Conventional-Flow Liquid ChromatographyâMass Spectrometry for

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Conventional-flow Liquid Chromatography-Mass Spectrometry for Exploratory Bottom-up Proteomic Analyses Juraj Lenco, Marie Vajrychova, Kristyna Pimkova, Magdaléna Prokšová, Marketa Benkova, Jana Klimentová, Vojtech Tambor, and Ondrej Soukup Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b00525 • Publication Date (Web): 27 Mar 2018 Downloaded from http://pubs.acs.org on March 28, 2018

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Analytical Chemistry

Conventional-Flow Liquid Chromatography-Mass Spectrometry for Exploratory Bottom-up Proteomic Analyses Juraj Lenčo,*,#,†,ǂ,‡ Marie Vajrychová, #,†,ǂ Kristýna Pimková,† Magdaléna Prokšováǂ, Markéta Benková,† Jana Klimentová,ǂ Vojtěch Tambor,† Ondřej Soukup† †

Biomedical Research Center, University Hospital Hradec Králové, Sokolská 581, 500 05 Hradec Králové, Czech Republic Department of Molecular Pathology and Biology, Faculty of Military Health Sciences, University of Defence, Třebešská 1575, 500 01 Hradec Králové, Czech Republic ‡ Department of Analytical Chemistry, Faculty of Pharmacy, Charles University in Prague, Heyrovského 1203, 500 05 Hradec Králové, Czech Republic⁑ ǂ

ABSTRACT: Due to its sensitivity and productivity, bottom-up proteomics based on liquid chromatography-mass spectrometry (LC-MS) has become the core approach in the field. The de facto standard LC-MS platform for proteomics operates at sub-µL/min flow rates, and nanospray is required for efficiently introducing peptides into a mass spectrometer. Although this is almost a “dogma”, this view is being reconsidered in light of developments in highly efficient chromatographic columns, and especially with the introduction of exceptionally sensitive MS instruments. Although conventional-flow LC-MS platforms have recently penetrated targeted proteomics successfully, their possibilities in discovery-oriented proteomics have not yet been thoroughly explored. Our objective was to determine what are the extra costs and what optimization and adjustments to a conventional-flow LC-MS system must be undertaken to identify a comparable number of proteins as can be identified on a nanoLC-MS system. We demonstrate that the amount of a complex tryptic digest needed for comparable proteome coverage can be roughly fivefold greater, providing the column dimensions are properly chosen, extra-column peak dispersion is minimized, column temperature and flow rate are set to levels appropriate for peptide separation, and the composition of mobile phases is fine-tuned. Indeed, we identified 2,835 proteins from 2 µg of HeLa cells tryptic digest separated during a 60 min gradient at 68 µL/min on a 1.0 × 250 mm column held at 55 °C and using aqua-acetonitrile mobile phases containing 0.1% formic acid, 0.4% acetic acid and 3% dimethyl sulfoxide. Our results document that conventional-flow LC-MS is an attractive alternative for bottom-up exploratory proteomics.

Current bottom-up (or “shotgun”) exploratory proteomics performed on state-of-the-art, high-sensitivity, high-resolution and high-acquisition-rate instruments is able to identify several thousand proteins in a single analysis run.1,2 Despite the differences in particular hardware solutions underlying the instrument performance, a de facto standard liquid chromatography-mass-spectrometry (LC-MS) platform in proteomics involves a nano-flow chromatograph hyphenated via a nanoelectrospray to a mass spectrometer. Since nanoelectrospray was introduced and initially applied to analysis of proteins and peptides more than 20 years ago,3-5 it has generally been viewed as indispensable to proteomics due to its unprecedented sensitivity, and thus, requirement for only very small sample amounts. This paradigm is now being challenged. Along with the development of highly efficient sub-2-µm and superficially porous particles, and particularly with the introduction of extremely sensitive MS instruments offering better ionization efficiency, ion sampling, transmission and detection, the utilization of conventional-flow (or ‘high-flow’) LC-MS platforms for targeted proteomic analyses is now being explored. Inspiring pioneering research from the Borchers group using triple quadrupole instruments has demonstrated the application of conventional-flow LC-MS to various targeted quantitative proteomics studies.6-9 The seminal work by Percy et al. demonstrated its performance merits relative to a nanoLC-MS

platform using as its test model an optimized subset of cardiovascular disease-related proteins in a human plasma digest.10 In contrast to targeted quantitative proteomics, the state of affairs in exploratory proteomics is completely different. To the best of our knowledge just one report has been published in this area. However, judging from the amount of protein digest that had to be injected to identify a reasonable number of proteins (40 µg), that single study did not exploit the full potential of current LC-MS.11 Although it was not so long ago when 50 µg and more of material had to be loaded onto a twodimensional electrophoresis gel for proteomic analysis, sacrificing such an amount of sample would scarcely convince practitioners today that such a sample-devouring LC-MS platform is viable. Hence, the question has remained unanswered whether high-efficiency conventional-flow chromatography hyphenated to advanced MS instruments had advanced sufficiently to be adopted into bottom-up exploratory proteomic analyses. In order comfortably to analyze proteomics samples as well as samples containing small molecules in our shared analytical laboratory at the Biomedical Research Center, we configured a Q Exactive Plus mass spectrometer with both a nano- and a conventional-flow chromatograph that may alternate in delivering analytes into the MS. This rather exceptional setup enabled us to probe what are the extra costs of using convention-

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al-flow LC-MS for analyses of a complex protein digest to achieve identification results comparable to those from a standard nanoLC-MS analysis performed on physically the same MS instrument. To make our results widely implementable without substantial costs and technical difficulties, we intended to exploit only inexpensive LC-MS accessories that were commercially available, and avoided such improvements on a research and development basis as multiple electrospray ionization emitters12 and electrosonic spray ionization.13

METHODS Sample Preparation. A combined rLys-C/trypsin digest of HeLa cells lysate as well as tryptic digest of bovine serum albumin (BSA, UniProt No. P02769) and equine myoglobin (EMg, UniProt No. P68082) were prepared for the study (Supporting Information). NanoLC-MS Analyses. An UltiMate 3000 RSLCnano system (Thermo Scientific) was used for nanoLC-MS. The trapanalytical column configuration consisted of a PepMap100 C18, 3 µm, 100 Å, 0.075 × 20 mm column and a PepMap RSLC C18, 2 µm, 100 Å, 0.075 × 250 mm column (both from Thermo Scientific). The analytical column was placed inside the ion source and connected by a conductive union, which was used to apply high voltage. The column terminated with a 10 µm PicoTip emitter (New Objective). The samples were loaded onto the trap column at 8 µL/min of loading phase (2% ACN, 0.1% TFA) for 5 min. Peptides were separated by a gradient formed by mobile phase A (2% ACN, 0.1% FA) and B (80% ACN, 0.1% FA), running from 0.2% to 46.2% in 60 min at 250 nL/min. Eluted peptides were introduced into a Q Exactive Plus using a Nanospray Flex ion source at 1.8 kV (Thermo Scientific). A full Top12 data-dependent acquisition (DDA) setup was used. MS1 spectra were acquired within m/z 350-1,600 with a 3 × 106 AGC target at 35,000 resolution and with a maximum ion time (IT) of 110 ms. Precursor ions with ≥ 2 ≤ 8 charges and threshold intensity of 2 × 104 were selected for higher-energy C-trap dissociation (HCD) with an exclusion window that should allow each precursor to be fragmented no more than twice. An isolation window of 2 m/z and normalized collision energy of 28 were used. MS2 spectra were acquired at resolution 17,500 with a 1 × 105 AGC target and a maximum IT of 50 ms. All experiments were run in triplicate. Narrow-Bore and Microbore LC-MS Analyses. An UltiMate 3000 binary RSLC system (Thermo Scientific) was used for conventional-flow LC-MS (Supporting Information). The system was exploited in the initial configuration prepared by the service engineer at the installation and later in a fully optimized configuration assembled within this study. Components of the original configuration that were readily replaceable are listed in the Supporting Information. The narrow-bore and microbore columns included Poroshell 120 SB-C18, 2.7 µm, 120 Å, 2.1 × 100 mm and 1 × 100 mm columns (Agilent Technologies), as well as a Halo Peptide ES-C18, 2.7 µm, 160 Å, 1 × 250 mm column (Advanced Materials Technology). Flow rate was 300 µL/min for the 2.1 mm column and 68 µL/min for the 1.0 mm columns. Peptides were introduced into the mass spectrometer using a HESI-II probe at 3 kV. Values suggested in the HESI-II user guide for additional ESI settings were interpolated for flow rates applied within the study. If not indicated otherwise, all chromatographic conditions and such MS settings as mobile phases, gradient length,

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ACN gradient span, and DDA setup were kept identical to those used in the nanoLC-MS analyses. All experiments were run in triplicate. Determination of Dwell Volume and Peak Dispersion. The dwell volume was determined by a simple procedure described in the Supporting Information. For determining the system peak dispersion, a column was replaced by a union. Human Glu1-fibrinopeptide B was dissolved in 20% ACN, 0.1% FA to 100 pmol/µL. Five 0.2 µL injections were introduced at eight flow rates that followed a progression an = 400 × 0.75n for n= 0-7. MS1 spectra were acquired with a 3 × 106 AGC target at resolution 17,500 and with a maximum IT of 50 ms, allowing MS1 cycle time of 0.16 s. The chromatographic profiles of the first monoisotopic peak of doubly charged Glu1-fibrinopeptide B were extracted from the RAW files using Skyline software.14 The volumetric peak dispersion was calculated from the crude peak profiles using the second central moment (µ2) method on the basis of the peak width at 5σ according to Equation 1:15,16

μ =

( ) ∙()

()

,

(1)

where t is time; c(t) is intensity as a function of time; and µ1 is the first central moment calculated by Equation 2:

μ =

∙()

()

.

(2)

Equations 1 and 2 were translated to Equations S-1 and S-2 for numerical integration into MS Excel (Supporting Information). Protein Identification. Acquired data were first subjected to MaxQuant v1.5.4.1 for processing.17,18 The created APL peak lists converted to MGF format were submitted to Preview v2.8.2 (Protein Metrics) to determine optimum search settings.19 The final search was performed in Proteome Discoverer v1.4 (Thermo Scientific) using Mascot v2.2 (Matrix Science) with two-pass workflow in order to cope efficiently with trypsin autolytic peptides (Supporting Information). Percolator was used for rescoring the Mascot results.20 Only peptide identifications with false discovery rate (FDR) ≤ 0.01 were taken into consideration. The following identification parameters were considered: number of protein groups (referred to as proteins), number of modification-specific peptides (referred to as peptides), number of peptide-spectrum matches (PSMs), and number of recorded MS2 spectra. Identification redundancy was calculated as a ratio of PSMs to peptides. Lastly, spectra identification rate was calculated as PSMs/recorded MS2 spectra × 100%. Peptide intensities were extracted from the MaxQuant result files. Data are available via ProteomeXchange with identifier PXD005481. Assessing Conventional-Flow LC-MS Performance. The LC-MS performance in terms of peak widths at 50% height (W50%) and MS signal intensities was assessed by injections of 500 fmol of BSA/EMg tryptic peptides. Four 15 min steep gradients were applied for each conventional-flow condition. The first injection confirmed the identity of peaks and was done using a Top3 DDA setup. Protein identification is described in the Supporting Information. Sequences of 21 BSA and 5 EMg peptides identified with FDR ≤ 0.01 across all runs were imported into the Skyline. Identified spectra for these peptides along with information about the peptide retention time were extracted from the Proteome Discoverer .MSF files and stored in a Skyline library. Next, three injections were run


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using MS1 scan in m/z range 350-1,200 with resolution 17,500, AGC target 3 × 106, and maximum IT 50 ms. From recorded RAW files, Skyline extracted precursor ion chromatograms of the first monoisotopic peaks of 2+, 3+ and 4+ precursors of 26 BSA/EMg peptides. Correct match between extracted chromatographic peak and a peptide sequence was confirmed by matching its retention time with that stored in the spectral library. Only the most intense precursor charge state for each peptide in each experimental condition was evaluated. Peak heights were relativized to a default experimental condition for comparison. Peak capacity (Pc) on the basis of the peak width at 4σ was determined from the gradient time (tg) and W50% of the peptides, as follows (Equation 3): = 1

. ∙ %

.

(3)

was extended to 110 ms, which allowed for increasing resolution to 35,000. To maintain a rate of collecting MS1 scans similar to that from the original Top12 method (maximum cycle time 1.17 s), at maximum six precursors were allowed to be fragmented amid MS1 scans (maximum cycle time 1.07 s). Despite that the search inputs decreased by 32%, extended IT enabled recording spectra of better quality, as gauged by the identification rate, which rose to 47.6%. Collectively, 32% fewer recorded MS2 spectra and a roughly threefold higher spectra identification rate led to identification of 6,500 peptides and 938 proteins (experiment #03; Figure 1). With almost 1,000 proteins identified from 2 µg of digest in a 1 h gradient, this LC-MS platform could compete with earliergeneration nanoLC-MS systems.22

RESULTS AND DISCUSSION Benchmark NanoLC-MS Analyses. The nanoLC-MS configuration employed was that routinely used for long-term best results in our hands. The 61.8% of explained MS2 spectra from injection of 500 ng of HeLa tryptic peptides implied potential saturation of the nanoLC-MS platform. Subsequently, we injected 375 ng, 250 ng, and 125 ng of the sample. All identification indicators diminished with decreasing load. Injections of 250 ng already yielded all main identification indicators (proteins, peptides, and PSMs) with statistical inferiority compared to 375 ng of peptides. The results from 375 ng injections were thus regarded as benchmark values (2,381 identified proteins, 15,920 identified peptides, 23,515 PSMs, etc.; Figure S-1) and are shown for quick comparison in graphs for conventional-flow analyses as a dotted line. Narrow-Bore Column Analyses. A 2.1 × 100 mm column operating at 300 µL/min was used for the initial conventionalflow analyses. The maximum LC-MS related settings as from the nanoLC-MS analyses were preserved. We anticipated that the 500 ng of sample as used for nanoLC-MS would be predestined to achieve poor yields due to the theoretical dilution factor of 784 predicted by Equation 4: =

!

"

,

(4)

where dA and dB are the column inner diameters. Indeed, although 14,214 MS2 spectra were recorded, only 102 proteins and 355 peptides were identified (experiment #01; Figure 1). It was thus inevitable that the sample load needed to be increased. In the subsequent experiment #02, 2 µg of HeLa peptides were injected under the same LC-MS settings. Fourfold more sample increased the identification rate to 14.0%, leading to identification of about sevenfold more peptides and fivefold more proteins (Figure 1). The roughly 500 proteins identified from 2 µg of HeLa digest compared to the 2,381 proteins identified on nanoLC-MS scarcely could make such exploratory analyses viable. Obviously, continuing to increase the load would have resulted in approaching the identification level obtained by nanoLC-MS. A significant portion of proteomic samples are of limited availability, however, and hence, rather than test how much sample is needed to yield comparable results, we fixed 2 µg as the largest sample load to be injected in the comparison to nanoLC-MS and focused on other aspects of the LC-MS that might maximize protein identification. Being inspired by the Olsen group’s work,21 we increased the sensitivity of the DDA setup. Maximum IT for MS2 spectra

Figure 1. Results from conventional-flow LC-MS analyses of HeLa digest on a 2.1 × 100 mm column. Peptides were resolved at flow rate 300 µL/min on a column held at RT. Specific conditions for experiment #01: 0.5 µg of peptides, Top12 DDA; #02: 2 µg of peptides, Top12 DDA; #03: 2 µg of peptides, Top6 DDA. Error bars represent standard deviation.

Microbore Column Analyses. We next aimed to examine theoretically the most feasible way to enhance sensitivity of the conventional-flow LC-MS platform: namely, by decreasing the column inner diameter. The relationship can be quantified by Equation 4, which indicates that replacing a 2.1 mm column with a 1.0 mm microbore column increases the sensitivity 4.41-fold. The flow rate was decreased by the same factor, to 68 µL/min, which led to significantly reduced solvent consumption and more economical and “greener” chromatography. The slow flow rates, however, produce a significant dwell time if the dwell volume is not minimized. The dwell volume of the LC system was 425 µL, and adjustments decreased it to 160 µL (Supporting Information), This resulted in an acceptable dwell time of 2.35 min at 68 µL/min. Disappointingly, identified proteins and peptides increased by just 18% and 4%, respectively (experiment #04; Figure 2). Search inputs increased only by 9%. Overall intensity of the total ion chromatogram (TIC) increased barely twofold. Scrutiny of the TIC also revealed that despite the column length and chemistry being the same the peaks suffered from extensive dispersion. The literature suggests extra-column peak dispersion # as the most probable underlying phenomenon for deteriorated microbore chromatography.23-26 To obtain a more accurate view on the matter, we assumed a theoretical 15 min gradient carried out on a 100 mm column with a desired peak capacity of Pc = 250 (W50% ~ 2.15 s). This peak capacity can be translated to roughly 7,000 gradient plates $ ∗ . Subsequently, based on Equation S-3 and the subsequent ones, we predicted the remaining efficiency Er for 4.6 mm, 2.1 mm and 1.0 mm columns for # in the range 0.01-100 µL2. Indeed, to



preserve 90% column efficiency, # must be at most 0.18 µL2 for a 1.0 mm column (Figure 3 A). After an initial optimiza tion as described in the Supporting Information, total # 2 decreased only to the range 2.8-4.3 µL (Figure 3 B). Nevertheless, the initial gradient allows peptides to be basically trapped at the head of the column in gradient separations. Hence, we focused on contributors downstream the column and replaced the outlet 0.075 × 250 mm capillary and 0.004′′ × 148 mm spray needle with a 0.050 × 350 mm capillary and 0.003′′ × 148 mm needle in the final adjustments. The experi mentally determined total # was then 2.2 µL2 at 68 µL/min. Eventually, we calculated the peak dispersion attributable only to the outlet and spray needle capillary based on Equation S14 and added the time-dependent contribution of the mass spectrometer (maximum IT of 50 ms; Equation S-17). Theoretically, 91.6% of the remaining separation efficiency of the 1.0 mm column should be preserved for the assumed gradient with N* = 7,000 and calculated post-column # = 0.15 µL2 * (or 93.1% for truly observed N ~ 5,600). In our hands, no other theoretical or experimental determination of the peak dispersion downstream the column matched better to the same W50% observed on the 2.1 mm and 1.0 mm columns.

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same W50% on the 2.1 mm and 1.0 mm columns implies that the configuration was approaching the limit as to the impact of # on achieving microbore column efficiency. The system as finally optimized exerted sufficient efficiency for microbore separations, which in turn was observed in a gain in MS sensitivity. The relative gain in sensitivity between narrow-bore and microbore separation on the system as finally optimized was on average 6.6-fold, which better corresponded to that gain predicted by Equation 4.

Figure 3. Predicted remaining column efficiency Er for 100 × 4.6 mm, 2.1 mm, and 1.0 mm columns depending on extra-column peak dispersion. The horizontal dotted line indicates 90% Er (A). of the original and optimized Extra-column peak dispersion # conventional-flow LC-MS configurations (B).

Figure 4. Performance of the narrow-bore 2.1 × 100 mm and microbore 1.0 × 100 mm columns in the original (experiments s01 and s02), initially optimized (s03, s04), and finally optimized (s05, s06) LC-MS configurations. Intensity fold-change was relativized to experiment s01.

Figure 2. Results from microbore-flow LC-MS analyses of 2 µg of HeLa digest on microbore columns. If not specified otherwise, injections were at 68 µL/min on a 1.0 × 100 mm column held at RT in the system as finally optimized and analyzed using a Top12 DDA setup (experiment #06). Specific conditions for experiment #04: original configuration, Top6 DDA; #05: Top6 DDA; #07: 40 °C; #08: 55 °C; #09: 70 °C; #10: 55 °C, 102 µL/min; #11: 55 °C, 136 µL/min; #12: 1.0 × 250 mm column, 55 °C, 68 µL/min. Error bars represent standard deviation. The # reduction became readily evident by inspecting chromatographic peaks of BSA/EMg peptides (Figures 4 and 5). W50% was on average 11.4 s wide in the original configuration on a 1.0 mm column, and it decreased to 2.4 s in the system as finally optimized. The matched injections on the 2.1 mm column had W50% of 4.0 s and 2.4 s, respectively, for the original configuration and that as finally optimized. Observing the

Figure 5. Effect of minimizing the extra-column peak dispersion on a microbore column peptide separation. Peptides were separated at 68 µL/min on a 1 × 100 mm column.


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Increased efficiency in both crucial LC-MS parameters was amply translated into protein identifications (experiment #05; Figure 2). Although the number of MS2 spectra increased only slightly, the boost in explained search inputs (72.9%) allowed extracting of 68% more PSMs leading to identification of 9,827 peptides and 1,448 proteins. The enhancement in identification rate motivated us to run the analysis again with the previous Top12 DDA setup (experiment #06; Figure 2). As expected, the spectra identification rate dropped to 58.4%. Due to a 35% increase in recorded MS2 spectra, however, this jointly led to an increase in identifications. Providing the system is well optimized, our data confirm that separation on microbore columns can provide a good compromise between system robustness and enhanced sensitivity. Temperature. It was evident from previous literature that higher column temperature leads to more efficient peptide separation.27-30 Prior to conducting experiments at elevated temperature, we attempted to determine the still-safe temperature at which the peptides would not be degraded even during very long proteomic gradients. Although a bare minimum of peptides resided on the column until the very end of the gradient across the experiments within our study, our observations regarding peptides’ thermal degradation described in the Supporting Information, 70 °C was set as the highest temperature applied for conventional-flow LC-MS analysis of peptides. The average W50% of BSA/EMg peptides remained unchanged when comparing separation at RT and 40 °C (experiment s07; Figure 6). The most apparent change was observed with the shift from 40 °C to 55 °C (experiment s08; Figure 6). With further increase to 70 °C, the distribution of peak widths became less dispersed (experiment s09; Figure 6). Nevertheless, the narrowing was not accompanied by further enhancement of the mean peak width, which remained the same as at 55 °C. Surprisingly, elevated temperature essentially did not change MS signals. This is in contradiction with the previously reported signal increase in nanoLC-MS.31 The effect of elevated temperature was more noticeable in the 60 min analysis of HeLa peptides. A temperature shift from RT to 40 °C had already been shown to enhance all main indicators (experiment #07; Figure 2). A major change could be seen in decreased spectra identification redundancy which dropped to 1.41, and concomitantly with an increased number of MS2 spectra that resulted in identifying of 19% more peptides and proteins. The trend was maintained, albeit to a lesser extent, when the temperature rose to 55 °C (experiment #08; Figure 2). Although most MS2 spectra were recorded at 70 °C (experiment #09; Figure 2), the number of identified proteins, peptides, and PSMs did not exceed those resulting when the temperature was at 55 °C. The underlying causes of this may relate, at least in part, to cleavage of peptides after Asp, as revealed in the thermal degradation test, because this effect was found to occur at the highest rate at 70 °C (237 peptides vs 121 at RT). Based on these results and also being mindful of column lifetime, all succeeding gradients were run while maintaining the column at 55 °C. Flow Rate. The most apparent effect of higher temperature is lower back pressure, while less attention is given to a shift in the minimum at the Van Deemter plot toward faster flow rates. 29 Faster flow rates can improve separation of complex mixtures and diminish the ion suppression, which, along with adjusted desolvation conditions, might result in identifying

more proteins. We therefore tested 1.5- and 2-fold higher flow rates using both BSA/EMg peptides and HeLa digest. In concordance with theory, peak widths decreased to 2.1 s at 102 µL/min and further to 2.0 s at 136 µL/min (experiments s10 and s11; Figure 6). The trend suggested that the separation efficiency could be further improved. On the other hand, despite adjusted desolvation parameters, the peak MS intensities decreased with faster flow rates due to the concentrationdependent behavior of the HESI-II source. We next sought to explore which of the observed effects of faster flow rates would prevail during the analysis of HeLa digest (experiments #10 and #11; Figure 2). With faster flow rates, fewer MS2 spectra were recorded and the identification rate diminished. Consequently, fewer peptides were identified, and that resulted in lower HeLa proteome coverage. Our results thus show that so long as the adjustments in HESI-II desolvation parameters cannot compensate for its concentration-dependent behavior, proteomics analyses of complex samples based on LC-MS may not benefit from the fact that higher flow rates produce thinner peaks due to the prevailing effect of peak dilution.

Figure 6. Effect of temperature, flow rate, and column length on performance of a microbore 1.0 mm column. If not specified otherwise, peptides were separated at 68 µL/min on a 100 mm column held at 55 °C (experiment s08). Specific conditions for experiment s07: 40 °C; s09: 70 °C; s10: 102 µL/min; s11: 136 µL/min; s12: 250 mm column. Intensity fold-change was relativized to experiment s06.

Column Length. Improving separation efficiency by faster flow rate is generally emphasized as a way to cope efficiently with complex mixtures.27,32 Such effort is usually doomed to disappointing results, however, in exploratory proteomics (see Figure 2 in this article (experiments #06 and #07) and work by Xu et al.33). Hence, we focused on an extended column length that increases Pc without impairing MS sensitivity. 34,35 Similarly to the Poroshell 120 C18 column used in previous experiments, a custom-prepared Halo Peptide ES-C18 microbore column 250 mm long was filled with particles comprising 1.7 µm solid core and 0.5 µm porous layer. We thus assumed that differences in performance between a 100 mm Poroshell 120 and a 250 mm Halo Peptide column were primarily because of the 2.5-fold longer dimension of the latter (Supporting Information). The 250 mm microbore column provided peaks 1.9 s wide in the steep gradient, and, in line with theory, Pc increased to 287.28 Concurrently, the peak heights increased 1.2-fold (experiment s12; Figure 6 and S-6). For the first time in the study, the LC-MS analysis of HeLa peptides yielded at least one of the main indicators, namely the number of peptides, superior to the benchmark analysis (experiment #12; Figure 2). A considerable increase was achieved in identified peptides, argua-



bly due to higher identification rate (62.1%) and lower identification redundancy (1.29) rather than due to the number of recorded MS2 spectra. Taken as a whole, our data show that longer microbore columns should not be overlooked by the manufacturers, because they are highly advantageous for identifying of components from a complex mixture available in limited quantity if used on an optimized LC-MS system. Mobile Phase Acidifiers. Recently, 0.1% acetic acid (HAc) has been proposed as a substitute for 0.1% FA in LC-MS to enhance peptides’ signals.36 HAc forms weaker ion pairs with peptides than does FA, which is appealing for the sake of MS sensitivity but may be detrimental to separation efficiency. We replicated the intensity boost due to 0.1% HAc but also observed an overall negative influence on peak widths (experiment s13; Figure 7). The peptide intensity increase lagged expectations based upon the reported data. Most likely, this is because the authors chose peak areas for gauging the acidifier effect. Indeed, peak areas were 1.8-fold larger in our analysis. It was difficult to judge which factor prevailed in the HeLa digest analysis – the negative effect of 0.1% HAc on separation or its positive effect on the MS signal (experiment #13; Figure 8). About 4% more MS2 spectra were acquired, but the identification rate decreased to 60.5%. This, along with poorer identification redundancy, led to identifying about 3% fewer peptides, although this was not translated into the number of identified proteins, which remained essentially the same as in the analysis carried out using 0.1% FA. Nevertheless, HAc should not be rejected outright as an acidifier in peptide LC-MS analysis. It has been exploited effectively by some proteomics research groups at a concentration of 0.5%.37 Hence, we probed 0.5% HAc using our conventional-flow LC-MS platform (experiments s14 and #14; Figures 7 and 8). The separation featured efficiency close to that achieved using 0.1% FA, and concurrently, the MS signal rose 1.4-fold, thereby suggesting that the higher concentration of HAc may be the most suitable in proteomics. Indeed, more identifications were obtained from the HeLa digest using 0.5% HAc than when using 0.1% FA. The number of recorded MS2 spectra remained at a level similar to that obtained when using with 0.1% HAc, but the identification rate increased to 62.7%. This, along with lower identification redundancy, led to a 2% gain in the numbers of proteins and peptides identified. The small difference in performance whether using 0.1% FA or 0.5% HAc inspired us to test a combination of the two acids in the hope that FA would help to achieve high separation efficiency and the prevailing HAc would boost the MS signal. Indeed, 0.1% FA/0.4% HAc provided peaks 2.0 s wide and the signal intensity was 1.2-fold greater compared to that for 0.1% FA (experiment s15; Figure 7). At the same time, the blend yielded the best proteomic identification results from the HeLa digest (experiment #15; Figure 8). Most importantly, the identification rate jumped to 65.3%, and, despite a lower number of collected MS2 spectra, this yielded 5% more peptides and 4% more proteins as compared to an analysis carried out using 0.1% FA. This blend of acids was therefore adopted for the following experiments. Dimethyl Sulfoxide (DMSO). A markedly different charge state distribution of fragmented precursors was noticeable when comparing nano- and microbore-flow results. MS2 spectra of 2+ precursors constituted only 36% of the microboreflow LC-MS data. The majority of MS2 spectra and PSMs were from 3+ precursors (Figure 9 A, B). Moreover, MS2

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spectra of precursors with more than three charges represented a significant portion of the data (17%). Collisional dissociation spectra of highly charged precursors are generally recognized to be disadvantaged in terms of confident identification due to their usually less complete and/or more complicated fragmentation patterns. DMSO has been shown to improve proteomic analyses by reducing peptide charge states and charge state coalescence. Furthermore, DMSO has been shown also to enhance the MS signal in nanoLC-MS.38,39 These observations motivated us to explore the effects of DMSO in microbore LC-MS (Supporting Information).

Figure 7. Effect of acidifiers and DMSO on performance of a microbore 1 × 250 mm column. Specific conditions for experiment s13: 0.1% HAc; s14: 0.5% HAc; s15: 0.1% FA/0.4% HAc; s16: 0.1% FA/0.4% HAc, 3% DMSO; s17: 0.1% FA, 3% DMSO. Intensity fold-change was relativized to experiment s12.

Figure 8. Results from microbore-flow LC-MS analyses of 2 µg of HeLa digest on a 1 × 250 mm column. Peptides were resolved on a column held at 55 °C at 68 µL/min. Specific conditions for experiment #13: 0.1% HAc; #14: 0.5% HAc; #15: 0.1% FA/0.4% HAc; #16: 3% DMSO, 0.1% FA/0.4% HAc; #17: 3% DMSO, 0.1% FA. Error bars represent standard deviation.

W50% increased modestly in the presence of 3% DMSO (experiment s16; Figure 7). The MS signal increased 1.3-fold, which was rather less than we expected based on the work by Hahne et al. 38 In line with the modest signal gain, the TIC of resolved HeLa peptides was essentially unchanged (experiment #16; Figure S-7). Despite the discouraging initial output, the numbers of identified proteins and peptides increased by 22% and 17%, respectively (Figure 8). This increment was not due to collecting more MS2 spectra or a higher identification rate. There was a noteworthy decrease in identification redundancy caused by rarer occurrence of peptides identified from more than one precursor charge state (Figure 9 C). This confirmed the charge state coalescence effect of DMSO in microbore LC-MS. Another difference was found in the distribution of


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precursor and PSM charge states, respectively, which shifted toward lower values (Figures 9 A, B; S-8). This corroborated the DMSO effect on reduction of peptide charge state. 38,39 Consequently, dominantly fragmented doubly-charged precursors increased the median ion score across identified peptides (Figure S-9). Collectively, 3% DMSO in microbore-flow LCMS analysis contributed to better identification yields, primarily due to precursor charge state coalescence and charge reduction. Its positive effect on MS response was not so evident as that reported in nanoLC-MS (Supporting Information). 38 Arguably, this could have been caused by leverage of nebulizing and desolvation gases in HESI-II, which effect might have surpassed the favorable effect of DMSO on desolvation/ionization described in nanoLC-MS operating without desolvation gases. Aside from DMSO, reduction of precursor charge state might also be attained by adjusting desolvation parameters in the HESI-II source. A design of experiments analysis revealed, however, that, compared to desolvation parameters, DMSO in the mobile phases most reduces the charge state for all but one of the tested BSA/EMg peptides (Supporting Information). Its effect is thus more universal and does not require any additional optimization.

tic peptides from HeLa cells and analyzing the resulting peptide fractions in a 2D LC-MS manner. In total, more than 6,700 proteins were identified from an equivalent of 20 µg of the starting material by eight 60 min second-dimension gradients. The higher loadability of the 1 mm column was exploited in an exploratory analysis of non-depleted human amniotic fluid, yielding 380 proteins and 559 proteins, respectively, from the injections corresponding to 2 µL of the fluid. Similarly, an injection corresponding to 0.05 µL of non-depleted human plasma yielded 225 proteins. Superior stability of the conventional-flow LC-MS system in terms of both retention time and MS response allowed reliable quantitation of proteins even with one order concentration differences using a labelfree approach. In addition, the iTRAQ quantification showed itself to be rather less burdened by the precursor co-isolation issue in comparing to results obtained earlier using nanoLCMS. Most likely, this was due to better chromatographic separation of peptides. The system was also tested for analysis of phosphopeptides sequentially enriched using TiO2 and FeIMAC chromatography. We identified almost 3,000 phosphopeptides with phosphoRS isoform probability ≥ 0.95 from an equivalent of 500 µg of Jurkat cell digest.

CONCLUSION

Figure 9. Effect of DMSO on charge states of precursors (A) and PSMs (B), and its effect on charge state coalescence (C). Error bars represent standard deviation.

In the final experiments (experiment s17 and #17), we tried to ascertain whether the benefit of 0.1% FA/0.4% HAc is preserved in 3% DMSO. W50% in the steep gradient improved marginally to 1.9 s whereas the signal intensities decreased 0.9-fold as compared to elution with 0.1% FA/0.4% HAc in the mobile phases (Figure 7). Although the difference in complex sample analysis was not so apparent as that seen in analyses without DMSO, the blend yielded more identifications than did FA alone. Its use can thus be recommended to be used for maximum performance together with DMSO (Figure 8). Scrutiny of the data further showed that the effect of DMSO on precursor charge state reduction and coalescence is not affected by the acidifier. Applications of the Conventional-Flow LC-MS System. The performance of the adapted system was examined in miscellaneous analyses of various samples (Supporting Information). Based on the results obtained in experiment #16, we purposely preferred the conventional-flow LC-MS to nanoLCMS for explorative analyses of cells (neonatal rat ventricular cardiomyocytes) and tissues (adult rat ventricular myocardium and adult rat skeletal muscle) potentially related to the H9c2 cell line. The resulting peptide library enabled us to readily design a targeted LC-MS assay for elaborately assessing H9c2 cells phenotype.40 Furthermore, we used physically the same conventional-flow chromatograph for both fractionating tryp-

We set out to examine the possibilities and limitations of a state-of-the art conventional-flow LC-MS platform for bottom-up exploratory proteomics. Our goal was to determine and show to practitioners how to adjust the conventional-flow LC-MS hardware and optimize the method in order to obtain protein identification as nearly comparable as possible to that obtained from a customary proteomic setup, and to do so without sacrificing a significant amount of sample. Although we have not exploited all options for improving LC-MS performance, we conclusively show that conventional-flow exploratory proteomic analyses are feasible. Providing the instrumentation and method are adjusted appropriately, the extra sample amount need not correspond to the theoretical 178-fold greater burden. Our results have significant implications for those bioanalytical laboratories that deliberate whether to conduct LC-MS analyses of proteins or peptides but abandon their efforts because they lack a costly nanoLC system and MS add-ons (the list price of the Nanospray Flex ion source alone, for instance, is approximately $20,000). Microbore-flow LCMS may be of special interest to proteomics practitioners who prefer robustness, sample throughput, and loadability to absolute sensitivity and whose needs have not been met by capillary LC-MS platforms recently put into practice. Indeed, throughout the present study, we greatly valued the unprecedented robustness of the conventional-flow LC-MS. From the time of its installation in experiment #05, the 0.003′′ spray needle did not have to be replaced, and from experiment #12 onward all separations were carried out using the identical 1 × 250 mm column without any signs of debased separation although even tryptic peptides from 5 µL of human amniotic fluid were injected. Similar robustness can be seen very rarely in nanoemitters and nanocolumns. In addition, conventionalflow LC methods were approximately 7 min shorter, thus increasing sample throughput. Lastly, we were impressed by the chromatographic performance in terms of peak widths that is scarcely attainable in nanoLC-MS. We are convinced that the potential for conventional-flow LC-MS analyses in proteomics will grow along with further developments in instrumentation sensitivity. It should be also noted, however, that if



a conventional-flow chromatograph is operated at flow rates below 100 µL/min, gradient separations using 1 mm columns are very sensitive to solvent leakage from the binary pumps. In our study, we clearly demonstrated that there is no vital need for nanoLC and nanospray to do exploratory proteomics, and the extra sample demands should not be a limiting factor for most conventional-flow proteomic analyses.

ASSOCIATED CONTENT Supporting Information Supporting Methods (chemicals and reagents, sample preparation details, specifics of microbore analyses with regard to remaining column efficiency, details on dwell volume and peak dispersion determination, details on protein identification and how the performance of the conventional-flow LC-MS system was assessed); Supporting Results and Discussion (results from the benchmark nanoLC-MS analyses, details on the thermal degradation test, information related to the column length and dimethyl sulfoxide in mobile phases); Applications of the conventional-flow LC-MS system (1D and 2D analyses, label-free quantification, isobaric quantification); Supporting Conclusion regarding nanoLC-MS efficiency; Supporting References; Tables S-1 and S-2 (list of all conventional-flow LC-MS analyses performed on narrow-bore and microbore columns); Table S-3 (list of peptides evaluated for assessing performance of the conventional-flow LC-MS system); Table S-4 (Plackett-Burman design of DoE); Table S-5 (cycle times of different DDA methods for label-free quantification). Eighteen Supporting figures (Figures S-1 to S-18).

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]. Fax: + 420 495 512 451 # These authors contributed equally to this work ⁑ Current address of the corresponding author. E-mail [email protected].

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS The LC-MS instrumentation was acquired using funds from MH CZ–DRO (UHHK, 00179906). Additional costs were covered from the same source as well as by the Internal Grant Agency of the Ministry of Health of the Czech Republic (project NT/13599). Juraj Lenčo has been supported by the STARSS project (Reg. No. CZ.02.1.01/0.0/0.0/15_003/0000465), co-funded by ERDF since September 2017. We wish to thank Eugene Kapp from the Walter and Eliza Hall Institute of Medical Research (Melbourne, Australia) for providing the APLtoMGF converter. The authors are grateful to Ian McColl and František Švec for assistance with the manuscript. The final manuscript was edited by G. A. Kirking, Editorin-Chief at English Editorial Services, s.r.o. (Czech Republic).

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