PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Proteome Res. Author manuscript; available in PMC 2010 August 1.
Published in final edited form as:
PMCID: PMC2757279
NIHMSID: NIHMS132613

Analysis of RP-HPLC loading conditions for maximizing peptide identifications in shotgun proteomics

Abstract

Substantial energy and resources have been invested in improving mass spectrometry (MS) instrumentation, up-stream sample preparation protocols, and database search strategies to maximize peptide and protein identifications. The role of HPLC sample loading methods in maximizing MS identifications has been largely overlooked, and there exists an immense heterogeneity in the methods employed in the proteomics literature. We sought to optimize loading methods by testing multiple loading conditions (buffer composition, resin, initial gradient) using tryptic digests of an 18 protein mixture and whole yeast lysate. The loading buffer acetonitrile (ACN) concentration greatly affected peptide identifications: up to a 26% increase in peptide identifications was observed by decreasing the ACN concentration from 5% to 2% during sample loading. Hydrophilic peptides were the main contributors to the increase in peptide identifications and, at higher ACN concentrations, were washed from the pre-column during desalting. Sampling of the hydrophilic peptides was enhanced by using a shallow initial ACN gradient. The results were found to be resin-specific and not generalizable. Our investigation demonstrates the often unappreciated importance of optimizing sample loading conditions to reflect the aims of the research and the characteristics of the LC configurations employed.

Keywords: HPLC, sample loading, proteomics, mass spectrometry, hydrophobicity

INTRODUCTION

In recent years, the field of proteomics has devoted substantial energy and resources to improving sample preparation, MS instrumentation and quantification methods1, and informatics approaches2 to facilitate the high-throughput study of whole proteomes. For example, new and expensive instrument platforms such as the Thermo Scientific LTQ Orbitrap-ETD and the Waters SYNAPT™ HDMS™ system both boast high mass accuracy and additional features, such as electron transfer dissociation in the former and ion mobility spectrometry in the latter, that seek to increase peptide identifications. While new instrumentation has enticed proteomics researchers with promises of increased proteome coverage, and research on improving sample preparation and database search strategies to extract peptide identifications has likewise seen much advancement, less attention has been given to optimizing the reversed phase (RP)-HPLC parameters used during sample loading, a crucial step in the “typical” LC-MS experiment practiced in most proteomics laboratories.

The “typical” LC-MS proteomics workflow entails the digestion of a biological sample with a protease, usually trypsin, an off-line first dimension of separation (e.g. SCX3, IEF4) or sub-proteome enrichment (e.g. IMAC, immuno-depletion and/or -enrichment5), and then an on-line RP-HPLC separation coupled via an electrospray ionization source to a mass spectrometer6. While most of the components of this workflow have been the subject of technological advancement and extensive optimization, especially LC system and column technologies7, few systematic investigations of the optimum conditions for sample loading during the second dimension RP-HPLC are reported in the literature. Issaq et al.'s8 study on the effect of various experimental parameters on the separation of peptides by HPLC, for example, determined optimum gradient conditions and ion pairing reagents for the separation of single protein digest. Isaaq and colleagues did not investigate loading buffer compositions other than 5% ACN in acidified water despite noticing decreased peak resolution at the start of the elution profile. More recently, Lee et al.9 described the optimization of RP-HPLC conditions to achieve high resolution separations for peptide quantification by ICAT (isotope-coded affinity tags). An optimum ACN gradient range and slope to maximize peptide identifications and minimize peak broadening was determined for a complex biological digest. Again, however, only loading buffers containing 5% ACN were employed. One study outside of the proteomics field, using HPLC-solid phase extraction (SPE)-NMR did address the influence of buffer composition during sample loading on the retention and downstream NMR detection of natural products compounds. Clarkson and colleagues10 investigated the retention of 25 model compounds on various column resins, including RP-C18 resin, after sample loading in 0, 3, 6, 9, 12, and 15% ACN-containing buffers. Their study stressed that successful experimental outcomes depended strongly on appropriate loading buffer choice.

A look at research articles published over the last two years in major proteomics journals demonstrates an immense heterogeneity in sample loading methods. For example, out of nearly 200 papers employing RP-HPLC-MS published in the Journal of Proteome Research (August, October, November 2007, and ASAP articles available online on January 9, 2009), Molecular and Cellular Proteomics (August – November 2007, April – May 2008, and In Press articles available online on January 9, 2009) and Proteomics (August 2008), approximately 40% use buffers containing greater than or equal to 5% ACN (N = 79) during sample loading, 38% use buffers containing less than 5% ACN (N = 75), and nearly 21% (N = 41) of the articles do not report the parameters used for their HPLC separations. Loading ACN concentrations were found to range from completely aqueous (0% ACN) to 15% ACN, with 5% ACN being the most common choice. No relationship was discernable between the loading conditions chosen and column resins employed. Only one paper mentioned optimizing their chromatographic conditions to maximize their peptide identifications11. Based on our perusal of the literature, we could identify no definitive standard method for sample loading.

Our proteomics facility has, until recently, routinely loaded samples in buffers containing 5% ACN as part of our standard operating protocol. This standard protocol was borne in our lab, as in other labs, out of a desire to maximize peptide identifications and throughput while maintaining column integrity. Limited investigation went into its establishment. However, after noticing a dramatic increase in peptide identifications after a one-time deviation from our standard operating protocol for analysis of our facilities’ standard protein digest, we were spurred to investigate our RP-HPLC loading conditions more closely. Here, we detail those investigations and provide guidelines for optimization of loading conditions in proteomics studies.

EXPERIMENTAL

Materials

Unless otherwise noted, all reagents and ISB standard 18 protein mixture components were purchased from Sigma Aldrich (St. Louis, MO) and used without modification. HPLC grade ACN was purchased from EMD (San Diego, CA), HPLC grade water-0.1% formic acid (FA) from J. T. Baker (Phillipsburg, NJ), tris(2-carboxyethyl)phosphine (TCEP) from Pierce (Rockford, IL), sequencing grade trypsin from Promega (Madison, WI), and Oasis MCX columns from Waters (Milford, MA).

Standard 18 protein mixture (18 mix) preparation

The ISB standard 18 protein mixture was prepared as described previously12. Briefly, each protein component was dissolved at a final concentration of 1 μM in ammonium bicarbonate containing 0.05% SDS for a total protein concentration of 970 μg/mL. The sample was reduced with TCEP, alkylated with iodoacetamide, and digested overnight with sequencing grade trypsin. Samples were dried and desalted on an Oasis MCX (Waters) column. The dried eluate was resuspended in 1% ACN in acidified water for mass spectrometry analysis. Four microliters, ~250 ng total protein, was used for each analysis.

Yeast digest preparation

A lysate of strain JRY193, grown to an OD600 of 1.0 in YPD media, was prepared by snap freezing in liquid nitrogen followed by mechanical disruption using a Retsch (Newtown, PA) PM 100 planetary ball mill. The yeast lysate was denatured with EDTA-containing urea in Tris buffer. Cysteines were reduced with dithiothreitol (DTT), alkylated with iodoacetamide, and alkylation quenched with additional DTT. After dilution with Tris buffer, the sample was digested for 3 hrs with LysC before an overnight incubation with sequencing grade trypsin. The digested sample was desalted on a Grace (Colombia, MD) Vydac Bioselect SPE C18 cartridge. The dried eluate was reconstituted at 1 mg/mL in 1% ACN in acidified water for preliminary yeast analyses using the Magic C18Aq RP resin. The digest was diluted to 250 μg/mL for subsequent yeast analyses.

Liquid chromatography-MS/MS analyses

LC-MS/MS was performed on a Thermo Scientific (Waltham, MA) LTQ linear ion trap coupled to an Agilent (Santa Clara, CA) HP 1100 series LC system through an electrospray source. Confirmatory analyses utilized the Thermo Scientific LTQ XL Orbitrap coupled to an Agilent 1100 Nano-LC. Peptides were trapped on a fused silica fritted capillary pre-column packed with Magic C18Aq RP spherical silica (2 cm × 75 μm ID, 5 μm, 200 Å; Michrom Bioresources, Auburn, CA) and separated over a 10 cm Magic C18Aq RP analytical column (75 μm ID, 5 μm, 100 Å). For analyses using an alternative column packing, the same-dimension pre-column and analytical column were packed with Waters Atlantis dC18 resin mined from a guard column (5 μm, 100 Å).

For preliminary analyses on the LTQ, a binary solvent system consisting of Buffer A (0.1% FA) and Buffer B (50% ACN, 0.1% FA) was employed to load samples over sets of consecutive runs in 5%, 4%, 3%, and 2% ACN. Acetonitrile loading concentrations less than 2% were considered in preliminary experiments and found not to net any benefit over the 2% ACN concentration and thus were not included in this final study. After a 10 min loading program, a 30 min “discontinuous” gradient from 10% to 35% ACN was employed, delivering eluate to the mass spectrometer at a tip flow rate of 200 nL/min. Following elution, each run within a set re-equilibrated for 15 min at the ACN concentration required for loading the next sample in the set, which was at a loading ACN concentration reduced by 1%. For the 18 mix analyses, the fourth run in the set (utilizing a 2% ACN loading concentration) re-equilibrated back to 5% ACN for the next set. For the yeast lysate analyses, the fourth run in the set (2% ACN loading concentration) re-equilibrated to 2% ACN and was followed by a fifth run from 2% ACN using a “continuous” gradient that re-equilibrated finally to 5% ACN for the next set. The “continuous” gradient program was created by extending the slope of the “discontinuous” gradient between 15 and 40 min (0.6% ACN/min) back to 10 min, lengthening the total run time by 8 min. Each set of four 18 mix runs and five yeast runs was replicated 10 and 4 times, respectively. Supplementary Figure 1 graphically depicts the loading and gradient programs used. Supplementary Table 1 presents both the discontinuous and continuous gradients in tabular format appropriate for programming an HPLC system.

Confirmatory analyses on the LTQ Orbitrap utilized a nano-LC system, in which 18 mix samples were loaded onto the pre-column with an isocratic pump over 10 min in 1% ACN, 0.1% FA. The column was then washed for 10 min using the nano-LC system in 2% or 5% ACN during MS acquisition to mimic the loading conditions utilized in the preliminary analyses. Peptides were eluted using the “discontinuous” elution gradient. The column was re-equilibrated to either 5% or 2% ACN for the subsequent analysis. Each set of 2 runs was replicated 2 times.

All MS analyses were performed in positive ion mode. Data were collected in data-dependent mode with 5 data-dependent MS/MS scans per full MS scan (m/z 250−2000) in centroid mode. Data-dependent MS/MS scans were collected at 35% normalized collision energy with dynamic exclusion enabled. The dynamic exclusion parameters were as follows: mass width, m/z 3; repeat count, 1; repeat duration, 30 s; exclusion list size, 50; and exclusion duration, 180 s.

Data processing and analysis

18 mix data were searched using SEQUEST13 against a custom Haemophilus influenzae database containing the 18 mix proteins as described previously12. The yeast data were searched against the yeast.nci.20060720 database. Both datasets were searched with carbamidomethylated cysteines as a static modification. Peptide identification numbers were obtained by analysis with PeptideProphet and Trans-Proteomics Pipeline software14,15 employing a minimum PeptideProphet probability of 0.9 (FDR ≤ 1%). Peptide relative hydrophobicity was calculated via the Sequence Specific Retention Calculator version 3.0 (SSRCalc 3.0) for 100 Å sorbents16 (available online at http://hs2.proteome.ca/SSRCalc/SSRCalc.html). Variance was analyzed by one-way ANOVA for correlated samples with Tukey HSD test performed on significant F-values (available online at http://faculty.vassar.edu/lowry/VassarStats.html).

RESULTS

Chromatographic Trends

Peptide elution began earlier as the ACN concentration during sample loading decreased. We compared the base peak elution profiles for ten sets of successive LC-MS/MS analyses in which the standard 18 protein mixture (18 mix) was loaded on a Magic C18Aq RP pre-column in 5%, 4%, 3%, and 2% ACN. The base peak elution profiles show that elution begins earlier with each drop in ACN concentration (Figure 1A, 1B). This trend was observed over all ten sets of runs (40 individual analyses) and was not a result of a general retention time shift. A plot of ten peptides observed in all analyses shows very little run-to-run variation (Figure 1C). The start of the peptide elution differed by as great as 3 min (18 mix) and 6 min (for yeast; data not shown) between loading ACN concentrations, whereas the variation in retention time for the ten monitored peptides was no greater than 40 s for either sample and not correlated to loading ACN concentration.

Figure 1
(A) Base peak elution profile for 4 successive 18 mix analyses loaded in (from top) 2%, 3%, 4%, and 5% ACN. Boxed portion from 15 to 25 min is shown in detail in panel B. (B) Detail view of the start of each elution profile, denoted by arrows. (C) Elution ...

Peptide Identification and Hydrophobicity Trends

Total unique peptide identifications increased and average peptide hydrophobicity decreased as the loading ACN concentration decreased. The number of unique peptides identified in each 18 mix analysis was determined by searching the data with SEQUEST and applying a PeptideProphet probability cut-off ≥ 0.9 (FDR ≤ 1%). The number of unique peptides in each analysis was averaged across all ten runs for each loading condition. An increase in average unique peptide identifications was observed as the sample was loaded in successively decreasing ACN concentrations from 5% to 2% ACN, a trend analogous to that observed in base peak elution profiles. These data are represented in Figure 2 for 18 mix; numerical data are included in Supplementary Table 2. Each decrease in loading ACN concentration resulted in a highly significant increase in the average number of peptides identified from the 18 mix sample. Overall, a 26% increase in average peptide identifications resulted from loading in 2% ACN versus 5% ACN (p < 0.01, n = 10).

Figure 2
Average unique peptide identifications over ten 18 mix analyses increase with decreasing ACN loading concentration. All averages are statistically significant (*p<0.01, ^p<0.05; n = 10); error bars represent 95% confidence intervals. Brackets ...

Four replicates at each loading condition performed using the yeast sample on the same instrument had similar results. The number of unique peptide identifications increased with each successive decrease in loading ACN concentration (from 5% ACN to 2% ACN: 1359 ± 88.92, 1382 ± 29.10, 1426 ± 36.39, 1477 ± 23.27; Supplementary Table 2). Expectedly, the magnitude of the significance of this trend decreased due to the greater sample complexity of the yeast sample, and therefore a greater bulk of peptides eluting mid-gradient compared to peptides eluting at the start of the gradient.

For each consistently identified peptide (identified in at least six of ten 18 mix or three of four yeast analyses), we assigned a relative hydrophobicity (HP) score using the Sequence Specific Retention Calculator version 3.0 (SSRCalc 3.0) established by Krokhin et al.17-20 for 100 Å sorbents. This commonly used model, constructed based on the retention time of 2700 tryptic peptides, calculates the effective HP/retention time of peptide on a chromatographic column by summation of each amino acid's retention coefficient and the inclusion of various correction factors. Amino acid retention coefficients in this model range from 11.0 (tryptophan), highly hydrophobic, to −1.9 (lysine), highly hydrophilic; as such, higher HP scores indicate greater overall hydrophobicity. The correction factors take into consideration such characteristics as protein length, the proximity of certain amino acids to the N- and C- termini, the amino acid distribution uniformity, pI, and missed cleavages, among others 17.

The average HP score calculated at each loading condition for all consistently identified 18 mix peptides was found to increase approximately 0.78 (HP score) per percent ACN (R2 = 0.98). The peptides eluting prior to 2000 s were found to be the primary contributors to the difference in average HP between each loading condition (increasing 0.73 per percent ACN; R2 = 0.99), whereas there was little change in the average HP score of peptides eluting after 2000 s across loading conditions (0.09; R2 = 0.78) (Figure 3).

Figure 3
Average HP score of all peptides ([diamond with plus]), peptides eluting before 2000 s (▲), and peptides eluting after 2000 s (■) identified in at least six of ten 18 mix runs at each loading condition. Best fit lines and slope (m) are indicated ...

Similar results were obtained in the yeast analyses

The average HP score of all consistently identified peptides at each loading ACN concentration increased 0.41 per percent ACN (R2 = 0.81). Again, peptides eluting prior to 2500 s were found to be the primary contributors to this trend, with HP scores increasing 0.39 per percent ACN (R2 = 0.94). The average HP score of peptides eluting after 2500 s, on the other hand, changed less (−0.12; R2 = 0.19).

Furthermore, each decrease in loading ACN concentration corresponded to identification of new, highly hydrophilic peptides clustered at the start of the elution profile. We compared the peptides consistently identified between each successive loading condition (5% vs. 4%, 4% vs. 3%, 3% vs. 2%, and 2% vs. 5%). Figure 4 plots each 18 mix peptide as a function of retention time and HP score in the 2% versus 5% ACN comparison (for plots of each individual comparison, see Supplementary Figure 2). In all comparisons, 18 mix peptides identified only at the lower loading ACN condition were found to be situated at the start of the elution profile with earlier retention times and more hydrophilic HP scores than the peptides shared between both analyses. Peptides identified only at the higher loading ACN condition were distributed throughout elution profile and had average HP scores and retention times more similar to the peptides shared between both analyses, especially at the 5% and 4% loading conditions. Table 1 displays the average HP scores and retention times for peptides shared in each comparison and those identified only at the lower ACN concentration for all comparisons.

Figure 4
Plot of 18 mix peptides identified in at least 6 of 10 analyses for the 5% and 2% ACN loading conditions. Peptides new at 2% ACN (An external file that holds a picture, illustration, etc.
Object name is nihms-132613-ig0005.jpg, pink) are clustered at the beginning of the gradient compared to peptides shared in both analyses (●, black) or ...
Table 1
Average HP scores and retention times for consistently identified peptides from the lower ACN concentration and from both analyses for each comparison of 18 mix and yeast loading conditions.

The same analysis was carried out for the consistently identified peptides in the yeast runs at each loading condition (not including the continuous gradient analysis at 2% ACN). This analysis further corroborated the results reported thus far (Table 1). Plots of each comparison are available in the supplementary data (Supplementary Figure 3).

The average length and amino acid content (classified as basic, acidic, polar, or non-polar residues) of the peptides consistently identified only at the higher loading ACN condition were compared to those identified by both analyses and the lower ACN condition combined for each analysis set. For both datasets, neither peptide length nor amino acid content were significantly different for the peptides not identified in each comparison by loading at the lower ACN concentration (data not shown). On average, peptides in both datasets (yeast, 18 mix) and all categories (5% vs. 4%, etc.) were 15 amino acids long and contained ~51% non-polar, ~23% polar, ~17% acidic, and ~9% basic residues. This suggests that the peptides missed when loading at lower ACN concentrations are not of a specific class and that lower ACN loading concentrations do not lead to experimental bias against any particular peptide class.

Atlantis Resin Analyses

We employed the Waters Atlantis dC18 resin to determine the generalizability of our observations. The Atlantis resin was chosen because of the apparent dependence of the observed trends on hydrophilic peptide retention, and the manufacturer's claim of enhanced polar analyte retention over other C18 resins (http://www.waters.com/waters/nav.htm?&cid=513211). Using the same HPLC and MS setup, we repeated the yeast experiment described above for each loading condition using a 1:3 dilution of the same yeast digest. Note that due to sample dilution, this experiment and the previously described experiment using the Magic resin are not comparable on absolute terms; however, a relative comparison of the trend is possible. None of the trends observed in elution profile start time, unique peptide identifications, or hydrophobicity were present using the Atlantis resin. However, the trends remained intact for the Magic C18Aq. Two sets of additional analyses immediately following the Atlantis experiment under the exact instrumental conditions again demonstrated the loss of hydrophobic peptides during loading at 5% ACN compared to 2% ACN (Figure 5). The performance differences between the two resins are likely attributable to the unique physical characteristics of each stationary phase and substantiate the manufacture's claim regarding the retention of polar analytes.

Figure 5
Average unique peptide identifications over four yeast digest analyses using Waters Atlantis dC18 resin (dark gray) show no statistically significant change with decreasing ACN loading concentration. Error bars represent 95% confidence intervals. Two ...

Continuous Gradient Analyses

The use of a continuous LC gradient increased MS sampling and hydrophilic peptide identifications using both resins. For both resins, the yeast digest was also analyzed using a method with a continuous elution gradient and 2% ACN loading buffer. As mentioned previously, the continuous elution gradient lengthens the time for peptide elution between 2% and 10% ACN to 13 min (compared to 5 min in the discontinuous gradient method). We hypothesized that the shallower initial ACN gradient would allow for better separation and reduce MS under-sampling of the hydrophilic peptides eluting at the start of the ACN gradient, the crucial portion of the gradient for maximizing peptide identifications when using the Magic resin.

The continuous gradient method netted a 5% (Magic resin) and 4% (Atlantis resin) increase in the number of total peptide identifications compared the 2% ACN loading condition with discontinuous gradient (see Supplementary Table 2). Concordantly, the average HP of all peptides identified decreased and hydrophilic peptides (HP scores < 20) eluted over a longer time period compared to the 2% ACN discontinuous gradient (data not shown). 241 hydrophilic peptides separated over the Magic resin in 600 s using the discontinuous gradient, whereas the continuous gradient separated 346 hydrophilic peptides over 1000 s. Using the Atlantis resin, 215 hydrophilic peptides were separated in 800 s and 262 peptides were separated in 1200 s by the discontinuous and continuous gradients, respectively (data not shown). These data support our hypothesis that the continuous gradient increases MS sampling as it has the effect of decreasing the rate of peptide introduction to the MS, increasing peptide identifications. The effect of increased sampling of hydrophilic peptides with temporal adjustments to the chromatography parallels the benefits described herein using concentration adjustments to increase hydrophilic peptide identifications. In combination, temporal and concentration-based modifications sum to give the largest overall increase in hydrophilic peptide sampling and identifications.

Nano-LC Analyses

We used a separate HPLC system (nano-HPLC) and mass spectrometer to confirm that the trends observed herein were not related to HPLC pump function. We studied the 18 mix sample at the 2% and 5% ACN loading conditions on the Magic resin with two replicates at each condition. The previous experiments were simulated with this HPLC system by loading and washing at 1% ACN with an isocratic loading pump, and then washing the pre-column/column for an additional 10 min at either 2% or 5% ACN with the nano-LC pump to approximate the load/wash on the other HPLC system. The remainder of the analysis was performed as described before. This setup allowed for data acquisition during the 2% or 5% ACN wash step, and thus detection of peptides not retained on the column at those ACN concentrations.

We found that total peptide identifications were similar under both the 2% and 5% ACN wash conditions (5% ACN: 418, 377; 2% ACN: 430, 429). As seen in Figure 6, a plot of all peptides identified in the four analyses, the majority of peptides with HP scores less than 20 were found to elute during the ten minute 5% ACN wash (prior to ~1400 s, accounting for column delay), but after the 10 min 2% ACN wash (after ~1400 s). The early eluting hydrophilic peptides in the 5% ACN wash analysis were found to be the same peptides eluting after ~1400 s in the 2% ACN wash analysis (data not shown).

Figure 6
Plot of all 18 mix peptides identified in 5% (An external file that holds a picture, illustration, etc.
Object name is nihms-132613-ig0009.jpg, blue) and 2% ACN (An external file that holds a picture, illustration, etc.
Object name is nihms-132613-ig0010.jpg, pink) wash analyses (2 runs combined) on a nano-LC system. Peptide retention time varies greatly between the 5% and 2% ACN wash analyses.

These data further explain the trend in peptide identifications and hydrophobicity seen using the Magic resin on the first HPLC system. Hydrophilic peptides, like those with disparate retention times in the 5% wash analysis using the nano-LC system, are not retained on the column during 5% ACN loading but are retained during 2% ACN loading using the first split-flow HPLC system. The retained peptides, identified by MS, account for the differences observed in total peptide identifications and average peptide hydrophobicity.

DISCUSSION

We have shown that the ACN concentration employed during sample loading and washing is critical to the mass spectrometry analysis. This work has demonstrated that: 1) reduced ACN concentration during sample loading and washing is strongly correlated with more peptide identifications, 2) new peptide identifications occur at the earliest parts of the analysis and are hydrophilic peptides, 4) an extension of the initial portion of the gradient to generate a continuous shallow gradient enhances identification of hydrophilic peptides, 5) differences in peptide identifications are due to a loss of peptides that occurs during the washing step, and 6) this phenomenon is resin-specific and not generalizable.

Our results are corroborated by observations obtained by other researchers. In their study of the retention of natural products compounds under various loading buffer conditions in a HPLC-SPE-NMR study, Clarkson, et al.10 also found that compound retention factors decreased as the proportion of organic solvent in the loading buffer increased. Thus, for polar analytes that weakly interact with the stationary phase, maximization of the water content in the loading solvent was found to be critical. Lee, et al.'s9 study on the effect of the solvent gradient on peptide identification and quantification corroborated our finding that, as expected, peptide elution is highly dependent on ACN composition and that shifts in base peak chromatograms with different gradient profiles are secondary to this dependence. Furthermore, Lee, et al. also found that use of a shallow gradient is best for obtaining greater sample coverage (i.e. maximizing peptide identifications).

An obvious criticism for loading samples in highly aqueous conditions comes from the well-documented problem of “phase collapse” with conventional RP column materials under such conditions21. The classical explanation for phase collapse is that, in aqueous conditions, long alkyl-chains of the hydrophobic bonded phase are likely to self-associate to minimize surface energy rather than interact with the solvent and analyte molecules. The result is a de-wetting of the porous resin structure, leading to a reduction in column performance: loss of analyte retention or run-to-run chromatographic reproducibility, degraded peak shapes, and lengthy column regeneration times22. In other investigations in our laboratory not detailed here, we attempted to force phase collapse for both of the resins employed in this study. We did not observe any degradation in column performance with the two particular resins studied after the column was held at pure aqueous conditions for up to 8 hours. The peak shape, retention time, and number of peptide identifications were not affected by any amount of exposure to aqueous conditions up to 8 hours. This seemingly contradictory result likely arises from improved RP resin technologies. A 2002 report from Majors and Przybyciel23 listed many commercial columns that can be used for separation of hydrophilic analytes in highly aqueous environments. These columns, like many of the packing materials employed in the proteomics studies reviewed in the introduction, are resistant to phase collapse due to incorporation of polar or hydrophilic end-capping chemistries or polar-embedded alkyl phases. The commercial availability and already widespread use of such columns makes testing lower ACN concentration loading buffers a viable option for all proteomics researchers.

The magnitude of our findings is not trivial considering the time and cost of mass spectrometry-based proteomic analyses. In our study, a simple optimization of loading and gradient conditions led to a 26% increase in peptide identifications using a standard 18 protein mixture and 14% using a complex yeast lysate. These gains were quite easily obtained compared to the time and expense of obtaining similar improvements through complex search strategies or new instrumentation. Furthermore, optimization of this sort may lead to substantial research gains in certain sub-areas of the proteomics field. In phosphoproteomics studies, optimization of loading conditions and initial ACN gradients may be crucial to maximizing phosphopeptide identifications. Protein phosphorylation, a prevalent post-translational modification that increases peptide hydrophilicity, is a critical cellular regulatory mechanism24. Phosphopeptides are routinely studied by RP-LCMS of a digest following phosphopeptide enrichment via immobilized metal affinity (IMAC)25, metal oxide26, or graphitic carbon27 chromatography. The time and effort of the enrichment could be for naught if the reversed phase portion of the analysis is not properly optimized for hydrophilic phosphopeptides. Therefore, our investigation should provide an impetus for those investigating proteomes, especially phosphoproteomes, to fully consider the design of their LC analyses. We suggest each laboratory perform a simple sequential loading analysis, such as that in supplementary figure 1, to define the optimal loading conditions for their resin of choice and instrumental configurations.

CONCLUSION

Our study clearly defines the need for loading condition optimization in RP-HPLC-MS assays, as well as identifies key factors affecting optimization. The phenomena described herein are well understood from a chromatographic point-of-view. From a chemistry perspective, it is not surprising that hydrophilic peptides are retained by a C18 resin at lower ACN concentrations. However, the heterogeneity of methods implemented in the proteomics literature is a strong indicator that many groups follow standard operating protocols that deserve careful scrutiny. While the research in our laboratory suggests that optimum performance is achieved using a 2% ACN loading buffer and continuous shallow initial ACN gradient, these results are specific to the resins used herein. We suggest that investigators devote a small amount of time to optimize loading conditions for their own particular LC configurations and proteomics experiments.

Supplementary Material

1_si_001

ACKNOWLEDGMENT

We thank Carly Sherwood for proofreading and suggestions during manuscript preparation. This work has been funded by the NIH grants R21 CA126216 and P50 GM076547/Center for Systems Biology (to D.B.M).

REFERENCES

1. Han X, Aslanian A, Yates JR., Iii Mass spectrometry for proteomics. Current Opinion in Chemical Biology. 2008;12(5):483–490. [PMC free article] [PubMed]
2. Deutsch EW, Lam H, Aebersold R. Data analysis and bioinformatics tools for tandem mass spectrometry in proteomics. Physiol. Genomics. 2008;33(1):18–25. [PubMed]
3. Washburn MP, Wolters D, Yates JR. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotech. 2001;19(3):242–247. [PubMed]
4. Wall DB, Kachman MT, Gong S, Hinderer R, Parus S, Misek DE, Hanash SM, Lubman DM. Isoelectric Focusing Nonporous RP HPLC:&nbsp; A Two-Dimensional Liquid-Phase Separation Method for Mapping of Cellular Proteins with Identification Using MALDI-TOF Mass Spectrometry. Analytical Chemistry. 2000;72(6):1099–1111. [PubMed]
5. Aebersold R, Goodlett DR. Mass Spectrometry in Proteomics. Chemical Reviews. 2001;101(2):269–296. [PubMed]
6. Motoyama A, Yates JR. Multidimensional LC Separations in Shotgun Proteomics. Analytical Chemistry. 2008;80(19):7187–7193. [PubMed]
7. Shen Y, Smith RD. Advanced nanoscale separations and mass spectrometry for sensitive high-throughput proteomics. Expert Review of Proteomics. 2005;2(3):431–447. [PubMed]
8. Issaq H, Fox S, Mahadevan M, Conrads T, Veenstra T. Effect of Experimental Parameters on the HPLC Separation of Peptides and Proteins. Journal of Liquid Chromatography & Related Technologies. 2003;26(14):2255–2283.
9. Lee H, Yi EC, Wen B, Reily TP, Pohl L, Nelson S, Aebersold R, Goodlett DR. Optimization of reversed-phase microcapillary liquid chromatography for quantitative proteomics. Journal of Chromatography B. 2004;803(1):101–110. [PubMed]
10. Clarkson C, Sibum M, Mensen R, Jaroszewski JW. Evaluation of on-line solid-phase extraction parameters for hyphenated, high-performance liquid chromatography-solid-phase extraction-nuclear magnetic resonance applications. Journal of Chromatography A. 2007;1165(1−2):1–9. [PubMed]
11. Schiess R, Mueller LN, Schmidt A, Mueller M, Wollscheid B, Aebersold R. Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry. Mol Cell Proteomics. 2008:M800172–MCP200. [PMC free article] [PubMed]
12. Klimek J, Eddes JS, Hohmann L, Jackson J, Peterson A, Letarte S, Gafken PR, Katz JE, Mallick P, Lee H, Schmidt A, Ossola R, Eng JK, Aebersold R, Martin DB. The Standard Protein Mix Database: A Diverse Data Set To Assist in the Production of Improved Peptide and Protein Identification Software Tools. Journal of Proteome Research. 2008;7(1):96–103. [PMC free article] [PubMed]
13. Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry. 1994;5(11):976–989. [PubMed]
14. Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search. Analytical Chemistry. 2002;74(20):5383–5392. [PubMed]
15. Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry. Analytical Chemistry. 2003;75(17):4646–4658. [PubMed]
16. Krokhin OV, Craig R, Spicer V, Ens W, Standing KG, Beavis RC, Wilkins JA. An Improved Model for Prediction of Retention Times of Tryptic Peptides in Ion Pair Reversed-phase HPLC: Its Application to Protein Peptide Mapping by Off-Line HPLC-MALDI MS. Mol Cell Proteomics. 2004;3(9):908–919. [PubMed]
17. Krokhin OV. Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. Anal Chem. 2006;78(22):7785–95. [PubMed]
18. Krokhin OV, Craig R, Spicer V, Ens W, Standing KG, Beavis RC, Wilkins JA. An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS. Mol Cell Proteomics. 2004;3(9):908–19. [PubMed]
19. Krokhin OV, Ying S, Cortens JP, Ghosh D, Spicer V, Ens W, Standing KG, Beavis RC, Wilkins JA. Use of peptide retention time prediction for protein identification by off-line reversed-phase HPLC-MALDI MS/MS. Anal Chem. 2006;78(17):6265–9. [PubMed]
20. Spicer V, Yamchuk A, Cortens J, Sousa S, Ens W, Standing KG, Wilkins JA, Krokhin OV. Sequence-specific retention calculator. A family of peptide retention time prediction algorithms in reversed-phase HPLC: applicability to various chromatographic conditions and columns. Anal Chem. 2007;79(22):8762–8. [PubMed]
21. Przybyciel M, Majors RE. Phase collapse in reversed-phase liquid chromatography. LCGC. 2002;20(6):516–523.
22. Reid TS, Henry RA. Compatibility of C18 HPLC columns with pure aqueous mobile phase. Am. Lab. 1999;31(14):24–28.
23. Majors RE, Przybyciel M. Columns for reversed-phase LC separations in highly aqueous mobile phases. LCGC. 2002;20(7):584–593.
24. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks. Cell. 2006;127(3):635–648. [PubMed]
25. Neville DC, Rozanas CR, Price EM, Gruis DB, Verkman AS, Townsend RR. Evidence for phosphorylation of serine 753 in CFTR using a novel metal-ion affinity resin and matrix-assisted laser desorption mass spectrometry. Protein Sci. 1997;6(11):2436–2445. [PubMed]
26. Pinkse MWH, Heck AJR. Essential enrichment strategies in phosphoproteomics. Drug Discovery Today: Technologies. 2006;3(3):331–337. [PubMed]
27. Larsen MR, Graham ME, Robinson PJ, Roepstorff P. Improved Detection of Hydrophilic Phosphopeptides Using Graphite Powder Microcolumns and Mass Spectrometry: Evidence for in Vivo Doubly Phosphorylated Dynamin I and Dynamin III. Mol Cell Proteomics. 2004;3(5):456–465. [PubMed]