Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions.

Area of Science:

• Biochemistry
• Nanotechnology
• Proteomics

Background:

• Deep plasma proteome profiling is crucial for biological insights but challenging with traditional methods.
• Existing workflows face limitations in precision, depth, and throughput for large-scale analysis.

Purpose of the Study:

• To develop and validate a novel nanoparticle-based workflow for deep plasma proteomics.
• To enhance the precision, depth, and throughput of proteomic analysis.
• To enable large-scale, multiomic studies by integrating proteomics with genomics.

Main Methods:

• Utilized surface-functionalized superparamagnetic nanoparticles for protein capture.
• Developed an automated workflow leveraging competitive nanoparticle-protein binding equilibria.
• Employed machine learning to analyze nanoparticle physicochemical properties and protein corona composition.

Main Results:

• Achieved superior performance in precision, depth, and throughput compared to conventional proteomics workflows.
• Demonstrated quantitative compression of the proteome's dynamic range using nanoparticle binding.
• Identified that nanoparticle functionalization can be tailored for specific protein sets.

Conclusions:

• The developed nanoparticle workflow offers a significant advancement for deep plasma proteomics.
• This method enables precise, unbiased proteomic analysis at a scale compatible with large-scale genomics.
• Facilitates future multiomic studies by providing a scalable proteomics solution.