Biology subjects

Teague, R.

Publications and source records attributed to Teague, R..

4 recordsLinked to original sources

bioRxiv2026

Scalable spatial DNA sequencing from archival tissue maps copy number subclones

Spatially resolved DNA sequencing holds promise due to its potential utility in understanding cancer intra-tumour heterogeneity and tumour evolution in relation to tissue architecture. However, it has so far been used to a limited extent due to technical challenges and high cost of existing methods. Hence we aimed to develop a high throughput spatial genomic assay to obtain copy number alteration (CNA) information at user-defined spatial resolution. We derived CNA profiles from ultra-low coverage whole genome sequencing at sub-millimetre resolution from archival samples using a novel method called Adaptive Resolution Multiscale Spatial DNA sequencing (ARMS DNAseq). We used it to profile CNAs from more than 766 regions (tiles) from 3 patients, covering a total area of over 300 mm2, with 1.2-2.6 million mapped reads per tile and tile sizes of 0.1-0.99mm2. Using ARMS DNAseq, we delineate tumour evolution in a spatial context, and identify more tumour subclones that were obscured or incompletely represented in bulk multi-region whole genome sequencing. Next, we show associations between tumour subclones and morphology, and prediction of subclone identity from deep learning-derived image representations. Finally, we demonstrate multi-omic integration by alignment with spatial transcriptomic data, showing subclone-specific immune cell co-occurrence as well as transcriptional programmes cutting across subclone boundaries. ARMS DNAseq converts low-throughput, region-by-region profiling into a scalable and adaptable workflow for direct spatial copy number profiling from archival tissue sections.

genomics↗

bioRxiv2025

Redefining the topology of the human bone marrow using augmented spatial transcriptomic analysis

The bone marrow (BM) is the main site of haematopoiesis in adult life. Our understanding of the pathogenesis of BM-derived blood cancers is limited by lack of spatial contextualisation. While emerging spatial transcriptomic (ST) platforms offer unprecedented opportunities for spatially-resolved cellular phenotyping, we recognise and incorporate the power of AI-based tissue feature detection to enhance ST workflows. We perform ST analysis to define the topology of the normal bone marrow (BM) and BM in myeloproliferative neoplasms (MPNs), profiling 5,104,452 cells across 30 human BM samples. Following rigorous histology-based QC, we identify spatially-restricted trajectories of haematopoiesis and extend our understanding of the haematopoietic stem cell (HSC) niche. We find that BM fibrosis in MPN is associated with expansion of distinct immune and stromal co-enriched cell neighbourhoods. We then present a machine learning (ML)-based model trained on ST data that quantifies BM microenvironmental deviation, identifying heretofore unrecognised inter- and intra-individual sample heterogeneity in MPN. Our study demonstrates the potential for AI-based augmented ST analysis, and redefines our understanding of human BM topology.

cancer biology↗

bioRxiv2025

Systematic molecular glue drug discovery with a high-throughput effector protein remodeling platform

Realising the promise of new medicines that operate through a targeted molecular glue-induced degradation mechanism requires systematic tools that can uncover the relevant principles of neomorphic protein-protein interactions. Whilst some monovalent glue degraders have been found through serendipity, the rules for small molecule attributes and the pairs or complexes of proteins that are amenable to drug-induced proximity control remain poorly articulated. Here we introduce a new approach to address this by using programmed libraries of intramolecularly edited proteins to expand protein surface landscapes and trigger new druggable interactions. We show that effector proteins, such as the E3 ligase Cereblon, can be engineered to provoke neomorphic activity by inducing the degradation of new client proteins and that these de novo interactions provide a blueprint from which new small molecule degraders can be built. As a demonstration of the approach, we use the platform to identify new non-IMiD molecular glue degraders of the oncology target GSPT1. SUMMARYO_LIMolecular glues are a highly important and promising new form of therapeutic agent, but rationalising their discovery has so far been impossible C_LIO_LIGlueSEEKER screening enables prospective monovalent drug discovery by using high-throughput deep mutational scanning to re-engineer the function of effector proteins like E3 ligases C_LIO_LIWe used this approach to enable the computational discovery of small molecule glues which degrade the oncology target GSPT1 and show how the technology can be used across new targets C_LI

bioengineering↗

bioRxiv2023

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx(R), Decipher(R), Prostadiag(R)) using a step-wise approach increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses. Patient summaryProstate cancers are very variable, including within a single tumour. Current genetic scoring systems, which are sometimes used to make decisions for how to treat patients with prostate cancer, are based on sampling methods which do not reflect these variations. We found, using state-of-the-art spatial genetic technology to simulate accurate assessment of variation in biopsies, that the current approaches miss important details which could negatively impact clinical decisions. Take home messageVirtual biopsies from spatial transcriptomic analysis of a whole prostate reveal that current genomic risk scores potentially deliver misleading results as they are based on bulk analysis of prostate biopsies and ignore tumour heterogeneity.

genomics↗