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

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.