Electrophoresis-Correlative Data-Independent Acquisition (Eco-DIA) Improves the Sensitivity of Mass Spectrometry for Limited Proteome Amounts.
Bowen ShenJerry ChenPeter NemesPublished in: Analytical chemistry (2024)
Capillary zone electrophoresis (CE) combines high separation power, scalability, and speed to limited proteome analyses by mass spectrometry (MS). However, compressed separation in CE challenges the duty cycle of tandem MS, even during data-independent acquisition (DIA). To help remedy this limitation, we introduce the concept of e lectrophoresis- co rrelative (Eco) data acquisition for CE-MS. We recognize CE electrospray ionization (ESI) to sort peptide ions into reproducible mass-to-charge ( m / z ) vs migration time (MT) trends in the solution phase, before subsequent ionization and m / z analysis. We proposed that such a correlation can be leveraged to improve the economy of data acquisition. We test this hypothesis using DIA frames that are tailored to the observed m / z -MT trends. The resulting Eco-DIA method substantially improves the bandwidth utilization of tandem MS during CE-MS. In proof-of-principle studies, Eco-DIA identified and quantified ∼38% more proteins from 1 ng of the HeLa proteome digest compared to the classical DIA, without the assistance of a project-specific tandem MS spectral library. Eco-DIA was able to quantify ∼51% more proteins with <10% coefficient of variation vs the control DIA approach. Based on label-free quantification, the proteins that were exclusively measured by Eco-MS occupied the lower dynamic range of the detected proteome concentration, revealing sensitivity enhancement. In addition to marking the inception of Eco-MS, this work lays the foundation for the development of next-generation data acquisition strategies that leverage electrophoretic ion sorting for high-sensitivity proteomics.
Keyphrases
- mass spectrometry
- liquid chromatography
- ms ms
- gas chromatography
- multiple sclerosis
- electronic health record
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- big data
- label free
- machine learning
- optical coherence tomography
- cell proliferation
- simultaneous determination
- magnetic resonance imaging
- computed tomography
- smoking cessation