Rapid Eco-AI CE–MS Enables Sub-7 Min Low-Input Proteome Profiling with Quantitative Validation

Abstract Low-input and single-cell proteomics by mass spectrometry (MS) have advanced rapidly, but balancing analytical speed and sensitivity remains challenging, especially for small, protein-scarce primary cells such as neutrophils. Here, we report Rapid Eco–AI, an optimized electrophoresis-correlative (Eco) capillary electrophoresis–mass spectrometry (CE–MS) workflow with artificial intelligence (AI)-assisted data processing for fast, high-efficiency, low-input proteome profiling. Relative to our 15 min Eco–AI method, Rapid Eco–AI compresses the effective separation window to 5–7 min while maintaining substantial peptide and proteome coverage, efficient precursor ion sampling, and quantitative precision. Real-time tracking of charge-dependent m/z and migration-time correlations, together with an optimized MS2 strategy, sustained high peptide-spectrum-match conversion under rapid CE, while AI-assisted interpretation of chimeric MS2 spectra (CHIMERYS) supported robust identification and label-free quantification. Implemented on a legacy Orbitrap platform (Q Exactive Plus), Rapid Eco–AI cumulatively identified 1,634 and 1,347 proteins from 300 pg of single-cell-equivalent HeLa digest across technical triplicates using 7 and 5 min effective separations, corresponding to <15 and <11 min injection-to-injection runtimes, respectively. Using the 7 min separation, 835 proteins were cumulatively identified from 75 pg of HeLa digest. Hybrid HeLa–yeast proteome mixtures validated quantitative performance, with median coefficients of variation <5% and measured mean B/A ratios of 0.57 and 1.60 for expected values of 0.60 and 1.80, respectively. As a proof-of-principle application, we analyzed fractional digest loads from 13 freshly isolated single human neutrophils, corresponding to ∼2 pg of digest per run on a manual CE–MS platform, and identified 135 proteins after conservative background filtering. Collectively, these results establish Rapid Eco–AI as a fast CE–MS workflow for low-input proteome profiling with quantitative validation and proof-of-principle transfer to neutrophil-derived digest analysis.