Advances of metabolomics in ocular diseases

Ocular diseases pose a significant challenge to global health. The field of metabolomics, which involves the systematic identification and quantification of metabolites within a biological system, has emerged as a promising research approach for unraveling disease mechanisms and discovering novel biomarkers. Through its application, metabolomics has yielded valuable knowledge pertaining to the initiation and advancement of various ocular diseases. This review presents an overview of metabolomics and examines recent research progess in four ocular diseases, specifically diabetic retinopathy, age-related macular degeneration, glaucoma, and dry eye, summarizing potential biomarkers and metabolic pathways associated with these diseases. Additionally, this review offers insights into the future prospects of utilizing metabolomics for the management and treatment of ocular diseases

New Insight into the Genotype-Phenotype Correlation of <i>PRPH2</i>-Related Diseases Based on a Large Chinese Cohort and Literature Review

Variants in PRPH2 are a common cause of inherited retinal dystrophies with high genetic and phenotypic heterogeneity. In this study, variants in PRPH2 were selected from in-house exome sequencing data, and all reported PRPH2 variants were evaluated with the assistance of online prediction tools and the comparative validation of large datasets. All variants were classified based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines. Individuals with pathogenic or likely pathogenic variants of PRPH2 were confirmed by Sanger sequencing. Clinical characteristics were summarized. Ten pathogenic or likely pathogenic variants of PRPH2 were identified in 14 families. In our cohort, the most frequent variant was p.G305Afs*19, accounting for 33.3% (5/15) of alleles, in contrast to the literature, where p.R172G (11.6%, 119/1028) was the most common variant. Nine in-house families (63.8%) were diagnosed with retinitis pigmentosa (RP), distinct from the phenotypic spectrum in the literature, which shows that RP accounts for 27.9% (283/1013) and macular degeneration is more common (45.2%, 458/1013). Patients carrying missense variants predicted as damaging by all seven prediction tools and absent in the gnomAD database were more likely to develop RP compared to those carrying missense variants predicted as damaging with fewer tools or with more than one allele number in the gnomAD database (p = 0.001). The population-specific genetic and phenotypic spectra of PRPH2 were explored, and novel insight into the genotype–phenotype correlation of PRPH2 was proposed. These findings demonstrated the importance of assessing PRPH2 variants in distinct populations and the value of providing practical suggestions for the genetic interpretation of PRPH2 variants

Efficient Sample Preparation System for Multi-Omics Analysis via Single Cell Mass Spectrometry

Mass spectrometry (MS) has become a powerful tool for metabolome, lipidome, and proteome analyses. The efficient analysis of multi-omics in single cells, however, is still challenging in the manipulation of single cells and lack of in-fly cellular digestion and extraction approaches. Here, we present a streamlined strategy for highly efficient and automatic single-cell multi-omics analysis by MS. We developed a 10-pL-level microwell chip for housing individual single cells, whose proteins were found to be digested in 5 min, which is 144 times shorter than traditional bulk digestion. Besides, an automated picoliter extraction system was developed for sampling of metabolites, phospholipids, and proteins in tandem from the same single cell. Also, 2 min MS2 spectra were obtained from 700 pL solution of a single cell sample. In addition, 1391 proteins, phospholipids, and metabolites were detected from one single cell within 10 min. We further analyzed cells digested from cancer tissue samples, achieving up to 40% increase in cell classification accuracy using multi-omics analysis in comparison with single-omics analysis. This automated single-cell MS strategy is highly efficient in analyzing multi-omics information for investigation of cell heterogeneity and phenotyping for biomedical applications