Biology subjects

Wilmott, J.

Publications and source records attributed to Wilmott, J..

4 recordsLinked to original sources

bioRxiv2025

OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more efficient alternative to full-panel staining, enabling detailed analysis of the tumour microenvironment. Existing One-Step Inference Models (OSIMs), primarily based on generative adversarial networks (GAN) or autoencoders, often generate suboptimal images with significant artifacts or reduced signal intensity. These limitations impair visual interpretability and reliability of the downstream immunotherapy response assessment. The challenge is further amplified when imputing cytoplasmic biomarkers such as CD8 from commonly used stains such as DAPI, due to the limited spatial correlation and the inherently complex structure of cytoplasmic signals. To address these limitations, we propose a self-refining diffusion model, OS2CR-Diff, which utilises the results from OSIMs as additional conditional representations. Unlike prior studies that rely on a single or limited conditional inputs, OS2CR-Diff incorporates three conditional inputs: the OSIM-imputed target biomarker image, OSIM-imputed complementary biomarker images, and non-antibody-stained images. Furthermore, we propose a feature fusion module that employs a cross-gated attention mechanism to effectively integrate these inputs, enabling context-aware feature refinement and improving the quality and reliability of imputed biomarker images. We evaluated OS2CR-Diff for CD8 biomarker imputation on mIF images of melanoma tissues. Our method out-performed state-of-the-art methods, achieving a 73.4% increase in the Structural Similarity Index Measure (SSIM), a 28.9% gain in the Peak Signal-to-Noise Ratio (PSNR), a 61.2% improvement in Mean Absolute Error (MAE), and significantly lower false positive rates compared to OSIM.

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↗

bioRxiv2023

Melanotransferrin Functions as a Pro-Oncogenic WNT Agonist: A Yin-Yang Relationship in Melanoma with the WNT Antagonist and Metastasis Suppressor, NDRG1

A persistent mystery in the melanoma field has been the function of one of the first melanoma tumor antigens characterized, namely p97 (melanotransferrin; MTf). While MTf expression increases melanoma cell proliferation, migration, and tumorigenesis, the molecular mechanism responsible is unknown. On the other hand, N-myc down-stream regulated gene 1 (NDRG1) is a potent metastasis suppressor and WNT antagonist. Expression of NDRG1 in melanoma cells suggests a role in inhibiting metastasis, with this study investigating MTfs role in oncogenic signaling. We demonstrate MTf acts as a pro-oncogenic WNT agonist, which down-regulates NDRG1, while silencing MTf increases NDRG1 expression. In contrast, silencing NDRG1 increases MTf expression. These observations demonstrate a bidirectional negative feedback loop and "Yin-Yang" relationship between MTf and NDRG1. Mechanistically, MTf was directly associated with the WNT co-receptor, lipoprotein-receptor 6 (LRP6), and increased total LRP6 expression, activated p-LRP6 (Ser1490), {beta}-catenin, and activated {beta}-catenin (Ser552) levels, with MTf expression inducing their nuclear accumulation. Additionally, MTf expression increased downstream WNT targets, namely cyclin D1 and c-Myc, with c-Myc down-regulating NDRG1 expression. Silencing c-Myc prevented the Yin-Yang relationship between NDRG1 and MTf, indicating c-Myc played a key role in their inverse regulation. Melanoma patient specimens demonstrated that a low NDRG1/MTf ratio was significantly (p = 0.008) associated with lower survival and metastasis. Chemotherapeutic agents that up-regulated NDRG1 depressed MTf and nuclear LRP6 and potently inhibited melanoma xenograft growth in vivo. This study demonstrates MTf acts as a WNT agonist, with a Yin-Yang relationship being observed with the WNT antagonist, NDRG1.

cancer biology↗

bioRxiv2017

bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays

Motivation: Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes are important tools in Gene Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene set selection bias induced during the construction of these platforms.\n\nResults: We propose bcGST, a bias-corrected Gene Set Test by introducing bias correction terms in the contingency table needed for calculating the Fishers Exact Test (FET). The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and TCGA cancer studies.\n\nAvailability: The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/\n\nContact: kevin.wang@sydney.edu.au

bioinformatics↗