# Harnessing New Tools for Old Challenges: Optimising Neat Plasma Proteomics with Automation and Gas-Phase Fractionation

## TL;DR

This paper introduces an automated workflow for plasma proteomics that improves efficiency and reproducibility, enabling large-scale biomarker discovery.

## Contribution

A novel automated and optimized workflow for neat plasma proteomics using gas-phase fractionation and deep spectral libraries is presented.

## Key findings

- Automation reduced hands-on time by 88% and improved robustness.
- Mixed-mode searching increased protein identifications by up to 31%.
- 936 proteins were quantified in a coronary artery disease cohort, with 42 dysregulated compared to healthy controls.

## Abstract

Advances in high-throughput
mass spectrometry have shifted the
bottleneck in plasma proteomics from data acquisition to sample preparation.
While enrichment and depletion strategies enable detection of low-abundance
proteins, their complexity and cost limit scalability and clinical
translation. Targeting midto-high abundance proteins from neat plasma
offers a practical, reproducible alternative aligned with clinical
workflows. Here, we combine fully automated sample preparation and
Evotip loading on the Bravo AssayMAP system with extensive method
optimization on the timsTOF HT and gas-phase fractionation deep spectral
libraries to advance neat plasma proteomics. Automation reduced hands-on
time by 88% and significantly improved robustness. Mixed-mode searching
with a 1788-protein library increased identifications by up to 31%
at a throughput of 100 samples per day, with less than 15% variation
across plates. In a coronary artery disease cohort, we quantified
936 biologically relevant proteins and found 42 dysregulated compared
to healthy controls. This streamlined, high-throughput workflow enables
deep, reproducible analysis of neat plasma at scale, paving the way
for population-level biomarker discovery and clinical implementation.