Biology subjects

Neilson, G.

Publications and source records attributed to Neilson, G..

4 recordsLinked to original sources

bioRxiv2025

TP53 and RB1 are predictive genetic biomarkers for sensitivity to cytarabine in gliomas

Therapeutic progress in glioma, one of the most lethal human cancers, has been limited by molecular heterogeneity and lack of biomarker-driven drug deployment. Here we used a proprietary large-scale CRISPRi screening in primary patient-derived glioma tumorspheres to identify genetic vulnerabilities and nominate pharmacologically tractable targets. DNA polymerase-linked dependencies emerged as top-ranked hits, which we validated through orthogonal viability assays. Network-based integration of dependency data with drug-target relationships nominated cytarabine, a nucleoside analogue already approved for intrathecal use, as a candidate agent targeting this axis. Dose-response profiling across molecularly diverse glioma models revealed substantial heterogeneity in cytarabine sensitivity (IC50 range: 0.04-9.8 {micro}M). Machine learning analysis of whole-genome sequencing data identified TP53 wild-type and RB1-wild-type status as dominant predictors of response, with double wild-type lines showing three standard deviations (3 s.d.) increased sensitivity compared to altered models. Prospective validation in an independent cohort confirmed that TP53/RB1 genotype stratifies cytarabine activity. These findings establish a mechanistically anchored, biomarker-restricted repurposing opportunity for cytarabine in leptomeningeal glioma, enabling rational prioritisation of an accessible therapy in a molecularly defined patient subset.

cancer biology↗

bioRxiv2025

Multi-modal single-cell foundation models via dynamic token adaptation

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in applying deep learning in genomics include DNA-language and single-cell foundation models. However, these models take only one data type as input. We introduce dynamic token adaptation and demonstrate how it combines these models to predict gene regulation at the single-cell level in different genetic contexts. Although the method is generalisable, we focus on an illustrative example by training an adapter from DNA-sequence embeddings to a single-cell foundation models token embedding space. As a qualitative evaluation, we assess the impact of DNA sequence changes on the models learned gene regulatory networks by mutating the transcriptional start site of the transcription factor GATA4 in silico, observing predicted expression changes in its target genes in fetal cardiomyocytes.

bioinformatics↗

bioRxiv2025

Detecting cell-level transcriptomic changes of Perturb-seq using Contrastive Fine-tuning of Single-Cell Foundation Models

Genome-scale perturbation cell atlases are an exciting new resource to understand the transcriptomic and phenotypic impact of single-gene activation or knockdown. However, in terms of differentially expressed genes identified, the signal detected in these data atlases is low, leading to the exclusion of most data from downstream analyses. Recent advances in single-cell foundation models have shown promise in capturing complex biological insights. However, their application to perturbation analysis, especially in predicting perturbed single-cell transcriptomes, remains limited. In this paper, we focus on learning representations of single-cell transcriptomes that capture subtle, yet important, transcriptome-wide changes, and we propose a novel fine-tuning strategy using contrastive learning to leverage single-cell foundation models for this task. We pre-train a single-cell foundation model and fine-tune on a genome-scale perturbation dataset using a contrastive loss, which minimises the distance between cell embeddings from unperturbed cells while maximising the distance between perturbed and unperturbed cells. We validate and test the model on unseen perturbations, demonstrating its ability to identify global biologically meaningful transcriptional changes not captured by traditional differential expression methods. Our approach provides a novel framework for analysing single-cell perturbation data and offers a more effective means of identifying perturbations that drive systemic gene expression changes.

bioinformatics↗

bioRxiv2020

Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Human DNA-methylation data have been used to develop biomarkers of ageing - referred to epigenetic clocks - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into training and testing samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

genomics↗