Exploiting the dynamic relationship between peptide separation

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Exploiting the dynamic relationship between peptide separation quality and peptide co-isolation in a mPSM approach offers a new strategy to optimize bottom-up proteomics throughput and depth Manuel I. Villalobos Solis, Richard J. Giannone, Robert L. Hettich, and Paul E. Abraham Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b00819 • Publication Date (Web): 10 May 2019 Downloaded from http://pubs.acs.org on May 11, 2019

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Analytical Chemistry

Exploiting the dynamic relationship between peptide separation quality and peptide coisolation in a mPSM approach offers a new strategy to optimize bottom-up proteomics throughput and depth Manuel I. Villalobos Solis1,2, Richard J. Giannone1, Robert L. Hettich1 and Paul E. Abraham1

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*Corresponding author:

Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States. 2 Department of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee 37996, United States.

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Paul E. Abraham Oak Ridge National Lab, Oak Ridge, TN 37831 Email: [email protected] Phone: (865) 574-4968 Fax: (865) 241-1555

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Keywords: tandem mass spectrometry, chimeric spectra, isolation interference, liquid chromatography

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Notice: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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ABSTRACT

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Peptide co-fragmentation leads to chimeric MS/MS spectra that negatively impact traditional

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single-peptide match-per-spectrum (sPSM) search strategies in proteomics. The collection of

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chimeric spectra is influenced by peptide co-elution and the width of precursor isolation windows.

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Although peptide co-fragmentation can be reduced by advanced chromatography, such as UHPLC

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and 2D-HPLC separation schemes, and narrower isolation windows, chimeric spectra can still be

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as high as 30-50% of the total MS/MS spectra collected. Alternatively, co-fragmented peptides in

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chimeric spectra and the use of wider isolation windows benefit multiple-peptide matches-per-

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spectrum (mPSM) algorithms, such as CharmeRT, which facilitate the identification of several co-

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fragmented peptides. Considering recent advancements in LC and mPSM methodologies, we

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present a comprehensive examination of the levels of chimeric spectra collected in the analysis of

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a HeLa digest measured using different LC modes of separation and isolation windows and

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compare the depth of identifications obtained when these data are annotated using a sPSM or a

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mPSM approach. Our results demonstrate that MS/MS data derived from 1D-HPLC strategies

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under different gradient schemes and searched with CharmeRT yielded higher average numbers

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of PSMs (11%-49%), peptide analytes (10%-16%), and peptide sequences (3%-10%) compared to

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data derived from 1D-UHPLC runs but searched with a sPSM strategy. Interestingly, data from a

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2D-HPLC separation strategy benefits more from the application of CharmeRT results when

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compared to a 50 cm 1D-UHPLC column employing a 500 min gradient. Overall, these results

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provide new insights into how to better configure LC-MS/MS measurements for improved

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throughput and peptide identification in complex proteomes.


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INTRODUCTION

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Despite advances in mass accuracy, resolving power, and scan speeds in mass spectrometry

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instrumentation, one of the remaining challenges of any high-throughput bottom-up proteomics

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experiment is that only a fraction of the collected tandem mass spectra (MS/MS) can be assigned

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to peptide sequences with high confidence (usually ≤ 60%).1 Although several reasons contribute

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to this effect,2 one that has been under scrutiny by the proteomics community is the occurrence of

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chimeric spectra (also known as mixture or co-fragmented spectra).

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Chimeric spectra are the result of the co-isolation and co-fragmentation of two or more

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peptide precursor ions with similar m/z and retention time. The complex nature of the samples

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commonly analyzed in bottom-up proteomics (>100,000 detectable peptide species) and the m/z

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isolation widths typically used in data-dependent acquisition (DDA) experiments (2-4 m/z)1, 2 can

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result in chimeric spectra representing 50% of the total MS/MS data collected.2 Recent

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investigations employing tryptic digests of HeLa cells demonstrated that even in experiments with

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a narrow isolation width of 2 m/z, 39% of the total MS/MS spectra collected is chimeric.3

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The negative effects of chimeric spectra in proteomics studies have been well-studied. For

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example, in database-driven peptide and protein identifications, the presence of chimeras

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deteriorates the search scores of true peptide assignments given by algorithms such as MASCOT

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and SEQUEST.2 In addition, chimeric spectra reduces the accuracy of quantitative isobaric

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tagging-based quantification methods such as iTRAQ or TMT, in which the contribution of

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reporter ion intensities from co-fragmented peptides, causes the under-estimation of

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protein/peptide abundance differences (a phenomenon termed as “ratio compression”).4-6 Due to

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these issues, experimental and computational approaches that minimize the negative impact of

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chimeric spectra in bottom up experiments have been developed.



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Reducing the complexity of samples prior to MS analysis has the advantage of minimizing

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the chance of co-eluting peptides.7, 8 By exploiting independent physicochemical properties of

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peptides, protocols coupling orthogonal chromatographic separations before MS detection have

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shown improved separation resolutions and increased peptide and protein identifications, albeit at

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the cost of increased analysis times.9-11 With the introduction of ultra-high-pressure

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chromatography (UHPLC), a new era of high-quality one-dimensional separations has resurfaced.

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The use of chromatographic pumps that can tolerate up to 10,000 psi and stationary phases with

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particles sizes of < 2 µM diameter,12, 13 not only has afforded narrower peptide elution profiles and

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increased ion sensitivities, but the improved column capacities also reduces co-elution of peptides

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and hence, the occurrence of chimeric spectra.1 This mode of operation has enabled “single-shot”

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proteomics studies, in which complex proteomes are analyzed in-depth with the aid of 1D reverse

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phase chromatographic columns of 50 cm or more in length and effective LC gradients times

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ranging from 300-500 mins.12, 14-16 Although 1D-UHPLC (including “one-shot” separations) and

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2D-HPLC based separations reduce the occurrence of chimeric spectra, even under the best

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chromatographic separations, the co-elution of thousands of peptides is still unavoidable.1

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Computationally, database searching algorithms aiming to deconvolute chimeric spectra

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collected in DDA experiments have been developed.17-21 These algorithms make use of the

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multiple peptides-per-spectrum-match approach (mPSM) that, in comparison to most commonly

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employed search strategies using a single-peptide match-per-spectrum approach (sPSM), try to

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assign more than one peptide per MS/MS spectrum. A newly developed computational workflow

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called CharmeRT demonstrated substantial improvement in peptide identifications when

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compared to other methods.3 This was achieved by implementing a second search strategy coupled

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with a highly accurate retention time prediction algorithm method. The second search option of


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CharmeRT is integrated into the database search engine MSAmanda, with validation of multiple

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peptide assignments to a given MS/MS spectrum performed by Elutator, a new tool built upon the

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foundations of Percolator that incorporates retention time (RT) prediction in FDR calculations.

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The impact of having RT prediction in FDR evaluations of first and second searches translated

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into 25%-62% more peptide identifications in runs of HeLa tryptic digests compared to more

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frequently used database search workflows.

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Further advancements in LC and MS technology will continue to improve the limit of

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detection for peptide sequencing in complex mixtures; however, these alone are unlikely to solve

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the problem with chimeric spectra. We contend that a concomitant evaluation of LC configurations

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and mPSM search algorithms is required to further increase the number of detectable peptides.

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Therefore, our study was designed to systematically evaluate several LC peptide separation

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techniques, ranging from short HPLC gradients to UHPLC and orthogonal separations, as well as

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precursor isolation m/z windows, to better understand the effects of each on the subsequent

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assignment of MS/MS spectra by a traditional sPSM search strategy compared to a mPSM one,

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specifically CharmeRT.

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EXPERIMENTAL PROCEDURES

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Standard sample for LC-MS/MS analyses

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Commercial Pierce HeLa Protein Digest Standards were purchased from Thermo Fisher

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Scientific (20 µg total amounts). The lyophilized peptides were resuspended in 40 µL of water

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with 0.1% formic acid as per the manufacturer instructions. Stock solutions of ~0.5 µg/µL were

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frozen at -80°C and used as needed. Volumes equivalent to 2 µg of HeLa protein digest were

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analyzed each time using different LC setups and gradients as described below. 4 ACS Paragon Plus Environment


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1D-LC-MS/MS runs of HeLa tryptic digest standards

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1D-LC-MS/MS runs were carried out using a Proxeon EASY-nLC 1200TM system (Thermo

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Fisher Scientific) interfaced with a Q Exactive Plus (Thermo Fisher Scientific) mass spectrometer

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equipped with a nano-electrospray source. For HPLC measurements, a single 2 µg injection of

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HeLa peptides were loaded in mobile phase A (0.1% formic acid, 2% acetonitrile) each time onto

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a trap column (150 mm x 100 μm ID) packed in-house with ~10 cm of 5 μm Kinetex C18 resin

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(Phenomenex). Peptide separation was conducted on an analytical column (250 mm x 75 μm ID)

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packed in-house with 5 μm particle size Kinetex C18 resin using a linear gradient from 2 to 22% of

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mobile phase B (0.1% formic acid, 80% acetonitrile) at a flow rate of 300 nL/min over 90, 210, or

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240 min depending on the experiment. Each gradient was followed by an increase to 35% B within

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5 minutes, a 5 mins hold in 35% B, and afterwards a decrease to 2% B in 5 minutes.

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To test the effects of smaller particle diameter size in number of identifications, the same

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LC gradients were used in an UHPLC setup, where the trap and analytical front columns were

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packed with 1.7 μm particle size Kinetex C18 resin. In addition, a single 2 µg injection of HeLa

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peptides was separated with an analytical 500 mm x 75 μm ID column packed with 1.7 μm Kinetex

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C18 which was placed in a column heater (Sonation GmbH) at a temperature of 60°C. The linear

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gradient for this configuration was from 2 to 22% solvent B over 500 mins at a flow rate of 250

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nL/min.

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Optimization of relevant MS parameters in the Q Exactive instrument were performed and

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were found to agree well with the Q Exactive benchmarking study.22 In brief, mass spectra were

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acquired with the Q Exactive Plus instrument in a top 10 data-dependent acquisition setup. Peptide

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precursor MS spectra was collected within 300 to 1500 m/z with automatic gain control (AGC)

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target value of 3 × 106 at a resolution of 70,000 with a maximum injection time (IT) of 25 ms. 5 ACS Paragon Plus Environment

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Precursor ions with charge states ≥2 and ≤ 5 and intensity threshold of 1.6 × 105 were selected for

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higher-energy C-trap collision dissociation (HCD) with a normalized collision energy of 27.

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Peptide precursor ions collected from the 1D HPLC and UHPLC gradient runs were isolated using

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a 1.6 m/z isolation width; whereas for the best gradients under HPLC or UHPLC conditions (see

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Results & Discussions), precursor m/z isolation widths of 0.8 and 3.0 m/z were additionally

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employed in order to test the effects of co-isolation interference. Fragment ion spectra were always

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acquired at a resolution of 17,500 at m/z 200 with an AGC target value of 1 × 105 and maximum

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IT of 50 ms. Dynamic exclusion was set to 20 s to avoid repeated sequencing of peptides. All runs

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were conducted in triplicate.

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2D-LC-MS/MS runs of HeLa tryptic digest standards

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2D-LC-MS/MS runs were performed using a Vanquish UHPLC interfaced with a Q

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Exactive Plus mass spectrometer (Thermo Fisher Scientific) outfitted with a 100 µM ID triphasic

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precolumn (RP-SCX-RP) coupled to a 250 mm x 75 µM ID nanospray emitter packed with 250

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mm of 5 µm Kinetex C18 RP resin.23 For each sample, a single 2 µg injection of HeLa peptides

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was loaded onto the precolumn with mobile phase A by direct flow (2 µL/min) then separated and

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analyzed across two successive salt cuts of ammonium acetate (35 mM and 500 mM), with each

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cut followed by a 210 min, split-flow (300 nL/min) organic gradient, wash, and re-equilibration:

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0% to 2% solvent B over 2 min; 2% to 22% solvent B over 208 min; 22% to 50% solvent B over

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10 min; 50% to 0% solvent B over 10 min, hold at 0% solvent B for 15 min. Mass spectra from

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the eluting peptides was collected using the same MS and MS/MS parameter settings on the Q

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Exactive Plus instrument as in the 1D-LC-MS/MS runs.

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PSMs, peptide, and proteins identifications by database search



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All MS/MS spectra collected were processed in Proteome Discoverer v.2.2. (PD) with

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MSAmanda v.2.2 and Percolator. Spectral data were searched against the most up-to-date human

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reference proteome database from UniProt (ID. UP000005640) to which common laboratory

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contaminants were appended. The following parameters were set up in MSAmanda to derive fully-

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tryptic peptides: MS1 tolerance = 5 ppm; MS2 tolerance = 0.02 Da; missed cleavages = 2;

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Carbamidomethyl (C, +57.021 Da) as static modification; oxidation (M, +15.995 Da) and

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carbamylation (n-terminus, +43.006 Da) as dynamic modifications. The percolator FDR threshold

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was set to 1% at the PSM and peptide level. In addition, MS/MS spectral data were searched with

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MSAmanda in which a second search option was enabled and Elutator v2.2 (the CharmeRT

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workflow). Parameters applied for MSAmanda second search were as described in the original

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CharmeRT publication, with the exception that a maximum of 3 additional precursors per PSMs

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were searched (referred as second searches).3 The Elutator FDR threshold was set to 1% at the

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PSM and peptide level.

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Data analysis

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The following identification parameters were considered to assess the performance of each

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1D and 2D LC-MS/MS runs: number of protein groups, number of modification-specific peptides

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with charge (referred to as peptide analytes), number of peptides without modification and charge

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(referred to as peptide sequences) and number of peptide-spectrum matches (PSMs). In addition,

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we considered the precursor isolation interference percentage calculated by Proteome Discoverer,

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as a measure of chimerism in the spectra collected:

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[ (

Eq.1 % 𝑖𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑟𝑓𝑒𝑟𝑒𝑛𝑐𝑒 = 100 × 1 ―

𝑝𝑟𝑒𝑐𝑢𝑟𝑠𝑜𝑟 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑖𝑛 𝑖𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑤𝑖𝑛𝑑𝑜𝑤 𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑖𝑛 𝑖𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑤𝑖𝑛𝑑𝑜𝑤

)]


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The full width at half-maximum (FWHM) region was calculated for each LC configuration

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using the FeatureFinderMetabo node24 of OpenMS25 using default parameters except for the

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expected chromatographic peak width (in seconds) setting, which was optimized for each

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configuration. All spectral data collected in this study was deposited at the ProteomeXchange

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Consortium via the MASSIVE repository. The project accession is PXD012635 and reviewers can

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access the data under the username [email protected] and password qd5l9bhm.

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Results & Discussion

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Performance metrics across a range of nanoLC peptide separation techniques

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Two common LC peptide separation techniques are HPLC and UHPLC. The former

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employs analytical columns packed with stationary phases having particle sizes > 2 µM and flow

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rates that operate below 450 bar,26 while the latter uses stationary phases with particle sizes ≤ 2

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µM that drive the backpressure of the LC system to 600-1300 bar.13 Our approach started with

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investigation of the performance of 5 µM C18 packed HPLC and 1.7 µM C18 packed UHPLC

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columns (250 mm x 75 μm ID) employing three different linear gradient lengths from 2 to 22%

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solvent B over 90 min, 210 min and 240 min at a constant flow rate of 300 nL/min.

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Not surprisingly, the 1D-UHPLC setups (250 mm x 75 μm ID column, 1.7 µM C18)

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outperformed the 1D-HPLC setups (250 mm x 75 μm ID column, 5 µM C18) in average number

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of identifications across the different gradients tested, with the best performance achieved in the

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longer analysis time (Figures S-1A-D). These data are undoubtedly explained by UHPLC

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providing narrower FWHM and boosting the sensitivity of the analysis with increased ion

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intensities (Figure S-2).14 In addition, the overall improved chromatographic resolution provided

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by UHPLC setups identified >90% of all the identified HPLC peptides and further yielded between



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38-44% new peptide sequences and 25-32% new protein groups not found by their HPLC

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counterparts (Figure S-1E).

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The comparison across gradient time lengths on the same LC setups demonstrated the peak

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dilution phenomena in HPLC.27, 28 For example, when the gradient lengths were adjusted from 210

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min to 240 min, minor returns in the number of identifications were observed for the UHPLC

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setup, with a maximum gain of 12% in the number of PSMs and less than 5% for the remaining

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identifications. However, for HPLC, only an increase of 6% PSMs was observed at 240 min, but

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the numbers of other identifications were reduced between 2%-4%, thereby suggesting longer

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gradients for the HPLC column results would likely bring in diminishing returns due to a decrease

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in peak heights which subsequently leads to loss of sensitivity and resolution.

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One way to combat peak dilution is to use extra-long 1D-UHPLC columns (> 300 mm) or

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to fractionate the sample via 2D-HPLC. While both these different modes of enhanced LC have

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been shown to offer in-depth proteome analyses with comparable identification metrics, we

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hypothesized that they would likely lead to differing degrees of co-isolation. Therefore, both 2D-

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HPLC (250 mm x 75 μm ID column, 5 µM C18) and long 1D-UHPLC (500 mm x 75 μm ID

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column, 1.7 µM C18) configurations were employed to assess their overall influence on chimeric

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MS/MS spectra. As observed in Figure S-3, the percentage increases in the average number of

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identifications were significant for both the 2D-HPLC and the 1D-UHPLC 500 min runs when

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compared to the results from the 250 mm HPLC and UHPLC configurations tested.

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Comparison of precursor isolation interferences across a range of nanoLC peptide separation techniques

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An obvious benefit provided by enhanced LC separation is a reduction in the number of

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co-eluting peptides. As fewer peptides with similar m/z ratios co-elute, the amount and degree of 9 ACS Paragon Plus Environment

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interfering precursor ions during isolation is expected to decrease. To evaluate this premise across

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the tested LC configurations, the degree of isolation interference imparted by co-eluting peptides

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was computed.

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Given the already established relationship between precursor ion abundance and the degree

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of isolation interference in MS/MS spectra,2 the median isolation interference percentages of

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identified PSMs in each LC-MS/MS run were plotted against different ranges of precursor ion

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abundance (Figure 1). As expected, higher precursor abundances have lower median isolation

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interferences in each LC configuration. In general, the majority of PSM precursor abundances

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ranged from 1e07-1e10. In this range, higher median isolation interference was observed in the

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1D-HPLC setups relative to the 1D-UHPLC setups, while the lowest isolation interferences were

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found in the 500 min 1D-HPLC runs and 2D-HPLC. Between these more specialized LC setups,

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precursors identified in the 2D-HPLC runs had a slightly lower median isolation interference

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percentage. Plotting the 2D-HPLC values for the independent fractions of peptides collected at

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each salt pulse revealed similar median isolation interference percentages across both fractions,

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suggesting they have similar peptide complexity (Figure S-4).

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Evaluation of the CharmeRT mPSM search algorithm across different LC peptide separation techniques

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Dorfer et al. (2018) benchmarked CharmeRT against other widely-used search algorithms,

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including MSAmanda, and demonstrated improved measurement depth. However, an in-depth

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evaluation of the performance gains across a broad range of peptide separation techniques has not

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been reported. Therefore, our objective was to compare the CharmeRT workflow (mPSM) against

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its foundational algorithms, MS Amanda and Percolator (sPSM), across the various LC

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configurations implemented. As expected, the application of CharmeRT increased the average

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numbers of identified PSMs between 31%-63% for the HPLC runs, 56%-58% for the UHPLC 10 ACS Paragon Plus Environment


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runs, and up to 64% and 26% for the more specialized 2D-HPLC and 500 min 1D-UHPLC runs,

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respectively. These numbers translated to increases in the number of peptide sequences identified

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overall with gains of 32%-55% per HPLC run, 45%-41% per UHPLC, 51% per 2D-HPLC and

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22% per 500 min 1D-UHPLC (Figure 2). The percentage gains in the average number of peptide

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sequences did not translate into comparable percentage of increases in the average number of

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protein groups. For example, we observed a slight decrease (~2-3%) in the number of protein

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groups identified in the 500 min 1D-UHPLC measurements, which we suspect is a reflection of

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saturation in protein measurement depth; this observation is also expected to vary across peptide

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mixtures of different complexities. Nearly all peptides and protein groups identified with

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MSAmanda-Percolator were also found with CharmeRT (Figure S-5). Moreover, most peptides

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identified from the second search mapped to protein groups identified in the first search (Figure

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S-6). Overall, these observations agreed well with results reported in the original CharmeRT

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publication.

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A major goal of this study was to better understand how varying the quality of peptide

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separation influences the performance of the CharmeRT search strategy and to determine whether

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one could achieve similar depth using either a UHPLC with MSAmanda-Percolator or an HPLC

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configuration with CharmeRT. A comparison of the average number of PSMs, peptide analytes,

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and peptide sequences between these two approaches revealed that HPLC combined with

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CharmeRT provided substantially more identifications than the conventional UHPLC Amanda-

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Percolator search (Figure 2). More importantly, comparable performance was observed between

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the HPLC 210 min CharmeRT and UHPLC 240 min Amanda-Percolator using the shorter

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analytical column (250 mm x 75 μm ID). Intriguingly, this observation suggests that peptide

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separation of a complex mixture on a HPLC column with shorter gradient times in combination


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with a mPSM search workflow can achieve identification metrics comparable to results given by

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a UHPLC column and a sPSM search strategy.

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Next, the degree of overlap between the newly identified peptides provided by either

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UHPLC or the application of CharmeRT was evaluated. More specifically, the newly identified

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peptide sequences and protein groups identified from HPLC runs searched with CharmeRT were

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compared to the results obtained from their UHPLC counterparts searched with MSAmanda-

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Percolator (Figure S-7). These comparisons demonstrate that the newly identified peptides for

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both approaches were mostly complementary, with only a small overlap (~10%).

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When comparing the CharmeRT performance between 2D-HPLC and the 1D-UHPLC 500

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min gradient runs (500 mm x 75 μm ID), the 2D-HPLC measurements had the highest percentage

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gains in terms of average number of PSMs, peptide analytes, peptide sequences and protein groups

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(64%, 63%, 51%, 13%, respectively) (Figure 2), despite 2D-HPLC having the least interference

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(Figure 1). To explore this further, the total number of PSMs derived from the 2D-HPLC and 1D-

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UHPLC 500 min setups at different ranges of isolation interference were binned and quantified

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based on their CharmeRT search depth (Figure 3, isolation width 1.6 m/z). Interestingly, across

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every isolation interference bin, the 2D-HPLC configuration afforded 2-4x more CharmeRT gains

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relative to the 1D-UHPLC 500 min configuration. Additionally, a greater search depth (i.e., a

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higher percentage of scans having 2-3 additional precursors derived from second searches) was

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observed at higher isolation interference ranges in 2D-HPLC. After exploring accompanying data

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related to data quality and PSM scoring, it’s not immediately obvious what metrics explain this

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phenomenon. However, a likely explanation is that the 2D separation scheme, which reduces the

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complexity of the loaded peptide mixture for each salt pulse, retains the CharmeRT performance



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gains like the 1D-HPLC 210 min separation scheme, whereas the 1D-UHPLC 500 min CharmeRT

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performance gains suffers from dilution of lower abundant secondary precursors.

314 315 316

Evaluation of spectra collected from LC peptide separation techniques with different isolation windows and searched with CharmeRT

317

Widely-acknowledged, precursor isolation widths can impact the performance in LC-

318

MS/MS measurements: narrower isolation widths lead to loss in signal and wider isolation widths

319

results in more precursor co-isolation/co-fragmentation. As previously demonstrated by Dorfer et

320

al., (2018) applying wider isolation widths during data acquisition improves the performance gains

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of the CharmeRT workflow. Therefore, our approach was to evaluate different isolation widths

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across the different LC configurations applied in this study.

323

To this end, three isolation widths (0.8, 1.6 and 3.0 m/z) that encompass the range often

324

employed in LC-MS/MS measurements were applied and performance assessed across the 1D-

325

HPLC 210 min and 1D-UHPLC 240 min setups, which gave the highest numbers of average

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identifications across each configuration either when the results were searched with Amanda-

327

Percolator or CharmeRT, as well as the 1D-UHPLC 500 min and 2D-HPLC setups.

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At each isolation width, the 1D-HPLC 210 min runs had the highest precursor isolation

329

interference medians, followed by the 1D-UHPLC 240 min runs. The 1D-UHPLC 500 min and

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2D-HPLC setups presented similar isolation interference values (Figure 4A). Given the lower

331

signal and reduced potential for co-fragmentation, the narrower isolation width of 0.8 m/z had the

332

fewest identifications and lowest gains by the CharmeRT workflow (Figures 4B and S8). Similar

333

to the results observed above, CharmeRT gains in peptide and protein group identifications were

334

higher in the HPLC configurations relative the UHPLC setups, particularly the 2D-HPLC which

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experienced the widest range in peptide identifications across the three different isolation widths.


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336

In agreement with the original CharmeRT publication, our results revealed CharmeRT

337

improvements as isolation widths increased, except for the 1D-UHPLC 500 min configuration.

338

This is counterintuitive, as the increase in precursor isolation interference becomes seemingly

339

more favorable for the CharmeRT workflow (Figure 3, isolation width 3 m/z and Figure S-9).

340

This novel insight into mPSM expectations implies that long 1D-UHPLC gradient times do not

341

experience the same benefit in the CharmeRT workflow as observed for the 2D-HPLC and other

342

more common HPLC configurations tested in this study.

343

Conclusions

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By using state-of-the-art LC-MS platforms, proteomes of simple organisms can now be

345

sequenced to near completion in just over an hour.29 However, comparable coverage is still not

346

readily achieved in more complex peptide mixtures, like those derived from higher-order

347

eukaryotes or microbial communities. While steady advancements in MS technology and LC will

348

continue to transform the achievable throughput and depth of proteomic analyses, the results

349

presented herein suggest that a better understanding of the dynamic relationship between peptide

350

separation and co-fragmentation can be leveraged by mPSM approaches, such as CharmeRT, to

351

enhance saturation in peptide identifications with minimal measurement duration or without

352

advanced LC configurations, i.e. 500 mm long, heated, UHPLC-driven peptide separations.

353

Moreover, this approach coupled to more specialized LC configurations, like 2D-HPLC, offers

354

even greater depth when compared to more traditional single-PSM approaches.

355

While our results revealed an improvement in peptide separation leads to the expected

356

increases in the number of peptides identified, similar gains can be achieved using shorter gradients

357

coupled with a mPSM approach. This performance differential became less as the peptide

358

separation improved. This observation has substantial implications for LC-MS/MS approaches



Page 16 of 26

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that use HPLC columns, as it suggests that a new approach towards improving peptide

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identification rates is to reduce peptide separation while employing a mPSM approach. Moreover,

361

for the 250 mm x 75 μm ID HPLC and UHPLC columns, a similar depth can be achieved when

362

HPLC measurements employ the CharmeRT wofklow. Importantly, this suggests that research

363

laboratories separating complex mixtures on a HPLC column with shorter gradient times can

364

achieve comparable identification metrics as if the sample were separated on a UHPLC column.

365

To maximize peptide identification and dynamic range of a proteome measurement, there

366

are many approaches that have been introduced in recent years. Many LC-MS proteome specialists

367

view the use of longer columns, packed with sub-2 μm separation particles, as the most direct

368

configuration toward improving peptide identifications.14 Yet, our results offer an alternative

369

perspective, in which the application of CharmeRT performed substantially better for a 2D-HPLC

370

strategy when compared to a long 1D-UHPLC column. Therefore, the 2D-HPLC strategy

371

combined with mPSM represents the best path toward improving the number of detectable

372

peptides, enabling a more rapid and comprehensive proteome analysis when compared to the

373

longer UHPLC columns. Moreover, because the demands of UHPLC configurations require LC

374

platforms that withstand very high pressures, which are often unavailable to non-specialists,

375

MS/MS spectral data derived from HPLC separations searched with a mPSM approach could be

376

become broadly adopted by the proteomics community.

377 378

Acknowledgments

379

This work was supported by the Plant-Microbe Interfaces Science Focus Area supported by the

380

U.S. Department of Energy (DOE) and the Office of Biological and Environmental Research

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(OBER). The manuscript has been authored by UT-Battelle, LLC, under contract no. DE-AC05-

382

00OR22725 with the U.S. Department of Energy. 15 ACS Paragon Plus Environment

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383

Supporting Information

384

A total of nine supporting figures (S-1 to S-9) with additional identification results and analyses

385

are presented in the Supplementary Results section.

386

Conflict of Interest Disclosure

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The authors declare no financial conflicts of interest.

388



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Figure Legends:

390

Figure 1. Boxplots showing the median isolation interference of all identified PSMs precursors

391

across a range of abundances in all initial LC configurations tested. A distribution of the total

392

number of PSMs precursors per abundance ranges is also shown in blue bars. All spectral data

393

were searched with Amanda-Percolator.

394

Figure 2. Results from the LC-MS/MS analyses of 2µg of HeLa digest using different LC

395

configurations and MS/MS spectra search algorithms. (A-D) Bar charts depicting the average

396

numbers of identifications obtained from the spectral data collected for each LC setup tested (x-

397

axis) and searched with Amanda-Percolator or the CharmeRT workflow. Percentage increase or

398

decrease of the CharmeRT results compared to the ones obtained with Amanda-Percolator are

399

shown above or close to each pair of bars per LC setup. Error bars are the standard error of the

400

mean (n= 3 technical replicates).

401

Figure 3. Search depth achieved by CharmeRT in the spectral data collected from (A) 2D-HPLC

402

and (B) 1D-UHPLC 500 mins runs at different isolation windows. Histograms of the number of

403

PSMs per isolation interference range and identified in the first search of CharmeRT are shown.

404

The colored stacked bars below each histogram represent the percentage (above each stacked bar)

405

of PSMs identified in the first search of CharmeRT from which no additional PSMs derived from

406

second search were identified (green color, Depth 1); from which just one additional PSM was

407

identified (orange color, Depth 2, i); from which two additional PSMs were identified (green color,

408

Depth 2, ii); from which three additional PSMs were identified (pink color, Depth 2, iii).

409

Figure 4. Results from the spectral data collected with a range of isolation windows from the best

410

LC configurations. (A) Boxplots showing the median isolation interference of all identified PSMs

411

precursors from the spectral data of the optimized LC gradients collected under a range of isolation 17 ACS Paragon Plus Environment

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412

windows. (B) Bar charts depicting the average numbers of peptide sequences and protein groups

413

obtained from the spectral data collected for each LC setup (x-axis) under a range of isolation

414

windows. Percentage increase or decrease afforded from the CharmeRT results compared to those

415

from Amanda-Percolator are shown above or close to each pair of bars per setup and isolation

416

window. Error bars are the standard error of the mean (n= 3 technical replicates). The average

417

numbers of PSMs and peptides analytes are presented in Figure S-8.

418



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Exploiting the dynamic relationship between peptide separation

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