Practical Implementation of 2D HPLC Scheme with Accurate Peptide Retention Prediction in Both Dimensions for High-Throughput Bottom-Up Proteomics

Abstract

We describe the practical implementation of a new RP (pH 10 - pH 2) 2D HPLC-ESI/MS scheme for large-scale bottom-up analysis in proteomics. When compared to the common SCX-RP approach, it provides a higher separation efficiency in the first dimension and increases the number of identified peptides/proteins. We also employed the methodology of our sequence-specific retention calculator (SSRCalc) and developed peptide retention prediction algorithms for both LC dimensions. A diverse set of approximately 10,000 tryptic peptides from the soluble protein fraction of whole NK-type cells gave retention time versus hydrophobicity correlations, with R (2) values of 0.95 for pH 10 and 0.945 for pH 2 (formic acid) separation modes. The superior separation efficiency and the ability to use retention prediction to filter out false-positive MS/MS identifications gives promise that this approach will be a method of choice for large-scale proteomics analyses in the future. Finally, the "semi-orthogonal" separation selectivity permits the concatenation of fractions in the first dimension of separation before the final LC-ESI MS step, effectively cutting the analysis time in half, while resulting in a minimal reduction in protein identification.

Quantitative Proteomic Analyses of Influenza Virus-Infected Cultured Human Lung Cells

“…Lyophilized tryptic digests were dissolved in 200 l of 20 mM ammonium formate (pH 10) (buffer A for first-dimension separation), injected onto a 1-by 100-mm XTerra (Waters, Milford, MA) column, and fractionated using a 0.67% acetonitrile-per-minute linear gradient (Agilent 1100 Series high-performance liquid chromatography [HPLC] system; Agilent Technologies, Wilmington, DE) at a 150-l/min flow rate. Sixty 1-min fractions were collected (covering an ϳ40% acetonitrile concentration range) and concatenated using procedures described elsewhere (22,67); the last 30 fractions were com-bined with the first 30 fractions in sequential order (i.e., 1 with 31; 2 with 32, etc.). Combined fractions were vacuum dried and redissolved in buffer A for the second-dimension RP separation (0.1% formic acid in water).…”

“…There also have been improvements in peptide fractionation (22,67). Therefore, we decided to apply newer quantitative approaches to more fully probe the richness of influenza virus-infected host cell proteomes to attempt to identify additional potential antiviral targets.…”

Quantitative proteomic analyses of mammary organoids reveals distinct signatures after exposure to environmental chemicals

“…Peptides from the three multiplexes were first separated offline into 20 fractions using alkaline pH reversed-phase HPLC (84). Peptides from each fraction were separated using a nanoLC Ultra 2D Plus system (SCIEX) interfaced with a 5600 Triple TOF mass spectrometer (SCIEX).…”

Peptide retention time prediction for immobilized artificial membrane phosphatidylcholine stationary phase: method development and preliminary observations

“…Our Sequence-Specific Retention Calculator (SSRCalc) model has been a benchmark tool in this field since 2004 (available on-line at http://hs2.proteome.ca/SSRCalc/SSRCalcQ.html), and followed the same trend but with the addition of models for trifluoroacetic acid [19] and high pH reversed-phase [20]. Other peptide separation modes have largely excluded because of the poor compatibility of the eluents with ESI-MS.…”

“…All of the listed prediction models that we have generated are the most accurate models reported for the respective modes of peptide separation. Application of 2D LC-MS/MS of the complex tryptic digests was the key innovation allowing collection of large retention datasets for peptide separations not possible with on-line ESI-MS detection [20,21]. Standard RP (formic acid) LC-MS/MS was used in the second dimension as a "standard detection device", while the first-dimension separation information was used as a modelling dataset: e.g.…”