Performance Evaluation of the Q Exactive HF-X for ... - ACS Publications

Subscriber access provided by READING UNIV

Technical Note

Performance evaluation of the Q Exactive HF-X for shotgun proteomics Christian D Kelstrup, Dorte B. Bekker-Jensen, Tabiwang N. Arrey, Alexander Hogrebe, Alexander Harder, and Jesper V. Olsen J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00602 • Publication Date (Web): 29 Nov 2017 Downloaded from http://pubs.acs.org on November 29, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 38

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

Journal of Proteome Research

Performance evaluation of the Q Exactive HF-X for shotgun proteomics Christian D. Kelstrup1, Dorte B. Bekker-Jensen1, Tabiwang N. Arrey2, Alexander Hogrebe1, Alexander Harder2, Jesper V. Olsen*,1 1)

The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen,

DENMARK 2)

Thermo Fisher Scientific, Bremen, GERMANY

KEYWORDS Bottom-up Shotgun proteomics Q Exactive HF-X DIA DDA phosphoproteomics TMT

ABSTRACT

Progress in proteomics is mainly driven by advances in mass spectrometric (MS) technologies. Here we benchmarked the performance of the latest MS instrument in the benchtop Orbitrap series, the Q Exactive HF-X, against its predecessor for proteomics applications. A new peak picking algorithm, a brighter ion source and optimized ion transfers enable productive MS/MS acquisition above 40 Hz at 7500 resolution. The hardware and software improvements collectively resulted in improved peptide and protein identifications across all comparable conditions, with an increase of up to fifty percent at short LC-MS gradients, yielding

ACS Paragon Plus Environment

1


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

Page 2 of 38

identification rates of more than one thousand unique peptides per minute. Alternatively, the Q Exactive HF-X is capable of achieving the same proteome coverage as its predecessor in approximately half the gradient time or at 10-fold lower sample loads. The Q Exactive HF-X also enables rapid phosphoproteomics with routine analysis of more than five thousand phosphopeptides with short single-shot 15-minute LC-MS/MS measurements, or 16,700 phosphopeptides quantified across ten conditions in six gradient hours using TMT10-plex and offline peptide fractionation. Finally, exciting perspectives for data independent acquisition are highlighted with reproducible identification of 55,000 unique peptides covering 5900 proteins in half-an-hour of MS analysis.

INTRODUCTION Mass spectrometry (MS)-based proteomics is the technology of choice for large-scale identification and quantification of proteins, their interactions, and post-translational modifications (PTMs).1 Over the last decades, proteomics has made a huge impact in addressing questions in cell biology and biomedicine and is slowly making its way into the clinic.2,3 The growth and progress in the field of proteomics have been fueled by improvements in biochemistry, bioinformatics, chromatography, and particularly by developments that dramatically increase the accuracy, speed, and sensitivity of the MS instrumentation. Different types of MS analyzers are employed in shotgun proteomics, with the high-resolution Orbitrap tandem mass spectrometers and especially the Q Exactive (QE) family being one of the most popular instruments, as evidenced from the overview of submitted data to proteomeXchange.4 The Q Exactive instrument series consist of benchtop quadrupole-Orbitrap mass spectrometers that incorporate an electrospray ion source inlet with a series of ion guides, a selection quadrupole, a combined Ctrap/octopole collision cell and an Orbitrap mass analyzer. Peptide sequencing in this type of instrument is performed by tandem mass spectrometry using higher-energy collisional dissociation (HCD).5 HCD


2

Page 3 of 38

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


enables rapid and efficient fragmentation of the peptide backbone with no low mass cutoff and generally allows site localization of many post-translational modifications with single-residue resolution.6 One of the most frequently used approaches for large-scale protein analysis is the shotgun proteomics workflow. This workflow is based on proteolytic digestion of complex protein mixtures and analysis of the resulting peptide mixtures by nano-scale liquid chromatography (LC) coupled to a high-resolution tandem mass spectrometer (MS/MS). A key feature of this approach is ‘shotgun’ sequencing, where the MS instrument control software is operated in the so-called data-dependent acquisition (DDA) mode. In this mode the instrument automatically selects the ‘top-N’ most abundant precursor ions from a full MS spectrum for subsequent MS/MS fragment analysis, where N is usually a value around 10. Redundant resequencing of the same peptide precursor ions is avoided through the use of an exclusion system where selected precursors are excluded from being selected again within a fixed time window that typically matches the LC chromatographic elution profiles.7 Precursors are therefore only fragmented once and the single precursor spectrum therefore needs to be as optimal as possible to maximize the chance of identification. HCD fragmentation is a collision-induced dissociation (CID) method, also known as beam-type collisional-activated dissociation (CAD). The optimal kinetic energy supplied for conversion of precursor to fragment ions is strongly influenced by the amino acid sequence, charge-state and length of the peptide under investigation.8 The rule of thumb calculation of the ideal collision energy for peptide fragmentation shows a linear increase with the mass-to-charge of the peptide precursor in a charge-state dependent manner.9,10 Therefore, the precursor charge state is used by the DDA method to set the optimal collision energy for a given precursor m/z; peaks without charge state assignment are commonly excluded from the MS/MS candidate list. Too few precursors with determined charge states consequently result in a shorter MS/MS candidate list, and classical DDA top-N methods on fast acquisition instruments may therefore not reach the full top-N number of sequencing events per full MS cycle, as reported previously.11–13 Despite not saturating a full top-N method, we and others have previously demonstrated that the Q


3


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

Page 4 of 38

Exactive HF mass spectrometer can achieve peptide sequencing speeds above 20 Hz, and it routinely exceeds 10 peptide spectrum matches per second.12,13 This enables identification of ~4400 HeLa proteins in just one hour of LC-MS/MS gradient time.12 Moreover, we have shown that the Q Exactive HF can, in combination with extensive offline high-pH reversed-phase peptide fractionation, successfully cover essentially all proteins expressed in HeLa cells.14 Here we present a performance evaluation of a modified Q Exactive HF instrument available commercially under the Q Exactive HF-X brand. The modifications include a new software algorithm designed for better peak isotope detection and a brighter ion source designed for enhanced sensitivity, together with measures to minimize ion-transfer times designed for faster HCD acquisition speeds. We evaluate these improvements against the commercially-available Q Exactive HF instrument in the context of shotgun proteomics of human samples. Initial core optimization of instrument acquisition parameters is based on detailed measurements of acquisition speed for rational DDA method design with optimal parallel acquisition as previously published.15 Benchmarking of the Q Exactive HF-X is performed primarily by analyzing tryptic digests of HeLa whole cell lysates. The comparison to the Q Exactive HF is done by systematically evaluating the impact of sample amounts, gradient lengths and acquisition methods on peptide identifications. The improved performance of the Q Exactive HF-X is also evaluated in the context of pre-fractionated HeLa digests as well as phosphoproteomics with tandem isobaric mass tag (TMT) 10-plex based quantification. Finally, promising perspectives for data-independent acquisition (DIA) analysis on the Q Exactive HF-X instrument are presented.

MATERIALS AND METHODS

Instrument Modifications The instrument is based on the previous generation Q Exactive HF instrument design12,13 and while the overall layout does not differ, several modifications have been introduced to further optimize performance. The S-Lens was replaced with a brighter ion source, consisting of a High Capacity Transfer


4

Page 5 of 38

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


Tube (HCTT) and an Electrodynamic Ion Funnel, for improved ion transmission. The bent flatapole was modified to minimize solvent clusters and maintain correct operational pressure. To accommodate these changes, higher capacity fore-vacuum systems were used. Furthermore, the chronological interplay of ion optics was improved, which significantly reduced cycle time, especially for MS/MS acquisition. In addition to these changes, an improved peak detection algorithm, named Advanced Precursor Determination (APD), was incorporated into the Q Exactive HF-X. Compared to the previous algorithm, APD annotates overlapping isotopic envelopes more efficiently and improves pattern matching filters for charge state and monoisotopic m/z assignment and correlate assignments across the entire charge envelope (i.e. charge state deconvolution), in real-time. Furthermore, two new resolution options (7,500 and 45,000 resolution at m/z 200, respectively corresponding to transient lengths of 16 and 96 ms) have been implemented. Finally, a new feature enables real-time dynamic adjustment of retention time windows for PRM analysis (dRT-PRM).

Cells Human epithelial cervix carcinoma HeLa cells, human embryonic kidney HEK293 cells, lung adenocarcinoma A549 cells, human colon cancer HTC116 cells and human breast cancer MCF-7 cells were cultured in DMEM (Gibco, Invitrogen), supplemented with 10% fetal bovine serum, 100U/mL penicillin (Invitrogen), 100µg/mL streptomycin (Invitrogen), at 37°C, in a humidified incubator with 5% CO2. Human neuroblastoma SH-SY5Y cells were cultured in RPMI (Gibco, Invitrogen) with the same supplements as listed above.

Lysis and Digestion Cells were harvested at ∼80% confluence by washing twice with PBS (Gibco, Life technologies) and subsequently adding boiling lysis buffer16 (6 M guanidinium hydrochloride (GndCl), 5 mM tris(2carboxyethyl)phosphine, 10 mM chloroacetamide, 100 mM Tris, pH 8.5) directly to the plate. The cell lysate was collected by scraping the plate and boiled for additional 10 minutes followed by micro tip probe sonication (Vibra-Cell VCX130, Sonics, Newtown, CT, USA) for 2 minutes with pulses of 1


5


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

Page 6 of 38

second on and 1 second off at 50% amplitude. Protein concentration was estimated by Bradford assay, and the lysate was digested with LysC (Wako) in an enzyme/protein ratio of 1:100 (w/w) for 1 h followed by dilution with 25 mM Tris, pH 8.5, to 2 M GndCl and further digested overnight with a protease that cleaves C-terminal to arginine and lysine 1:100 (w/w).17,18 Protease activity was quenched by acidification with trifluoroacetic acid (TFA) to a final concentration of approximately 1%, and the resulting peptide mixture was concentrated on Sep-Pak (C18 Classic Cartridge, Waters, Milford, MA, USA). Elution was done with 2 mL 40% acetonitrile (ACN), followed by 4 mL 60% ACN. The combined eluate was reduced by SpeedVac (Eppendorf, Germany) and the final peptide concentration was estimated by measuring absorbance at 280 nm on a NanoDrop spectrophotometer (NanoDrop 2000C, Thermo

Fisher Scientific, Germany). For DIA samples, iRT peptides (Biognosys AB, Schlieren, Switzerland) were added prior to MS analysis according to the manufacturer's protocol.

Phosphopeptide Enrichment For phosphoproteomics experiments, cells were lysed as described above. After digestion, sample volume was doubled by addition of 80% ACN in 12% TFA and subsequently enriched with TiO2 beads (5 µm, GL Sciences Inc., Tokyo, Japan) as previously described but with slight modifications.19,20 Briefly, the beads were suspended in 20 mg/mL 2,5-dihydroxybenzoic acid (DHB), 80% ACN, and 6% TFA. The sample was incubated with the beads in a sample to bead ratio of 1:2 (w/w) in batch mode for 15 minutes with rotation. The beads were washed and collected on C8 STAGE-tips21 with first a 10% ACN and 6% TFA wash followed by a 40% ACN and 6% TFA wash and finally by 80% ACN and 6% TFA. Elution of phosphopeptides was accomplished with 5% NH3 followed by 10% NH3 in 25% ACN, which were finally evaporated in a SpeedVac. The enriched phosphopeptides were fractionated with high-pH reversed phase fractionation.

High-pH Fractionation Peptides were fractionated using a reversed-phase Acquity CSH C18 1.7 µm 1 × 150 mm column (Waters, Milford, MA, USA) on an UltiMate 3000 high-pressure liquid chromatography (HPLC) system


6

Page 7 of 38

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


(Dionex, Sunnyvale, CA, USA) operating at 30 µL/minute. Buffer A (5 mM ammonium bicarbonate) and buffer B (100% ACN) were used. Peptides were separated by a linear gradient from 5% B to 35% B in 55 minutes followed by a linear increase to 70% B in 8 minutes. For deep proteomes, 46 fractions were collected without concatenation, whereas for deep phosphoproteomes 12 fractions were collected in a concatenated manner.22,23 For nanoflow LC–MS/MS, the loading amount was kept constant at 1 µg per injection, estimated by measuring absorbance at 280 nm on a NanoDrop spectrophotometer.

TMT Labeling For the TMT10-plex labeling experiments, whole cell lysates of HeLa cervix carcinoma cells, SHSY5Y neuroblastoma cells, HEK293 human embryonic kidney cells, MCF7 breast cancer cells, HCT-116 colon cancer cells were prepared as described above. Biological replica of each cell line were lysed and digested and 200 µg peptide from each cell population was labeled with one of the ten different TMT10reagents according to the manufacturer’s protocol.

Nanoflow LC–MS/MS The peptide solution was adjusted in volume to an appropriate concentration and kept in loading buffer (5% ACN and 0.1% TFA) prior to autosampling. For the 46 fraction and single shot label free phosphoproteomes experiments, an in-house packed 15 cm, 75 µm ID capillary column with 1.9 µm Reprosil-Pur C18 beads (Dr. Maisch, Ammerbuch, Germany) was used while all other samples were analyzed using commercial Acclaim PepMap RSLC C18, 2 µm, 100 Å, 75 µm i.d. × 25 cm, nanoViper EASY-Spray columns (Thermo Fisher Scientific, Bellefonte, PA, USA). The LCs used were EASY-nLC 1200 systems (Thermo Fisher Scientific, San Jose, CA, USA). The column temperature was maintained at 45 °C using either the EASY-Spray oven or an integrated column oven (PRSO-V1, Sonation GmbH, Biberach, Germany) and interfaced online with the mass spectrometer. Formic acid (FA) 0.1% was used to buffer the pH in the two running buffers used. If the total gradient time is denoted x, the gradients went from 8% to 24% acetonitrile (ACN) in 0.83*x minutes followed by 24% to 36% in 0.17*x minutes. For all gradients, this was followed by a washout by a ½ minute increase to 64% ACN which was kept for 4½


7


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

Page 8 of 38

minutes. Flow rate was kept at 350 nL/minute. Re-equilibration was done in parallel with sample pickup and prior to loading with a minimum requirement of 1 µL 0.1% FA buffer at a pressure of 800 bar. The Q Exactive HF and Q Exactive HF-X instruments (Thermo Fisher Scientific, Bremen, Germany) were freshly cleaned and calibrated using Tune (version 2.9) instrument control software. Spray voltage was set to 2 kV, funnel RF level at 40, and heated capillary at 275 °C. Except where otherwise noted, instruments were configured for DDA using the Full MS/DD-MS/MS setup. Full MS resolutions were set to 60,000 at m/z 200 and full MS AGC target was 3E6 with an IT of 45 ms. Mass range was set to 3501400. AGC target value for fragment spectra was set at 1E5, and intensity threshold was kept at 2E5. Isolation width was set at 1.3 m/z except for TMT experiments which were set at 0.8 m/z. A fixed first mass of 100 m/z was used. Normalized collision energy was set at 28%. Peptide match was set to off, and isotope exclusion was on. All data were acquired in profile mode using positive polarity. Further details of the respective MS methods are explained in the figures and tables.

Raw Data Processing and Analysis All raw files that used DDA LC-MS/MS were analyzed by MaxQuant v1.5.8.3 software using the integrated Andromeda Search engine and searched against the human UniProt Reference Proteome without isoforms (April 2017 release with 21042 protein sequences).24 Four analysis groups were made in MaxQuant, enabling one combined analysis of all proteome, phosphoproteome, and TMT data. TMT correction factors were edited under the TMT labels. Trypsin was specified as the enzyme, cleaving after all lysine and arginine residues and allowing up to two missed cleavages. Carbamidomethylation of cysteine was specified as fixed modification and protein N-terminal acetylation, oxidation of methionine, and pyro-glutamate formation from glutamine were considered variable modifications for all groups. All TMT data were set to the first parameter group that further specified TMT10-plex as label with a reporter ion mass accuracy of 0.002 Da and phosphorylation as an additional variable modification of serine, threonine, and tyrosine residues with a maximum total of 4 variable modifications per peptide. All


8

Page 9 of 38

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


fractionated proteomes were set to the second parameter group that further considered the variable modifications deamidation of asparagine and glutamine together with possible phosphorylation of serine, threonine, and tyrosine residues with a maximum total of 2 variable modifications per peptide. All single shot phosphoproteome experiments were specified as the third parameter group where the variable modifications were the same as under TMT but with no label. All single shot proteome data were searched with the fourth parameter group that further considered the variable modifications deamidation of asparagine and glutamine with a maximum total of 1 variable modification per peptide. An experimental design was used where each raw file was considered an independent experiment, except for fractionated samples, where one experiment was used for each fractionation and fractions were specified instead. Raw files from the Q Exactive HF-X were specified in the experimental design as “Experiment A” and Q Exactive HF files as “Experiment B”. The other MaxQuant settings were that the “match between run” feature was disabled, the “maximum peptide mass” was set at 7500 Da, the “modified peptide minimum score” set to 25 and “minimum delta score” to 3 and everything else was set to the default values, including the false discovery rate limit of 1% on both the peptide and protein levels. Phosphorylation sites were considered localized at a site localization probability above 75%. DIA data were analyzed in Spectronaut v11.0.15038.4.29119 software (Biognosys AB, Schlieren, Switzerland) with a spectral library imported from a separate MaxQuant analysis of the two 12 fraction experiments and with settings as described above for fractionated samples. DIA files were analyzed using default settings with three files specified as three replicate experiments. Subsequent analysis of data was performed in R25 with the assistance of scripting in Perl for filtering of the very large result files. For all box-plots the whiskers were set to the default value meaning extension to one and a half times the interquartile range. Meta-data was extracted from the raw files using the XQLconsole and Raxport (https://code.google.com/archive/p/raxport/) command line tools. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium4 via the PRIDE partner repository with the data set identifier PXD006932. Reviewer account details:


9


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

Page 10 of 38

Username: [email protected] Password: MhQci68d

RESULT AND DISCUSSION The modifications introduced with the Q Exactive HF-X are a brighter ion source with a HCTT capillary and an electrodynamic ion funnel, optimized scan matrix for improved acquisition speed, APD algorithms to increase the number of available precursors during DDA experiments, and new available software options (Figure 1A). In the following sections, we describe testing and evaluation of the impact of these changes on the instrument performance for proteomics and phosphoproteomics.


10

Page 11 of 38


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 ACS Paragon Plus Environment

11


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

Page 12 of 38

Optimized Ion Movements The first instrument modification we evaluated was the optimized ion movement, in particular between the C-trap and HCD cell to enable faster HCD fragmentation. To this end, we set out to measure the optimal parameters for the parallel mode of operation. This mode refers to instrument conditions at which accumulation and fragmentation of one precursor can take place while ions generated by fragmentation of another precursor are measured inside the Orbitrap mass analyzer. From a user standpoint, two parameters can be controlled: the ‘maximum ion injection time’ (IT) parameter, which controls the duration of ion accumulation, and the ‘resolution’ parameter, which determines the length of the transient read-out time from the Orbitrap mass analyzer. All other parameters were kept constant. Ion movements can be mapped by measuring the spectrum to spectrum acquisition time as a function of IT and transient time as previously described.15 Performing these timing measurements on the Q Exactive HF-X reveals that delays between Orbitrap transients are reduced by ~3 ms relative to the Q

Exactive HF, down to 3-4 ms from 6-7 ms (Figure 1B). Looking at full MS spectra, this is found to uniformly accelerate acquisition independently of IT (Figure S-1). MS/MS HCD acquisition show a larger improvement where IT can be up to 15 ms longer while overall cycle times are maintained

constant indicating the majority of the ion movement optimization relates to transfers in and out of the HCD cell (Figure 1B). Notably, the speed improvement can be larger as the Q Exactive HF slows slightly down for larger precursor masses, whereas the Q Exactive HF-X does not show this behavior (Figure S-1). These faster ion transfers on the Q Exactive HF-X made it feasible to include a very short 16 ms Orbitrap transient option with a slightly lowered mass precision (Figure S-2). This transient length is available in the software as a resolution option of 7500 at m/z 200. Methods for DDA on the Q Exactive HF-X were designed by choosing resolution and IT settings that were optimal for maximum parallel mode of operation utilization of the instrument except for the fastest acquisition speed, where a slight increase in IT was valued over perfect parallelization (Figure 1B and Table 1). These acquisition


12

Page 13 of 38

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


methods formed the basis for the subsequent evaluation and as parallel mode of operation was kept for both instruments, the methods vary in acquisition speed. MS/MS Method name

Instrument

Resolution

IT

Example topN

Example time

cycle

acquisition time

Maximum speed

HF(23Hz)

Q Exactive HF

15,000

15

10

~ 0.6 s

~ 43 ms

~ 23 Hz

HF(14Hz)

Q Exactive HF

30,000

45

6

~ 0.6 s

~ 73 ms

~ 14 Hz

HF(7Hz)

Q Exactive HF

60,000

110

7

~ 1.2 s

~ 138 ms

~ 7.2 Hz

HF-X(41Hz)

Q Exactive HFX

7,500

11

18

~ 0.6 s

~ 24 ms

~ 41 Hz

HF-X(28Hz)

Q Exactive HFX

15,000

22

12

~ 0.6 s

~ 35 ms

~ 28 Hz

HF-X(15Hz)

Q Exactive HFX

30,000

54

6

~ 0.6 s

~ 67 ms

~ 15 Hz

HF-X(10Hz)

Q Exactive HFX

45,000

86

10

~ 1.2 s

~ 99 ms

~ 10 Hz

HF-X(8Hz)

Q Exactive HFX

60,000

118

7

~ 1.2 s

~ 131 ms

~ 7.6 Hz

New Peak Detection Algorithm The second instrument change we evaluated was the new APD algorithm for peak detection. To assess the improvements in the algorithm we quantified the number of peaks with and without charge state assignment across all acquired LC-MS/MS raw files. We found that approximately 75% of all peaks in full MS spectra from the Q Exactive HF-X were reproducibly assigned with charge state information (Figure 1C). This fraction of annotated peaks is more than twice as high as for the Q Exactive HF, which on average only assigns charge state to 35% of peaks at full MS level. A similar trend was also observed at the MS/MS level, although with a lesser difference (Figure 1C). This significantly improved algorithm


13


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

Page 14 of 38

for charge state assignment was found to saturate higher top-N methods (Figure S-3). However, a new algorithm is not better if it simply increases the number of annotated precursor charges, as it also needs to annotate peaks with the correct charge state. To investigate if this is the case, we took advantage of the MaxQuant software suite which - like many other raw MS data post-acquisition processing tools disregards any prior information about assigned charge states and relies on its own, independent peak and charge state detection algorithms. The overlap of precursor charge annotations with the same charge state assignment between MaxQuant and the MS instrument control software was very high, with close to 99% agreement, and showed little difference between the older instrument peak detection algorithm and the new APD (Figure 1D). The slight increase in variability of charge agreement seems to be explained by other differences between instruments, such as acquisition speed. In summary, the new APD algorithm is found to significantly increase the number of assigned precursor state charges with a low error rate that agrees well with other algorithms.

Redesigned Ion Source Interface The third instrument upgrade we evaluated was the brighter ion source, consisting of a new HCTT capillary and an electrodynamic ion funnel design, which provides a higher ion flux compared to the Slens interface on the Q Exactive HF. The brighter ion source design is very similar to that found on the Quantiva triple quad MS and the Orbitrap Fusion Lumos (Thermo Fisher Scientific, San Jose, CA). Previously, this new ion funnel design has been utilized for improving the ion flux in high flow LCMS/MS systems that are arguably less interesting for bottom-up proteomics experiments, which are usually performed in a low flow LC-MS/MS regime. Here, we have focused the evaluation of the brighter ion source in the context of nano-flow. Comparing the overall LC-MS/MS performance across instruments is challenging, as multiple parameters influence the final results. Here, to minimize possible technical biases we performed a set of controlled comparisons between MS instruments, where the LC instrument and LC column were different between different types of experiments, but constant within, i.e. the LC system and LC column were


14

Page 15 of 38

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


physically moved between MS instruments to keep conditions within an instrument comparison constant. Furthermore, comparisons were performed in alternate sequence with regard to which MS instrument was used first to avoid any bias in run order. This setup was used to evaluate the brighter source by pairwise comparisons of intensity values of all peptide spectrum matches (PSMs) from a DDA analysis of either high or low sample loads as well as variable LC gradient lengths ranging from 7.5 minute to 1 hour with peak capacity as shown (Figure 1E and Figure S-4). To keep the focus on the MS instruments and simplify interpretation, differences in LC overheads are largely excluded from the interpretation as this can be considered a separate optimization challenge. We also looked at other differences between the Q Exactive HF and HF-X instruments, particularly the quantitative performance or biases in peptide mass populations (Figure S-4) but without finding any meaningful differences. The optimal isolation widths were also found to be similar to what has been previously reported (Figure S-5).13 Under all conditions, we observed an increase in ion flux for the Q Exactive HF-X compared to the Q Exactive HF at a fixed LC flow-rate of 350 nL/minute during peptide elution. Aggregating all runs, we evaluated the intensities of the brighter ion source setup on the Q Exactive HF-X to be on average 70% higher compared to the traditional S-lens based setup on the Q Exactive HF, but with large variations (Figure 1F). There was a slight trend for the relative gain to be highest on the shortest gradients or with the highest load.


15


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

Page 16 of 38

Single Shot Proteomes To facilitate an overview of the differences of the designed DDA methods across the two instruments, a comparison is performed based on theoretical improvements in signal-to-noise (S/N) or sensitivity as a function of acquisition speed. This is done under the expectation and premise that IT is proportional to S/N and resolution scales with the square root of S/N.12 Visualizing the improvement for the new set of


16

Page 17 of 38

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


optimized acquisition methods, including the 70% improvement in precursor intensities, the Q Exactive HF-X show a significant simultaneous improvement in acquisition speed and sensitivity (Figure 2A). For example, where the Q Exactive HF can achieve 23Hz HCD acquisition with an IT of 15 ms using an Orbitrap resolution setting of 15,000, the Q Exactive HF-X instrument is capable of achieving 28Hz HCD acquisition at the same Orbitrap resolution setting, however, with a longer IT of 22 ms. These methods have been named HF(23Hz) and HF-X(28Hz) and the latter corresponds to an increased acquisition speed of 23% or five additional HCD spectra per second together with more ions measured per spectrum. Alternatively, a lower Orbitrap resolution of 7,500 enables 79% faster acquisition speed at 41Hz with an IT of 11ms, here termed HF-X(41Hz). However, it should be noted that fast acquisition at low resolution generally results in a drop in S/N. In the case of the 16ms transient (7500 resolution setting) the S/N dropped by approximately 15% (Figure 2A). This overview analysis (Figure 2A) also highlights that methods relying on higher IT, and higher resolutions, as parallel acquisition is desired, show lower relative gains for the new instrument compared to faster acquisition methods. We have previously shown12 that these fast acquisition methods are the best choice when analyzing abundant samples with short gradients and we therefore hypothesized these were the conditions where the new instrument would also show the largest relative improvements. This is confirmed experimentally as the fastest acquisition method, HF-X(41Hz), only provides the highest number of identifications on very short gradients (Figure 2B). The largest performance gain was observed with very short LC gradients, where the HF-X(41Hz) method enabled identification of above 1500 PSMs per minute or more than 1200 unique peptide sequences per gradient minute (Figure 2C). This extreme case corresponds to a 50% improvement in peptide identifications compared to a Q Exactive HF instrument. As expected from our hypothesis, the performance gain was found to be lower for longer gradients but was still around 20% for the one hour gradient. An alternative interpretation of these data was the possibility to keep the number of identifications constant while shortening the gradient. As an example, the one hour LC-MS/MS gradient on a Q Exactive HF can identify approximately 25,000


17


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

Page 18 of 38

unique peptide sequences and this can be achieved in approximately half the time on the Q Exactive HFX (Figure 2B and Figure 2D). The HF-X(28Hz) acquisition method would seem to have the highest relative gain in both sensitivity and speed on the new instrument (Figure 2A). This is shown in a sample dilution comparison analyzed on both instruments using 30 minute gradients (Figure 2E). Close to the same proteome depth is achieved when analyzing 100 ng tryptic HeLa digest on the Q Exactive HF-X as 1 µg on a Q Exactive HF using the HF(23Hz) that also uses 15,000 Orbitrap resolution. This demonstrates that the Q Exactive HF-X provides significant gains in sensitivity and that these gains can be used to lower the peptide amount while keeping the covered proteome depth constant. However, it is important to note that the fastest acquisition HF-X(41Hz) method results in the lowest number of identifications at the lower peptide loads, highlighting the need for high ion flux for this method to perform successfully.


18

Page 19 of 38

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


Comprehensive Proteomes While single shot analysis is preferable from ease of use and reproducibility perspectives, it has so far not been possible to get comprehensive coverage of mammalian proteomes without at least two dimensional fractionation. We therefore evaluated the Q Exactive HF-X performance in the context of two-dimensional peptide fractionation for in-depth proteome analysis as outlined (Figure 3A). This strategy is similar to our previously published workflows employing offline fractionation by high pH reversed-phase C18-based chromatography combined with fast LC-MS/MS gradients.14 The 11.5h of LCMS/MS gradient time with the HF-X(41Hz) method covers ~141,000 unique peptides, slightly more than the HF-X(28Hz) method which covers ~131,000 unique peptides (Figure 3B). This shows that even for


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

Page 20 of 38

fractionated samples, the HF-X(41Hz) method can outperform slower methods when the gradient time is kept short. Obviously, a challenge with short gradients is the total time is much higher than the gradient time, as LC overheads are significant. This high proteome coverage is achieved by the high separation power and complementarity of the offline high pH and online low pH reversed-phase chromatography, which results in roughly 5000 unique peptides identified in every single fraction with the exception of the first few and last few fractions (Figure 3C). These fast acquisition methods, together with extensive offline fractionation, seem like a very promising approach to generate comprehensive proteomes in a relatively short amount of time but also highlight the need to reduce LC overheads in the future.

Single Shot Phosphoproteomes An additional challenge for shotgun proteomics can be found in the area of phosphoproteomics, the global analysis of phosphopeptides enriched from protein digests. In phosphoproteomics, importance is

placed not only on identifying as many phosphopeptides as possible, but also on providing confident site


20

Page 21 of 38

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


localization with single amino acid resolution, which often rely on a few site determining fragment ions. This challenge has previously been shown to shift the optimum towards slower acquisition methods for phosphoproteomics.12 Here, we hoped to take advantage of faster acquisition methods, and investigated which of the methods for the new Q Exactive HF-X provided the highest number of identifications and site localizations in the context of very fast single-shot LC-MS/MS analysis of the HeLa phosphoproteome. We enriched 200 µg of HeLa digest and compared the four optimized resolution/IT settings on a fast 15 minute gradient (Figure 4A). This showed a potential to use the HF-X(28Hz) method as it achieves the highest number of PSMs, close to 6000 unique phosphopeptides and almost 4000 localized sites (Figure 4B). This phosphoproteome is similar in depth compared to another recent single shot approach that start with 200 µg of HeLa digest but here acquired much faster.26

The HF-X(28Hz) is still a slower method than the HF-X(41HZ) method that was found best for unfractionated proteomes on this 15 minute gradient (Figure 2B). The HF-X(28Hz) method results in identification of more than 300 phosphopeptides per minute, which is approximately 50% more than the HF-X(10Hz) (Figure 4C). Although the slower acquisition methods generate less phosphopeptide identifications per unit time on these short gradients, they do still give higher spectral quality indicated by their Andromeda peptide score distributions (Figure 4D).


21


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

Page 22 of 38

TMT and Deep Phosphoproteomes Coinciding with the release of the Q Exactive HF-X is the availability of a new transient length of 96ms, which corresponds to a resolution of 45,000 at m/z 200. This is particularly well-suited for quantitative proteomics experiments using 10-plex isobaric tandem mass tags, where a mass difference of 6.32 mDa needs to be resolved around m/z 130 in MS/MS mode (Figure 5A). Given the resolution settings for Orbitrap mass analyzers are provided at full-width half maximum (FWHM) and that two times FWHM is close to baseline separation, a simple calculation of a theoretical resolution requirement for TMT10-plex reporter ions should be 2*130/0.00632 ≅ 41,000 @ m/z 130. This equals a minimum resolution requirement of ~33,000 @ m/z 200 and because the 30,000 resolution setting may be seen as borderline sufficient, TMT10-plex samples have traditionally been analyzed using an HCD method with 60,000 resolution. But now it seems more sensible to use the new 45,000 setting. This is very promising for phosphoproteomics which, as discussed above, often requires higher quality spectra with higher IT and resolution to provide site-localization information. An initial explorative


22

Page 23 of 38

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


comparison was done by comparing the methods HF(7Hz) and HF-X(8Hz) with 60,000 resolution and the HF-X(10Hz) method with 45,000 resolution on a phosphopeptide enriched sample with one hour gradient elution (Figure S-6). This analysis shows that the HF-X(8Hz) compared to the HF(7Hz) using the 60,000 resolution is giving 5% more identifications which is in line with the speed improvement shown in Figure 2A. However, a larger potential for increasing the number of phosphopeptide identifications on the same one hour gradient length can be achieved by increasing the acquisition speed at the 45,000 resolution while keeping the score distributions high and similar between the three (Figure S-6). The next question was if the 45,000 resolution option would also give the same quantitative readout as the 60,000 option. In order to evaluate this, the quantitative performance in a TMT10-plex compared the basal phosphoproteome of five different human cancer cell lines in biological duplicates with readout on the two instruments with two different resolutions (Figure 5B). The pooled TMT sample was offline fractionated by high pH reversed-phase chromatography and concatenated into twelve fractions, which were analyzed individually with 30 minute LC-MS/MS gradients using either the HF(7Hz) or HFX(10Hz) methods. In line with the results from the single shot analysis, the HF-X(10Hz) method produced 23% more localized phosphosites than the HF(7Hz) method (Figure S-6). The abundance distribution profiles from the two phosphoproteomes show that the HF-X(10Hz) method allows a deeper sampling of lower abundant phosphopeptides (Figure 5D). Importantly, the two methods gave very similar quantitative readouts highlighted by a principal component analysis of the reporter ion ratios, which shows that biological differences group the channels and that differences in methods are similar to differences in replica within the TMT10-plex (Figure 5E). This is also highlighted by a comparison of coefficient of variation values (Figure S-6) and an unsupervised hierarchical clustering and heatmap visualization where the largest difference is between the different cell types (Figure 5F). We believe this seems to be an attractive approach for precise phosphoproteomics with the limitation that ratio accuracy is not perfect due to interference.27


23


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

Page 24 of 38

PRM and DIA Perspectives While focus to this point has been on benchmarking the Q Exactive HF-X for shotgun proteomics using classical DDA methods, the optimized settings for parallel acquisition can also be applied to targeted parallel reaction monitoring (PRM) or DIA workflows. We therefore speculated that the significant gains in speed and sensitivity between the Q Exactive HF-X and the Q Exactive HF would also extend to DIA and PRM measurements. Targeted PRM methods are known to be the most sensitive way to operate a


24

Page 25 of 38

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


mass spectrometer but classical tradeoffs exist between the number of targets that can be analyzed simultaneously and how much sensitivity can be achieved in each spectrum. As the relative performance gain of the Q Exactive HF-X as shown above is modest for slower acquisition methods, the largest improvements for targeted methods may be the inclusion of a new feature that enables real time dynamic adjustment of retention times for PRM analysis (dRT-PRM), which should significantly improve timescheduled PRM methods. In contrast, DIA is a completely different way of operating the MS instrument, where it quickly scans through the full mass range and generates a fragmentation map for many broader quadrupole isolation windows. A compromise between the mass range and number of MS/MS spectra and isolation widths is usually made such that a full MS cycle is performed within 2-3 seconds to fit the chromatographic performance of the LC system. As a consequence of this, DIA is usually performed with fast acquisition instruments and therefore the new Q Exactive HF-X may be very well suited for DIA. To test this, we devised a simple DIA method covering the precursor mass range, which covers the majority of tryptic HeLa peptides (m/z 386-1016) within 2.6 seconds by one full MS and 70x MS/MS HCD spectra with 9 m/z isolation widths using the HF-X(28Hz) method (Figure 6A). We performed triplicate technical replicates of 1 µg tryptic HeLa digest spiked with iRT peptides28 using a 30 minutes LC-MS/MS gradient and compared directly to the best and most optimized DDA method using the same gradient. To generate a sample and experiment specific MS/MS HCD spectral library, we analyzed 12 concatenated fractions from an offline high pH reversed-phase fractionated HeLa digest analyzed on the same gradient with DDA measured with HF(23Hz) and HF-X(28Hz) with a combined depth of approximately 118,000 unique peptides and 8300 protein groups. The resulting peptide library was used as the foundation to identify peptides and proteins in the DIA runs using the Spectronaut software with one percent falsediscovery rate at both peptide and protein level. To benchmark the performance of the Q Exactive HF-X in DIA mode we compared the results directly to the best-performing 30 minute single-shot DDA replica analyzed by MaxQuant. As shown previously, the DDA runs using the HF-X(41Hz) method generated 30,000 PSMs with a modest overlap of 75%


25


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

Page 26 of 38

between replica (Figure 6B). On average, 3850 protein groups were identified in each experiment of which 91% overlapped between pairwise replica. For the comparable DIA runs, 70,000 precursors were matched by Spectronaut in each replica with 88% overlap between pairwise replica runs (Figure 6C). This amassed to 5900 protein groups identified in each replica with a very high pairwise overlap of 99% between replica. The squared Pearson correlation coefficients were very high and similar between replica DDA (R2=0.96) and DIA (R2=0.97), but the quantified precursors seemed to span an extra order of magnitude in the DIA data (Figure 6D+E). Importantly, both software tools, MaxQuant and Spectronaut, claim to achieve 1% FDR level on both the peptide and protein level although this has not been independently validated. The overall data quality of the DIA analysis appear to be similar compared to DDA based on the overall high number of peptides per protein (Figure 6F) and the additional proteins identified show the expected abundance bias (Figure 6G). Given that these results were achieved with very little optimization, DIA on the Q Exactive HF-X seems very promising as it routinely identifies more than twice the number of peptides compared to DDA using the same LC-MS/MS gradients.

CONCLUSIONS In this study we have evaluated and benchmarked the performance of the Q Exactive HF-X quadrupole Orbitrap MS against the previous generation of this instrumentation type for shotgun proteomics. We measured significant improvement in most of the important parameters for complex peptide mixture analysis. Including a 70% increase in ion flux, faster acquisition due to optimized ion movements and faster Orbitrap transients which, in combination, yield an increase in HCD acquisition speed of up to

78% although this comes at a cost of roughly 15% less signal (S/N) and a slightly lower mass precision. The new APD algorithm available on the Q Exactive HF-X enables saturation of higher top-N methods as it improves the ‘on-the-fly’ assignments of precursor charge-states to approximately twothirds of all detected peaks, or twice of what was previously available on the previous generation Q Exactive HF.


26

Page 27 of 38

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


The hardware and software improvements collectively resulted in an overall improved number of peptide and protein identifications across all comparable conditions with an increase at very short LCMS/MS gradients of up to 50% in the DDA mode of operation, where we observed unprecedented identification rates of more than 1200 unique peptides per minute. On longer gradients the improvement was lower yet still significant. Alternatively, from our comparative analysis it is also evident that the new Q Exactive HF-X is capable of achieving the same proteome coverage with less sample material or in shorter MS measurement time. This is an attractive mode of usage of the new instrument, as increase in throughput is a key objective in many large-scale proteomics laboratories, where instrument time or sample amounts are limiting factors. However, high ion flux into the instrument is a requirement for productively faster HCD acquisition and on longer gradients or very dilute samples this may be difficult to achieve. We argue that the best usage of the instrument is therefore with shorter gradients and high sample loads. One of the most popular ways to increase proteome depth has been to analyze peptide mixtures with long extended LC-MS/MS gradients of more than four hours.29–32 A powerful alternative to this is to utilize multi-dimensional peptide chromatography and separately analyze the resulting fractions employing shorter LC-MS/MS gradients.33 Such an approach fits very well with the fast acquisition methods available on the Q Exactive HF-X. We have here shown that deep coverage of a human cell line proteome with more than 140,000 unique peptides is feasible in less than half a day of gradient time when employing massive offline high pH reversed-phase fractionation in combination with short online LC-MS/MS gradients. The Q Exactive HF-X also exhibits promise for global phosphoproteomics experiments as it enables routine analysis of more than 5000 phosphopeptides with short single-shot 15 minute LC-MS/MS measurements. Furthermore, we also show that offline high pH reversed-phase fractionation in combination with TMT10-plex quantification and a new 45,000 resolution HCD acquisition method is an effective and reproducible methodology to employ for multiplexed quantitative analysis of phosphoproteomes. Finally, our data produced by operating the Q Exactive HFX in the DIA mode resulted in more than twice the number of peptide identifications and spanned an


27


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

Page 28 of 38

additional order of magnitude of dynamic range compared to a corresponding DDA analysis. This suggests that it will be an instrument of choice for DIA-based label-free quantification of human proteomes in the future. DIA does not rely on ‘on the fly’ precursor assignments and is therefore largely insensitive to improvements in the APD feature. Instead one has to compromise on the charge-state dependency when choosing collision energy settings for DIA experiments, which comes at a cost of fragmentation spectra quality. Improvements and optimization of these parameters may further enhance the DIA capabilities of this instrument. Future challenges exist around instrument robustness for the Q Exactive HF-X, as this seems similar to that of Q Exactive HF and it is therefore recommended to use QC samples to monitor and ensure that instrument performance stays high. Since the sweet spot for operation of the instrument with standard nanoflow LC-MS/MS settings is at high peptide loads and short gradients, it would be desirable to increase ion flux either using lower flow-rate for higher sensitivity or with additives in the mobile phase to increase ionization efficiency.34,35 Similarly, superior chromatographic performance to increase peak capacity or increase peptide load capacity should also be a good match for this instrument.36 However, while MS instruments have improved dramatically over the last decade, nanoflow UHPLC instruments have seen less improvement outside research models, particularly from a robustness perspective.37–39

Thus, while we can demonstrate that short gradients provide great results on the Q Exactive HF-X, additional overhead between samples is still significant with the current generation of nano-UHPLC systems.

FIGURES Figure 1. Low level evaluation of the changes in the Q Exactive HF-X. (A) Hardware overview of the Q Exactive HF-X instrument. (B) Plot of MS/MS acquisition times as a function of resolution and IT measured at m/z 192, z = 1. (C) A box plot depicting the fraction of peaks that


28

Page 29 of 38

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


are annotated with a charge in raw files from either the Q Exactive HF or the Q Exactive HF-X on either full MS or MS/MS level. (D) A box plot depicting the fraction of agreement in full MS charge assignment with identified precursors using MaxQuant. (E) Scatter plot of PSMs from one microgram of HeLa peptides analyzed on a 30 minute gradient with the same LC and column on both the Q Exactive HF and Q Exactive HF-X. The light blue line depicts a linear regression while the green is showing a 1:1 relationship where a difference in medians is used to quantify the improvements. (F) Boxplot of median improvements of the Q Exactive HF-X over the Q Exactive HF for all pairwise comparisons for different gradients and peptide amounts. Figure 2. Comparative analysis of the Q Exactive HF and Q Exactive HF-X for ‘single shot’ proteomes. (A) Scatter plot showing the combined relative increase for all designed methods. (B) Number of unique peptide sequences identified is plotted versus gradient length for the three fastest acquisition methods with triplicate replicates. (C) Number of unique peptide sequences identified per minute of gradient length is plotted versus gradient lengths for the three fastest acquisition methods. (D) Number of protein groups identified is plotted versus gradient lengths for the three fastest acquisition methods. (E) Bar chart showing differences in unique peptide sequences identified in 30 minutes with 10 times lower peptide amount loaded on column across the three fastest acquisition methods. Figure 3. Example of a ‘multi-shot’ proteome. (A) Workflow overview where HeLa cells are lysed in boiling GndCl , digested, and 100 µg of peptides are fractionated into 46 fractions using high pH reversed phase fractionation and analyzed on fast 15 minute gradients. (B) Results from two different MS methods are shown where gradient time refers to the second dimension low pH gradient elution period. Total time comes from the raw file time stamp and refers to all time used including LC overheads. The amount refers to the peptide amount subjected to high pH


29


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

Page 30 of 38

fractionation. (C) The number of unique peptides sequences per fraction is shown for all the fractions with the two Q Exactive HF-X methods. Figure 4. Example of a ‘single shot’ phosphoproteome. (A) Workflow overview where 200 µg of HeLa peptides are enriched for phosphorylation with TiO2 beads and analyzed by short 15 minute gradients. (B) Bar chart showing the number of PSMs, unique phosphopeptide sequences, and number of localized phosphorylation sites. Error bars depict the standard deviation of replica measurements for the four different methods tested. (C) Number of phosphopeptides identified per minute for the 15 minute gradient for two selected MS methods. (D) Boxplot showing the Andromeda scores of all PSMs for the four different MS methods. Figure 5. Example of ‘multi-shot’ phosphoproteomes with TMT10-plex labeling. (A) Zoom of two overlaid spectra comparing different resolutions for baseline resolving two TMT10-plex reporter ions around m/z 130. (B) Workflow overview where five cell lines are lysed and digested in replica and labeled using TMT10-plex. Two technical replicas are enriched with TiO2 and fractionated offline into twelve fractions where each replica is analyzed on a different instrument. (C) Smooth histogram comparing the precursor intensity distributions of the localized phosphorylation sites quantified in each setup. (D) Principal component analysis of all modification specific peptides where the first two principal components are depicted. The two TMT10-plex experiments group tightly based on which cell line they belong to. (E) Heatmap and cluster analysis of the localized phosphorylation site reporter ion intensities from the two TMT10-plex experiments. Figure 6. Application for DIA analysis. (A) Classical MS method setup for DIA showing the m/z scale measured and the MS/MS windows and the total duty cycle time. (B) Workflow for the


30

Page 31 of 38

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


DDA setup where three single shot runs with 30 minute gradients are analyzed with the fastest MS method and analyzed using the MaxQuant software tool. The numbers reflect the average of a single analysis and the round brackets percentages indicate average pairwise overlap between two replica. (C) Workflow for the DIA setup where three single shot runs with 30 minute gradients are analyzed using a spectral library from DDA analysis of two 12 fraction proteomes analyzed on the same gradient. Spectronaut is used as the software tool and the numbers reflect single analysis averages where the percentage in round brackets indicates average pairwise overlap between two replica. (F) A bar chart depicting how many unique peptide sequences are mapping to each protein for the three data sets used in the analysis. (G) Histogram showing the intensity based absolute quantification (iBAQ) values for all fractionated proteins and which are found in each dataset. TABLES Table 1. Details of method names and instrument settings ASSOCIATED CONTENT Supporting Information The following files are available free of charge at ACS website http://pubs.acs.org: The supporting information for publication contains supplementary figures: Figure S-1: Detailed timing experiments; Figure S-2: Detailed mass error and S/N dependence; Figure S-3: Top-N analysis; Figure S-4: Features of variable gradient lengths; Figure S-5: Isolation width optimization; Figure S-6: Detailed comparison of phosphoproteomes. AUTHOR INFORMATION


31


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

Page 32 of 38

Corresponding Author *Phone: +45-353-25022. E-mail: [email protected]. Notes The authors declare the following competing financial interest(s): T.N.A. and A.H. are employees of Thermo Fisher Scientific, the manufacturer of the Q Exactive HF and the Q Exactive HF-X instrument used in this research.

ACKNOWLEDGMENT Work at The Novo Nordisk Foundation Center for Protein Research (CPR) is funded in part by a generous donation from the Novo Nordisk Foundation (grant no. NNF14CC0001). Part of this work has been funded as part of the MSmed project that has received funding from the European Union’s Horizon 2020 Research and innovation program under grant agreement no. 686547. We would like to thank the PRO-MS Danish National Mass Spectrometry Platform for Functional Proteomics and the CPR Mass Spectrometry Platform for instrument support and assistance. J.V.O. was supported by the Danish Cancer Society (R90-A5844 KBVU project grant). The authors would like to thank Yue Xuan from Thermo Fischer Scientific for input on the DIA experiments and members of the CPR Proteomics Program for critical input on the manuscript. ABBREVIATIONS ACN, acetonitrile; APD, advanced precursor determination; DDA, data dependent acquisition; DIA, data independent acquisition; FA, formic acid; FWHM, full width half maximum; GndCl, guanidinium chloride; HCD, higher-energy collisional dissociation; HCTT, high capacity transfer tube; IT, maximum injection time; LC, liquid chromatography; m/z, mass-to-charge; MS, mass spectrometry; MS/MS, tandem mass spectrometry; PRM, parallel reaction monitoring; PSMs, peptide spectrum matches; PTMs, post translational modifications; QE, Q Exactive; S/N, signal-to-noise; TFA, trifluoroacetic acid; TMT, tandem mass tags.


32

Page 33 of 38

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


REFERENCES (1)

Olsen, J. V.; Mann, M. Status of large-scale analysis of post-translational modifications

by mass spectrometry. Mol. Cell. Proteomics 2013, 12 (12), 3444–3452. (2)

Geyer, P. E.; Kulak, N. A.; Pichler, G.; Holdt, L. M.; Teupser, D.; Mann, M. Plasma

Proteome Profiling to Assess Human Health and Disease. Cell Syst 2016, 2 (3), 185–195. (3)

Larance, M.; Lamond, A. I. Multidimensional proteomics for cell biology. Nat. Rev. Mol.

Cell Biol. 2015, 16 (5), 269–280. (4)

Vizcaíno, J. A.; Deutsch, E. W.; Wang, R.; Csordas, A.; Reisinger, F.; Ríos, D.; Dianes,

J. A.; Sun, Z.; Farrah, T.; Bandeira, N.; et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32 (3), 223–226. (5)

Olsen, J. V.; Macek, B.; Lange, O.; Makarov, A.; Horning, S.; Mann, M. Higher-energy

C-trap dissociation for peptide modification analysis. Nat. Methods 2007, 4 (9), 709–712. (6)

Marx, H.; Lemeer, S.; Schliep, J. E.; Matheron, L.; Mohammed, S.; Cox, J.; Mann, M.;

Heck, A. J. R.; Kuster, B. A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat. Biotechnol. 2013, 31 (6), 557–564. (7)

Davis, M. T.; Spahr, C. S.; McGinley, M. D.; Robinson, J. H.; Bures, E. J.; Beierle, J.;

Mort, J.; Yu, W.; Luethy, R.; Patterson, S. D. Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. II. Limitations of complex mixture analyses. Proteomics 2001, 1 (1), 108–117.


33


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

(8)

Page 34 of 38

Downard, K. M.; Biemann, K. The effect of charge state and the localization of charge on

the collision-induced dissociation of peptide ions. J. Am. Soc. Mass Spectrom. 1994, 5 (11), 966– 975. (9)

Neta, P.; Simon-Manso, Y.; Yang, X.; Stein, S. E. Collisional energy dependence of

peptide ion fragmentation. J. Am. Soc. Mass Spectrom. 2009, 20 (3), 469–476. (10) Haller, I.; Mirza, U. A.; Chait, B. T. Collision induced decomposition of peptides. Choice of collision parameters. J. Am. Soc. Mass Spectrom. 1996, 7 (7), 677–681. (11) Senko, M. W.; Remes, P. M.; Canterbury, J. D.; Mathur, R.; Song, Q.; Eliuk, S. M.; Mullen, C.; Earley, L.; Hardman, M.; Blethrow, J. D.; et al. Novel parallelized quadrupole/linear ion trap/Orbitrap tribrid mass spectrometer improving proteome coverage and peptide identification rates. Anal. Chem. 2013, 85 (24), 11710–11714. (12) Kelstrup, C. D.; Jersie-Christensen, R. R.; Batth, T. S.; Arrey, T. N.; Kuehn, A.; Kellmann, M.; Olsen, J. V. Rapid and deep proteomes by faster sequencing on a benchtop quadrupole ultra-high-field Orbitrap mass spectrometer. J. Proteome Res. 2014, 13 (12), 6187– 6195. (13) Scheltema, R. A.; Hauschild, J.-P.; Lange, O.; Hornburg, D.; Denisov, E.; Damoc, E.; Kuehn, A.; Makarov, A.; Mann, M. The Q Exactive HF, a Benchtop mass spectrometer with a pre-filter, high-performance quadrupole and an ultra-high-field Orbitrap analyzer. Mol. Cell. Proteomics 2014, 13 (12), 3698–3708. (14) 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.; et al. An Optimized Shotgun


34

Page 35 of 38

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


Strategy for the Rapid Generation of Comprehensive Human Proteomes. Cell Syst 2017, 4 (6), 587–599.e4.

(15) Kelstrup, C. D.; Young, C.; Lavallee, R.; Nielsen, M. L.; Olsen, J. V. Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole orbitrap mass spectrometer. J. Proteome Res. 2012, 11 (6), 3487–3497. (16) Kulak, N. A.; Pichler, G.; Paron, I.; Nagaraj, N.; Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 2014, 11 (3), 319–324. (17) Olsen, J. V.; Ong, S.-E.; Mann, M. Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Mol. Cell. Proteomics 2004, 3 (6), 608–614. (18) Poulsen, J. W.; Madsen, C. T.; Young, C.; Poulsen, F. M.; Nielsen, M. L. Using guanidine-hydrochloride for fast and efficient protein digestion and single-step affinitypurification mass spectrometry. J. Proteome Res. 2013, 12 (2), 1020–1030. (19) Olsen, J. V.; Blagoev, B.; Gnad, F.; Macek, B.; Kumar, C.; Mortensen, P.; Mann, M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006, 127 (3), 635–648. (20) Lundby, A.; Andersen, M. N.; Steffensen, A. B.; Horn, H.; Kelstrup, C. D.; Francavilla, C.; Jensen, L. J.; Schmitt, N.; Thomsen, M. B.; Olsen, J. V. In vivo phosphoproteomics analysis reveals the cardiac targets of β-adrenergic receptor signaling. Sci. Signal. 2013, 6 (278), rs11.


35


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

Page 36 of 38

(21) Rappsilber, J.; Ishihama, Y.; Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 2003, 75 (3), 663–670. (22) Wang, Y.; Yang, F.; Gritsenko, M. A.; Wang, Y.; Clauss, T.; Liu, T.; Shen, Y.; Monroe, M. E.; Lopez-Ferrer, D.; Reno, T.; Moore, R. J.; Klemke, R. L.; Camp, D. G., 2nd; Smith, R. D. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 2011, 11 (10), 2019-26. (23) Batth, T. S.; Francavilla, C.; Olsen, J. V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J. Proteome Res. 2014, 13 (12), 6176–6186. (24) Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26 (12), 1367–1372. (25) Team, R. C. The R Project for Statistical Computing: R Foundation for Statistical Computing, Vienna, Austria; ISBN 3-900051-07-0. Available: http://www.R-project.org/. (26) Post, H.; Penning, R.; Fitzpatrick, M. A.; Garrigues, L. B.; Wu, W.; MacGillavry, H. D.; Hoogenraad, C. C.; Heck, A. J. R.; Altelaar, A. F. M. Robust, Sensitive, and Automated Phosphopeptide Enrichment Optimized for Low Sample Amounts Applied to Primary Hippocampal Neurons. J. Proteome Res. 2017, 16 (2), 728–737. (27) Bantscheff, M.; Boesche, M.; Eberhard, D.; Matthieson, T.; Sweetman, G.; Kuster, B. Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol. Cell. Proteomics 2008, 7 (9), 1702–1713.


36

Page 37 of 38

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


(28) Escher, C.; Reiter, L.; MacLean, B.; Ossola, R.; Herzog, F.; Chilton, J.; MacCoss, M. J.; Rinner, O. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 2012, 12 (8), 1111–1121. (29) Köcher, T.; Pichler, P.; Swart, R.; Mechtler, K. Analysis of protein mixtures from wholecell extracts by single-run nanoLC-MS/MS using ultralong gradients. Nat. Protoc. 2012, 7 (5), 882–890. (30) Horie, K.; Kamakura, T.; Ikegami, T.; Wakabayashi, M.; Kato, T.; Tanaka, N.; Ishihama, Y. Hydrophilic interaction chromatography using a meter-scale monolithic silica capillary column for proteomics LC-MS. Anal. Chem. 2014, 86 (8), 3817–3824. (31) Thakur, S. S.; Geiger, T.; Chatterjee, B.; Bandilla, P.; Fröhlich, F.; Cox, J.; Mann, M. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell. Proteomics 2011, 10 (8), M110.003699. (32) Zhou, F.; Lu, Y.; Ficarro, S. B.; Webber, J. T.; Marto, J. A. Nanoflow low pressure high peak capacity single dimension LC-MS/MS platform for high-throughput, in-depth analysis of mammalian proteomes. Anal. Chem. 2012, 84 (11), 5133–5139. (33) Washburn, M. P.; Wolters, D.; Yates, J. R., 3rd. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19 (3), 242–247. (34) Hahne, H.; Pachl, F.; Ruprecht, B.; Maier, S. K.; Klaeger, S.; Helm, D.; Médard, G.; Wilm, M.; Lemeer, S.; Kuster, B. DMSO enhances electrospray response, boosting sensitivity of proteomic experiments. Nat. Methods 2013, 10 (10), 989–991.


37


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

Page 38 of 38

(35) Yu, P.; Hahne, H.; Wilhelm, M.; Kuster, B. Ethylene glycol improves electrospray ionization efficiency in bottom-up proteomics. Anal. Bioanal. Chem. 2017, 409 (4), 1049–1057. (36) Shen, Y.; Zhao, R.; Berger, S. J.; Anderson, G. A.; Rodriguez, N.; Smith, R. D. Highefficiency nanoscale liquid chromatography coupled on-line with mass spectrometry using nanoelectrospray ionization for proteomics. Anal. Chem. 2002, 74 (16), 4235–4249. (37) Hosp, F.; Scheltema, R. A.; Eberl, H. C.; Kulak, N. A.; Keilhauer, E. C.; Mayr, K.; Mann, M. A Double-Barrel Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) System to Quantify 96 Interactomes per Day. Mol. Cell. Proteomics 2015, 14 (7), 2030–2041. (38) Falkenby, L. G.; Such-Sanmartín, G.; Larsen, M. R.; Vorm, O.; Bache, N.; Jensen, O. N. Integrated Solid-Phase Extraction–Capillary Liquid Chromatography (speLC) Interfaced to ESI– MS/MS for Fast Characterization and Quantification of Protein and Proteomes. J. Proteome Res. 2014, 13 (12), 6169–6175. (39) Livesay, E. A.; Tang, K.; Taylor, B. K.; Buschbach, M. A.; Hopkins, D. F.; LaMarche, B. L.; Zhao, R.; Shen, Y.; Orton, D. J.; Moore, R. J.; et al. Fully automated four-column capillary LC-MS system for maximizing throughput in proteomic analyses. Anal. Chem. 2008, 80 (1), 294–302.


38

Performance Evaluation of the Q Exactive HF-X for ... - ACS Publications

Recommend Documents