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Naderi-Meshkin, H.

Publications and source records attributed to Naderi-Meshkin, H..

2 recordsLinked to original sources

Impaired Function in Diabetic Patient iPSCs-derived Blood Vessel Organoids Stem from a Subpopulation of Vascular Cells

The presence of both endothelial cells (ECs) and mural cells are central to the proper function of blood vessels in health and pathological changes in diseases including diabetes. Although iPSCs-derived vascular organoids (VOs) provide an appealing in vitro disease model and platform for drug screening, whether these organoids recapitulate human disease remains debatable. Here, we show human diabetic (DB)-VOs represent impaired vascular function including enhanced ROS activity, with higher mitochondrial content and activity, increased pro-inflammatory cytokines, and less regenerative potential in vivo. Using single-cell RNA sequencing, we identify all specialized types of vascular cells (artery, capillary, vein, lymphatic and tip cells, as well as pericytes and vSMCs) within vascular organoids, while demonstrating the dichotomy landscape of ECs and mural cells. Furthermore, we reveal basal heterogeneity within vascular organoids and demonstrate differences between diabetic and non-diabetic VOs. Of note, a subpopulation of ECs significantly enrich for ROS and oxidative phosphorylation hallmarks in DB-VOs, may represent early signs of aberrant angiogenesis in diabetes. This study helps to identify key biomarkers for diabetic disease progression and find signalling molecules amenable to drug intervention.

cell biology↗

MmCMS: Mouse models' Consensus Molecular Subtypes of colorectal cancer

BACKGROUNDColorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODSUsing transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n=577) and mouse (n=57 across n=8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTSWe developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONSWhen applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package with three options helps researchers select suitable mouse models of human CRC subtype for their experimental testing.

cancer biology↗