. Further, we demonstrate that trajectory-derived genes reveal cross-cohort molecular programs and show consistently higher reproducibility than traditional case-control comparisons. Finally, prototype learning provides intrinsic model interpretability and enables the characterization of cell type-specific disease states. Taken together, Phenoverse offers an interpretable disease-phenotyping approach to dissecting sample heterogeneity, and our results highlight its utility in translating complex single-cell transcriptomic data into patient-level biological insights.

bioinformatics↗

bioRxiv2026

Generalisable tissue-wide molecular reconstruction from histology

Spatial transcriptomics technologies measure gene expression within intact tissues but remain difficult to scale across large tissue sections and patient cohorts. Consequently, many studies rely on tissue microarrays (TMAs) or sparse spatial profiling designs, where molecular measurements are available for only limited tissue regions and are often generated using heterogeneous gene panels. Existing H&E to spatial gene expression prediction methods remain challenged by sparse molecular measurements, partially overlapping gene panels and tissue-wide reconstruction across heterogeneous spatial datasets. Here, we present GHIST+, a framework for tissue-wide reconstruction of single-cell molecular states from H&E histology. GHIST+ integrates cellular morphology, local tissue context and shared tissue representations to extend sparse molecular measurements into tissue-wide molecular maps across heterogeneous spatial datasets. Across multiple cancer types and GTEx breast tissues, GHIST+ reconstructs biologically meaningful tissue-wide molecular organisation from sparse TMA-derived measurements while preserving spatial tissue structure, cell-type organisation and age-associated tissue states across cancer and non-cancer settings. GHIST+ establishes a scalable framework for transforming sparse spatial profiling experiments into tissue-wide molecular maps, enabling cohort-scale molecular reconstruction from routine histology under heterogeneous spatial transcriptomic settings.

bioinformatics↗

bioRxiv2026

Comparison of Machine Learning Surrogate Models for Prediction of Single-Fiber Activation in Deep Brain Stimulation

Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to electrical stimulation, while minimizing tradeoffs between computational expense and accuracy. Previous work has developed high accuracy artificial neural network (ANN) and convolutional neural network (CNN) surrogate models that predict activation of individual, myelinated axons, to extracellular electrical stimulation for subsets of DBS programming configurations. Moreover, more traditional machine learning methods including extreme gradient boosting (XGBoost) have been used effectively for peripheral-nerve single-fiber activation predictions. We build upon the previous work and compare ANN, CNN and XGBoost methods to a much expanded set of electrode programming configurations including: monopolar, bipolar, tripolar, quadrupolar, multiple monopolar, and multiple cases of directional leads. Training used datasets generated from a finite-element model of an implanted DBS lead together with multi-compartment cable models of synthetically generated axons. We evaluated the machine learning predictors using white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Our ANN and CNN models successfully predicted neural fiber activation for almost all electrode configurations with low error, expanding the scope of our previous predictor model. Results also showed key limitations of XGBoost models and superior performance of CNNs for more complex electrostatic fields of the directional leads.

neuroscience↗

bioRxiv2026

Combining xenium in situ spatial transcriptomics and imaging mass cytometry on a single tissue section

Spatial imaging technologies provide an expansive view of tissue microenvironments through high-plex profiling of protein and molecular targets in situ. Imaging mass cytometry (IMC; Standard BioTools) is a trusted method for defining immune phenotypes based on up to 40 protein targets, whilst Xenium in situ spatial transcriptomics (Xenium; 10x Genomics) is an emerging platform that can measure up to 5000 mRNA markers simultaneously. Although these platforms can reveal valuable insights on their own, there is an increasing need to analyse samples using a multi-omics approach to further our understanding of complex biological processes. To address this, we have assessed a novel dual-platform workflow that combines Xenium and IMC on a single formalin-fixed paraffin-embedded tissue section to enable the spatial profiling of both mRNA and protein targets at single-cell resolution. The feasibility of the workflow was determined by comparing the staining quality of IMC performed after Xenium to that of IMC performed alone on an adjacent tissue section, confirming that Xenium has little to no negative impact on subsequent IMC protein staining. Although the location of transcripts picked up by Xenium correlated with the corresponding proteins picked up by IMC at a global scale, discrepancies between the two technologies were apparent at the single-cell level. This is to be expected, as biologically transcript expression does not always correlate with protein, and both platforms have their own technical limitations. However, when we analyse T cells identified by both technologies, as opposed to T cells identified by Xenium or IMC alone, it produces the most biologically meaningful results at both the transcript and protein level for specific T cell markers. These results highlight how integration of the two platforms, identifying the presence of both RNA and protein, can foster a more comprehensive view of cellular landscapes and provide a greater depth of functional capabilities and cellular interactions.

immunology↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

Differential Tissue Coupled Powering for Battery-Free Injectable Electroceuticals

Electroceutical implants that deliver targeted neural stimulation have shown therapeutic potential for a wide range of neurological and peripheral disorders, yet wirelessly powering ultra-miniaturized, fully injectable systems remains a critical challenge. Here we report a Thread-like Injectable Neural TechnologY (TINY), powered via a differential tissue-coupled powering (DTCP) scheme. DTCP employs mid-frequency differential potentials applied across external electrodes on a compact, wearable transmitter to deliver energy through tissue to an ultra-miniaturized, thread-like implant that integrates a custom ASIC and PEDOT-coated receiver and stimulation electrodes. Benchtop experiments in agar phantoms characterize the power-transfer efficiency (PTE) and reveal that PTE increases with implant length while maintaining strong tolerance to angular misalignment. In vivo tests in rat hindlimbs further demonstrate wireless activation of the sciatic nerve through tissue at centimeter-scale depths, confirming effective transcutaneous energy delivery for neurostimulation. A 20-day implantation study shows stable positioning of the device with minimal tissue response, indicating excellent chronic compatibility. These findings address long-standing challenges in wirelessly powering injectable electroceuticals and establish DTCP as a scalable and alignment-robust powering strategy for future minimally invasive neuromodulation therapies.

bioengineering↗

bioRxiv2025

Acetylation-dependent clustering of BRD2 instructs transcription dynamics

BET protein inhibitors represent one of the most prominent classes of epigenetic drugs. However, the specific contributions of individual BET family members to transcription regulation remain largely unknown. Here, by acutely co-depleting BET proteins, we uncover an essential role of BRD2 in maintaining RNA Polymerase II recruitment at promoters, which becomes especially critical in the absence of BRD4 or when pause release is inhibited. Combining rapid protein degradation, chemogenomics, and super-resolution microscopy, we show that H4 acetylation, particularly H4K16ac, serves as a template for BRD2 function, driving sub-diffraction BRD2 cluster formation at chromatin. Accordingly, depletion of BRD2 or the writer of H4K16ac, MOF decreases sub-diffraction RNA Polymerase II clusters, which are associated with transcription initiation. Thus, this study highlights the importance of acetylated chromatin in transcriptional coactivator organization and the distinct role of BRD2 in transcription initiation dynamics.

genomics↗

bioRxiv2025

dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

Multi-parameter cytometry technologies enable high-dimensional analysis of immune cell populations at single-cell resolution. Deep learning has emerged as a transformative tool for analyzing these datasets, but existing methods often struggle with transferability across datasets due to technical variability, batch effects and identification of biologically relevant cell populations, limiting their utility in clinical research. We present dioscRi, a transferable deep learning framework that integrates a maximum mean discrepancy variational autoencoder for normalization and de-noising, enhancing cross-dataset compatibility. Changes in cell type proportions and marker expression are identified by structuring these features within biologically or empirically derived cell type hierarchies. These hierarchies are incorporated directly into an overlapping group LASSO model, improving the prediction of clinical outcomes. When applied to a coronary artery disease study, dioscRi recapitulated several known immune associations. Benchmarking across multiple datasets demonstrated dioscRis ability to generalize and outperform existing methods, establishing it as a versatile and interpretable tool for cytometry data analysis.

bioinformatics↗

bioRxiv2025

Interpretable deep generative ensemble learning for single-cell omics with Hydra

Single-cell omics enable the dissection of cellular heterogeneity, yet the high dimensionality, inherent noise, and sparsity present significant challenges. These challenges are amplified for rare cell populations, which are often difficult to annotate reliably but can be central to development and disease. As single-cell assays increasingly capture multiple molecular layers, the integrative analysis of such multimodal data further increases complexity. Here, we propose Hydra, a deep generative framework based on an ensemble of variational autoencoders for effective learning of unimodal and multimodal single-cell omics data. Hydra implements interpretable modules for capturing cell type-specific molecular signatures. The ensemble of such interpretable modules enables reproducible feature selection and robust cell type annotation, with particular effectiveness for rare populations. We benchmarked Hydra on a repertoire of 21 datasets, including unimodal and multimodal single-cell omics data. Our results demonstrate that Hydra offers comparable to superior performance to several state-of-the-art methods. Finally, we highlight the utility of Hydra in robustly annotating brain cellular subtypes and preserving disease-relevant signatures using our previously published dataset that profiles Alzheimers disease.

bioinformatics↗

bioRxiv2025

Tissue-wide profiling of human lungs reveals spatial sequestration of macrophages in tuberculosis

The immune response to human tuberculosis (TB), particularly in the context of complex lung pathology, remains incompletely understood. Here, we employed whole-slide spatial proteomics to map immune cell organization in TB-affected human lung tissues. Our analysis revealed pronounced spatial segregation of major immune cell populations in non-necrotizing TB lesions. At the tissue level, macrophages and lymphocytes formed distinct cellular communities associated with specific pathological features. At the lesion level, macrophages and B cells showed an inverse relationship in both abundance and spatial distribution. Proinflammatory T cells preferentially accumulated in macrophage-rich lesions but remained largely separated from macrophages. Interestingly, lesions exhibiting clear segregation between T cells and macrophages were more common in subclinical TB than in active disease. These findings suggest that spatial isolation of macrophages from effector lymphocytes may help temper inflammation and potentially prevent lesion progression to necrosis, while also enabling immune evasion by Mycobacterium tuberculosis. One Sentence SummaryXiao et al. reveal spatial segregation of immune cells in TB-lung tissue and link the microenvironmental dynamics to disease states of tuberculosis.

immunology↗

bioRxiv2024

SpatioMark: Quantifying the impact of spatial proximity on cell phenotype

As research advances in spatially resolving the biological archetype of various diseases, technologies that capture the spatial relationships between cells are demonstrating increasing value. Whilst there are an increasing number of analytical methods being developed to identify the complex web of interactions between cells, the downstream impacts of these cell-cell relationships are under explored. Here, we present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression in relation to the spatial proximity of different cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets and link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localisation of cells and propose corrections which reduce artefact induced relationships. SpatioMark is implemented in the Statial R package hosted on the Bioconductor Project, ensuring interoperability with existing spatial analysis tools. Ultimately, this work highlights strategies for identifying and interpreting changes in cell phenotype associated with cellular relationships in spatial omics data, with broad applicability across various multiplexed imaging platforms.

bioinformatics↗

bioRxiv2024

A highly adaptable protocol for mapping spatial features of cellular aggregates in tissues

Multiplex imaging technologies have developed rapidly over the past decades. The advancement of multiplex imaging has been driven in part by the recognition the spatial organisation of cells can represent important prognostic biomarkers and that simply studying the composition of cells in diseased tissue is often insufficient. There remains a lack of tools that can perform spatial analysis at the level of cellular aggregates (a common histopathological presentation) such as tumors and granulomas, with most analysis packages focussing on smaller regions of interest and potentially missing patterns in the overall lesion structure and cellular distribution. Here we present a protocol to quantify the cellular structure of entire tissue lesions built around two novel metrics. The Total Cell Preference Index reports whether a lesion tends to change in density in its central vs peripheral areas and can indicate the extent of necrosis across the entire lesion. The Immune Cell Preference Index then reports whether each immune cell-type is located more centrally or peripherally across the entire lesion. The output of both indexes is a single number readout for simple interpretation and visualization, and they can be applied to lesions of any size or shape. Additionally, this protocol can be applied to any slide-scanning multiplexed imaging system, either based on protein and nucleic acid staining. Finally, the protocol uses the open-source software QuPath and can be utilized by researchers with a basic understanding of QuPath with the full protocol able to be applied on pre-generated images within one hour.

immunology↗

bioRxiv2024

Kontextual: Reframing analysis of spatial omics data reveals consistent cell relationships across images

State-of-the-art spatial proteomic and transcriptomic technologies can deeply pheno-type cells in their native tissue environment, providing a high throughput means to effectively quantify spatial relationships between diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method which considers alternative frames of reference for contextualising spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells which are consistent across regions. By modelling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and spatial transcriptomics datasets that modelling spatial relationships this way is biologically meaningful. We also demonstrate how this approach can be used in a classification setting to improve prediction of patient prognosis.

bioinformatics↗

bioRxiv2024

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broad clinical adoption. Consequently, methods have been created to predict spot-based gene expression from routinely-collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstrating its flexibility across different spatial resolutions and superior performance. Our results underscore the utility of in silico generation of single-cell spatial gene expression measurements and the capacity to enrich existing datasets with a spatially resolved omics modality, paving the way for scalable multi-omics analysis and new biomarker discoveries.

bioinformatics↗

bioRxiv2024

Spatial mapping reveals unique cellular interactions and enhanced tertiary lymphoid structures in responders to anti-PD-1 therapy in mucosal head and neck cancers.

Survival in recurrent/metastatic head and neck mucosal squamous cell carcinoma (HNmSCC) remains poor. Anti-programmed death (PD)-1 therapies have demonstrated improved survival with lower toxicity when compared to standard chemotherapy. However, response to anti-PD-1 therapy remains modest, at 13-17%. We evaluated the tumor microenvironment (TME) using Imaging Mass Cytometry (IMC) on 27 tumor specimens from 24 advanced HNmSCC patients prior to receiving anti-PD-1 based treatment. We show significantly increased central memory T cells and B cells in responders (n=8) when compared to non-responders (n=16). Spatial mapping identified interactions between phenotypically distinct malignant squamous cells with CD8+ T cells, CD4+ Tregs and endothelial cells in responders, and avoidance of these cells in non-responders. Importantly, regional analysis shows responders have more abundant tertiary lymphoid structures (TLS), with TLS proportion >20% also associated with longer progression free survival. Together these findings define the immune landscape associated with response to anti-PD-1 treatment in HNmSCCs.

cancer biology↗

bioRxiv2023

Benchmarking the translational potential of spatial gene expression prediction from histology

Spatial transcriptomics has enabled the quantification of gene expression at spatial coordinates, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have offered the opportunity of enhancing the utility of readily obtainable and cost-effective haematoxylin-and-eosin-stained histology images. To this end, we conducted a comprehensive benchmarking study encompassing six developed methods. These methods were reproduced and evaluated using HER2-positive breast tumour and human cutaneous squamous cell carcinoma datasets, followed by external validation using The Cancer Genome Atlas data. Our evaluation incorporates diverse metrics which capture the performance of predicted gene expression, model generalisability, translational potential, usability and computational efficiency of each method. Our findings demonstrate the capacity of methods to spatial gene expression from histology and highlight key areas that can be addressed to support the advancement of this emerging field.

bioinformatics↗

bioRxiv2023

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, including cell-cell interactions, enabling great potential in biological discovery.

bioinformatics↗