Biology subjects

Marsh, L. M.

Publications and source records attributed to Marsh, L. M..

4 recordsLinked to original sources

bioRxiv2026

A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort

Lung neuroendocrine tumours (lung NETs) are rare neoplasms comprising approximately 2% of lung cancers. Recent studies have identified distinct molecular groups based on transcriptome and methylome data, but genomic and morphological features remain underexplored due to limited whole-genome and imaging data. We have generated the largest multi-omic dataset of lung NETs to date (201 participants, for a total of n = 294 tumours), including RNA sequencing, EPIC 850K methylation arrays, and whole-genome sequencing. This multiomic dataset also include multi-regional whole-genome sequencing for 41 participants, allowing for the quantification of intra-tumoural heterogeneity. We additionally generated spatial proteomics (64 participants), spatial transcriptomics (4 participants) and whole-slide histopathology images for 212 cases. This dataset enables a comprehensive characterization of lung NET molecular groups and the identification of group-specific morphological features using deep learning algorithms. All quality control analyses, processed data, and scripts are provided to ensure reproducibility. This dataset is available as a basis for further molecular and morphological analysis of lung NETs, and for future research on multi-scale integration.

bioinformatics↗

bioRxiv2024

Connectivity Map and perturbation-based sensitivity analysis identifies MEK inhibitors as senolytics in human lung fibroblasts

Recently, the elimination of the disease-associated accumulation of senescent cells using senolytics has been shown to exert health benefits in animal studies. However, due to the heterogeneity of cell senescence and its unrecognized master regulators, drug development faces a complexity that must be handled. Bioinformatic elucidation of genes and pathways involved in senolysis and prediction of senolytic activity of compounds can cut costs and facilitate faster achievements in the field. In the present investigation, after obtaining the consensus gene signature of senescent fibroblasts of lung origin and deriving its anti-apoptotic module, we utilized Connectivity Map (CMap) alongside small molecule and genetic perturbation sensitivity data in cancer cell lines to identify drugs and genetic interventions that might induce apoptosis or sensitize senescent cells to apoptosis. Through bioinformatic evaluations, we speculate that activation of early stages of autophagy which contributes to the formation of autophagosomes, concurrent with the activation of waste protein concealment system by the mean of p62 and chaperoning system alongside an increase in JUNB gene expression can secure the survival of the senescent cells even when homeostasis of different cellular processes is disrupted. Moreover, our bioinformatic evaluation proposed selumetinib, a MEK inhibitor, as a senolytic against senescent lung fibroblasts. The senolytic activity of a variety of MEK inhibitors in senescent lung fibroblasts was confirmed using human lung fibroblasts in vitro.

pharmacology and toxicology↗

bioRxiv2024

Adventitial fibroblasts direct smooth muscle cell-state transition in pulmonary vascular disease

Pulmonary vascular remodeling is a progressive pathological process characterized by functional alterations within pulmonary artery smooth muscle cells (PASMC) and adventitial fibroblasts (PAAF). Mechanisms driving the transition to a diseased phenotype remain elusive. Utilizing a combination of transcriptomic and proteomic profiling, along with phenotyping of source-matched cells from healthy controls and individuals with idiopathic pulmonary arterial hypertension (IPAH), our investigation uncovered that while PASMC and PAAF retained their original cellular identities, they acquired distinct disease-associated states. Though both cell types exhibited reduced mitochondrial content and hyperpolarization, IPAH-PASMC displayed heightened glycosaminoglycan production and downregulation of contractile machinery, contrasting a hyperproliferative phenotype of IPAH-PAAF. We investigated the involvement of cellular crosstalk in regulating cell state dynamics and identified pentraxin-3 and hepatocyte growth factor as potential modulators of PASMC phenotypic transition orchestrated by PAAF. Our findings contribute to a deeper understanding of pulmonary vascular mesenchyme dynamics in disease pathogenesis.

cell biology↗

bioRxiv2020

Machine learning analysis of the bleomycin-mouse model reveals the spatial and temporal pulmonary inflammatory fingerprint

The bleomycin mouse-model is the extensively used model to study pulmonary fibrosis, however, the inflammatory cell kinetics and their compartmentalisation is still incompletely understood. Here we assembled historical flow cytometry data, totalling 303 samples and 16 inflammatory-cell populations, and applied advanced data modelling and machine learning methods to conclusively detail these kinetics. Three days post-bleomycin, the inflammatory profile was typified by acute innate inflammation, pronounced neutrophilia, especially of SiglecF+ neutrophils, and alveolar macrophage loss. Between 14 and 21 days, rapid-responders were increasingly replaced by T and B cells, and monocyte-derived alveolar macrophages. Multi-colour imaging revealed the spatial-temporal cell distribution and the close association of T cells with deposited collagen. Unbiased immunophenotyping and data modelling exposed the dynamic shifts in immune-cell composition over the course of bleomycin-triggered lung injury. These results and workflow provides a reference point for future investigations, and can easily be applied in the analysis of other datasets.

immunology↗