Biology subjects

Small, M.

Publications and source records attributed to Small, M..

4 recordsLinked to original sources

bioRxiv2025

Identifying Neuroimaging Biomarkers of Resilience and Vulnerability to Chronic Stress in An Animal Model: An Exploratory Analysis

Stress is a main contributor to mood disorders, with individuals displaying great heterogeneity in response to stressful life events and adversity. Identifying biomarkers of vulnerability and resilience to stress would facilitate a prevention-based approach to mental illness that benefits individuals and reduces healthcare costs. The present study adopted a multivariate machine-learning approach to track neuroimaging biomarkers predictive of resilience and vulnerability following a chronic restraint stress (CRS) model. 96 male Sprague-Dawley rats underwent two sessions of MRI and behavioral tests, before and after CRS. Resilience and vulnerability to CRS were assessed with elevated plus maze and forced swimming tests. Hierarchical clustering was applied to construct brain networks. Partial correlation was used to compute network connectivity. Repeated nested cross validation with a support vector machine was employed to identify rs-fMRI biomarkers predictive of resilience and vulnerability following CRS. No strong group effect size of behavioral changes following CRS was observed within the same animals, suggesting the presence of resilient and vulnerable subgroups. Although the average model performance was modest (area under the receiver operating characteristic curve: 0.3 ~ 0.67), baseline functional connectivity across cerebellum, brainstem, striatum, prefrontal and salience-orbitofrontal regions, as well as functional alteration across hippocampus, striatum, prefrontal regions, auditory thalamus, cerebellum, inferior colliculi and brainstem were identified as stable features. The present study is the first to identify connectome-based neuroimaging biomarkers predictive of resilience and vulnerability using an animal model. The results may provide insights into neuroimaging biomarkers to aid diagnosis and prevention of mood disorders in humans.

neuroscience↗

bioRxiv2024

Decipher: A computational pipeline to extract context-specific mechanistic insights from single-cell profiles

The advent of single-cell profiling technologies has revolutionized our understanding of the cellular and molecular states that underpin disease. However, current computational tools struggle to recover both known and novel mechanistic insights at distinct layers of biological regulation. Here, we present Decipher, a novel computational pipeline that builds integrated cell signalling networks from single-cell profiles in a context-specific, data-driven manner and identifies the key cellular and molecular events that drive disease. We benchmarked Decipher against existing tools and found it could recover known, experimentally determined cytokine signalling pathways, whilst maintaining the flexibility to detect novel pathways and context-specific effects. Notably, Decipher produces global cell-to-cell signalling maps that are interpretable. We utilised Decipher to unveil the cellular and molecular mechanisms driving a novel population of inflammatory monocytes enriched with interferon stimulated genes that is markedly increased in frequency following secondary immunization with the Pfizer-BioNTech COVID-19 mRNA vaccine. Finally, we employed Decipher to interrogate regulon profiles from covid-19 patients with mild versus severe disease, and we found that progression to severe disease was associated with a loss of interferon signalling transcription factors (Irf7, Irf9, STAT1, STAT2) and a gain of factors that drive inflammation and cellular stress responses (NFkB, HIF-1a, ATF3, ATF4). Taken together, our findings demonstrate that Decipher can decode signalling pathways and report on ligand-receptor mediated transcription factor-target gene networks that underlie processes in homeostasis, disease, and cellular responses to therapies. We present Decipher as an invaluable new tool for the discovery of novel therapeutic targets and the development of new medicines.

bioinformatics↗

bioRxiv2023

Spectral detection of condition-specific biological pathways in single-cell gene expression data

Single cell RNA sequencing is an ubiquitous method for studying changes in cellular states within and across conditions. Differential expression (DE) analysis may miss subtle differences, especially where transcriptional variability is not unique to a specific condition, but shared across multiple conditions or phenotypes. Here, we present CDR-g (Concatenate-Decompose-Rotate genomics), a fast and scalable strategy based on spectral factorisation of gene coexpression matrices. CDR-g detects subtle changes in gene coexpression across a continuum of biological states in multi-condition single cell data. CDR-g collates these changes and builds a detailed profile of differential cell states. Applying CDR-g, we show that it identifies biological pathways not detected using conventional DE analysis and delineates novel, condition-specific subpopulations in single-cell datasets.

bioinformatics↗

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↗