A Deep Quantitative Proteome Turnover Platform for Human iPSC-Derived Neurons

Abstract Quantitative evaluation of protein turnover in human neurons is crucial for understanding neuron homeostasis and guiding drug development for neurological diseases. However, measuring protein turnover in postmitotic neurons remains challenging because of the broad dynamic range of neuronal protein half-lives and limited proteome coverage in SILAC (stable isotope labeling by amino acids in cell culture) experiments. Here, we established a comprehensive analytical platform for protein turnover measurements in human induced pluripotent stem cell (iPSC)-derived neurons by integrating optimized dynamic SILAC labeling, extensive offline peptide fractionation, data-independent acquisition (DIA) LC−MS/MS, and a streamlined computational workflow with stringent kinetic modeling and filtering criteria. As a result, this platform enabled the quantification of 10,792 apparent protein half-lives from 162,854 unique peptides, substantially expanding the coverage of low-abundance regulatory proteins and therapeutically relevant targets, including 72% of the human kinome, 45% of FDA-approved drug targets, and 72% of potential drug targets. Comparative analyses of glutamatergic cortical neurons and spinal motor neurons revealed globally conserved proteome dynamics alongside subtype-specific differences in protein turnover and abundance. To maximize the accessibility and utility of these datasets, we developed NeuronProfile (https://www.neuronprofile.com/), an interactive resource for exploring protein turnover, abundance, protein copy number, peptide-level kinetics, subcellular localization, and therapeutically relevant proteins in human neurons. Together, this work establishes a robust analytical platform and community resource for quantitative studies of neuronal proteostasis, therapeutic target prioritization, and neurological disease research.