In-depth and high-throughput spatial proteomics for whole-tissue slice profiling by deep learning-facilitated sparse...

Area of Science:

• Proteomics
• Systems Biology
• Biotechnology

Background:

• Cellular heterogeneity and spatial organization are key to mammalian tissue function.
• Spatial transcriptomics has advanced, but spatial proteomics lags due to protein detection limitations.
• Accurate spatial proteome mapping is crucial for understanding disease mechanisms and identifying biomarkers.

Purpose of the Study:

• To develop a novel method for high-resolution, large-scale spatial proteome mapping.
• To overcome the limitations of current spatial proteomics techniques in terms of throughput and sensitivity.
• To generate the most comprehensive spatial proteome dataset to date for a complex organ.

Main Methods:

• Developed a sparse sampling strategy for spatial proteomics (S4P).
• Utilized computationally assisted image reconstruction for data analysis.
• Applied the S4P strategy to centimeter-sized mouse brain tissue samples.

Main Results:

• Generated the largest spatial proteome dataset to date, identifying over 9000 proteins.
• Achieved deep proteome coverage in centimeter-sized tissue samples.
• Discovered potential novel regional and cell-type-specific protein markers in the mouse brain.

Conclusions:

• The S4P strategy significantly enhances spatial proteomics capabilities for large tissue samples.
• This approach offers improved sensitivity and throughput compared to existing methods.
• S4P is expected to be broadly applicable to various tissues for future biological and medical research.