Biology subjects

Barrow, R.

Publications and source records attributed to Barrow, R..

4 recordsLinked to original sources

bioRxiv2023

A Human Breast Cell Atlas Mapping the Homeostatic Cellular Shifts in the Adult Breast

One of the barriers for breast cancer prevention and treatment is our poor understanding of the dynamic cellular shifts that naturally occur within the breast and how these changes contribute to tumour initiation. In this study we report the use of single cell RNA sequencing (scRNAseq) to compile a Human Breast Cell Atlas (HBCA) assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mammoplasties. The data from more than 800,000 cells identified 41 cell subclusters distributed across the epithelial, immune, and stromal compartments. We found that the contribution of these different clusters varied according to the natural history of the tissue. Breast cancer risk modulating factors such as age, parity, and germline mutation affected the homeostatic cellular state of the breast in different ways however, none of the changes observed were restricted to any one cell type. Remarkably, we also found that immune cells from BRCA1/2 carriers had a distinct gene expression signature indicative of potential immune exhaustion. This suggests that immune escape mechanisms could manifest in non-cancerous tissues during very early stages of tumour initiation. Therefore, the Atlas presented here provides the research community with a rich resource that can be used as a reference for studies on the origins of breast cancer which could inform novel approaches for early detection and prevention.

cancer biology↗

bioRxiv2023

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy

Glioblastoma (GBM) brain tumours lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumours almost always fatally recur. Using RNAseq data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumours, we identified two responder subtypes based on therapy-driven changes in gene expression. In two thirds of patients a specific subset of genes is up-regulated from primary to recurrence (Up responders) and in one third the same genes are down-regulated (Down responders). Characterisation of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms. In Up responders treatment enriches for quiescent proneural GBM stem cells and differentiated neoplastic cells with increased neurotransmitter signalling, whereas Down responders commonly undergo therapy-driven mesenchymal transition. Stratifying GBM tumours by response subtype may lead to more effective treatment. In support of this, modulators of gamma aminobutyric acid (GABA) neurotransmitter signalling differentially sensitise Up and Down responder GBM models to standard treatment in vitro.

cancer biology↗

bioRxiv2022

SpheroidAnalyseR - an online platform for analysing data from 3D spheroids or organoids grown in 96-well plates

Spheroids and organoids are increasingly popular three-dimensional (3D) cell culture models. Spheroid models are more physiologically relevant to a tumour compared to two-dimensional (2D) cultures and organoids are a simplified version of an organ with similar composition. Both can be used in cell migration studies, disease modelling and drug discovery. A drawback of these models is, however, the lack of appropriate analytical tools for high throughput imaging and analysis over a time course. To address this, we have developed an R Shiny app called SpheroidAnalyseR: a simple, fast, effective open-source app that allows the analysis of spheroid or organoid size data generated in a 96-well format. SpheroidAnalyseR processes and analyses datasets of image measurements that can be obtained via a bespoke software, described herein, that automates spheroid imaging and quantification using the Nikon A1R Confocal Laser Scanning Microscope. However, templates are provided to enable users to input spheroid image measurements obtained by user-preferred methods. SpheroidAnalyseR facilitates outlier identification and removal followed by graphical visualisation of spheroid measurements across multiple predefined parameters such as time, cell-type and treatment(s). Spheroid imaging and analysis can, thus, be reduced from hours to minutes, removing the requirement for substantial manual data manipulation in a spreadsheet application. The combination of spheroid generation in 96-well ultra-low attachment microplates, imaging using our bespoke software, and analysis using SpheroidAnalyseR toolkit allows high throughput, longitudinal quantification of 3D spheroid growth whilst minimising user input and significantly improving the efficiency and reproducibility of data analysis. Our bespoke imaging software is available from https://github.com/GliomaGenomics. SpheroidAnalyseR is available at https://spheroidanalyser7.azurewebsites.net, and the source code found at https://github.com/GliomaGenomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/481039v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1f779fborg.highwire.dtl.DTLVardef@698103org.highwire.dtl.DTLVardef@94f252org.highwire.dtl.DTLVardef@b1180e_HPS_FORMAT_FIGEXP M_FIG Graphical abstract - SpheroidAnalyseR main steps. A summary of the process followed within the SpheroidAnalyseR app. Dotted boxes are the steps required to produce the input data for SpheroidAnalyseR. Solid boxes show the 5 main steps of SpheroidAnalyseR: Data input, Preview, Outlier removal, Merging and Plotting. C_FIG

cell biology↗

bioRxiv2019

Volatile molecules secreted by the wheat pathogen Parastagonospora nodorum are involved in development and phytotoxicity

Septoria nodorum blotch is a major disease of wheat caused by the fungus Parastagonospora nodorum. Recent studies have demonstrated that secondary metabolites, including polyketides and non-ribosomal peptides, produced by the pathogen play important roles in disease and development. However, there is currently no knowledge on the composition or biological activity of the volatile organic compounds (VOCs) secreted by P. nodorum. To address this, we undertook a series of growth and phytotoxicity assays and demonstrated that P. nodorum VOCs inhibited bacterial growth, were phytotoxic and suppressed self-growth. Mass spectrometry analysis revealed that 3-methyl-1-butanol, 2-methyl-1-butanol, 2-methyl-1-propanol and 2-phenylethanol were dominant in the VOC mixture and phenotypic assays using these short chain alcohols confirmed that they were phytotoxic. Further analysis of the VOCs also identified the presence of multiple sesquiterpenes of which four were identified via mass spectrometry and nuclear magnetic resonance as {beta}-elemene, -cyperone, eudesma-4,11-diene and acora-4,9-diene. Subsequent reverse genetics studies were able to link these molecules to corresponding sesquiterpene synthases in the P. nodorum genome. However, despite extensive testing, these molecules were not involved in either of the growth inhibition or phytotoxicity phenotypes previously observed. Plant assays using mutants of the pathogen lacking the synthetic genes revealed that the identified sesquiterpenes were not required for disease formation on wheat leaves. Collectively, these data have significantly extended our knowledge of the VOCs in fungi and provided the basis for further dissecting the roles of sesquiterpenes in plant disease.

microbiology↗