Patient-derived organoids from induced pluripotent stem cells have emerged as a model for studying human diseases beyond conventional two-dimensional (2D) cell culture. Briefly, these three-dimensional organoids are highly complex, capable of self-organizing, recapitulate cellular architecture, and have the potential to model diseases in complex organs, such as the brain. For example, the hallmark of Parkinson's disease (PD) - proteostatic dysfunction leading to the selective death of neurons in the substantia nigra - present a subtle distinction in cell type specificity that is lost in 2D cell culture models. As such, the development of robust methods to study global proteostasis and protein turnover in organoids will remain essential as organoid models evolve. To solve this problem, we have designed a workflow to reproducibly extract proteins from brain organoids, measure global turnover using mass spectrometry, and statistically investigate turnover differences between genotypes. We also provide robust methodology for data filtering and statistical treatment of turnover data. Using human midbrain organoids (hMO) as a model system, our method accurately characterized the half-lives of 773 midbrain proteins. We compared these half-lives both to Parkin knockout hMOs and to previously reported data from primary cell cultures and in vivo models. Overall, this method will facilitate the study of proteostasis in organoid models of human disease and will provide an analytical and statistical framework to measure protein turnover in organoids of all cell types.
Keywords: Isotope labeling; Mass spectrometry; Midbrain; Organoids; Parkinson; Proteomics; Turnover.
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