Real-Time Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with AI-Based Data Processing Broadens...

Single-cell mass spectrometry (MS) offers unprecedented sensitivity for profiling cellular proteomes, yet widespread adoption is hindered by the cost of advanced instrumentation. Here, we broaden access to single-cell proteomics by combining capillary electrophoresis (CE), data-dependent acquisition (DDA) with electrophoresis-correlative (Eco) ion sorting, and artificial intelligence (AI)-assisted spectral deconvolution via CHIMERYS (Eco-AI). This "Real-Time Eco-AI" workflow was implemented on a custom-built CE platform coupled to a legacy hybrid quadrupole-orbitrap mass spectrometer (Q Exactive Plus). Despite slower scan speed, lower resolution, and inferior ion transmission efficiency, real-time Eco-DDA sampling and CHIMERYS processing enabled identification of up to ∼15 peptides per spectrum-performance on par with modern Orbitrap Fusion Lumos tribrid systems. From 1 ng of HeLa digest, 2142 proteins were identified, surpassing the 969 proteins detected on a contemporary nanoLC Orbitrap Fusion Lumos. Even from ∼250 pg (a single-cell equivalent), 1799 proteins were identified in <15 min of effective separation, raising a theoretical throughput of 48 samples per day. As proof of principle, Real-Time Eco-AI profiled 1524 proteins from single precursor cells (50-75 µm diameter) in Xenopus laevis blastulae, revealing proteome asymmetry during neural versus epidermal fate specification. These results establish Real-Time Eco-AI as a budget-conscious yet powerful strategy for single-cell proteomics using CE-MS.