Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology

Connor J. Greatbatch, Qinyi Lu, Sandy Hung, Son N. Tran, Kristof Wing, Helena Liang, Xikun Han, Tiger Zhou, Owen M. Siggs, David A. Mackey, Guei Sheung Liu, Anthony L. Cook, Joseph E. Powell, Jamie E. Craig, Stuart MacGregor, Alex W. Hewitt

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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Abstract

Purpose: Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation. 

Design: Experimental study. Subjects: Primary TMCs collected from human donors. 

Methods: Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network 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. 

Main Outcome Measures: Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls. 

Results: Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC (P < 0.05) between genes, allowing for pathological gene prioritization. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). 

Conclusions: We 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. 

Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Original languageEnglish
Article number100504
Number of pages11
JournalOphthalmology Science
Volume4
Issue number4
Early online date5 Mar 2024
DOIs
Publication statusPublished - 1 Jul 2024

Keywords

  • CRISPR
  • Genetics
  • Glaucoma
  • Morphological profiling
  • Transcriptomics

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