@article {2070,
	title = {Liquid Biopsy Proteomics in Ophthalmology.},
	journal = {J Proteome Res},
	volume = {23},
	year = {2024},
	month = {2024 Feb 02},
	pages = {511-522},
	abstract = {<p>Minimally invasive liquid biopsies from the eye capture locally enriched fluids that contain thousands of proteins from highly specialized ocular cell types, presenting a promising alternative to solid tissue biopsies. The advantages of liquid biopsies include sampling the eye without causing irreversible functional damage, potentially better reflecting tissue heterogeneity, collecting samples in an outpatient setting, monitoring therapeutic response with sequential sampling, and even allowing examination of disease mechanisms at the cell level in living humans, an approach that we refer to as TEMPO (Tracing Expression of Multiple Protein Origins). Liquid biopsy proteomics has the potential to transform molecular diagnostics and prognostics and to assess disease mechanisms and personalized therapeutic strategies in individual patients. This review addresses opportunities, challenges, and future directions of high-resolution liquid biopsy proteomics in ophthalmology, with particular emphasis on the large-scale collection of high-quality samples, cutting edge proteomics technology, and artificial intelligence-supported data analysis.</p>
},
	keywords = {Artificial Intelligence, Biopsy, Humans, Liquid Biopsy, Ophthalmology, Proteins, Proteomics},
	issn = {1535-3907},
	doi = {10.1021/acs.jproteome.3c00756},
	author = {Wolf, Julian and Franco, Joel A and Yip, Rui and Dabaja, Mohamed Ziad and Velez, Gabriel and Liu, Fei and Bassuk, Alexander G and Mruthyunjaya, Prithvi and Dufour, Antoine and Mahajan, Vinit B}
}
@article {2058,
	title = {Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in~vivo.},
	journal = {Cell},
	volume = {186},
	year = {2023},
	month = {2023 Oct 26},
	pages = {4868-4884.e12},
	abstract = {<p>Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson\&$\#$39;s disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.</p>
},
	keywords = {Aging, Artificial Intelligence, Biopsy, Humans, Liquid Biopsy, Proteomics},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2023.09.012},
	author = {Wolf, Julian and Rasmussen, Ditte K and Sun, Young Joo and Vu, Jennifer T and Wang, Elena and Espinosa, Camilo and Bigini, Fabio and Chang, Robert T and Montague, Artis A and Tang, Peter H and Mruthyunjaya, Prithvi and Aghaeepour, Nima and Dufour, Antoine and Bassuk, Alexander G and Mahajan, Vinit B}
}
