A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy

Sarah R Weber; Yuanjun Zhao; Jingqun Ma; Christopher Gates; Felipe da Veiga Leprevost; Venkatesha Basrur; Alexey I Nesvizhskii; Thomas W Gardner; Jeffrey M Sundstrom

doi:10.1186/s12014-021-09328-8

A validated analysis pipeline for mass spectrometry-based vitreous proteomics: new insights into proliferative diabetic retinopathy

Clin Proteomics. 2021 Dec 3;18(1):28. doi: 10.1186/s12014-021-09328-8.

Authors

Affiliations

¹ Department of Ophthalmology, Penn State College of Medicine, 500 University Drive, Hershey, PA, 17033, USA.
² Kellogg Eye Center, University of Michigan Medical School, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
³ Department of Pathology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
⁴ Bioinformatics Core, Biomedical Research Core Facilities, University of Michigan Medical School, 2800 Plymouth Road, Ann Arbor, MI, 48109, USA.
⁵ Department of Pathology, University of Michigan Medical School, 1301 Catherine Street, Ann Arbor, MI, 48109, USA.
⁶ Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, MI, 48109, USA.
⁷ Department of Ophthalmology, Penn State College of Medicine, 500 University Drive, Hershey, PA, 17033, USA. jsundstrom@pennstatehealth.psu.edu.
⁸ Kellogg Eye Center, University of Michigan Medical School, 1000 Wall Street, Ann Arbor, MI, 48105, USA. jsundstrom@pennstatehealth.psu.edu.

Abstract

Background: Vitreous is an accessible, information-rich biofluid that has recently been studied as a source of retinal disease-related proteins and pathways. However, the number of samples required to confidently identify perturbed pathways remains unknown. In order to confidently identify these pathways, power analysis must be performed to determine the number of samples required, and sample preparation and analysis must be rigorously defined.

Methods: Control (n = 27) and proliferative diabetic retinopathy (n = 23) vitreous samples were treated as biologically distinct individuals or pooled together and aliquoted into technical replicates. Quantitative mass spectrometry with tandem mass tag labeling was used to identify proteins in individual or pooled control samples to determine technical and biological variability. To determine effect size and perform power analysis, control and proliferative diabetic retinopathy samples were analyzed across four 10-plexes. Pooled samples were used to normalize the data across plexes and generate a single data matrix for downstream analysis.

Results: The total number of unique proteins identified was 1152 in experiment 1, 989 of which were measured in all samples. In experiment 2, 1191 proteins were identified, 727 of which were measured across all samples in all plexes. Data are available via ProteomeXchange with identifier PXD025986. Spearman correlations of protein abundance estimations revealed minimal technical (0.99-1.00) and biological (0.94-0.98) variability. Each plex contained two unique pooled samples: one for normalizing across each 10-plex, and one to internally validate the normalization algorithm. Spearman correlation of the validation pool following normalization was 0.86-0.90. Principal component analysis revealed stratification of samples by disease and not by plex. Subsequent differential expression and pathway analyses demonstrated significant activation of metabolic pathways and inhibition of neuroprotective pathways in proliferative diabetic retinopathy samples relative to controls.

Conclusions: This study demonstrates a feasible, rigorous, and scalable method that can be applied to future proteomic studies of vitreous and identifies previously unrecognized metabolic pathways that advance understanding of diabetic retinopathy.

Keywords: Diabetic retinopathy; Mass spectrometry; Power analysis; Precision medicine; Proteomics; Retinal disease; Vitreous.

Abstract

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