Biology subjects

Forrest, A. R. R.

Publications and source records attributed to Forrest, A. R. R..

6 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomics reveals tumour cell-intrinsic and microenvironmental features associated with survival in high-grade serous ovarian carcinoma

High-grade serous ovarian carcinoma (HGSOC) is the most common form of ovarian carcinoma and accounts for the majority of deaths from the disease, with fewer than half of patients surviving five years after diagnosis. Bulk transcriptomic profiling has identified molecular subtypes and gene-expression signatures associated with shorter or longer survival. However, because bulk profiling measures a composite of tumour and microenvironmental gene expression, the cellular origins of these signatures and their relationship to the tumour microenvironment remain unresolved. To address this, we applied Xenium spatial transcriptomics to treatment-naive HGSOC tumours from 122 patients using a custom 480-gene panel designed to measure tumour-intrinsic gene expression and distinguish immune and stromal cell populations in the surrounding microenvironment. Analysis of tumour-intrinsic gene expression identified 61 genes nominally associated with overall survival, from which Elastic-Net modelling prioritised five genes associated with longer survival and nine associated with shorter survival. Notably, 46 of the 61 associations did not reach the nominal significance threshold when expression was instead aggregated across all cells from the same patients, highlighting how whole-tissue expression can obscure tumour-cell-specific survival associations. Complementary analysis of tumour-proximal cell populations revealed that multiple immune populations were associated with longer survival, whereas two fibroblast populations were associated with shorter survival. Integrating these analyses revealed that survival-associated tumour-intrinsic gene expression was correlated with the abundance of neighbouring immune and stromal populations that were themselves associated with survival. Pairwise Cox regression demonstrated substantial overlap in the prognostic information captured by these features, while analysis of individual gene-cell combinations showed that survival associations differed according to the abundance of neighbouring populations. Consensus clustering further identified four patient groups with distinct combinations of tumour-intrinsic expression and microenvironmental composition, broadly separating patients with longer and shorter survival. Together, this cohort-scale spatial analysis identifies tumour-intrinsic and microenvironmental features associated with HGSOC survival and reveals how their correlated patterns and combined associations relate to patient outcome.

cancer biology↗

bioRxiv2026

A Joint Promoterome-Proteome Atlas Highlights the Molecular Diversity of Human Skeletal Muscles

More than 600 distinct skeletal muscles constitute up to 40% of the total mass of the human body. Human skeletal muscles differ in anatomical position, morphology, origin, and function, but the diversity of their molecular phenotypes, the gene expression and protein abundance profiles, remains poorly explored. Here, we report the large-scale CAGE-Seq promoterome profiling of 75 human skeletal muscles, complemented by 22 matched proteomes obtained with mass spectrometry. We identified 37001 transcribed regulatory elements and 1804 protein groups encompassing 1895 proteins, 80% of which demonstrated non-uniform expression across different muscles. The skeletal muscles of the eye, tongue, and diaphragm had the most distinctive molecular phenotypes, while the overall diversity was driven by hundreds of transcription factors with tissue-specific activity. By analyzing the allelic imbalance of CAGE-Seq reads, we discovered 6653 allele-specific single-nucleotide variants often coinciding with muscle-related GWAS SNPs, including muscle volume. Finally, we provide an interactive online atlas of transcriptomic and proteomic molecular phenotypes, facilitating further studies of gene regulation and heritable pathologies of skeletal muscles.

genomics↗

bioRxiv2022

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

High-grade serous ovarian carcinoma (HGSOC) is characterised by recurrence, chemotherapy resistance and overall poor prognosis. Genetic heterogeneity of tumour cells and the microenvironment of the tumour have been hypothesised as key determinants of treatment resistance and relapse. Here, using a combination of spatial and single cell transcriptomics (10x Visium and Chromium platforms), we examine tumour genetic heterogeneity and infiltrating populations of HGSOC samples from eight patients with variable response to neoadjuvant chemotherapy. By inferring gross copy number alterations (CNAs), we identified distinct tumour subclones co-existing within individual tumour sections. These tumour subclones have unique CNA profiles and spatial locations within each tumour section, which were further validated by ultra-low-pass whole genome sequencing. Differential expression analysis between subclones within the same section identified both tumour cell intrinsic expression differences and markers indicative of different infiltrating cell populations. The gene sets differentially expressed between subclones were significantly enriched for genes encoding plasma membrane and secreted proteins, indicative of subclone-specific microenvironments. Furthermore, we identified tumour derived ligands with variable expression levels between subclones that correlated or anticorrelated with various non-malignant cell infiltration patterns. We highlight several of these that are potentially direct tumour-stroma/immune cell relationships as the non-malignant cell type expresses a cognate receptor for the tumour derived ligand. These include predictions of CXCL10-CXCR3 mediated recruitment of T and B cells to associate with the subclones of one patient and CD47-SIRPA mediated exclusion of macrophages from association with subclones of another. Finally, we show that published HGSOC molecular subtype signatures associated with prognosis are heterogeneously expressed across tumour sections and that areas containing different tumour subclones with different infiltration patterns can match different subtypes. Our study highlights the high degree of intratumoural subclonal and infiltrative heterogeneity in HGSOC which will be critical to better understand resistance and relapse.

cancer biology↗

bioRxiv2022

Single Nucleus RNA Sequencing of Pre-Malignant Liver Reveals Disease-Associated Hepatocyte State with HCC Prognostic Potential

Current approaches to stage chronic liver diseases have limited utility to directly predict liver cancer risk. Here, we employed single nucleus RNA sequencing (snRNA-seq) to characterize the cellular microenvironment of healthy and chronically injured pre-malignant livers using two distinct mouse models. Analysis of 40,748 hepatic nuclei unraveled a previously uncharacterized disease-associated hepatocyte transcriptional state (daHep). These cells were absent in healthy livers, but were increasingly prevalent as chronic liver disease progressed towards hepatocarcinogenesis. Gene expression deconvolution of 1,439 human liver transcriptomes from publicly available datasets revealed that daHep frequencies highly correlate with current histopathological liver disease staging systems. Importantly, we show that high daHep levels precede carcinogenesis in mice and humans and predict a higher risk of hepatocellular carcinoma (HCC) development. This novel transcriptional signature with diagnostic and, more importantly, prognostic significance has the potential to change the way chronic liver disease patients are staged, surveilled and risk-stratified.

cancer biology↗

bioRxiv2020

Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

Cell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligandreceptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

systems biology↗

bioRxiv2019

Systematic bias assessment in solid tissue 10x scRNA-seq workflows

Single-cell and single-nucleus RNA sequencing have been widely adopted in studies of heterogeneous tissues to estimate their cellular composition and obtain transcriptional profiles of individual cells. However, the current fragmentary understanding of artefacts introduced by sample preparation protocols impedes the selection of optimal workflows and compromises data interpretation. To bridge this gap, we compared performance of several workflows applied to adult mouse kidneys. Our study encompasses two tissue dissociation protocols, two cell preservation methods, bulk tissue RNA sequencing, single-cell and three single-nucleus RNA sequencing workflows for the 10x Genomics Chromium platform. These experiments enable a systematic comparison of recovered cell types and their transcriptional profiles across the workflows and highlight protocol-specific biases important for the experimental design and data interpretation.

genomics↗