<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Shen B</submitter><funding>NIA NIH HHS</funding><funding>Chan-Zuckerberg Initiative Foundation</funding><funding>National Institute of General Medical Sciences</funding><funding>National Institute on Aging</funding><funding>NIGMS NIH HHS</funding><funding>Arnold and Mabel Beckman Foundation</funding><pagination>e202510692</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12582007</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>64(45)</volume><pubmed_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 spe</pubmed_abstract><journal>Angewandte Chemie (International ed. in English)</journal><pubmed_title>Real-Time Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with AI-Based Data Processing Broadens Access to Single-Cell Mass Spectrometry Proteomics.</pubmed_title><pmcid>PMC12582007</pmcid><funding_grant_id>Beckman Young Investigator Award</funding_grant_id><funding_grant_id>1R01AG088147</funding_grant_id><funding_grant_id>R35GM124755</funding_grant_id><pubmed_authors>Shen B</pubmed_authors><pubmed_authors>Zhou F</pubmed_authors><pubmed_authors>Nemes P</pubmed_authors></additional><is_claimable>false</is_claimable><name>Real-Time Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with AI-Based Data Processing Broadens Access to Single-Cell Mass Spectrometry Proteomics.</name><description>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 spe</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Nov</publication><modification>2026-06-05T10:45:27.262Z</modification><creation>2026-05-16T03:08:31.659Z</creation></dates><accession>S-EPMC12582007</accession><cross_references><pubmed>40847748</pubmed><doi>10.1002/anie.202510692</doi></cross_references></HashMap>