Real‐Time Eco–AI, Electrophoresis‐Correlative Data‐Dependent Acquisition with AI‐Based Data Processing Broadens Access to Single‐Cell Mass Spectrometry Proteomics

Abstract

Abstract 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.