A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons.

Area of Science:

• Neuroscience
• Proteomics
• Systems Biology

Background:

• Quantifying protein turnover in human neurons is vital for understanding neurological diseases and homeostasis.
• Existing methods like Stable Isotope Labeling by Amino acids in Cell culture (SILAC) face challenges in human neurons, including limited proteome coverage and a wide range of protein half-lives.

Purpose of the Study:

• To establish a comprehensive platform for quantifying protein half-lives in human induced pluripotent stem cell (iPSC)-derived neurons.
• To compare protein turnover and abundance between different human neuronal subtypes.
• To create a publicly accessible resource for exploring neuronal protein dynamics.

Main Methods:

• Optimized dynamic SILAC labeling in human neuron cultures.
• Extensive peptide fractionation and advanced LC-MS/MS acquisition (data-dependent and data-independent).
• Streamlined computational pipeline for deep and accurate proteome-wide half-life measurements.

Main Results:

• Successfully quantified 10,792 protein half-lives from 162,854 unique peptides in human iPSC-derived neurons.
• Revealed globally conserved proteome dynamics between glutamatergic cortical neurons and spinal motor neurons.
• Identified subtype-specific differences in protein turnover and abundance consistent with specialized neuronal functions.

Conclusions:

• The developed platform provides a robust method for assessing human neuronal proteostasis.
• The findings offer a foundational resource for neurological disease research and therapeutic development.
• The interactive web platform NeuronProfile enhances accessibility to neuronal proteomic data.