Biology subjects

James, N. D.

Publications and source records attributed to James, N. D..

3 recordsLinked to original sources

bioRxiv2025

Fractionated radiation alters the extracellular matrix produced by muscle-invasive bladder cancer cells

Muscle-invasive bladder cancer (MIBC) is a prevalent disease that can be treated with radiotherapy, but has a poor prognosis. Radiation-induced extracellular matrix (ECM) remodelling and fibrosis can induce tumour resistance and recurrence, but has not been studied in MIBC. Here we aimed to characterise the impact of radiation on the ECM composition of MIBC. Materials and MethodsThree MIBC cell lines (T24, UMUC3, J82) were treated with fractionated radiation. We used proteomics to analyse the ECM composition produced by surviving cancer cells and immunofluorescence to investigate changes in the morphology and number of ECM fibres. We evaluated the RNA expression of identified ECM proteins (FN1, COL5A1, COL1A1, TNF6AIP6, FLG) in one cystectomy (TCGA-BLCA, n=397) and two radiotherapy (BC2001, n=313; BCON, n=151) cohorts. ResultsThere were 613 proteins affected by radiation (padj<0.05, fold change >2 or <-2), 68 of which were ECM-associated proteins. There was a general increase in proteases and protease regulators but heterogeneity across cell lines. Enrichment analysis showed ECM organisation was the primary pathway affected. Immunofluorescence confirmed radiation affected ECM structure, generally, reducing the number, length and width of fibres. High FN1, COL5A1, COL1A1, TNF6AIP6 and FLG mRNA levels were adverse prognostic markers in clinical cohorts. ConclusionRadiation alters the composition and structure of the ECM produced by MIBC. As a proof-of-concept, we showed the expression of radiation-affected ECM genes are prognostic markers in MIBC. Future studies should validate these radiation-induced ECM changes in clinical samples.

cancer biology↗

bioRxiv2023

The Tabulae Paralytica: Multimodal single-cell and spatial atlases of spinal cord injury

Here, we introduce the Tabulae Paralytica--a compilation of four atlases of spinal cord injury (SCI) comprising a single-nucleus transcriptome atlas of half a million cells; a multiome atlas pairing transcriptomic and epigenomic measurements within the same nuclei; and two spatial transcriptomic atlases of the injured spinal cord spanning four spatial and temporal dimensions. We integrated these atlases into a common framework to dissect the molecular logic that governs the responses to injury within the spinal cord. The Tabulae Paralytica exposed new biological principles that dictate the consequences of SCI, including conserved and divergent neuronal responses to injury; the priming of specific neuronal subpopulations to become circuit-reorganizing neurons after injury; an inherent trade-off between neuronal stress responses and the activation of circuit reorganization programs; the necessity of reestablishing a tripartite neuroprotective barrier between immune-privileged and extra-neural environments after SCI; and a catastrophic failure to form this barrier in old mice. We leveraged the Tabulae Paralytica to develop a rejuvenative gene therapy that reestablished this tripartite barrier, and restored the natural recovery of walking after paralysis in old mice. The Tabulae Paralytica provides an unprecedented window into the pathobiology of SCI, while establishing a framework for integrating multimodal, genome-scale measurements in four dimensions to study biology and medicine.

genomics↗

bioRxiv2021

Confronting false discoveries in single-cell differential expression

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

bioinformatics↗