ArticlebioRxiv : the preprint server for biology2026
A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 due to the high dynamic range of protein half-lives and limited proteome coverage in SILAC (Stable Isotope Labeling by Amino acids in Cell culture) experiments. Despite broad applications of dynamic SILAC proteomics to measure protein turnover in rodent tissues and primary neurons, few studies have measured protein half-lives in human neurons with limited proteome coverage. Here, we established a comprehensive platform to quantify protein half-lives in human induced pluripotent stem cell (iPSC)-derived neurons. By integrating optimized dynamic SILAC labeling in human neuron cultures, extensive peptide fractionation, optimized data-dependent and data-independent LC-MS/MS acquisition methods, and a streamlined computational pipeline, we achieved deep and accurate measurement of 10,792 protein half-lives from 162,854 unique peptides. We then compared the protein turnover and abundances in iPSC-derived glutamatergic cortical neurons and spinal motor neurons, revealing globally conserved proteome dynamics alongside subtype-specific differences consistent with specialized neuronal functions. To enable broad community access, we created NeuronProfile (www.neuronprofile.com), an interactive web platform for exploring protein turnover, abundance, and subcellular location in human neurons. Together, this work provides a comprehensive analytical platform to assess human neuronal proteostasis and a foundational resource for neurological disease research and therapeutic development.
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