Development of a Sensitive, Scalable Method for Spatial, Cell-Type-Resolved Proteomics of the Human Brain

Area of Science:

• Neuroscience
• Proteomics
• Biochemistry

Background:

• Bulk tissue proteomics lacks spatial resolution, averaging protein abundance across diverse cell types.
• Current proteomics methods struggle with the sensitivity and throughput required for deep single-cell proteome studies.
• Cell sorting enriches specific cell populations but sacrifices spatial context and resolution.

Purpose of the Study:

• To develop and optimize a method for proteomic analysis of neurons isolated from post-mortem human brain using laser capture microdissection (LCM).
• To achieve high protein identification and quantitative performance from limited brain tissue samples.
• To demonstrate the workflow's capability in resolving cell-type-specific proteomes within human brain tissue.

Main Methods:

• Optimized sample collection, lysis buffers, and digestion methods for neuronal proteomic analysis.
• Utilized laser capture microdissection (LCM) to isolate specific neuronal populations from human brain tissue.
• Analyzed proteomes from both bulk cerebellar molecular layer samples and individual Betz and Purkinje cells.

Main Results:

• Identified approximately 1500 proteins from a 60,000 µm² area of 10 µm thick cerebellar molecular layer with excellent reproducibility.
• Successfully isolated and analyzed proteomes of individual Betz and Purkinje cells.
• Resolved cell-type-specific proteomes, identifying 2800 to 3600 proteins per cell type.

Conclusions:

• The optimized LCM-based proteomic workflow enables high-resolution analysis of neuronal protein expression in the human brain.
• This method overcomes limitations of bulk tissue and cell sorting techniques by preserving spatial context.
• The workflow provides deep proteome coverage for individual neurons, facilitating the study of cell-type-specific functions and disease mechanisms.