Here we present quantitative proteomics data used in the evaluation of quantitative accuracy. A human cell line, MCF7 was split into 9 aliquotes that were spiked with a dilution series of 57 protein standards of known amounts spanning 5 orders of magnitude. The protein extracts were trypsinized, and the peptides were analysed by LC-MS using either a label-free or a label-based (TMT 6-plex and iTRAQ 8-plex) quantification approach. The iTRAQ- and TMT-labelled samples were co-analysed and separated by HiRIEF (high resolution isoelectric focusing) prior LC-MS. Raw MS data was identified and quantified under the software platform Proteome Discoverer 1.3.0.339 (Thermo Fisher Scientific Inc.) or MaxQuant software (version 1.2.0.18) (label-free data). For both protein identification and quantification at least 1 unique (i.e. a peptide that occurs in not more than one database entry) peptide was required. The false discovery rate (FDR) for peptide identification was set to 5% in all analyses. For the iTRAQ and TMT labeled samples, all MS/MS spectra were searched by SEQUEST combined with the Percolator algorithm (version 2.0) for PSM search optimization. Searches were performed against a custom made database consisting of SwissProt human sequences(uniprot.org 2012-01-17, 20242 entries), and the spiked in protein standards (57 protein sequences). Peptide FDR was calculated by a target – decoy approach.

Plasma proteomics has regained attention in recent years through advancements in mass spectrometry instrumentation and sample preparation, as well as new high-throughput affinity-based technologies. Here, we evaluate the analytical performance of the new Olink Reveal platform, a proximity extension assay based technology quantifying 1,034 proteins across biological pathways. Using spiked-in recombinant Interleukin-10 (IL-10) and vascular endothelial growth factor D (VEGF-D) in the NIST SRM 1950 plasma standard, we assessed the linearity, sensitivity, precision and accuracy of the Olink assay. The results demonstrated strong linear relationships (R² 0.922–0.953) for both IL-10 and VEGF-D across spiked-in concentrations, confirming the robust technical performance of Olink Reveal and underscoring its suitability for relative quantitation in large-scale studies. The resulting data contains no sensitive or personally identifiable information, and is available in public repositories, and therefore suitable for use in benchmarking and software development.

To unbiasedly evaluate the quantitative performance of different quantitative methods, and compare different popular proteomics data processing workflows, we prepared a benchmark dataset where the various levels of spikeed-in E. Coli proteome that true fold change (i.e. 1 fold, 1.5 fold, 2 fold, 2.5 fold and 3 fold) and true identities of positives/negatives (i.e. E.Coli proteins are true positives while Human proteins are true negatives) are known. To best mimic the proteomics application in comparison of multiple replicates, each fold change group contains 4 replicates, so there are 20 LC-MS/MS analysis in this benchmark dataset. To our knowledge, this spike-in benchmark dataset is largest-scale ever that encompasses 5 different spike level, >500 true positive proteins, and >3000 true negative proteins (2peptide criteria, 1% protein FDR), with a wide concentration dynamic range. The dataset is ideal to test quantitative accuracy, precision, false-positive biomarker discovery and missing data level.

Spike-in of standards of known concentrations used in proteomics-based workflows is an attractive approach for both accurate and precise multiplexed protein quantification. Here, a quantitative method based on targeted proteomics analysis of plasma proteins using isotope-labeled recombinant standards originating from the Human Protein Atlas project has been established. The standards were individually quantified prior to being employed in the final multiplex assay. The assays are mainly directed toward actively secreted proteins produced in the liver, but may also originate from other parts of the human body. This study included 21 proteins classified by the FDA as either drug targets or approved clinical protein biomarkers. We describe the use of this multiplex assay for profiling a well-defined human cohort with sample collection spanning over a one-year period. Samples were collected at four different time points, which allowed for a longitudinal analysis to assess the variable plasma proteome within individuals. Two assays toward APOA1 and APOB had available clinical data, and the two assays were benchmarked against each other. The clinical assay is based on antibodies and shows high correlation between the two orthogonal methods, suggesting that targeted proteomics with highly parallel, multiplex analysis is an attractive alternative to antibody-based protein assays.

Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.

Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined. We found, that while libraries of prefractionationed samples are generally larger, the actually yielded identifications are not increased compared to repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantifications is also highly dependent on the applied spectra library and also whether the peptide or protein level is analysed. Overall, the reproducibility and accuracy of DIA is superior to DDA in all analysed approaches.

Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined. We found, that while libraries of prefractionationed samples are generally larger, the actually yielded identifications are not increased compared to repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantifications is also highly dependent on the applied spectra library and also whether the peptide or protein level is analysed. Overall, the reproducibility and accuracy of DIA is superior to DDA in all analysed approaches.

Label free quantification (LFQ) and isobaric labelling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT 10-plex workflow to label free single shot data-independent acquisition (DIA) method on a controlled sample set. The sample set consisted of ten samples derived from 10 different mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. To match instrument time between the methods, the combined TMT sample was fractionated into ten fractions. The LC-MS data were acquired at two facilities to assess inter-laboratory reproducibility. Both methods resulted in a high proteome coverage (>5,000 proteins) with low missing values on protein level (<2%) The TMT workflow led to 15-20% more identified proteins and a slightly better quantitative precision whereas the quantitative accuracy was better for the DIA method. The quantitative performance was benchmarked by the number of true positives (UPS2 proteins) within the top 100 candidates. TMT and DIA performed similar. The quantitative performance of the DIA data could be even improved by searching them directly against a database instead of using a project specific library. Our experiments also demonstrated that both methods can be easily transferred between facilities.

Spectral counting is a straightforward label-free quantitation strategy used in bottom-up proteomics workflows. The application of spectral counting in label-free top-down proteomics workflows can be similarly straightforward but has not been applied as widely as quantitation by chromatographic peak areas or peak intensities. In this study, we evaluate spectral counting for quantitative comparisons in label-free top-down proteomics workflows by comparison with chromatographic peak areas and intensities. We tested these quantitation approaches by spiking standard proteins into a complex protein background and comparing relative quantitation by spectral counts with normalized chromatographic peak areas and peak intensities from deconvoluted extracted ion chromatograms of the spiked proteins. Ratio estimates and statistical significance of differential abundance from each quantitation technique are evaluated against the expected ratios and each other. In this experiment, spectral counting was able to detect differential abundance of spiked proteins for expected ratios ≥2, with comparable or higher sensitivity than normalized areas and intensities. We also found that while ratio estimates using peak areas and intensities are usually more accurate, the spectral-counting-based estimates are not substantially worse. Following the evaluation and comparison of these label-free top-down quantitation strategies using spiked proteins, spectral counting, along with normalized chromatographic peak areas and intensities, were used to analyze the complex protein cargo of exosomes shed by myeloid-derived suppressor cells collected under high and low conditions of inflammation, revealing statistically significant differences in abundance for several proteoforms, including the active pro-inflammatory proteins S100A8 and S100A9.

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC–MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (

The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Global and targeted approaches were applied to nasal swabs from healthy and COVID-19-confirmed cases within 24 hours post-positive-confirmation and at 3 weeks post-infection to observe changes in proteome and metabolome. We found that the tryptophan/kynurenine metabolism pathway is a pinch-point regulator of canonical and non-canonical transcription activation, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course. Methods Nasal epithelial swabs were self-collected by participants in this study. Swabs were resuspended in 80% methanol with 6mg of 1.0 mm zirconium beads and used cell shearing to extract proteins and metabolites. The method is described in Wasinger et al., 2020 [1]. Proteins were pelleted and the supernatant containing metabolites stored at -80°C until required. Protein pellet was resuspended in digestion buffer and 50 µg enzymatically treated with trypsin overnight at room temperature. Proteomic mass spectrometry Mass spectrometry was carried out using a QExactive (Thermo Electron, Bremen, Germany) run in DDA mode using 1.5 μg (2.0 μL from 10μL) as previously described [2]. Peptides were eluted using a linear gradient of H2O:CH3CN (98:2, 0.1% formic acid) to H2O:CH3CN (64:36, 0.1% formic acid) at 250 nL min-1 over 60 min. Statistical Analysis Proteins were identified using Mascot Daemon v2.5.1 (Matrix Science, London, UK) searched against the SwissProt and SARV19 database (downloaded February 2021, containing 563,972 sequences; and July 2020, containing 271,909 sequences, respectively). Search parameters were set to carbamidomethyl (C); variable modifications, oxidation (M), phospho (STY); enzyme, semi-Trypsin; and maximum missed cleavages, 1; peptide tolerance, ± 5 ppm; fragment tolerance, 0.05 Da. Scaffold software (version 4.6.1, Proteome Software Inc., Portland, OR, USA) was used to compare the proteome. Peptide identifications were accepted if they could be established at greater than 95% probability using the Scaffold delta-mass correction. Protein identifications were accepted if they could be established at less than 1% false discovery rate (FDR) and contained at least 2 identified peptides. Expression changes across the samples were measured via spectral count, normalised to total ion count. ANOVA was used to report abundance changes controlled by the Benjamini-Hochberg procedure for multiple comparisons, with p-values set to <0.05. The studies reached a power ≥ 90% and were calculated using PASS software based on a mean abundance values and standard deviation between groups. The proteomic dataset of differentially abundant proteins was assessed for enriched pathways using Ingenuity Pathway Analysis (IPA® Qiagen, CA, USA). The core analysis was carried out using the default settings with only direct relationships and only experimentally observed confidence considered based on the IPA knowledge base (genes only). The P-value for the correlation between identified proteins and a given canonical pathway was calculated by Fisher's exact test. Targeted proteins were analysed using Skyline Software, and peptides were accepted based on retention time and sequence with at least 3 transitions required. Peak area under curve of the parent ion was used to assess relative abundance of the marker panel. Log2 transformed data were evaluated using Student T-test, and Receiver Operating Characteristic (ROC) probability curves to measure ability to distinguish between binary classifiersPRM targeted analysis applied transitions listed in Attachment. Quantification of Kynurenine Pathway Mixed standards and 100 µl aliquots of Nasal methanolic extracts were spiked with an internal standard mixture containing labelled KP metabolites; dried, and reconstituted in 100 µl of water, filtered through 4 mm syringe filters with 0.2 μm membrane into reduced volume LC vials; 20 µl aliquots were injected for analysis. MRM LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C using a gradient of 0.1% formic acid to 10% methanol over 2 min, then ramped to 60% B to 4min, and then ramped to 100%B by 8mins. Quantification of NAD+ome metabolites LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C. The mobile phase consisted of aqueous 0.1% formic acid (A) and methanol (B). The gradient elution was programmed as follows: start at 10 % B, hold 2 minutes, ramp to 60%B in 4min, then to 100%B in 8min. In 0.4min set to 10 % B and equilibrate for 5.6 min. Total run time is 20 min. Mass spectrometric detection was performed using multiple reaction monitoring (MRM) with heated electrospray ionization (HESI) source in positive mode. MSD parameters were optimised using Anthranilic acid direct infusion, and the tune file created was used in the created method. The conditions were: ion spray voltage, 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision argon gas 1 Torr, sheath and auxiliary gas valves (nitrogen) set at 20 and 10 arbitrary units respectively. The MRM transitions for all analytes were optimised using a syringe infusion pump and are shown in Attachment 1. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). NAD+ome LCMS/MS assay of nasal epithelial (NE) swab extracts Methods followed Bustamante et al. [3]. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex, USA) solvent delivery system/autosampler using an adaptation of a previously published method by Bustamante et al. [5]. Isotopically enriched (2H) internal standards were purchased from Toronto Research Chemicals and primary standards from Sigma-Aldrich. HESI-MS parameters: Ion spray voltage 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision gas 1.0 Torr. These parameters were optimised using NMN solution in positive ion mode. Calibrators of known concentrations (0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.4 μM) of NADOME metabolites were prepared by mixing aliquots of standards with a fixed volume of internal standard mixture. Similarly, NE extracts were mixed with internal std. cocktail, dried and reconstituted in 50 µl of 100 mM ammonium acetate in water. Samples were filtered into LC vials and 20μL injected for analysis. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). Mobile phases consisted of 5mM ammonium acetate in water pH 9.5 (A); 100 % Acetonitrile (B) according to Table S5 using a Phenomenex Luna 3 µm NH2 100 Å 150 x 2 mm column. Racemic amino acid analysis Methods were adapted from Ayon et al. [4]. Briefly, 40 µl of colon biopsies extracts were mixed with 2H4-alanine as internal standard. Samples were dried and derivatised with 20 µl of 10mM Marfey’s reagent in acetone and 5 µl of triethylamine and incubated at 37˚C for 3 hours, the reaction was quenched with 10 µl 0.5 M HCl. Samples were diluted with 120 µl of 30 % ACN in 0.1% aqueous formic acid. Phenomenex SPE Strata-X cartridges (30 mg) were preconditioned with methanol, followed by 0.1 % formic acid in water, and samples were loaded and washed with 0.1 % formic acid in water, and then eluted with 70 % acetonitrile in 0.1 % aqueous formic acid. Eluants were dried and reconstituted in 0.1 % aqueous formic acid before analysis. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer as described in Attachment 1. GCMS/MS assay of nasal epithelial (NE) swabs of picolinic and quinolinic acid GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column Agilent J&W DB5-MS UI 30mx 0.25mm x 0.25μm. Methods followed those described by Smythe et al. [5]. Single Ion Monitoring (SIM) GC-MS assay of picolinic and quinolinic acid in nasal swab extracts. Picolinic and quinolinic acid in NE extracts were assayed by GC–MS in electron-capture negative ionization mode; a very sensitive method with on-column limit of detection for QUIN and PIC < 1 femtomol on column (Smythe et al. 2003). Briefly, standards and NS extracts (100-200μl) were spiked with 2H4 -Pic and 2H3-Quin in 13x100mm glass cell culture tubes, and dried in a Speedvac before derivatisation with 60μL TFAA and 60μL of HFP. Capped tubes were placed in a heating block at at 60°C for 30 min to produce the hexafluoro-isopropyl esters of the respective acids. Samples were then dissolved in 80μl of toluene, washed with 1ml of 5% sodium bicarbonate and 1ml of water to remove by-products. The upper toluene layer was passed through anhydrous sodium sulphate mini columns (approx. 500 mg) into autosampler vials, and 2μl were injected into the GC/MS system. Sample concentrations of Pic and Quin were calculated from the standard curves generated.
Monitored SIM ions for 2H4 -Pic, Pic, 2H3-Quin and Quin are m/z 277, m/z 273, m/z 467 and m/z 470 respectively.
Injector temperature 250˚C, transfer line temperature 280˚C; run time 15.2 minutes using T program below: GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column

An LC-MS/MS assay based on a signature peptide was developed and fully validated for the quantitation of bovine lactoferrin in infant formulas. Three unreported signature peptides were derived and identified from the tryptic peptides of bovine lactoferrin. The peptide ETTVFENLPEK was used for quantification based on assay performance. The blank matrix camel milk powder and bovine lactoferrin protein standards were mixed and spiked with stable isotope-labeled internal standard to establish a calibration curve. The established method was extensively validated by determining the linearity (R2 > 0.999), sensitivity (limit of quantitation, 0.16 mg/100 g), recovery (83.1–91.6%), precision (RSD < 5.4%) and repeatability (RSD < 7.7%). To validate the applicability of the method, four different brands of infant formulas in China were analysed. The acquired contents of bovine lactoferrin were 52.60–150.56 mg/100 g.

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome-wide scale. To this end, label-free LC-MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low cost and easily implemented quantification methods (APEX, emPAI, iBAQ, Top3 and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; i.e. using spiked in SIS peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of mis-cleaved, modified and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy. The .raw data submitted to PRIDE correspond to replicate DDA LC-MS/MS analysis of the UPS2 mix (Universal Proteomics Standard, UPS2, Sigma-Aldrich), as well as triplicate DDA LC-MS/MS analysis of the UPS2 mix spiked into samples from the following organisms M. pneumoniae, L. interrogans and D. melanogaster. The accompanying R-script called calculatePQIs.R was used to calculate the different Quantification Indices, provided that the peptide XICs or Spectral Counts have been loaded into a data.frame R object (which can be produced by SafeQuant).

The human serum proteome has been extensively screened for biomarkers. However, the large dynamic range of protein concentrations in serum and the presence of highly abundant and large molecular weight proteins, make identification and detection changes in the amount of low-molecular weight proteins (LMW, molecular weight ≤ 30kDa) difficult. Here, we developed a gel-filter method including four layers of different concentration of tricine SDS-PAGE-based gels to block high-molecular weight proteins and enrich LMW proteins. By utilizing this method, we identified 1,576 proteins (n = 2) from 10 μL serum. Among them, 559 (n = 2) proteins belonged to LMW proteins. Furthermore, this gel-filter method could identify 67.4% and 39.8% more LMW proteins than that in representative methods of glycine SDS-PAGE and optimized-DS, respectively. By utilizing SILAC-AQUA approach with labeled recombinant protein as internal standard, the recovery rate for GST spiked in serum during the treatment of gel-filter, optimized-DS, and ProteoMiner was 33.1 ± 0.01%, 18.7 ± 0.01% and 9.6 ± 0.03%, respectively. These results demonstrate that the gel-filter method offers a rapid, highly reproducible and efficient approach for screening biomarkers from serum through proteomic analyses.

Quantification of individual proteins is an essential task in understanding biological processes. For example, determination of concentrations of proteins transporting and metabolizing xenobiotics is a prerequisite for drug disposition predictions in humans based on in vitro data. So far, this task has frequently been accomplished by targeted proteomics. This type of analyses requires preparation of stable isotope labeled standards for each protein of interest. The selection of appropriate standard peptides is usually tedious and the number of proteins that can be studied in a single experiment by these approaches is limited. In addition, incomplete digestion of proteins often affects the accuracy of the quantification. To circumvent these constrains in proteomic protein quantification, label- and standard-free approaches, such as “total protein approach” (TPA) have been proposed. Here we directly compare an approach using stable isotope labeled (SIL) standards and TPA for quantification of transporters and enzymes in human liver samples within the same LC-MS/MS runs. We show that TPA is a convenient alternative to SIL-based methods. Optimization of the sample preparation beyond commonly used single tryptic digestion, by adding consecutive cleavage steps, improves accuracy and reproducibility of the TPA method to a level, which is achievable by analysis using stable isotope-labeled standard spiking.

Major advances have been made to improve the sensitivity of mass analyzers, spectral quality, and speed of data processing enabling more comprehensive proteome discovery and quantitation. While focus has recently begun shifting toward robust proteomics sample preparation efforts, a high-throughput proteomics sample preparation is still lacking. We report the development of a highly automated universal 384-well plate sample preparation platform with high reproducibility and adaptability for extraction of proteins from cells within a culture plate. Digestion efficiency was excellent in comparison to a commercial digest peptide standard with minimal sample loss while improving sample preparation throughput by 20- to 40-fold (the entire process from plated cells to clean peptides is complete in ∼300 min). Analysis of six human cell types, including two primary cell samples, identified and quantified ∼4,000 proteins for each sample in a single high-performance liquid chromatography (HPLC)–tandem mass spectrometry injection with only 100–10K cells, thus demonstrating universality of the platform. The selected protein was further quantified using a developed HPLC-multiple reaction monitoring method for HeLa digests with two heavy labeled internal standard peptides spiked in. Excellent linearity was achieved across different cell numbers indicating a potential for target protein quantitation in clinical research.

PDBs were generated using molecular dynamics. See DESRES_README.txt for more details on molecular dynamics simulation. PDBs were converted to volumetric data using EMAN2. The image stack contains 100 000 projection images each of the 10 states (see PDBs), at an SNR of 1/10 in the following order: state00 (closed) state01 (closed) state02 (closed) state10 (intermediate) state11 (intermediate) state12 (intermediate) state13 (intermediate) state20 (open) state21 (open) state22 (open) Projections were made using relion_project. White gaussian noise with standard deviation 1.0 CTF multiplied signal High signal-to-noise ratio Image size 96x96x96 MRC-files used for the projections not included, but can be generated using the PDB files. Final RELION reconstruction resolution is 5.33334 Angstrom (Nyqvist is at 5.33334). Command line for RELION reconstruction: relion_refine_mpi --o refine3d/run --auto_refine --split_random_halves --i rot_trans_ctf_noise/stack.star --ref pdb2mrc/state21.mrc --ini_high 20 --dont_combine_weights_via_disc --preread_images --pool 30 --pad 2 --ctf --particle_diameter 130 --flatten_solvent --zero_mask --oversampling 1 --healpix_order 2 --auto_local_healpix_order 4 --offset_range 5 --offset_step 2 --low_resol_join_halves 40 --norm --scale --j 2 --gpu --fristiter_cc --grad This dataset is generated as a testbed for cryo-EM heterogeneity analysis.

The current state of proteomics requires a choice between targeted and discovery methods. The former provides exceptional quantitation and data completeness, but only with few analytes. Discovery proteomics can identify hundreds and thousands of proteins in a sample, but with poor quantitation and data completeness. We have established and optimized a method that combines targeted and data-independent acquisition for absolute quantitation of all plasma proteins in a single sequential window acquisition of all theoretical fragment ions (SWATH) acquisition run using a panel of spike-in standards (SIS). We compared the absolute quantitation (AQ) of SWATH and high-resolution multiple-reaction monitoring (MRM-HR) acquisition methods using the 100 protein PlasmaDive SIS panel spiked into human plasma. SWATH provided equivalent quantitation and differentially abundant protein profiles as MRM-HR. Absolute quantities of the SIS peptides from the SWATH data were used to estimate the absolute quantities (eAQ) for all the proteins in the run. The eAQ values provided similar quantitation and differentially abundant protein profiles as AQ and protein group (PG) values, and the eAQ method was the only scheme where all selected proteins were verified by MRM-HR. We applied the eAQ method to a cohort of 16 non-small cell lung cancer (NSCLC) patients receiving immunotherapeutics and found that fibronectin (FN1) and proteoglycan 4 (PRG4) were useful markers for predicting patients who would stay on these agents for at least 6 months (area under the curve of 0.8438 and 0.6735, respectively). Thirteen additional plasma samples confirmed that FN1 and PRG4 are putative biomarkers (positive predictive value is 100% and 85.7%, respectively) for a prolonged (>6 months) response to immunotherapeutic agents. In conclusion, we describe an optimized method for absolute quantification of all proteins in a SWATH run, and this approach is amenable for the identification of plasma biomarkers.

Soon after commencement of the SARS-CoV-2 disease outbreak of 2019 (COVID-19), it became evident that the receptor-binding domain of the viral spike protein is the target of neutralizing antibodies that comprise a critical element of protective immunity to the virus. This study addresses the relative lack of information regarding actual antibody concentrations and binding affinities in convalescent plasma (CP) samples from COVID-19 patients and extends these analyses to post-vaccination (PV) samples to estimate protective IgG antibody (Ab) levels. A direct enzyme-linked immunosorbent assay (ELISA) was used to measure IgG anti-spike protein (SP) antibodies (Abs) relative to human chimeric spike S1 Ab standards. Microplate wells were coated with recombinant SP. Affinities of Ab binding to SP were determined by previously described methods. Binding affinities were also determined in an RBD-specific sandwich ELISA. Two indices of protective immunity were ...

In this manuscript, we present a proof-of-concept study for targeted relative protein quantitation workflow using chemical labeling in the form of dimethylation, coupled with selected reaction monitoring (dimethyl-SRM). We first demonstrate close to complete isotope incorporation for all peptides tested. The accuracy, reproducibility, and linear dynamic range of quantitation are further assessed based on known ratios of nonhuman standard proteins spiked into human cerebrospinal fluid (CSF) as a model complex matrix. Quantitation reproducibility below 20% (CV < 20%) was obtained for analyte concentrations present at a dynamic range of 4 orders of magnitude lower than that of the background proteins. An error of less than 15% was observed when measuring the abundance of 44 out of 45 major human plasma proteins. Dimethyl-SRM was further examined by comparing the relative quantitation of eight proteins in human CSF with the relative quantitation obtained using synthetic heavy peptides coupled to stable isotope dilution-SRM (SID-SRM). Comparison between the two methods reveals that the correlation between dimethyl-SRM and SID-SRM is within 0.3–33% variation, demonstrating the accuracy of relative quantitation using dimethyl-SRM. Dimethyl labeling coupled with SRM provides a fast, convenient, and cost-effective alternative for relative quantitation of a large number of candidate proteins/peptides.