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3D HPLC-MS with Reversed-Phase Separation Functionality in All Three Dimensions for Large-Scale Bottom-Up Proteomics and Peptide Retention Data Collection

Spicer, Vic, Ezzati, Peyman, Neustaeter, Haley, Beavis, Ronald C., Wilkins, John A., Krokhin, Oleg V.
Analytical chemistry 2016 v.88 no.5 pp. 2847-2855
data collection, high performance liquid chromatography, hydrophobicity, mass spectrometry, pH, peptides, prediction, proteins, proteome, proteomics
The growing complexity of proteomics samples and the desire for deeper analysis drive the development of both better MS instrument and advanced multidimensional separation schemes. We applied 1D, 2D, and 3D LC-MS/MS separation protocols (all of reversed-phase C18 functionality) to a tryptic digest of whole Jurkat cell lysate to estimate the depth of proteome coverage and to collect high-quality peptide retention information. We varied pH of the eluent and hydrophobicity of ion-pairing modifier to achieve good separation orthogonality (utilization of MS instrument time). All separation modes employed identical LC settings with formic-acid-based eluents in the last dimension. The 2D protocol used a high pH–low pH scheme with 21 concatenated fractions. In the 3D protocol, six concatenated fractions from the first dimension (C18, heptafluorobutyric acid) were analyzed using the identical 2D LC-MS procedure. This approach permitted a detailed evaluation of the analysis output consuming 21× and 126× the analysis time and sample load compared to 1D. Acquisition over 189 h of instrument time in 3D mode resulted in the identification of ∼14 000 proteins and ∼250 000 unique peptides. We estimated the dynamic range via peak intensity at the MS² level as approximately 10⁴.², 10⁵.⁶, and 10⁶.² for the 1D, 2D, and 3D protocols, respectively. The uniform distribution of the number of acquired MS/MS, protein, and peptide identifications across all 126 fractions and through the chromatographic time scale in the last LC-MS stage indicates good separation orthogonality. The protocol is scalable and is amenable to the use of peptide retention prediction in all dimensions. All these features make it a very good candidate for large-scale bottom-up proteomic runs, which target both protein identification as well as the collection of peptide retention data sets for targeted quantitative applications.