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Nov 13, 2014 - A novel fully automatable two-dimensional liquid chromatography (2DLC) platform has been integrated into a modified commercial off-the-...
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Online Two-Dimensional Porous Graphitic Carbon/Reversed Phase Liquid Chromatography Platform Applied to Shotgun Proteomics and Glycoproteomics Yun Zhao,† Samuel S. W. Szeto,† Ricky P. W. Kong,† Chun Hin Law,† Guohui Li,† Quan Quan,† Zaijun Zhang,‡ Yuqiang Wang,‡ and Ivan K. Chu*,† †

Department of Chemistry, The University of Hong Kong, Hong Kong, China Institute of New Drug Research and Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine, Jinan University College of Pharmacy, Guangzhou, 510632, China

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S Supporting Information *

ABSTRACT: A novel fully automatable two-dimensional liquid chromatography (2DLC) platform has been integrated into a modified commercial off-the-shelf LC instrument, incorporating porous graphitic carbon (PGC) separation and conventional low-pH reversed-phase (RP) separation for both proteomics and N-glycomics analyses; the dual-trap column configuration of this platform offers desirable high-throughput analyses with almost no idle time, in addition to a miniaturized setup and simplified operation. The total run time per analysis was only 19 h when using eight PGC fractions for unattended large-scale qualitative and quantitative proteomic analyses; the identification of 2678 nonredundant proteins and 11 984 unique peptides provided one of the most comprehensive proteome data sets for primary cerebellar granule neurons (CGNs). The effect of pH on the PGC column was investigated for the first time to improve the hydrophobic peptide coverage; the performance of the optimized system was first benchmarked using tryptic digests of Saccharomyces cerevisiae cell lysates and then evaluated through duplicate analyses of Macaca fascicularis cerebral cortex lysates using isobaric tags for relative and absolute quantitation (iTRAQ) technology. An additional plug-and-play PGC module functioned in a complementary manner to recover unretained hydrophilic solutes from the low-pH RP column; synchronization of the fractionations between the PGC-RP system and the PGC module facilitated simultaneous analyses of hydrophobic and hydrophilic compounds from a single sample injection event. This methodology was applied to perform, for the first time, detailed glycomics analyses of Macaca fascicularis plasma, resulting in the identification of a total 130 N-glycosylated plasma proteins, 705 N-glycopeptides, and 254 N-glycosylation sites.

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greatly improving the protein and proteome coverage.4 While one-dimensional liquid chromatography (1DLC) has limited resolving capacity, multidimensional liquid chromatography (MDLC), combining two or more orthogonal dimensions of LC, can enhance the peak capacity and resolving power during such analyses;3 effective front-end separation remains crucial for successful analyses of complex biological samples. One of the most prevalent online MDLC techniques combines strong cation exchange (SCX) with reversed-phase (RP) liquid chromatography (SCX-RP);5,6 the cationic peptides that elute from the SCX column are amenable to the second-dimension RP column, thereby simplifying practical implementation. Despite good orthogonality between these two LC dimensions, the system is limited by the poor resolution of SCX separation (based mainly on peptide charge) and the need for high concentrations of salts, which must be removed prior

ass spectrometry (MS) based proteomics has become an essential component in the tools applied to understanding the molecular mechanisms of biological processes. Shotgun proteomics is one of the most common strategies for the analyses of complex protein mixtures; it combines proteolytic digestion of biological samples with analysis through liquid chromatography (LC)/tandem mass spectrometry (MS/ MS)1 to overcome many problems related to direct protein level identification.2 At present, the major challenges facing comprehensive proteomics analyses include the sheer complexity of proteomes, the huge dynamic range of protein abundances (over 5 orders of magnitude), and unwitting ion suppression in MS analyses caused by the far-too-complex coeluates of tryptic digests subjected to electrospray ionization (ESI).2 These challenges can lead to severe undersampling of the proteome, as well as a bias toward proteins with high abundance or relatively more “ionizable” peptides.3 Recent advances in hyphenated separation technologies and MS have dramatically enhanced the dynamic range, throughput, and accuracy of qualitative and quantitative proteomics analyses, © XXXX American Chemical Society

Received: August 20, 2014 Accepted: November 13, 2014

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to ESI.7 Multiply charged tryptic peptides typically carry no more than five charges, with doubly and triply protonated ions predominating (>80% in total); the very uneven distribution of peptides across the fractions limits the number of possible gradient steps that can be employed in the first SCX dimension. This phenomenon tends to decrease the separation efficiency and increase the sample complexity. Thus, SCX-RP separation with extensive fractionation would lead to peptide spillover across different SCX fractions (because of the intrinsically limited resolution), resulting in decreased sensitivity of individual peptides in a single fraction as well as wasted duty cycles in redundant sampling;7 the number of identified unique peptides decreases when the product ion spectra of potentially yet-to-be-sampled peptides are missing because of the finite duty cycle time in the mass spectrometer. Other first-dimensional separation systems that have been employed in MDLC include hydrophilic interaction chromatography (HILIC),8−10 size exclusion chromatography,11 and high-pH RP chromatography.7,12−14 A particularly promising and rapidly emerging platform among the existing 2DLC alternatives developed to improve separation power is high-pH RP chromatography followed by low-pH RP chromatography (hereafter simplified as “RP-RP”); the separations in both dimensions of high-/low-pH RP-RP are based on hydrophobicity, with each dimension exhibiting different selectivity mechanisms as a result of the different charges that the amino acid side chains of the peptides bear at different values of pH.7 Although HILIC-RP 2DLC exhibits very high orthogonality between its two LC dimensions, peptides must be dissolved in highly organic solvents to be compatible with first-dimension HILIC fractionation. This requirement can potentially affect the performance of this 2DLC system because of the relatively poor solubility of peptides in such solvents.15 Online MDLC facilitates automation with minimal sample loss or contamination, as well as requiring only small sample quantities; despite these attractive features, the development of online MDLC systems has been hindered by the issues of solvent incompatibility between the different LC dimensions as well as the need for synchronization among the sampling time, frequency, and fraction volume between the two dimensions in an automatable manner. Online coupling of most MDLC systems without concomitant deterioration of the performance and sensitivity of the second-dimension conventional low-pH RP separation generally remains a significant challenge, hindered by the issues of solvent incompatibility of the eluents between the two dimensions and the need for special types of tailor-made MDLC instruments; thus, most of the MDLC alternatives have been applicable so far only in an offline manner, providing greater flexibility and enabling uncompromised optimization of each individual dimension.15 Therefore, commercially available online 2D MDLC systems have mainly been limited to and engineered as combinations of SCX-RP; recently, nano-RP-RP 2DLC was also demonstrated on commercial MDLC instruments, operating under a discontinuous step gradient in the first dimension and sample fractionation stages.16 In previous studies, we reported integrated methodologies to circumvent the incompatibility issues, performing online reconstitution of the buffers prior to sample loading into the second-dimension low-pH RP column for ESI-MS/MS analysis.12,13 Synchronization of the sampling time, frequency, and fraction volume between the two dimensions was achieved using a stop-flow mechanism to eliminate solvent flow to and from the first-dimension column

during second-dimension analytical LC separation; such methodologies minimize losses of nonretained peptides during fractionation through effective online sampling. Significantly, the integrated methodologyfeaturing a sample mixing loop with a solvent conversion interfacehas been extended to recent reports on the development of other variants of MDLC systems with different column chemistries and degrees of sophistication, including HILIC-RP8 and RP-RP17 with the addition of porous graphitic carbon (PGC) for unattended proteomics and glycoproteomics applications. PGC is a two-dimensional (2D) form of graphite that has sufficient stability throughout the entire pH range (from pH 0− 14) and is compatible with a large number of solvent systems;18 PGC possesses many unique properties in comparison with those of materials used in conventional RP or HILIC LC columns. The retention of analytes on PGC occurs through a balance of hydrophobic and charge-induced interactions, which depend on both the contact surface area and the interacting functional groups.18,19 This mixed retention mechanism has facilitated the application of PGC in the separation of a variety of chemical species, such as isomers, polar compounds, and charged solutes. The separation of these species would mostly likely not be possible using RP columns (indeed, these materials might not even be retained on RP columns). To the best of our knowledge, only a few reports have been published describing the use of PGC chemistry in proteomics analyses; the offline combination of PGC and RP separations has been demonstrated to provide excellent performance in proteomics applications.20 The objective of this study was to develop a fully automatable, efficient, online PGC-RP platform using a commercially available MDLC system for the simultaneous and unattended identification of proteins, as well as for the mapping of N-glycosylation, a post-translational modification (PTM); our strategy was similar to the ones employed in our recent successes in the design and construction of a variety of fully automatable MDLC platforms with online MS/MS, by applying a solvent-conversion loop between the first and second dimensions. This new online system also adopts an SCX-RP dual-trap design, which greatly reduces the total 2D LC/MS analysis time. PGC columns are most useful for the separation of polar glycans and glycopeptides;19 when glycoproteomics analysis is required, an additional plug-and-play PGC module can be integrated into the system to recapture the hydrophilic glycans and glycopeptides flowing through from the second-dimension RP column. More importantly, this platform can be configured into a commercial off-the-shelf system to enable researchers in many medical and biological disciplines to adopt the technology in a relatively easy manner for large-scale proteomics analyses.

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EXPERIMENTAL SECTION Because of space considerations, experimental methods related to materials, sample preparation, liquid chromatography, mass spectrometry, and data analysis are provided in the Supporting Information. Online 2D PGC-RP, 2D PGC-RP/PGC, and 2D RP-RP Platforms. The online 2D PGC-RP and 2D RP-RP LC systems were both operated using an Eksigent nanoLC 2D Plus LC-valve system, with minor modifications on the triggering wiring to adapt to the control of the newly designed 2DLC system. Briefly, the online 2D PGC-RP LC system comprised a PGC column as the first dimension; a 30-μL loop and two SCX−RP trap columns for solvent conversion and sample B

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transferring between the two LC dimensions; and an RP column as the second dimension (Figures 2A and 2B). The setup was identical for the online 2D RP-RP LC system, with the exception that the first dimension of the online 2D RP-RP LC was an RP column operated at pH 10 (Figure 2B). The LC gradients used for the first dimension are detailed in Table S-1. The gradient for the second dimension RP separation was performed at 300 nL/min as follows: 0−1 min, 0−5% B; 1−91 min, 5−35% B; 91−106 min, 35−80% B; 106−111 min, 80% B; 111−116 min, 80−0% B; 116−140 min, 0% B. Solvent A was 2% ACN and 0.5% formic acid in H2O and solvent B was 2% H2O and 0.5% formic acid in ACN. The detailed operational steps of the online 2D PGC−RP LC system are presented in the Experimental Section of the Supporting Information. The gradient details of the second-dimension RP LC and the positions of the two valves in each step are displayed in Figures 2C and 2D. The online 2D pH 10 PGCRP and RP-RP LC platforms were operated in the same manner as the online 2D pH 2 PGC-RP LC system, with the notable difference that the pump 1 solvents C and D (pH 10) were used. Solvent C was 2% ACN in 20 mM ammonium formate adjusted to pH 10 with ammonium hydroxide and solvent D was 10% solvent C and 90% ACN. Hereafter, the PGC-RP system operated at pH 2 is denoted as “PGCpH2-RP,” while the platform operated at pH 10 is denoted as “PGCpH10RP.” The online 2D PGC-RP/PGC LC system configuration with an additional plug-and-play PGC module was quite similar to the online 2D PGC-RP LC platform described above, with two exceptions (details in the Experimental Section of the Supporting Information, Figure S-1). Mass Spectrometry and Data Analysis. All the data from the 1D LC/MS experiments were acquired using an AB Sciex QSTAR XL Q-TOF mass spectrometer; those from the online 2DLC/MS experiments were acquired using a TripleTOF 5600 system (AB Sciex). Both mass spectrometers were fitted with nanospray sources. For proteomics analysis, the acquired MS/ MS spectra were analyzed using ProteinPilot 4.521 (AB Sciex); those acquired for glycoproteomic analysis were analyzed with the assistance of the software Byonic22 (v. 1.4-5, Protein Metrics) and manual inspection. Detailed optimized MS acquisition parameters and related data analysis methods are presented in the Experimental Section of the Supporting Information.

Figure 1. (A) Schematic representation of the different separation performances of the 1D PGC and 1D RP LC systems toward glycans, peptides, and glycopeptides of various hydrophobicities. The glycans are represented by the combination of the monosaccharides mannose (pale green circles) and N-acetylglucosamine (blue squares). The peptides are composed of amino acids (dark green and red circles; peptides composed of more dark green circles are represented to be more hydrophilic while those composed of more red circles are represented to be more hydrophobic). Very hydrophobic peptides were trapped in the PGC column and could not be eluted out (represented by the lock) (B) HI value distributions plotted against the retention time of the uniquely identified analytes from standard glycoprotein tryptic digests using the 1D RP and PGC LC/MS systems. The RP flow-through analytes are represented by magenta dots; those uniquely identified by the RP LC system are represented by orange dots; those uniquely identified by the PGC LC system are represented by blue dots. The space occupied by each population is circled with its respective color.

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RESULTS AND DISCUSSION PGC Properties in Proteomics and Glycomics. Although we have evaluated the properties of PGC chromatography using 1D LC with mixtures of proteolytic peptides and glycans from standard proteins, the detailed mechanisms of separation will not be elaborated herein. Analysis of the Hydrophobicity Index (HI) values23,24 of the unique identified peptides against their observed retention time provides a foundation for utilizing analytical features of PGC chromatography in large-scale proteomics and glycomics analyses; briefly, the PGC column led to the discovery of a different peptide profile than the one detected from RP under the similar gradient conditions (Figure 1). A clear distinct cluster of polar eluents was uniquely scattered at relative low HI values during the PGC elution; it was also not surprising that N-glycans were also identified during the 1D PGC separation (Table S-2). However, none were detected during the 1D RP LC separation while the N-glycan signal was only observed in the RP flow-through portion during the sample trapping

(Figure 1B, the magenta dot at 7 min, possessing hypothetically very low HI value). Table S-2 presents the mass spectrometric details of some representative polar eluents identified using both platforms. Highlighted in Figure 1B are three representative examples of different hydrophilic species eluted from the PGC column: an N-glycan of heptamannose (Hex7HexNAc2), an N-glycopeptide (Hex7HexNAc2-NLTK), and a very hydrophilic decapeptide (VEQGASVDKR; HI: 0.30; RT: 61.8 min). Their low-energy collision-induced dissociation (CID) spectra are shown in Figure S-2A−C. Therefore, our results suggested that PGC was an appropriate support that enhanced the coverage of polar eluents. PGC columns display many unique properties with respect to other conventional LC columns, such as SCX or low-pH RP. PGC separation occurs through a balance of hydrophobic and the electronic interactions between the polarizable groups of the analytes and the graphite surface; it depends on both the contacting surface area and the nature of the functional groups.18,19 This C

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mixed retention mechanism has emerged as a useful means for separating polar solutes that are not readily retained by RPbased chromatography. The elution properties of glycopeptides from the PGC and RP columns depended on the structure of both the glycan and the peptide; Table S-2 displays the hydrophobic peptide sequences LCPDCPLLAPLNDSR (HI: 15.73) and KLCPDCPLLAPLNDSR (HI: 13.57), which were commonly identified by both PGC and RP chromatography; in contrast, polar glycopeptides incorporating the short tetrapeptide NLTK (HI −0.54; Table S-2) were retained only by the PGC column. These findings suggest that an important advantage of using PGC to complement RP chromatography is the ability to retain and resolve polar components of extreme hydrophilicity, including peptides, glycopeptides, and glycans, which are typically diverted into the waste in the flow-through during sample trapping involving an RP column. A drawback of using a PGC column is poor recovery of some hydrophobic components because of their strong retentive characteristics;19 because PGC has been reported previously to act as a very strong adsorbent, we speculate that some of the higher-mass hydrophobic analytes might have been trapped, but were not able to elute from the PGC stationary phase.19,25 For example, the very hydrophobic peptide ASGDLSMLVLLPDEVSGLER (HI 24.75; RT 95.2 min) and glycopeptides with lengthy peptide sequences of VVHAVEVALATFNAESNGSYLQLVEISR (HI 23.18) and RPTGEVYDIEIDTLETTCHVLDPTPLANCSVR (HI 18.35) were found exclusively when using the RP column. Online 2D PGC-RP System. The analytical performance of the PGC chromatography system demonstrated its overall suitability for relatively hydrophilic eluents, suggesting it has potential to function as the first dimension of 2DLC, coupled with conventional low-pH RP separation, for proteomics and N-glycomics applications. The offline combination of PGC and RP systems has demonstrated excellent performance in previous proteomic applications;20 their online coupling without concomitant deterioration of the resolution of the second-dimension separation remains a challenge, due to the issue of solvent incompatibility of the eluents between the two dimensions. Based upon our earlier successes in designing and constructing a variety of fully automatable MDLC systems,8,12,13,26 here we developed an efficient online 2D PGCRP platform: a PGC column as the first dimension and conventional RP separation at pH 2 as the second dimension (Figures 2A and B). Our approach was similar to the one employed for the 2D RP-RP LC system, with gradient elution of the PGC column proceeding from a highly aqueous content to a highly organic content in the mobile phase; a solventconversion loop introduced between the dimensions to moderate the solvent composition; and an extra SCX trap column to improve the system performance with minimal peptide spillover, thereby extending protein and proteome coverage.13 We first attempted to implement the new online 2D PGC-RP platform within a commercially available MDLC system featuring a dual-trap column configuration, to minimize the system idle time in sample fractionation, trapping, and desalting steps (Figure 2B). While gradient elution of the second-dimension low-pH RP analytical column was used for the online MS/MS analysis of peptides from the first trap column, the alternate trap column was reconditioned and then used to enrich peptides from the subsequent first-dimension

Figure 2. (A) Schematic representation of the online 2D PGC-RP design. (B) Detailed layout of the online 2D PGC-RP setup on the Eksigent nanoLC apparatus. It contains the first-dimension PGC column for the sample fractionation, two SCX-RP trap columns for efficient simultaneous sample transfer to and from the LC dimensions, and the second-dimension RP LC coupled to the mass spectrometer. (C) Solvent flow diagram of pump 2 with the corresponding time frame [green: pump 2 solvents flowed through SCX-RP trap column 1. orange: pump 2 solvents flowed through SCX-RP trap column 2. F1 and F2 in the blue circle of the upper diagram are enlarged in the lower one. Steps 1−9 are labeled in red and correspond to those described in “Online 2D PGC-RP and 2D RP-RP platforms” of the Supporting Information]. (D) Positions of valves 1 and 2 during Steps 1−9 in application of the online 2D PGC-RP platform for proteomics analyses. This cycle repeats for the other subsequent fractions (i.e., F3 and F4) until completion of the experiment.

PGC fraction. As expected, the dual-trap design offered a desirable high-throughput feature. Performance of 2D PGCpH2-RP LC in Proteomics Applications. The new design exhibited its robustness and applicability through duplicate analyses of tryptic digests of primary cerebellar granule neurons (CGNs), a well-known model for studying oxidative stress in relation to neurodegenerative disease,27 using isobaric tags for relative and absolute quantitation (iTRAQ).28 This new technology performed well and achieved almost no idle time; the total run time per analysis was 19 h for eight fractions of 140 min second-dimension RP separation and downstream online ESIMS/MS analyses. The 2D PGC-RP system identified 23.9% more proteins (2678 vs 2161) and 9.4% more unique peptides (11984 vs 10953) at 1% global FDR, respectively, than did the 2D RP-RP system; nearly 60% of the proteins were observed in the duplicates, demonstrating the good reproducibility of both 2DLC systems in proteomics analysis (Figure 3A). The performance of the 2D PGC-RP LC system, evaluated using D

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Figure 3C compares the analyses of the identified peptides of various hydrophobicities when using the 2D PGCpH2-RP and 2D RP-RP LC-MS/MS systems; in accordance with results from 1D PGC and RP LC analyses (Figure 1B), a greater proportion of hydrophilic peptides (73.25 vs 62.56%; HI < 15) were detected using the PGCpH2-RP system, while more hydrophobic peptides (HI > 15) were found through the 2D RP-RP system. The majority of hydrophobic peptides (HI > 15) identified in the 2D RP-RP LC were relatively acidic with quite low isoelectric points (average pI of 4.4), while the average pI for corresponding peptides identified with the 2D PGCpH2-RP was 5.7 (Figure S-3A). The tryptic peptides characterized using the RP-RP system generally exhibited more medium to long peptides (>15 amino acid residues in the peptide, Figure S-3C), with the bulk of them having higher molecular weights (MWs) (Figure S-3D) and low values of pI (Figure S-3B). PGC has been reported previously to be a very strong adsorbent with poor hydrophobic recovery for higher molecular mass analytes.19,25 The PGCpH2-RP system exhibited an apparent loss of acidic long peptides (peptide length >15: 13.3% unique peptides with pI < 4 in 2D PGCpH2-RP versus 38.1% unique peptides with pI < 4 in 2D RP-RP), presumably because these peptides were present in their neutral forms at pH 2 and exhibited very high hydrophobicities. Effect of pH on the Online 2D PGC-RP LC System. To examine the effects of pH on the first-dimension PGC separation performance, we investigated the use of both high and low pH conditions. A particularly promising tandem approach is high-pH PGC chromatography with subsequent low-pH RP chromatography (PGCpH10-RP); this strategy maintained most of the acidic hydrophobic peptides in a deprotonated form, for ease of PGC elution, potentially resulting in a higher peptide coverage. Apparent incompatibilities in pH between the two dimensions could, however, pose a significant challenge. Having developed an effective online solvent conversion loop methodology in the previous PGCpH2RP experiment, the remaining problem to be solved was pHincompatibility in the proposed online PGCpH10-RP system. The use of a modified solvent conversion loop circumvented this issue, while the two mobile phases, despite having different values of pH, were similarly amenable to second-dimension RP separation without requiring any changes in hardware. We examined the performance of the online 2D PGCpH10-RP system through the analyses of S. cerevisiae tryptic digeststhe complete proteome of which has been studied extensively along with the expression levels of its individual proteins, thereby making it a good tool for benchmarking of platform performance (Figure S-4).31−33 Figure 4A compares the analyses of the S. cerevisiae lysate using 2D PGCpH10-RP and 2D PGCpH2-RP LC-MS/MS; the former identified a total of 9700 distinct peptides from the 2152 nonredundant proteins; the latter identified 7277 peptides and 1895 proteinswith 1552 of these proteins common to both sets. Significantly, the PGCpH10-RP system led to the discovery of considerably more hydrophobic (HI > 15; 2462 vs 1228, an increase of 100.5%, Figure 4B), long (n > 15; 1959 vs 860, an increase of 127.8%, where n is the number of amino acid residues, Figure 4D), and high-MW (MW > 1500; 3253 vs 1649, an increase of 97.3%, Figure 4C) peptides than those recovered from the 2D PGCpH2-RP system. The majority of the hydrophobic peptides (HI > 15) identified by the PGCpH10-RP system were large acidic peptides [average pI 4.22; n = 15−20 (37.5%) and n > 20 (23.7%); MW > 2000 (34.3%)]. These results suggest that the

Figure 3. (A) Protein and peptide identification from iTRAQ-labeled CGNs protein digests by the technical duplicate runs of the 2D PGCpH2-RP and 2D RP-RP LC platforms, respectively, as well as a Venn diagram comparison of the two LC platforms using the combined duplicate data. (B) PGC fractions from the 2D PGCpH2-RP LC analysis of the CGNs protein digests with iTRAQ tags, examined for their HI distributions, as represented by a box-whisker plot (box range 25−75%; whisker range 5−95%), the percentage of amino acid residues with aromatic groups in the peptides, and the normalized number of uniquely identified peptides with different charges. (C) HI distributions of the number (left y axis columns) and percentage (right y axis lines) of sequence-unique peptides identified from iTRAQlabeled CGNs protein digests when using the online 2D PGCpH2-RP (in blue) and 2D RP-RP LC (in red) platforms.

CGNs, was satisfactory;29,30 we envision that increasing the number of fractions, increasing the acquisition time, and/or employing more advanced MS instrumentation would increase the number of proteins and peptides that we could identify confidently while also improving the proteome coverage. The separation chemistry of PGC is mixed, depending on interactions based on the planar contact area and hydrophobicity, as evidenced in Figure 3B. Across PGC fractions 2− 8, a relatively high and clear correlation existed between the peptide hydrophobicity [average HI from 8.3 to 17.0] and the percentage of aromatic amino acid residues in the peptides (tyrosine, tryptophan, phenylalanine; from 1.9−10.8%), substantiating a separation mechanism based on a mixture of planar-based interactions and hydrophobicity-driven retention. The normalized number of unique peptides with +3 and +4 charges increased proportionally with a concomitant decrease in those with a +2 charge, suggesting a charge-centric mechanism, in agreement with the expectation that PGC can provide effective retention and resolution to very polar analytes as a result of charge-induced interactions.18 E

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glycosylation processes in glycoproteins.35 Our experimental findings strongly suggested that the PGCpH10-based platform would be applicable not only to proteomics applications but also for the characterization of N-glycoproteins in complex samples, facilitating unattended concomitant analyses of peptides, glycopeptides, and glycans. The effective loading and retention of hydrophilic analytes onto an RP column is a constant challenge because very hydrophilic analytes, including peptides, glycopeptides, and glycans, might be too polar to be effectively retained by the RP column matrix, eventually flowing through and being lost in the void volume.17,36 The outstanding retention properties of PGC chromatography for very polar compounds makes it a promising complementary alternative to recapture the nonretained hydrophilic analytes for subsequent analysis, thereby minimizing information losses; this step can be performed by incorporating an additional plug-and-play PGC module onto the optimized PGCpH10-RP system. We designed and engineered a system for synchronization of the fractionations between PGCpH10-RP and PGC modules (for details, see the Experimental Section of the Supporting Information, Figure 2, and Figure S-1). This platform configuration enabled identification of both hydrophobic and hydrophilic compounds in analyses of complex mixtures featuring a diverse range of hydrophobicities. Armed with the online PGCpH10-RP/PGC platform, we evaluated its effectiveness and performance using a complex biological sample (cynomolgus monkey plasma) for the discovery and simultaneous characterization of N-glycoproteins from a single sample injection event; the platform identified 130 nonredundant N-glycoproteins consisting of 288 Nglycoforms and 254 N-glycosylation sites (Figure 5A, Table S-3), of which the PGC module uniquely discovered 12 glycoforms and 14 N-glycosylation sites. For the same sample, the 2D RP-RP LC system exhibited comparable separation performance, with 126 nonredundant N-glycoproteins consisting of 306 N-glycoforms and 243 N-glycosylation sites being identified (Figure 5A, Table S-3). The hydrophobicities of the peptide backbones of the N-glycopeptides identified by the two LC system were, however, different. As revealed in Figure 5B, the number of hydrophilic peptides with HI values of less than 5 identified by the online 2D PGCpH10-RP/PGC LC system was greater than that of the 2D RP-RP LC system (98 vs 75). This finding highlights the advantage of the 2D PGCpH10-RP/ PGC LC system in identifying more N-glycopeptides with hydrophilic backbones relative to those identified by the 2D RP-RP LC system. Figure 5C displays a representative MS/MS spectrum of an N-glycopeptide identified using the online 2D PGCpH10-RP/PGC system. The spectrum is of the triply charged protonated N-glycopeptide, YAEDKFNETTEK-HexNAc4Hex5NeuGc2, at m/z 1237.8158; the peptide portion is particularly hydrophilic, with a low HI value of 4.50. After comparing the N-glycopeptides data generated by both 2DLC platforms with the 135 N-glycosites identified by the deglycosylation method,37 328 N-glycopeptides containing 68 N-glycosites were matched in the 2D PGCpH10-RP/PGC LC, while 316 N-glycopeptides containing 77 N-glycosites were matched in the 2D RP-PR LC (Figure S-6A, Table S-4). Although the platforms exhibited comparable performances, a higher percentage (35.7% vs 27.8%) of the identified Nglycopeptides with a low peptide backbone HI value (

Figure 4. (A) Venn diagrams of protein and peptide identifications and distributions of sequence-unique peptides identified from tryptic digests of yeast protein lysates according to (B) peptide HI, (C) peptide molecular weight, and (D) peptide length (number of amino acid residues) between the 2D PGCpH2-RP and 2D PGCpH10-RP systems. The percentage values above the orange columns in B−D represent the percentage increases in the number of peptides identified by the 2D PGCpH10-RP platform over the 2D PGCpH2-RP platform for the corresponding parameter.

hydrophobic peptide coverage of the PGCpH10-RP system would be superior to that found using the PGCpH2-RP system; the high-pH PGC separation provided reasonable recovery, and the peak-parking elution method allowed sharp band sampling. Unlike the case during 2D RP-RP LC,7,14 the effect of the pH of the mobile phase on the performance of the 2D PGC-RP system was subtle. The PGCpH10-RP and PGCpH2-RP systems had comparable orthogonality (Figure S-5A), dynamic ranges spanning from the cellular abundances of approximately 50 to 1.26 × 106 copies/ cell [including 35 and 43 lowly abundant proteins ( 15), respectively. Clearly, our results demonstrate that an automated PGCpH10-based 2DLC platform performs as well as, if not better than, the wellknown RP-RP 2DLC system and has the ability to discover hydrophilic peptides orthogonal and complementary to the hydrophobic peptides discovered using the latter technique. Glycoproteomics Applications. Many important proteins undergo N-glycosylation, one of the most common PTMs, in order for them to carry out their proper biological functions.34 The structural complexity of the glycan portions of glycoproteins are not directly predictable from known genomes; hence, the ability to reliably identify and determine characteristic heterogeneous glycan structures is an important analytical step toward exploring the functional roles of NF

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published previously;38−40 Table S-5 provides a detailed comparison of the number of identified N-glycoproteins, sites of N-linked glycosylation and N-linked glycans in Macaca fascicularis plasma relative to those in human serum, as determined using LC-MS.39,40 The PGCpH10-RP/PGC system could be readily configured into a commercial off-the-shelf platform to allow biomedical researchers to adopt the technology for large-scale proteomics analyses. We are encouraged by our preliminary successful development of the online 2DLC system; it might indeed be possible to couple the developed methodologies with dissociation techniques featuring higher energy collision dissociation (HCD) and electron transfer dissociation (ETD) options41,42 that have become commercially available, although the caveat is that they require special mass spectrometric hardware. We envision that the combination of PGC-RP and multiple MS fragmentation technologies will emerge as a powerful tool for simultaneous proteomics and glycomics applications.

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CONCLUSIONS We have designed and implemented a fully automatable online 2D PGC-RP LC technology featuring a novel dual-trap design into a commercial off-the-shelf platform for the efficient frontend separation of proteolytic peptides prior to high-throughput MS based proteomic analyses. We evaluated the effectiveness and performance of this 2D PGC-RP system using primary CGNs cell lysates with iTRAQ tags, leading to the mostcomprehensive CGNs proteome ever assembled; the total run time per analysis was only 19 height fractions of 140 min second-dimension RP separationfrom the initial sample injection event to all the downstream online ESI-MS/MS analyses. The technology facilitates unattended, robust, and scalable analyses of samples on a submicrogram scale, with the number of fractions chosen being a trade-off between the proteome depth and the run time. We typically performed an injection every day, with continuous operation of the system for a period of up to a few weeks, without experiencing any major complications. The recovery of hydrophobic peptides with the high-pH PGC system was substantially better than its low-pH counterpart. We benchmarked the performance of our optimized system through analyses of S. cerevisiae cell lysates, from which approximately 50% of the yeast proteome, with a dynamic range spanning over approximately 5 orders of magnitude, was accounted for in a single analysis. We extended the application of this online 2D PGC-RP LC to the Nglycoproteomic analysis of monkey plasma, after the integration of a PGC trap column to recapture the very hydrophilic flowthrough from the 2D PGC-RP LC system for subsequent separation of those PGC-trapped analytes. Overall, this online 2D PGC-RP LC system, with an additional plug-and-play PGC module, appears to be a promising and useful separation platform for concomitant proteomics and glycoproteomics analyses, with the number of hydrophilic analytes identified being greater than that observed using the 2D RP-RP LC system.

Figure 5. (A) Venn diagram of identified N-glycoproteins, Nglycopeptides, N-glycosites, and N-glycans and (B) distributions across the different HIs of the peptide backbones of the identified sequence-unique N-glycopeptides from monkey plasma between the online 2D PGCpH10-RP/PGC and 2D RP-RP LC platforms. (C) MS/ MS spectrum of the N-glycopeptide [YAEDKFNETTEK-HexNAc4Hex5NeuGc2]3+ at m/z 1237.8158 identified from monkey plasma using the online 2D PGCpH10-RP/PGC LC system. Characteristic peaks for the b- and y-ions derived from the peptide backbone cleavage are labeled in red, and the B- and Y-ions from the glycosidic cleavage are labeled in black.

10) were identified by the 2D RP-RP LC system (Figure S-6B). In addition, the N-glycopeptides containing the nine Nglycosites uniquely identified by the 2D PGCpH10-RP/PGC system were more hydrophilic than those with 18 N-glycosites uniquely identified by the 2D RP-RP LC system (average HI 9.9 vs 15.9; Table S-4). Therefore, the 2D PGCpH10-RP/PGC LC system could be used to detect more N-glycopeptides with hydrophilic peptide backbones than could the 2D RP-RP LC system in the N-glycoproteomics application. Our methodology performs equally well in relation to the majority of those

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S Supporting Information *

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: (852) 2857 1586. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the Hong Kong Research Grants Council (project no. HKU 701613P), Hong Kong Special Administrative Region, China. Y.Z., R.P.W.K., C.H.L., G.L., and Q.Q. thank the Hong Kong RGC for supporting their studentships. We thank AB SCIEX (Hong Kong) Limited for loan of the Eksigent NanoLC ultra 2D plus system; and the school of biological Science, HKU, for access to the 5600 mass spectrometer; Professor R. S.S. Wu, Dr. Tina Settineri, and Mr. W.-Y. Yuen for their comments and suggestions. We also thank Dr. Herman C. Lam and Ms. Yuko P. Y. Lam for helpful discussions.

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