We generated two comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent and Data-Indepentdent Acquisition modes. This dataset contain a balanced two-class design (cell lines: A549 vs K562), allowing for investigating mixed effects from class, batch and acquisition method. Investigators can also compare and integrate DDA and DIA platforms, delve into the various patterns and mechanisms of missing values, benchmark batch effects correction algorithms and assess confounding between different technical issues.

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.

LC-MS (DDA) for proteomics tutorial dataset (for MS-DIAL5)

This dataset includes raw data (*.raw) for the LC-MS proteomics analysis tutorial of MS-DIAL5.
The *.raw files in these data were obtained from https://repository.jpostdb.org/entry/JPST000200.
The human_proteins_ref.fasta was obtained from https://www.uniprot.org/.

In global proteomic analysis, it is estimated that proteins span from millions to less than 100 copies per cell. The challenge of protein quantitation by classic shotgun proteomic techniques relies on the presence of missing values in peptides belonging to low-abundance proteins that lowers intraruns reproducibility affecting postdata statistical analysis. Here, we present a new analytical workflow MvM (missing value monitoring) able to recover quantitation of missing values generated by shotgun analysis. In particular, we used confident data-dependent acquisition (DDA) quantitation only for proteins measured in all the runs, while we filled the missing values with data-independent acquisition analysis using the library previously generated in DDA. We analyzed cell cycle regulated proteins, as they are low abundance proteins with highly dynamic expression levels. Indeed, we found that cell cycle related proteins are the major components of the missing values-rich proteome. Using the MvM workflow, we doubled the number of robustly quantified cell cycle related proteins, and we reduced the number of missing values achieving robust quantitation for proteins over ∼50 molecules per cell. MvM allows lower quantification variance among replicates for low abundance proteins with respect to DDA analysis, which demonstrates the potential of this novel workflow to measure low abundance, dynamically regulated proteins.

State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 minute LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

To mimic a complex marine sample, a dilution series of R. pomeroyi and T. pseudonana was created at different cellular ratios. These mixtures were filtered and proteins were extracted from the filter for tryptic digestion and LC-MS/MS analysis.

Biological fractions were lysed, digested and analyzed using proteomic mass spectrometry.

Data are available for download at the EBI PRIDE Archive.
Homepage:   https://www.ebi.ac.uk/pride/archive
Project URL: https://www.ebi.ac.uk/pride/archive/projects/PXD004799
Data URL:   https://www.ebi.ac.uk/pride/archive/projects/PXD004799/files

Raw data of nanoLC-IMS-MS/MS (DDA-PASEF) from HeLa whole proteome digest.

HeLa cells were lysed in a urea-based lysis buffer (7 M urea, 2 M thiourea, 5 mM dithiothreitol (DTT), 2% (w/v) CHAPS) assisted by sonication for 15 min at 4°C in high potency using a Bioruptor instrument (Diagenode). Proteins were digested with Trypsin using a filter-aided sample preparation (FASP) [Wisniewski et al., 2009] as previously detailed [Distler et al., 2016]. 200 ng of peptide digest were analyzed using a nanoElute UPLC coupled to a TimsTOF PRO MS (Bruker). Peptides injected directly in an Aurora 25 cm x 75 µm ID, 1.6 µm C18 column (Ionopticks) and separated using a 120 min. gradient method at 400 nL/min. Phase A consisted on water with 0.1% formic acid and phase B on acetonitrile with 0.1% formic acid. Sample was injected at 2% B, lineally increasing to 20% B at 90 min., 35% B at 105 min., 95% at 115 min. and hold at 95% until 120 min. before re-equilibrating the column at 2%B. The MS was operated in DDA-PASEF mode [Meier et al., 2018], scanning from 100 to 1700 m/z at the MS dimension and 0.60 to 1.60 1/k0 at the IMS dimension with a 100 ms TIMS ramp. Each 1.17 sec MS cycle comprised one MS1 and 10 MS2 PASEF ramps (frames). The source was operated at 1600 V, with dry gas at 3 L/min and 200°C, without nanoBooster gas. The instrument was operated using Compass Hystar version 5.1 and timsControl version 1.1.15 (Bruker).

We generated two comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent and Data-Indepentdent Acquisition modes. This dataset contain a balanced two-class design (cell lines: HCC1806 vs HS578T), allowing for investigating mixed effects from class, batch and acquisition method. Investigators can also compare and integrate DDA and DIA platforms, delve into the various patterns and mechanisms of missing values, benchmark batch effects correction algorithms and assess confounding between different technical issues.

Shotgun proteomics is one of the key "omics" methods, the methodology of which is rapidly developing. Mass spectrometers of the TimsToF series (Bruker) with ion mobility are one of the young technological platforms for shotgun proteomics in which both data dependent (DDA) and data independent acquisition (DIA) proteomics methods might be performed. However, only a few comparisons of the effectiveness of DDA and DIA proteomics on TimsToF have been published, carried out mainly on the test samples. From the other hand, peculiarities of osteogenic differentiation of human valve interstitial cells (VICs) are fruitful therapeutic target for calcific aortic valve disease (CAVD) treatment. Still, there is no data whether pathological osteogenic differentiation of VICs similar to normal ossification. Combining this technical and biological tasks we performed comparative proteomics analysis of osteogenic differentiation of human VICs and osteoblasts by DIA- and DDA-PASEF proteomics on TimsToF Pro.

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

MaxDIA is a universal platform for analyzing data-independent acquisition proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves cutting-edge proteome coverage with significantly better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate estimates on both library-to-DIA match and protein levels, also when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA – a framework for the hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s novel bootstrap-DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies, BoxCar acquisition and trapped ion mobility spectrometry, both lead to deep and accurate proteome quantification.

The integration of proteomic datasets, generated by non-cooperating laboratories using different LC-MS/MS setups can overcome limitations in statistically underpowered sample cohorts but has not been demonstrated to this day. In proteomics, differences in sample preservation and preparation strategies, chromatography and mass spectrometry approaches and the used quantification strategy distort protein abundance distributions in integrated datasets. The Removal of these technical batch effects requires setup-specific normalization and strategies that can deal with missing at random (MAR) and missing not at random (MNAR) type values at a time. Algorithms for batch effect removal, such as the ComBat-algorithm, commonly used for other omics types, disregard proteins with MNAR missing values and reduce the informational yield and the effect size for combined datasets significantly. Here, we present a strategy for data harmonization across different tissue preservation techniques, LC-MS/MS instrumentation setups and quantification approaches. To enable batch effect removal without the need for data reduction or error-prone imputation we developed an extension to the ComBat algorithm, ´ComBat HarmonizR, that performs data harmonization with appropriate handling of MAR and MNAR missing values by matrix dissection The ComBat HarmonizR based strategy enables the combined analysis of independently generated proteomic datasets for the first time. Furthermore, we found ComBat HarmonizR to be superior for removing batch effects between different Tandem Mass Tag (TMT)-plexes, compared to commonly used internal reference scaling (iRS). Due to the matrix dissection approach without the need of data imputation, the HarmonizR algorithm can be applied to any type of -omics data while assuring minimal data loss

The goal of this project is to compare label free quantification, chemical labeling with tandem mass tags, and data independent acquisition discovery proteomics approaches using lung squamous cell carcinomas and adjacent lung tissues. This additional single sample LC-MS/MS analysis with data dependent acquisition was performed to enable direct comparison to the PRIDE dataset, titled, "Comparison of Lung Cancer Proteome Profiles 3: DIA," where single samples were analyzed with LC-MS/MS using data independent acquisition.

Opportunistic infections of the respiratory tract often succeed under a weakened immune response caused by an underlying illness or hospitalization. The human fungal pathogen, Cryptococcus neoformans, and the bacterial pathogen, Klebsiella pneumoniae, are both well-characterized microbes that cause severe infections within immunocompromised individuals. In this study, we simulate a concentration-dependent pulmonary co infection of a bacterial and fungal pathogen, and profile the proteomic changes by DDA vs. DIA. Dual perspective profiling provides new insights into host defense regulation of infection and pathogenic mechanisms of invasion.

This dataset contains key characteristics about the data described in the Data Descriptor A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. The current standard for quantitative proteomics in plants is the use of data-dependent acquisition (DDA) analysis with or without the use of chemical labels. However, the DDA approach preferentially measures higher abundant proteins, and often requires data imputation due to quantification inconsistency between samples. In this study we systematically benchmarked a recently developed library-free data-independent acquisition (directDIA) method against a state-of-the-art DDA label-free quantitative proteomics workflow for plants. We next developed a novel acquisition approach combining MS1-level BoxCar acquisition with MS2-level directDIA analysis that we call BoxCarDIA. DirectDIA achieves a 33% increase in protein quantification over traditional DDA, and BoxCarDIA a further 8%, without any changes in instrumentation, offline fractionation, or increases in mass-spectrometer run time. BoxCarDIA, especially, offers wholly reproducible quantification of proteins between replicate injections, thereby addressing the long-standing missing-value problem in label-free quantitative proteomics. Further, we find that the gains in dynamic range sampling by directDIA and BoxCarDIA translate to deeper quantification of key, low abundant, functional protein classes (e.g., protein kinases and transcription factors) that are underrepresented in data acquired using DDA. We applied these methods to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies. Our results establish BoxCarDIA as the new method of choice in quantitative proteomics using Orbitrap-type mass-spectrometers, particularly for proteomes with large dynamic range such as that of plants.

Project description:

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.

Sample processing protocol:

A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10⁶, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10⁵, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10⁶, max. injection time of 55 ms and an isolation window of 1.4 m/z.

Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10⁶, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10⁵, max. injection time of 50 ms and an isolation window of 2 m/z.

To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10⁶, max. injection time of 100 ms and an isolation window of 1.4 m/z.

Data processing protocol:

DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).

Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here, we present an ultra-fast scanning data-independent acquisition (DIA) strategy consisting on 2-Th precursor isolation windows, dissolving the differences between data-dependent and independent methods. This is achieved by pairing a Quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer that provides >200 Hz MS/MS scanning speed, high resolving power and sensitivity, as well as low ppm-mass accuracy. Narrowwindow DIA enables profiling of up to 100 full yeast proteomes per day, or ~10,000 human proteins in half-an-hour. Moreover, multi-shot acquisition of fractionated samples allows comprehensive coverage of human proteomes in ~3h, showing comparable depth to next-generation RNA sequencing and with 10x higher throughput compared to current state-of-the-art MS. High quantitative precision and accuracy is demonstrated with high peptide coverage in a 3-species proteome mixture, quantifying 14,000+ proteins in a single run in half-an-hour.

This dataset consists of 44 raw MS files, comprising 27 DIA (SWATH) and 15 DDA runs on a TripleTOF 5600 and of two raw mass spectrometry files acquired on a Q Exactive. The composition of the dataset is described in the manuscript by Tsou et al., titled: "DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics", Nature Methods, in press Raw files are deposited here in ProteomeXchange and are associated with the DIA-Umpire processed data. All DIA-Umpire processed results for each sample together with DDA results are deposited in separated folders. Also see the "DataSampleID.xlsx" associated with this Readme file. Internal reference from the Gingras lab ProHits implementation: Project 94, Export version VS2 (Tsou_DIA-Umpire)

Proteomic modifications linked to non-enzymatic glycation and glycoxidation are common in all tissues under hyperglycemic conditions, as present in metabolic syndrome, type 2 diabetes (T2D), mice bearing a homozygous mutation in leptin (so-called Ob/Ob mice, a common model of obesity and T2D) and mice subjected to a high fat diet (HFD). It has been already shown that the circulating levels of glycated or glycoxidated proteins (e.g., glycated hemoglobin or albumin) are commonly employed as a reliable indicator of overall glycemic status. Albeit these initial non-enzymatic reactions are reversible, following a series of chemical rearrangements protein glycation and glycoxidation become irreversible, generating the so-called advanced glycation end-products (AGEs). AGEs are commonly detected in tissue proteins with an extended half-life, including dermal collagens as well as extracellular matrix proteins. In these proteins, Nϵ-carboxymethyllysine (CML), pentosidins, and glucosepane represent the most prevalent AGEs. As an additional, novel mechanism linking protein PTMs to altered immunity, the research presented herein highlights that several components of the MHC class II antigen processing and presentation machinery are glycated in metabolic conditions associated with increased oxidative stress, leading to qualitative and quantitative changes in the MHC class II immunopeptidome. To investigate the impact of T2D and metabolic syndrome on the proteome of antigen presenting cells (APCs) we isolated dendritic cells (DCs, which are key for the initiation of antigen-specific immunity) from the lymph nodes of Ob/Ob mice and syngeneic, age-matched control C57BL/6 (B6) mice and mapped the oxidized proteins at the molecular and cellular level by employing label-free mass spectrometry using at least three biologically independent whole lysates of DCs. Analysis of the type of post-translational modifications (PTMs) accumulated in the proteome from the combined three biological Ob/Ob samples ranked the formyl-lysine and site-specific carboxymethylation on lysine (CML) as the most abundant AGEs found in the glycated proteome; with CML having about two-fold increase in their total PTM-modified spectral counts in the Ob/Ob vs control dendritic cell proteome. Additional glycoxidation-specific PTMs, that mapped only on the Ob/Ob proteome, albeit at a smaller amount than CML and formyl lysine, were glyceryl lysine and 3-deoxyglucosone. Finally, a separate proteomic analysis, performed on gradient purified late endosomes also mapped an increased number of PTM-modified proteins in the Ob/Ob organelles. Ingenuity pathway analysis (IPA) analysis identified metabolic pathways as well as phagocytosis, antigen processing and presentation and phagosome maturation amongst the top pathways including a higher number of proteins modified by AGEs or carbonylation in primary DCs from Ob/Ob vs. control mice. The site-specific amide-AGE modifications that we detected also mapped to proteins involved in response to DC signaling, immune cell trafficking, actin and cytoskeleton signaling, cell movement, and carbohydrate, nucleic acids and protein metabolism. Overall, these findings indicate that the DC proteome of Ob/Ob mice has an increased number of carbonyl PTMs and AGEs as compared to DC of C57BL/6 mice. The results presented herein are one of the first to map the redox mediated PTM in the primary mouse DC in healthy vs T2DM type syndrome physiological conditions.