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