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Trimodal mixed mode chromatography enables efficient offline 2D peptide fractionation for proteome analysis Peng Yu, Svenja Petzoldt, Mathias Wilhelm, Daniel Zolg, Runsheng Zheng, Xuefei Sun, Xiaodong Liu, Günter Schneider, Andreas F. Huhmer, and Bernhard Kuster Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b01356 • Publication Date (Web): 01 Aug 2017 Downloaded from http://pubs.acs.org on August 2, 2017

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Analytical Chemistry

Trimodal mixed mode chromatography enables efficient offline 2D peptide fractionation for proteome analysis Peng Yu1; Svenja Petzoldt1,2,3; Mathias Wilhelm1; Daniel P Zolg1; Runsheng Zheng1; Xuefei Sun4; Xiaodong Liu4; Günter Schneider5, Andreas Huhmer6; Bernhard Kuster1,2,3,7,8* 1

Technical University of Munich, Freising, Germany; 2German Cancer Consortium (DKTK), Munich, Germany; 3German Cancer Center (DKFZ), Heidelberg, Germany; 4Thermo Fisher Scientific, Sunnyvale, CA; 5Department of Medicine II, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, 6Thermo Fisher Scientific, San Jose, CA; 7Center for Integrated Protein Science Munich, Freising, Germany; 8Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany

Fax: +49.8161.71.5931 Email: [email protected] Abstract Offline two-dimensional chromatography is a common means to achieve deep proteome coverage. To reduce sample complexity and dynamic range and to utilize mass spectrometer (MS) time efficiently, high chromatographic resolution of and good orthogonality between the two dimensions is needed. Ion exchange and high pH reversed phase chromatography are often used for this purpose. However, the former requires desalting to be MS-compatible and the latter requires fraction pooling to create orthogonality. Here, we report an alternative firstdimension separation technique using a commercial trimodal phase incorporating polar embedded reversed phase, weak anion exchange and strong cation exchange material. The column is capable of retaining polar and nonpolar peptides alike without noticeable breakthrough. It allows separating ordinary and TMT-labelled peptides under mild acidic conditions using an acetonitrile gradient. The direct MS compatibility of solvents and good orthogonality to online coupled C18 columns enable a straightforward workflow without fraction pooling and desalting while showing comparable performance to the other techniques. The method scales from low to high microgram sample quantity and is amenable to full automation. To demonstrate practical utility, we analyzed the proteomes of ten human pancreatic cancer cell lines to a depth of >8,700 quantified proteins.

Key words: Trinity P1, 2D-LC, orthogonality, peptide fractionation, proteomics, pancreatic cancer Running title: Trinity P1 enables facile peptide fractionation

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Introduction One of the major technical challenges in proteomic research lies in the enormous molecular complexity and dynamic expression range of proteomes, making it difficult to probe any proteome to very high depth1. Finite loading capacity of chromatographic columns together with the dominance of a few highly abundant proteins can result in a lack of sensitivity for proteins at the low end of the concentration range. One of the most effective strategies to mitigate this problem is to introduce another dimension of separation prior to online LC-MS/MS such as gel electrophoresis, isoelectric focusing, or chromatographic separation at the protein or peptide level2-4. Among those, offline peptide fractionation, sometimes applied at multiple levels5,6, has developed into the most widely applied approach due to its relatively high resolution and ease of operation. Recently, online comprehensive 2D-nanoLC-MS/MS for proteome analysis was also reported7. A number of offline fractionation methods, including high pH reversed phase (hRP)8,9, strong cation exchange (SCX)10,11, strong anion exchange (SAX)12,13, mixed mode14,15, and hydrophilic interaction chromatography (HILIC) are practiced16,17. SCX has been used early on to take advantage of the presence of positive charges on peptides following trypsin digestion5. As silica manufacturing technologies advanced, it became possible to use reversed phase material to fractionate peptides at pH 8-10, which afforded much higher resolution than SCX and eliminated the necessity for desalting prior to LC-MS/MS analysis18. The difference in peptide charge distribution between offline high pH and online low pH reversed phase chromatography results in a considerable difference in selectivity and consequently provides orthogonality. Orthogonality is, however, only partial possibly because reversed phase interactions still play a dominant role at both low and high pH8. Therefore, most current implementations of high pH fractionation uses fraction pooling to improve orthogonality19-22. In a recent study, the pooling process has been automated by employing two sets of pumps and online fraction storage via a ten-port valve21. Another implementation involved using a programmable switch valve to enable automatic pooling22. Hydrophilic strong anion exchange (hSAX) chromatography has also been introduced for proteomic application and showed both good chromatographic resolution and orthogonality to online low pH reversed phase chromatography13. However, the use of salt gradients necessitates a desalting step prior to LC-MS/MS. Hydrophilic interaction chromatography (HILIC), also exhibits good orthogonality to C18, but has not seen widespread adoption yet16. There have also been some efforts to go beyond two-dimensional separations to gain extra depth in proteome coverage6. Despite impressive results, particularly at the peptide level, these may not be very practical for high-throughput applications. In this study, we report the use of a commercial trimodal stationary phase, called Acclaim Trinity P1 (Trinity P1 for short), for offline peptide fractionation. It was originally designed for the analysis of active pharmaceutical ingredients. The base silica (3 µm diameter, 300 Å pore size) surface is bonded with a terminal tertiary amine modified alkyl chain providing weak anion exchange and reversed phase interactions. Nano-sized sulfonated polystyrene-divinylbenzene copolymers are then permanently attached on the outer surface via electrostatically driven selfassembly to provide strong cation exchange functionality23 . This spatial configuration enables all three modes of interaction to function simultaneously. Our results demonstrate high orthogonality to online low pH C18 separations when running an acetonitrile gradient at mild acidic conditions (pH 5). Trinity P1 separates ordinary or TMT-labelled peptides and does not


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require fraction pooling or desalting prior to LC-MS/MS. It provides similar performance in terms of peptide and protein identification compared to the other techniques which makes it an attractive alternative for high-throughput or in-depth proteomics experiments demonstrated by the analysis of ten human pancreatic cancer cell lines to a depth of >8,000 proteins.



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Experimental Section Cell culture PanC-1 cells were used for all of the experiments, except the following. The phosphopeptides were from a four cell line mix: Jurkat, MV4-11, MCF7 and SK-N-BE (2). The human pancreatic cancer cell lines were provided by the Schneider group and authenticated by Single Nucleotide Polymorphism (SNP)-Profiling conducted by Multiplexion (Multiplexion GmbH, Heidelberg, Germany) or short tandem repeat locis (STRs) profiling conducted by Microsynth (Mycrosynth, Balgach, Switzerland). These cells were cultured with standard procedures as recommended by the supplier, also described previously24. Cells were lysed with either 8M urea or 0.8 % NP-40 buffer, and centrifuged at 20,000× g for 20min for clarification. Please refer to supplementary information for a full description of cell culture procedure. Sample preparation The cleared lysate for full proteome analysis was reduced with 10 mM DTT and alkylated with 50 mM chloroacetamide before digested overnight with trypsin (Promega, Madison, WI) at a 1: 50 ratio. The digest was then desalted with C18 SepPak columns (Waters, MA) and the eluate was dried down with a Univapo 150 ECH vacuum concentrator (UniEquip, Planegg, Germany) and kept at -80°C. TMT labeling was carried out with TMT0 or TMT10 reagents (Thermo Fisher Scientific) essentially according to the manufacturer’s instruction except that a TMT to peptide ratio of 1:2 (w:w) was used. First dimension separations All first dimension column fractionations were carried out on a Dionex Ultimate 3000 Quaternary Micro HPLC (Thermo Dionex, Germering, Germany) equipped with an IonPac AS24 2 × 250 mm, 7um column (Thermo Fisher Scientific) for hSAX, an XBridge Peptide BEH C18 2.1 × 150 mm, 3.5um column (Waters, MA) for hRP, or an Acclaim Trinity P1 2.1 × 150 mm, 3 um column (Thermo Fisher Scientific) for TP1 separations. The hSAX procedure used in this study was detailed in a previously published protocol25. Briefly, 100 µg peptides were injected and fractionated using a salt gradient. Solvent A was 5 mM Tris·HCl, pH 8.5, and solvent B was 5 mM Tris·HCl, 1M NaCl, pH 8.5. The gradient was: 0-3 min, 0% B; 3-27 min, 0-25% B; 27-40 min, 25-100% B; 40-44 min, 100% B; 44-45 min, 100-0% B; 45-50 min, 0% B. Flow rate was 250 µl·min-1 and 38 one-minute fractions were collected and pooled into 36 based on the UV trace. Fraction 3 and 4, 37 and 38 were combined respectively. The fractions were subsequently desalted by C18 Stage-Tips prior to LC-MS/MS analysis26. For hRP column fractionations, 100 µg peptides were injected. Solvent A was 20 mM ammonium formate, pH 10, and solvent B was 20 mM ammonium formate in 90% acetonitrile, pH 10. The gradient was: 0-0.01 min, 2-5% B; 0.01-64 min, 5-33% B; 64-66 min, 33-60% B; 6667 min, 60-90% B; 67-73 min, 90% B; 73-74 min, 40% B. Flow rate: 0-66 min, 200 µlmin-1; 6667 min, 200-300 µlmin-1; 67-70 min, 300 µlmin-1; 70-71 min, 300-400 µlmin-1; 71-73 min, 400 µlmin-1; 73-74 min, 20 µlmin-1. Seventy-two one-minute fractions were collected from 1 to 73 min and pooled into 36 by overlaying the second half of the fractions onto the first half. No desalting was performed. For TP1 fractionations, two different gradient length were used. Peptide loading amount is indicated in the text. Solvent A was 10 mM ammonium acetate, pH 4.7, and B was 10 mM


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ammonium acetate in 95% acetonitrile, pH 5.3. The solvents were made from a 200 mM ammonium acetate, pH 5.1 stock solution and diluted twenty fold without further pH adjustment. The difference in pH was a result of ELGA purified water having a pH of 4.6 and consequent dilution using this water or acetonitrile to produce the respective working solvents. Initial fractionations was performed with a gradient of 0-2 min, 15% B; 2-30 min, 15-50% B; 30-31 min, 50-84% B; 31-34 min, 84% B; 34-36 min, 84-50% B; 36-38 min, 50% B. Flow rate: 0-30 min, 200 µlmin-1; 30-31 min, 200-300 µlmin-1; 31-34 min, 300 µlmin-1; 34-36 min, 300-400 µlmin-1; 36-38 min, 400-20 µlmin-1. Thirty-two one-minute fractions were collected from 2 min to 34 min into a 96 well plate. Replicate injections were made where indicated in the text. For the 36 fraction scheme, the gradient was 0-2 min, 15% B; 2-50 min, 15-42% B; 50-53 min, 42-84% B; 53-57 min, 84% B; 57-58 min, 84-50% B; 58-59 min, 50% B. Flow rate: 0-56 min, 200 µlmin-1; 56-57 min, 200-400 µlmin-1; 57-58 min, 400 µlmin-1; 58-59 min, 400-20 µlmin-1. From 2 min to 56 min, 36 1.5-min fractions were collected. The fractions were lyophilized and reconstituted for LC-MS/MS measurement. No pooling was performed.

LC-MS/MS measurements All samples were measured on a Dionex Ultimate 3000 nanoLC (Thermo Fisher Scientific) coupled to an Orbitrap Q Exactive HF mass spectrometer (Thermo Fisher Scientific). NanoLC separation was carried out using an in-house packed capillary column (75 µm × 45 cm) filled with 3 µm Reprosil Gold C18 particles (Dr. Maisch GmbH, Ammerbuch, Germany) at 300 nL·min-1. Sample was loaded onto a trap column (75 µm × 2 cm) in 0.1 % formic acid at 5 µLmin−1 for 10 min. The trap column was packed with 5 µm Reprosil ODS-3 particles. The analytical column was heated to 50 °C using a 30-cm capillary column heater (ASI, Pompton Plains, NJ). Solvent A was 0.1 % formic acid (FA), 5% DMSO in water; solvent B was 0.1 % FA, 5% DMSO in acetonitrile27,28. For the analysis of regular peptides, the gradient was 0–1 min, 24% B; 1–52 min, 4-32% B; 52–54 min, 35–80% B; 54–56 min, 80% B; 56–58 min, 80-2% B; 58– 60 min, 2% B. A top 20 data dependent acquisition (DDA) method was used for MS. The survey scan was acquired at 60,000 resolution with a mass range of 360-1300 m/z and AGC target value of 3e6. The maximum injection time (max IT) was 50 ms. MS/MS spectra were acquired at 15,000 resolution with fixed first mass at 100 m/z. AGC target was 1e5 and max IT was 25 ms. Isolation window was 1.7 m/z and normalized collision (NCE) energy was 25. An underfill ratio of 1.0% was used and +1, +7 and higher, and unknown charge states were excluded. Further setting included “peptide match preferred” and the “exclude isotopes” option was turned on. Dynamic exclusion was set to 20 s. For analysis of TMT-labeled peptides, the LC conditions were identical but with some changes to the MS parameters as follows. Survey scan max IT was 100 ms. TopN was 25. MS2 resolution was 30,000. AGC target was 2e5 and max IT was 57 ms. Fixed first mass was set at 120 m/z and NCE was 33. For phosphopeptides, the LC gradient was adjusted to 0-25 min, 2-15% B; 25-40 min, 15-27% B; 40-42 min, 27-80% B; 42-44 min, 80% B; 44-45 min, 80-2% B; 45-50 min, 2% B. There were also changes to the MS parameters compared to the regular peptide method as follows. Max IT was 10 ms and 120 ms for full MS and MS2 scans respectively. MS2 AGC target was 2e5 and the top 12 peaks were picked for MS/MS. Dynamic exclusion was set to 25 s.

Data processing



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MS instrument .raw files were processed by MaxQuant (v.1.5.3.30)29 and MS2 spectra searched against the UniProtKB Human Reference Proteome database (v22.07.13, 88,381 entries). Default MaxQuant parameters were used and the match-between-runs feature was enabled. The retention time alignment window was 10 min and the match window was 0.5 min. For comparisons across different fractionation techniques, the fractions are numbered consecutively only within experiment. For example, 101-136 for hSAX, 201-236 for hRP, 301336 for TP1. This configuration allows matching only within experiments, but not across. For convenience, number of protein groups are simply reported as proteins and number of unique peptides sequences are reported as peptides in the text. TMT labeled samples were searched with fixed TMT modifications. The resulting text files were further processed in R to generate orthogonality and fractionation plots. Isoelectric point (pI) and hydrophobicity (GRAVY) score calculation for unmodified peptides was performed using the R Peptides package30. A theoretical Human UniprotKB database digest was performed in silico assuming 100% cleavage efficiency at the C termini of lysine and arginine residues. Peptide length between 7 and 52 amino acids were kept, corresponding to the length of identified peptides in the measured samples when a 1% peptide spectrum match and identification false discovery rate was applied.

Data deposition The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository31 with the dataset identifier . Reviewer account details: Username: [email protected] Password: oFd2X7rp


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Results and Discussion TP1 shows even use of 2D separation space and good reproducibility To test the general performance and reproducibility of Trinity P1, we separated 20 µg peptides from PanC-1 cells into 32 straight fractions in triplicates. A sample UV chromatogram is shown in Figure S1A. This setup allowed fraction collection of up to three runs into a 96-well plate and direct LC-MS/MS measurement (1h per fraction) after lyophilization and re-solubilization, facilitating a workflow requiring minimum hands-on operation. We observed very good orthogonality and utilization of the 2D separation space (Figure 1A), translating into an even distribution of peptides among the fractions (Figure 1B). In most fractions, 5,000-6,000 peptides were detected. 53% of these were found in a single fraction only and 27%, 8% and 12% were identified to two, three and more than three fractions respectively. We used a geometric approach reported earlier to calculate 2D surface coverage and orthogonality18. A detailed description is provided in the supplementary text. Briefly, peptides were divided into bins as in Figure 1A, and a bin was counted as occupied if it contained equal or more peptides compared to the total number of peptides divided by the total number of bins. Surface coverage was then determined by dividing the number of occupied bins by the total number of bins in the analysis and in this case was 54%. For comparison, a random distribution of analytes into the same number of bins (described by Poisson distribution) at 100% orthogonality would have a surface coverage of 63%18, and the most orthogonal setup in the quoted publication (HILIC × RP) had a coverage of 51%. As a consequence of the even peptide distribution in a TP1 separation, we observed a steady increase in peptide identifications with every fraction added (Figure 1B). The number of identified proteins saturated much more quickly as one would expect and noted before by others (Figure 1C). At one third of all fractions analyzed, 7,111 (85%) out of 8,372 proteins were already identified. At half of the fractions, 8,258 proteins (99%) were identified. When restricting the database search using every other fraction, we still obtained 7,764 proteins and 56,045 peptides (out of 77,685 from the full analysis). This clearly indicates that substantial time savings are possible at a very moderate expense of protein identifications which would in turn support applications in which many samples have to be analyzed.



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Figure 1 Direct measurements of TP1 fractions without fraction pooling or desalting. 20 µg peptides were loaded onto D1 TP1 and 100% of each fraction injected onto D2 C18. The offline TP1 and online C18 setup showed high orthogonality and good utilization of the 2D separation space (A). The size of each dot denotes the number of peptides identified in each 2 min retention time bin. In each fraction 4,000 - 6,000 peptides were identified of which 2,000~3,000 were new identifications (B).. As expected, proteome coverage saturated more quickly with 7,111 (85%), 8,258 (99%), or 8,372 (100%) proteins identified after the first 11, 16, or 32 fractions measured respectively (C). Triplicate analysis of the same sample showed that 98% of all proteins and 70% of all peptides were identified in all three analysis (D).


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In triplicate experiments, 8,737 out of 8,939 proteins (98%) were shared among all three replicates and 66,722 out of 95,215 peptides (70%) were commonly found (Figure 1D), indicating excellent reproducibility at the protein and fairly good reproducibility at the peptide level. To estimate the resolution of the Trinity P1 column, we separated 100 pmol BSA digest (Figure S1B) and obtained a peak width of about 30 s at base (based on 5 peaks). In a 32 min gradient, this translates to a peak capacity of about 64. Higher peak capacity may be achieved via longer gradients, if desired.

Downscaling of TP1 separations We subsequently tested the potential of downscaling sample quantities for TP1 separations. For the commercial column dimension of 2.1 × 50 mm, we found the practical limit to be ~4 µg below which sample losses outweighed the gains of separation. Collecting four fractions (in triplicate) and measuring half of each fraction for 1h on an Orbitrap HF instrument, resulted in the identification of ~6,000 proteins and 28,000 peptides in each replicate (Figure S2). The overlap at the protein level was 97% and 81% at peptide level (Figure S2). When using the match-between-run feature of MaxQuant in a combined search with a sample separated into 36 fractions, the number of proteins in a single replicate of the 4 µg separation increased to ~7,400 proteins and ~34,000 peptides respectively. Such an approach can be very useful for say clinical applications when many (similar) samples of limited quantity have to be analyzed and for which one ‘master sample’ may be available that can be analyzed to great depth. Further downscaling can be envisaged by packing columns of smaller inner diameter.

Orthogonality of D1 TP1 and D2 C18 The above observation that TP1 shows good orthogonality to C18, led us to analyze the properties of the identified peptides in more detail. When plotting the GRAVY score of the peptides against the D1 TP1 fraction number (a proxy for retention time) and D2 online C18 retention time (Figures 2 A, B), there was good correlation between hydrophobicity and retention time for C18 but not for TP1. This was not entirely expected since an acetonitrile gradient was used for the elution of the TP1 column. A likely explanation for this observation is that ion exchange and polar interaction play important roles in peptide retention at the pH employed for the separation. Next, we analyzed the pI distribution of the identified peptides. It was obvious from Figure 2C, D that the pI values of TP1 peptides were not uniformly distributed. The TP1 data clearly showed a group of less retained peptides clustered around pH 6.2 and more retained peptides at around pH 4.1. These peptides would be positively and negatively charged respectively. The stronger retention of negatively charged peptides was likely due to spatial proximity of weak anion exchange and reversed phase ligands. The former afforded by the tertiary amine would then repel the positively charged peptides, while the strong cation exchange ligand located on the outer surface retained them, somewhat resembling the electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) process32. The transition between cation and anion exchange happened in this case around fraction 8, where a less populated area could be seen in Figure 2A. Consequently, pH 5 is a good choice for the mobile phase buffer, yielding maximum charge discrimination of the peptides and still keeping within the recommended pH range of 3 to 6 for the column. In contrast, C18 showed a more even distribution across different pI values. Similarly to C18, peptide retention on TP1 can likely be



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altered by altering the pH of the mobile phase. When we compared the mass distributions of peptides, there was a clear correlation between molecular weight and retention time for C18 separations, but not for TP1 (Figures 2E, F). This well-known behavior of peptides on C18 material can be explained by the fact that longer/larger peptides have more hydrophobic residues and more potential to form ion pairs and consequently stronger interactions with the C18 phase.

Figure 2 Analysis of the distribution of peptide hydrophobicity (GRAVY), isoelectric point (pI) and mass among D1 TP1 and D2 C18 retention time. TP1 retention time was approximated by fraction number. Peptide properties were either plotted against TP1 fraction number (A, C, E) or C18 retention time (B, D, F). There was a strong correlation between peptide hydrophobicity and C18 retention (B) but not TP1 retention (A). C18 evenly separated peptides of different


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charges (reflected by pI) (D). Under the conditions employed, negatively charged peptides (streak around pI 4.2) clearly showed stronger retention than positively charged peptides (streak around pI 6.2) on TP1 (C). There was no correlation between peptide mass and TP1 retention (E) but a trend could be observed for C18 as noted before (F).

Separation of tandem mass tag modified peptides In order to assess if TP1 separations would be generically useful for proteomic applications, we next performed separations with TMT-labelled peptides. We indeed observed an even distribution of TMT-labeled peptides in TP1 separations and with very good orthogonality to online C18 (Figure 3A, Figures S3 AB). In this experiment, ~64,000 peptides and 8,600 proteins were identified using 32 fractions and 1h measurement time each. Interestingly, while it is well established that TMT labeling leads to much stronger retention of peptides to C18, we only observed a very small such effect for TP1 (Figures 3B,C), likely the result of its mixed mode retention mechanism. Calculating pI and GRAVY scores for the identified peptides (without the TMT moiety because no models are available for this modification) confirmed that retention still largely depended on hydrophobicity for C18, but not for TP1 (Figures S3C,D). However, the peptides formerly observed to cluster at pH ~6.2 if not TMT-labeled, were now retained more strongly on TP1 (Figure S3E) under identical gradient conditions, likely due to the added hydrophobicity provided by the TMT moiety. As generally observed in the field, retention of TMT labeled peptides on C18 material generally shifted to later times, due to the strong dependence on hydrophobicity (Figures 2C and S3F).



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Figure 3 Analysis of TMT-labelled peptides. D1 offline TP1 and D2 online C18 separation of TMT labeled peptides showing that the two dimensions are fully orthogonal. However, the added retention on C18 provided by the TMT label led to the underutilization of the front part of the C18 separation (A). TMT labeling caused no significant shift in retention on TP1 (B), but a strong shift to later retention times on C18 (C).


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We also attempted the separation of phosphopeptides on TP1 using the same column and mobile phase system. Using Fe-IMAC enrichment for phosphopeptides24 from a 10 mg tryptic digest of a four cell line mixture containing Jurkat, MV4-11, MCF7 and SK-N-BE (2), we observed a number of distinct peaks toward the middle and end of the gradient, rather than an evenly distributed profile (Figure S4). Sixteen continuous fractions were collected for this experiment and measured for one hour each by LC-MS/MS, resulting in the identification of 9,481 phosphopeptides. Most of these were identified from fractions ten and sixteen. A singleshot analysis of the same sample (i.e. no TP1 separation) identified 6,737 phosphopeptides. A number of possible reasons for this chromatographic behavior and associated unsatisfactory identification performance compared to other approaches can be identified. Compared to peptides from full proteome digests, phosphopeptides contain an extra negative charge. They also tend to contain more missed protease cleavage sites which not only makes them longer on average (and thus potentially containing more charged residues)33, it also adds a strong positive charge to the peptide by way of the extra Arg or Lys residue. These charges likely give rise to strong ion exchange interactions with the TP1 stationary phase and together with reversed phase interactions result in the observed bimodal and compressed distribution. Consistent with this interpretation, there were only 546 (6%) doubly phosphorylated peptides in the TP1 separation compared to 1,223 (18%) in the single shot analysis.

TP1 shows similar performance compared to hSAX and hRP separations We next compared TP1 to hSAX and hRP fractionation (Figure 4). Here, peptides were separated into 36 fractions for each chromatography (standard hSAX procedure in the authors’ laboratory, no pooling25). For hRP, we collected 72 fractions and pooled them into 36 as is common practice to increase orthogonality. Loading amount was kept constant at 100 µg for the first dimension (D1) separation and 10% of each fraction was used for the second dimension (D2) analysis. As detailed in the methods section, hSAX was run at pH 8.5 and peptides eluted with a salt gradient, hRP and TP1 were run at pH 10 and 5 respectively, both with an acetonitrile gradient.



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Figure 4 Direct comparison of hSAX, hRP and TP1 D1 separation modes. 100 µg peptides were injected onto D1 and 10% of each fraction was injected onto D2. 87% of all proteins (A) and 18% of all peptides (B) are shared among all three D1 modes. The pI and GRAVY score distributions of unmodified peptides are shown as boxplots, where the center line is the median value and the boundaries are the first and third quartiles respectively (C, D). The notation “All” refers to the combined data for hSAX, hRP and TP1. Uniprot denotes the distributions for an in-silico digest of UniprotKB human database containing peptides between 7 and 52 amino acids in length.

hSAX, hRP and TP1 experiments led to the identification of ~9,800, 9,600 and 10,100 proteins and ~120,000, 127,000 and ~83,000 peptides, with 56%, 57% and 49% peptides unique to one fraction for each technique respectively. In total, all three chromatographic modes identified ~10,500 proteins and ~188,000 peptides indicating reasonably deep overall coverage of the sample. Among all peptides were ~3,300 phosphorylated peptides carrying ~3,500 phosphorylation sites. Interestingly, hSAX identified ~2,500 of these, while hRP and TP1 only covered ~1,200 and 500 respectively. This was probably due to the ability of hSAX to better retain negatively charged peptides. The overlap of identified proteins was 87% (Figure 4A). At the peptide level, the overlap drastically fell to 18% and 35 % of all peptides were unique to one of the three techniques (Figure 4B). While not entirely unexpected given that the three stationary phases exhibit different modes of peptide retention and separation, the extent of the observed complementarity was surprising. All three techniques showed good utilization of the 2D separation space (Figure S5 AB) but when analyzing the chemical properties of the identified peptides, substantial differences were observed. Acidic peptides (and polar peptides in general) were significantly over-represented in hSAX, while the difference to a theoretical digest of the Uniprot database was smaller for hRP and TP1 (Figure 4C). TP1 showed a slight bias for basic peptides, probably attributable to the SCX functionality. The difference in hydrophobicity was less pronounced between the three D1 modes (Figure 4D). We currently have no obvious explanation for why the number of identified peptides in TP1 separations is lower than that of hSAX and hRP given that the number of identified proteins is very similar. As a result of observed differences in peptide properties, median protein sequence coverage increased from 18-27% for a single chromatography to 39% when combining all three data sets (Figure S1D). More generally, these results imply that employing different first dimension separations can have a very similar effect on increasing sequence coverage as employing different proteases34.

Quantitative proteome profiling of pancreatic cells lines To demonstrate utility of TP1 separations for proteomic applications, we quantitatively profiled the proteomes of 10 human pancreatic cancer cell lines using TMT-10plex labeling. Because it is well established that multiplexing samples leads to lower proteome coverage, we performed three experiments collecting 32, 48, and 96 fractions respectively from the mixed proteomes (see supplementary methods for details) and used 1h of LC-MS/MS time on each fraction. Using 32 fractions, the 10 proteomes were covered to a depth of 6,635 proteins with 6,067 proteins quantified in all proteomes (Figure 5A). Increasing the number of fractions to 48, led to the identification of 7,247 proteins (6,793 quantified in all channels) and further increasing the number of fractions to 96 resulted in the identification of 9,021 proteins (8,781 quantified in all


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channels) proteins. We also notice that peptides unique to one fraction fell from 79%, to 73% and then to 38%, a consequence of deeper proteome coverage. To the best of our knowledge, the above represents the largest number and deepest coverage of pancreatic cancer cell line proteomes published to date35. While a full analysis of this data set is beyond the scope of this study, a few interesting observations may be made here. Overall, proteome expression across the 10 cell lines was found to be quite similar (median coefficient of variation of protein expression of 0.34; Figure 5B, S6). However, among the divergent proteome, there are a number of proteins with strong connections to pancreatic cancer. These include the tumor suppressor TP53, which is frequently mutated in the disease (Figure 5C) as well as oncogenes such as the kinase AKT2 (Figure 5D), which is frequently activated in pancreatic cancer, has been associated with resistance to tyrosine kinase inhibitors and is therefore a potential drug target36,37.

Figure 5 Analysis of ten pancreatic cancer cell lines. Number of identified protein increased from 6,635 to 7,247 to 9,021 when 32, 48 or 96 fractions were collected and measured respectively (A). Most part of the proteome were similar among the cell lines with a median coefficient of variation 0.34, while significant differences in certain proteins also existed (B). Differences in TP3 and AKT2 expression among different cell lines (C, D).

Conclusions and outlook In this study, we have shown that the commercial mixed mode column Trinity P1 is a viable alternative to other stationary phases for 2D-LC based deep proteome profiling experiments. The approach produces competitive protein identification and quantification results but also provides sequence coverage that is complementary to hSAX or hRP separations. Because no desalting and no sample pooling is required, manual handling can be reduced, losses avoided and the approach lends itself to automation. The commercial column format can effectively handle low to high microgram sample quantities but also holds considerable potential for



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downscaling. Compared to strong anion exchange or high pH reversed phase chromatography, Trinity P1 operates at mild acidic conditions, thus minimizing the risk of LC column and lining deterioration as well as degradation of base-labile post-translational modifications such as phosphorylation and glycosylation. Since retention on Trinity P1 is largely independent of the polarity of a peptide, it might prove useful in the study of very polar peptides when coupled online to a mass spectrometer. In combining orthogonality to C18 material, use of MS compatible mobile phases, and the lack of a peptide breakthrough, Trinity P1 is an attractive first dimension phase to construct an online LC×LC-MS/MS setup in the future. Such a combination may dramatically reduce sample loss, increase overall system sensitivity, and thus lead to more protein and peptide identifications and more robust quantification38.

Associated Content Supporting Information contains additional methods and six figures, as referenced in the main text.

Author Information Corresponding author: [email protected]

Declaration of competing financial interest: XS, XL and AH are employees of Thermo Fisher Scientific BK and MW are founders and shareholders of OmicScouts GmbH. They have no operational role in the company.

Acknowledgements We gratefully acknowledge the funding from Alexander von Humboldt Foundation and Carl Friedrich von Siemens Foundation (to PY). This work was in part funded by the German Federal Ministry for Education and Research (grant 031L0008A). The authors wish to thank Andreas Klaus and Andrea Hubauer for technical assistance, Drs. Chen Meng and Thomas Wieland for help with data processing, and many of the current and former members of the Kuster group for help with experiments and general discussion.

References (1) Zubarev, R. A. Proteomics 2013, 13, 723-726. (2) Dowell, J. A.; Frost, D. C.; Zhang, J.; Li, L. Anal. Chem. 2008, 80, 6715-6723. (3) Branca, R. M. M.; Orre, L. M.; Johansson, H. J.; Granholm, V.; Huss, M.; Perez-Bercoff, A.; Forshed, J.; Kall, L.; Lehtio, J. Nat Meth 2014, 11, 59-62. (4) Aebersold, R.; Mann, M. Nature 2003, 422, 198-207. (5) Di Palma, S.; Hennrich, M. L.; Heck, A. J. R.; Mohammed, S. J. Proteomics 2012, 75, 3791-3813.


Page 17 of 19

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(6) Spicer, V.; Ezzati, P.; Neustaeter, H.; Beavis, R. C.; Wilkins, J. A.; Krokhin, O. V. Anal. Chem. 2016, 88, 2847-2855. (7) Vonk, R. J.; Gargano, A. F. G.; Davydova, E.; Dekker, H. L.; Eeltink, S.; de Koning, L. J.; Schoenmakers, P. J. Anal. Chem. 2015, 87, 5387-5394. (8) Gilar, M.; Olivova, P.; Daly, A. E.; Gebler, J. C. J. Sep. Sci. 2005, 28, 1694-1703. (9) Toll, H.; Oberacher, H.; Swart, R.; Huber, C. G. J. Chromatogr. A 2005, 1079, 274-286. (10) Takahashi, N.; Takahashi, Y.; Putnam, F. W. J. Chromatogr. A 1983, 266, 511-522. (11) Washburn, M. P.; Wolters, D.; Yates, J. R. Nat. Biotechnol. 2001, 19, 242-247. (12) Takahashi, N.; Ishioka, N.; Takahashi, Y.; Putnam, F. W. J. Chromatogr. A 1985, 326, 407-418. (13) Ritorto, M. S.; Cook, K.; Tyagi, K.; Pedrioli, P. G.; Trost, M. J. Proteome Res. 2013, 12, 2449-2457. (14) Phillips, H. L.; Williamson, J. C.; van Elburg, K. A.; Snijders, A. P.; Wright, P. C.; Dickman, M. J. Proteomics 2010, 10, 2950-2960. (15) Ishihama, Y.; Rappsilber, J.; Mann, M. J. Proteome Res. 2006, 5, 988-994. (16) Di Palma, S.; Boersema, P. J.; Heck, A. J.; Mohammed, S. Anal. Chem. 2011, 83, 3440-3447. (17) Alpert, A. J. J. Chromatogr. A 1990, 499, 177-196. (18) Gilar, M.; Olivova, P.; Daly, A. E.; Gebler, J. C. Anal. Chem. 2005, 77, 6426-6434. (19) Dwivedi, R. C.; Spicer, V.; Harder, M.; Antonovici, M.; Ens, W.; Standing, K. G.; Wilkins, J. A.; Krokhin, O. V. Anal. Chem. 2008, 80, 7036-7042. (20) Wang, Y.; Yang, F.; Gritsenko, M. A.; Wang, Y.; Clauss, T.; Liu, T.; Shen, Y.; Monroe, M. E.; LopezFerrer, D.; Reno, T.; Moore, R. J.; Klemke, R. L.; Camp, D. G.; Smith, R. D. Proteomics 2011, 11, 2019-2026. (21) Lee, H.; Mun, D.-G.; So, J. E.; Bae, J.; Kim, H.; Masselon, C.; Lee, S.-W. Anal. Chem. 2016, 88, 1173411741. (22) Kulak, N. A.; Geyer, P. E.; Mann, M. Mol. Cell. Proteomics 2017, 16, 694-705. (23) Liu, X.; Pohl, C.; Woodruff, A.; Chen, J. J. Chromatogr. A 2011, 1218, 3407-3412. (24) Ruprecht, B.; Koch, H.; Medard, G.; Mundt, M.; Kuster, B.; Lemeer, S. Mol. Cell. Proteomics 2015, 14, 205-215. (25) Ruprecht, B.; Wang, D.; Chiozzi, R. Z.; Li, L.-H.; Hahne, H.; Kuster, B. Proteomics: Methods and Protocols 2017, 69-82. (26) Rappsilber, J.; Ishihama, Y.; Mann, M. Anal. Chem. 2003, 75, 663-670. (27) Meyer, J. G.; Komives, E. A. J. Am. Soc. Mass Spectrom. 2012, 23, 1390-1399. (28) Hahne, H.; Pachl, F.; Ruprecht, B.; Maier, S. K.; Klaeger, S.; Helm, D.; Medard, G.; Wilm, M.; Lemeer, S.; Kuster, B. Nature methods 2013, 10, 989-991. (29) Tyanova, S.; Temu, T.; Cox, J. Nat. Protocols 2016, 11, 2301-2319. (30) Osorio, D.; Rondón-Villarreal, P.; Torres, R. The R Journal 2015, 7, 4-14. (31) Vizcaíno, J. A.; Côté, R. G.; Csordas, A.; Dianes, J. A.; Fabregat, A.; Foster, J. M.; Griss, J.; Alpi, E.; Birim, M.; Contell, J. Nucleic Acids Res. 2013, 41, D1063-D1069. (32) Alpert, A. J. Anal. Chem. 2008, 80, 62-76. (33) Dickhut, C.; Feldmann, I.; Lambert, J.; Zahedi, R. P. J. Proteome Res. 2014, 13, 2761-2770. (34) Bekker-Jensen, D. B.; Kelstrup, C. D.; Batth, T. S.; Larsen, S. C.; Haldrup, C.; Bramsen, J. B.; Sørensen, K. D.; Høyer, S.; Ørntoft, T. F.; Andersen, C. L.; Nielsen, M. L.; Olsen, J. V. Cell Systems 2017, 4, 587599.e584. (35) Paulo, J. A.; Mancias, J. D.; Gygi, S. P. Pancreas 2017, 46, 690-698. (36) Altomare, D. A.; Tanno, S.; De Rienzo, A.; Klein-Szanto, A. J.; Tanno, S.; Skele, K. L.; Hoffman, J. P.; Testa, J. R. J. Cell. Biochem. 2002, 87, 470-476. (37) Banno, E.; Togashi, Y.; de Velasco, M. A.; Mizukami, T.; Nakamura, Y.; Terashima, M.; Sakai, K.; Fujita, Y.; Kamata, K.; Kitano, M. Int. J. Oncol. 2017, 50, 2049-2058. (38) Magdeldin, S.; Moresco, J. J.; Yamamoto, T.; Yates III, J. R. J. Proteome Res. 2014, 13, 3826-3836.



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