Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.


Cell Culture
The A549 and Calu-6 cells were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin.Cells were maintained in a 37 °C incubator with 5% CO2.After treatment with 0.25% (w/v) trypsin/EDTA treatment, cells were washed once with 10 mL cold PBS.The cell suspension was centrifuged at 1200 rpm for 2 min at 4 °C, with the cell pellet kept at -80 °C before analysis.

Protein extraction and digestion
Cell pellets were suspended in 50 µL cell lysis buffer (5% SDS, 1x protease inhibitor cocktail, 50 mM TEAB) by pipetting up and down.The cell suspension was then sonicated with a probe-tip sonicator for 2 pulses (10 sec on and 20 sec off for each pulse) on ice.The cell lysates were centrifuged at 13000 g for 15 min at 4 °C, with the supernatant transferred into a new 1.5 mL tube.
Extracted proteins were processed with the suspension trapping (S-Trap) method as reported before. 2 Briefly, proteins were first reduced in 20 mM DTT by heating at 95 °C for 10 min.After cooling down, iodoacetamide was added to a final concentration of 40 mM for alkylation in darkness at room temperature for 30 min.The cell lysate solution was acidified by aqueous phosphoric acid (a final concentration of ~1.2% phosphoric acid) and diluted by six volumes of the S-Trap buffer (90% aqueous methanol in 100 mM TEAB, pH 7.1).The acidified mixture was transferred onto a midi S-Trap column followed by centrifugation at 2000 g for 1 min.After washing with the S-Trap buffer three times, proteins on the column were digested with trypsin (an enzyme to substrate ratio of 1:50, w/w) at 37 °C overnight.The resulting peptides were eluted by adding 50 μL of 0.2% formic acid and 50% acetonitrile containing 0.2% formic acid subsequently.
The elutes were combined and dried down with SpeedVac.
For proteome quantification, four biological repeats were processed for each cell line samples (A549 and Calu-6), and the peptides were labeled with iTRAQ 8-plex reagents as follows: four repeats of A549 digests were labeled with iTRAQ reagents 113, 114, 115, 116, and Calu-6 digests were labeled with 117, 118, 119 and 121, respectively.After labeling, all the eight digests were combined and dried with a lyophilizer.The mixture sample was fractioned with the high pH reversed-phase fractionation kit from Thermo Fisher Scientific (Cat# 84868) according to the manufacturer's instructions.

Sample preparation of iTRAQ labeled three proteome mixture
In brief, as shown in Figure S1, we labeled yeast peptides with all six reagents and mixed the differentially labeled peptides so that the ratio of reporter ions at channels from 113 to 115 was 10:6:1, and the ratio of reporter ions at channels from 116 to 118 was 1:6:10.We then labeled equal amounts of human peptides with iTRAQ reagents that generate reporter ions at m/z of 113, 114, 115 and 119 and labeled equal amounts of E. coli peptides with iTRAQ reagents that generate reporter ions at m/z of 113, 114, 115 and 121, respectively.After labeling, all aliquots were combined and the mixture was desalted using spin columns.We used this sample to measure the interference effect.Without interference, we expected ratios of channels 113 to 114, 114 to 115 and 113 to 115 to be equal to the ratios of channels 118 to 117, 117 to 116 and 118 to 116, respectively, for each yeast peptide ion selected for MS 2 analysis.Interference from human or/and E. coli peptide ions on yeast ions in channels 113, 114 and 115 was responsible for a leveling out of yeast reporter ion intensities so that ratios measured for channels 113, 114 and 115 were less than those measured for channels 116, 117 and 118.Furthermore, spiking human and E. coli digests into channels 119 and 121 makes these two channels can reflect the interference from human and E. coli.

S-5
The LC gradient for analysis of K562 cell digests started at 2% acetonitrile with 0.1% formic acid and increased to 35% acetonitrile over 90 min, followed by a 15 min wash at 80% acetonitrile and a 15 min equilibration at 2% acetonitrile, for a total of 120 min.acquired with the mass spectrometer using an ion spray voltage of 2.3 kV, GS1 5 psi, GS2 0, CUR 30 psi and an interface heater temperature of 150 °C.Mass spectra was recorded with Analyst TF 1.7 software in the DDA mode.The data without dynamic exclusion (turboDDA) was obtained with the set of never exclude former target ions and precursors that exceeded a threshold of 100 cps were selected for MS 2 and 100 × 30 ms MS 2 candidate ion scans from 100-1800 Da were acquired in high sensitivity mode.Adjusted CE when iTRAQ reagent was used.

Data analysis for 1 ng, 10 ng and 100 ng of K562 digests acquired on SCIEX Triple TOF instrument
Mass spectrometry data generated were stored, searched, and analyzed using the ProHits laboratory information management system platform. 3Within ProHits, WIFF files were converted to an MGF format using the WIFF2MGF converter and to a mzML format using ProteoWizard as variable modifications, while the carbamidomethylation of cysteine residues was regarded as a fixed modification.Precursors and fragments had a mass tolerance of 10 ppm and 0.6 Da, respectively.Minimum and maximum peptide lengths were six and 144 amino acids, respectively.
The missed cleavage allowed for every peptide was two.The filtering of proteins had a maximum false discovery rate (FDR) of 0.01.The default settings of Proteome Discoverer were other parameters that were not mentioned.

MaxQuant database searching for iTRAQ labeled three proteome mixture samples
For protein identification and quantification of iTRAQ labeled three proteome mixture sample, the wiff files from the Sciex TripleTOF 6600 system were imported into MaxQuant (version 1.6.

( 4 Data analysis for 1
V3.0.10702) and the AB SCIEX MS Data Converter (V1.3 beta).The data were then searched using Mascot (V2.3.02) and Comet (V2016.01rev.2).The spectra were searched against the human and adenovirus sequences in the RefSeq database (version 57, January 30 th , 2013) acquired from NCBI, supplemented with "common contaminants" from the Max Planck Institute (http://lotus1.gwdg.de/mpg/mmbc/maxquant_input.nsf/7994124a4298328fc125748d0048fee2/$FILE/contaminants.fasta) and the Global Proteome Machine (GPM; https://www.thegpm.org/crap/),forward and reverse sequences (labeled "gi|9999" or "DECOY"), sequence tags (BirA, GST26, mCherry, and green fluorescent protein (GFP)) and streptavidin, for a total of 72,481 entries.Database parameters were set to search for tryptic cleavages, allowing up to two missed cleavage sites per peptide with a mass tolerance of 35 ppm for precursors with charges of 2+ to 4+ and a tolerance of 0.15 amu for fragment ions.Deamidated asparagine and glutamine and oxidized methionine were selected as variable modifications.Results from each search engine were analyzed through the Trans-Proteomic Pipeline (v.4.7 POLAR VORTEX rev 1) via the iProphet pipeline.ng and 10 ng of K562 digests acquired on a Thermo Orbitrap Eclipse instrument Proteome Discoverer Software (version 2.5, San Jose, CA) was used to process raw files for detecting features, searching databases and quantifying proteins/peptides.The search of MS/MS spectra was conducted against the UniProt human database (downloaded on June 27 th , 2022, containing 79,435 entries).Methionine oxidation and N-terminal protein acetylation were chosen

3 . 4 )
with integrated Andromeda database search engine.The MS/MS spectra were queried against the fasta file combined by three databases from human (65,536 entries), yeast (6,068 entries) and E. coli (4,306 entries).Database search employed the following parameters: Reporter ion MS2 with multiplicity 8plex for the iTRAQ 8-plex experiments, trypsin digestion with maximum 2 missed cleavages, oxidation of methionine and acetylation of protein N-termini as variable modifications, carbamidomethylation of cysteine as fixed modification, maximum number of modifications per peptide set at 5, minimum peptide length of 6, and protein FDR 0.01.The precursor intensity filtering (PIF) was set to 0, 0.25, 0.5 or 0.75 when testing their effects on identification and quantification.Instrument type was set to AB SCIEX Q-TOF and the isolation window used in

Figure S1 .
Figure S1.A three-proteome model to simulate complex samples.(A) Schematic of reporter ion intensities for samples consisting of six iTRAQ channel labeled yeast digests, four iTRAQ channel labeled human cell digests, and four iTRAQ channel labeled E. coli digests.(B) An ideal yeast peptide MS 2 spectrum without human and E. coli peptide interference in the first three channels would have identical and mirrored iTRAQ reporter ion intensities in the last three channels.(C) Typical yeast peptide spectra have some interference from human (red) or/and E. coli (blue) peptide, resulting in a ratio distortion toward 10:6:1.

Table S1 .
Optimization of acquisition parameters for the turboDDA and

Table S2 .
Total MS/MS spectra for turboDDA and DEDDA S-10
Each sample was analyzed on a For both acquisition method, adjusted CE was used when analyzing iTRAQ labeled three proteome mixture.The turboDDA method was optimized by testing acquisition parameters, including the use of low resolution Q1 or unit resolution Q1; the precursor intensity threshold (100 cps or 300 cps); and the MS/MS accumulation time (30ms or 45ms); the same cycle time (3.25s) but different number of peptide ions selected for MS/MS fragmentation (top 50 or top 100) and different MS/MS accumulation time (60 ms or 30 ms).The DEDDA method was also optimized by testing several acquisition parameters, including the use of different dynamic exclusion time (7s or 20s); precursor exclusion after 1 time triggered or after 3 times triggered; different number of peptide ions selected for MS/MS fragmentation (top 10 or top 100) and different MS/MS accumulation time (100 ms or 30 ms).to 1,500).For DEDDA method, dynamic exclusion was used for 20 s to exclude all charge states for a specified precursor.For turboDDA, no dynamic exclusion was used and top 100 MS/MS candidate ion scans were acquired.The collection of MS2 spectra was completed in the linear ion trap (isolation window: 1.6 m/z; scan rate: rapid; AGQ target: standard; MaxIT: Auto; HCD CE: 35%; data type: centroid).

Table S1 .
Optimization of acquisition parameters for the turboDDA and DEDDA methods (10 ng or 1 ng of the K562 digest was used as standard for testing; n = 2).

Table S3 .
The number of proteins identified from 250 ng and 25 ng of K562 cell digests analyzed by turboDDA and DEDDA methods. S-12