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Greatbatch, C. J.

Publications and source records attributed to Greatbatch, C. J..

2 recordsLinked to original sources
bioRxiv2023

High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma

INTRODUCTIONPrimary open angle glaucoma (POAG) is a leading cause of blindness globally. Characterised by progressive retinal ganglion cell degeneration, the precise pathogenesis remains unknown. Genome-wide association studies (GWAS) have uncovered many genetic variants associated with elevated intraocular pressure (IOP), one of the key risk factors for POAG. This study sought to investigate the morphological and transcriptional consequences of perturbation of key genes at IOP loci in trabecular meshwork cell (TMC); the cellular regulators of IOP. We aimed to identify genetic and morphological variation that can be attributed to TMC dysfunction and raised IOP in POAG. METHODS62 genes across 55 loci were knocked-out in a primary human TMC line. Each knockout group, including five non-targeting control groups, underwent single-cell RNA-sequencing (scRNA-seq) for differentially-expressed gene (DEG) analysis. Multiplexed fluorescent staining of key organelles, was coupled with high-throughput microscopy for single-cell morphological analysis using CellProfiler image analysis. RESULTSAcross many of the individual gene knockouts scRNA-seq highlighted genes relating to matrix metalloproteinases and interferon-induced proteins. Our work has prioritised genes at four loci of interest to identify gene knockouts that may contribute to the pathogenesis of POAG, including ANGPTL2, LMX1B, CAV1, and KREMEN1. Three genetic networks of gene knockouts with similar transcriptomic profiles were identified (ABO / CAV1 / MYOC, ANGPT2 / PKHD1 / TNS1 / TXNRD2, and CAPZA1 / KALRN / LMO7 / PLEKHA7 / GNB1L / TEX41), suggesting a synergistic function in trabecular meshwork cell physiology. TEK knockout caused significant upregulation of nuclear granularity on morphological analysis, whilst knockout of TRIOBP, TMCO1 and PLEKHA7 increased granularity and intensity of actin and the cell-membrane. CONCLUSIONHigh throughput analysis of cellular structure and function through multiplex fluorescent single-cell analysis and scRNA-seq assays enabled the direct study of genetic perturbations at the single-cell resolution. This work provides a framework for investigating the role of genes in the pathogenesis of glaucoma and heterogenous diseases with a strong genetic basis.

genetics↗
bioRxiv2023

Deep Learning-based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell and Organelle Morphology

PURPOSEThe exact pathogenesis of primary open-angle glaucoma (POAG) is poorly understood. Genome-wide association studies (GWAS) have recently uncovered many loci associated with variation in intraocular pressure (IOP); a crucial risk factor for POAG. Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs), the regulatory cells of IOP. METHODSSixty-two genes at fifty-five loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a five-channel fluorescent cell staining protocol. A convolutional neural network (CNN) was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. RESULTSCells where RALGPS1 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, SD 0.030), followed by LTBP2 (AUC 0.846, SD 0.029) and BCAS3 (AUC 0.845, SD 0.020). Of seven multi-gene loci, five had statistically significant differences in AUC (p<0.05) between genes, allowing for pathological gene prioritisation. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). CONCLUSIONSWe demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits.

genetics↗