Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Comprehensive Proteome Profiling of a Xanthomonas campestris pv. Campestris B100 Culture Grown in Minimal Medium with a Specific Focus on Nutrient Consumption and Xanthan Biosynthesis

Xanthan, a bacterial polysaccharide, is widespread in industrial applications, particularly as a food additive. However, little is known about the process of xanthan synthesis on the proteome level, even though Xanthomonas campestris is frequently used for xanthan fermentation. A label-free LC-MS/MS method was employed to study the protein changes during xanthan fermentation in minimal medium. According to the reference database, 2416 proteins were identified, representing 54.75 % of the proteome. The study examined changes in protein abundances concerning the growth phase and xanthan productivity. Throughout the experiment, changes in nitrate concentration appeared to affect the abundance of most proteins involved in nitrogen metabolism, except Gdh and GlnA. Proteins involved in sugar nucleotide metabolism stay unchanged across all growth phases. Apart from GumD, GumB, and GumC, the gum proteins showed no significant changes throughout the experiment. GumD, the first enzyme in the assembly of the xanthan-repeating unit, peaked during the early stationary phase but decreased during the late stationary phase. GumB and GumC, which are involved in exporting xanthan, increased significantly during the stationary phase. This study suggests that a potential bottleneck for xanthan productivity does not reside in the abundance of proteins directly involved in the synthesis pathways.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

An Introduction to Mass Spectrometry-Based Proteomics

Mass spectrometry is unmatched in its versatility for studying practically any aspect of the proteome. Because the foundations of mass spectrometry-based proteomics are complex and span multiple scientific fields, proteomics can be perceived as having a high barrier to entry. This tutorial is intended to be an accessible illustrated guide to the technical details of a relatively simple quantitative proteomic experiment. An attempt is made to explain the relevant concepts to those with limited knowledge of mass spectrometry and a basic understanding of proteins. An experimental overview is provided, from the beginning of sample preparation to the analysis of protein group quantities, with explanations of how the data are acquired, processed, and analyzed. A selection of advanced topics is briefly surveyed and works for further reading are cited. To conclude, a brief discussion of the future of proteomics is given, considering next-generation protein sequencing technologies that may complement mass spectrometry to create a fruitful future for proteomics.

Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition

Quantitative approaches encompassing parallel reaction monitoring (PRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays developed using PRM, DIA, and Tandem Mass Tag (TMT)-based DDA for quantitative proteomics have yet not been investigated. Here, we developed assays for glycopeptides identified from six glycoproteins, including Leucine-rich alpha-2-glycoprotein (LRG1), Prostaglandin-H2 D-isomerase (PTGDS), Aminopeptidase N (ANPEP), CD63 antigen (CD63), Clusterin (CLU), and Prostatic acid phosphatase (ACPP), using PRM, DDA, and DIA and evaluated the analytical performances of each assay using the different acquisition modes. We also compared assays in each acquisition mode on three different orbitrap instruments: Thermo Fisher Q Exactive, Exploris 480, and Lumos. We found that DIA showed the largest linear range, highest sensitivity, and most reproducibility. We then applied our developed DIA assays to urine samples from non-aggressive (n = 48) and aggressive (n = 35) prostate cancer patients. In conclusion, we developed assays for the six glycoproteins, evaluated the analytical performances of each assay in DIA, PRM, and PRM acquisition modes on three types of mass spectrometry instruments, and chose the DIA assays for the quantitative analysis of urine samples from patients with aggressive and non-aggressive prostate cancer.

Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

Proteomic Comparison of Three Wild-Type Pseudorabies Virus Strains and the Attenuated Bartha Strain Reveals Reduced Incorporation of Several Tegument Proteins in Bartha Virions

Pseudorabies virus (PRV) is a member of the alphaherpesvirus subfamily and the causative agent of Aujeszky's disease in pigs. Driven by the large economic losses associated with PRV infection, several vaccines and vaccine programs have been developed. To this day, the attenuated Bartha strain, generated by serial passaging, represents the golden standard for PRV vaccination. However, a proteomic comparison of the Bartha virion to wild-type (WT) PRV virions is lacking. Here, we present a comprehensive mass spectrometry-based proteome comparison of the attenuated Bartha strain and three commonly used WT PRV strains: Becker, Kaplan, and NIA3. We report the detection of 40 structural and 14 presumed nonstructural proteins through a combination of data-dependent and data-independent acquisition. Interstrain comparisons revealed that packaging of the capsid and most envelope proteins is largely comparable in-between all four strains, except for the envelope protein pUL56, which is less abundant in Bartha virions. However, distinct differences were noted for several tegument proteins. Most strikingly, we noted a severely reduced incorporation of the tegument proteins IE180, VP11/12, pUS3, VP22, pUL41, pUS1, and pUL40 in Bartha virions. Moreover, and likely as a consequence, we also observed that Bartha virions are on average smaller and more icosahedral compared to WT virions. Finally, we detected at least 28 host proteins that were previously described in PRV virions and noticed considerable strain-specific differences with regard to host proteins, arguing that the potential role of packaged host proteins in PRV replication and spread should be further explored. IMPORTANCE The pseudorabies virus (PRV) vaccine strain Bartha-an attenuated strain created by serial passaging-represents an exceptional success story in alphaherpesvirus vaccination. Here, we used mass spectrometry to analyze the Bartha virion composition in comparison to three established WT PRV strains. Many viral tegument proteins that are considered nonessential for viral morphogenesis were drastically less abundant in Bartha virions compared to WT virions. Interestingly, many of the proteins that are less incorporated in Bartha participate in immune evasion strategies of alphaherpesviruses. In addition, we observed a reduced size and more icosahedral morphology of the Bartha virions compared to WT PRV. Given that the Bartha vaccine strain elicits potent immune responses, our findings here suggest that differences in protein packaging may contribute to its immunogenicity. Further exploration of these observations could aid the development of efficacious vaccines against other alphaherpesvirus vaccines such as HSV-1/2 or EHV-1.

Enhanced Identification of Ginsenosides Simultaneously from Seven Panax Herbal Extracts by Data-Dependent Acquisition Including a Preferred Precursor Ions List Derived from an In-House Programmed Virtual Library

Data-dependent acquisition (DDA) is widely utilized for metabolite identification in natural product research and food science, which, however, can suffer from low coverage. A potential solution to improve DDA coverage is to include the precursor ions list (PIL). Here, we aimed to construct a PIL-containing DDA strategy based on an in-house library of ginsenosides (VLG) and identify ginsenosides simultaneously from seven Panax herbal extracts. VLG, combined with mass defect filtering, could efficiently screen the ginsenoside precursors and elaborate the separate PIL involved in DDA for each ginseng extract. Consequently, we could characterize 500 ginsenosides, including 176 ones with unknown masses. Using the Panax ginseng extract, the superiority of this strategy was embodied in targeting more known ginsenoside masses and newly acquiring the MS² spectra of 13 components. Conclusively, knowledge-based large-scale molecular prediction and PIL-DDA can represent a powerful targeted/untargeted strategy beneficial to novel natural compound discovery.

Application of Large-Scale Molecular Prediction for Creating the Preferred Precursor Ions List to Enhance the Identification of Ginsenosides from the Flower Buds of Panax ginseng

This work was designed to evaluate the coverage of data-dependent acquisition (DDA) extensively utilized in the untargeted metabolite/component identification in the food sciences and pharmaceutical analysis. Using saponins from the flower buds of Panax ginseng (PGF) as an example, precursor ions list (PIL)-including DDA on a Q-Orbitrap mass spectrometer could enable higher coverage than the other four MS² acquisition approaches in characterizing PGF ginsenosides. A "Virtual Library of Ginsenoside" containing 13,536 ginsenoside molecules was established by C-language-programmed large-scale molecular prediction, which in combination with mass defect filtering could create a new PIL involving 1859 PGF saponin precursors. We could newly obtain the MS² spectra of at least 17 components and characterize 36 ginsenosides with unknown masses, among the 164 compounds identified from PGF. Conclusively, a molecular-prediction-oriented PIL in DDA can assist to discover more potentially novel molecules benefiting to the development of functional foods and new drugs.

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.

A new mass spectral library for high-coverage and reproducible analysis of the Plasmodium falciparum-infected red blood cell proteome

Background

Plasmodium falciparum causes the majority of malaria mortality worldwide, and the disease occurs during the asexual red blood cell (RBC) stage of infection. In the absence of an effective and available vaccine, and with increasing drug resistance, asexual RBC stage parasites are an important research focus. In recent years, mass spectrometry–based proteomics using data-dependent acquisition has been extensively used to understand the biochemical processes within the parasite. However, data-dependent acquisition is problematic for the detection of low-abundance proteins and proteome coverage and has poor run-to-run reproducibility.

Results

Here, we present a comprehensive P. falciparum–infected RBC (iRBC) spectral library to measure the abundance of 44,449 peptides from 3,113 P. falciparum and 1,617 RBC proteins using a data-independent acquisition mass spectrometric approach. The spectral library includes proteins expressed in the 3 morphologically distinct RBC stages (ring, trophozoite, schizont), the RBC compartment of trophozoite-iRBCs, and the cytosolic fraction from uninfected RBCs. This spectral library contains 87% of all P. falciparum proteins that have previously been reported with protein-level evidence in blood stages, as well as 692 previously unidentified proteins. The P. falciparum spectral library was successfully applied to generate semi-quantitative proteomics datasets that characterize the 3 distinct asexual parasite stages in RBCs, and compared artemisinin-resistant (Cam3.II^R539T) and artemisinin-sensitive (Cam3.II^rev) parasites.

Conclusion

A reproducible, high-coverage proteomics spectral library and analysis method has been generated for investigating sets of proteins expressed in the iRBC stage of P. falciparum malaria. This will provide a foundation for an improved understanding of parasite biology, pathogenesis, drug mechanisms, and vaccine candidate discovery for malaria.

Data-Independent Acquisition Approach to Proteome: A Case Study and a Spectral Library for Mass Spectrometry-Based Investigation of Mycobacterium tuberculosis

Currently, mass spectrometry-based data-dependent acquisition protocols require several micrograms to milligram amounts of proteins to start with, and needs fractionation and enrichment or depletion protocols to identify low abundant proteins and their modifications. However, a data-independent acquisition (DIA) approach can help us to identify a large number of proteins irrespective of their abundance, from even a very low amount of protein. In the DIA protocol, mass spectrometry data are matched against a previously established tandem mass spectrometry (MS/MS) spectra for each peptide. Therefore, establishing a spectral library is a prerequisite for successful DIA protocol. However, the DIA protocol becomes extremely important to investigate biological systems, where there is a difficulty in gathering reasonable amounts of proteins. In this context, DIA can become a valuable tool to investigate proteome dynamics of slow growing pathogen such as Mycobacterium tuberculosis that causes tuberculosis. We report here a case study of the DIA approach that is ideal for M. tuberculosis, which cannot be scaled up easily as it requires specific BSL3 laboratory facilities to be grown. We generated a spectral library for M. tuberculosis proteome using six publicly available proteomic data sets. The in-house M. tuberculosis proteome spectral library contains MS/MS spectra for peptides corresponding to 88% of proteins when compared with the M. tuberculosis H37Rv proteome. We believe that the public availability of the M. tuberculosis spectral library is an important step forward to facilitate the research community to adopt DIA approaches, for example, to investigate M. tuberculosis proteome with greater depth and efficiency.

A comparative study of data-dependent acquisition and data-independent acquisition in proteomics analysis of clinical lung cancer tissues constrained by blood contamination

Proteomics analysis is often troubled by high-abundance proteins in samples such as plasma. However, many surgical tissue samples inevitably have got contaminated with blood before cryopreservation. Selection of an appropriate method to minimize the effect of high-abundance proteins is important for proteomics analysis of blood contaminated tissues. Here, we investigated and compared the abilities of data-independent acquisition (DIA) and data-dependent acquisition (DDA) strategies for the proteomics analysis of blood contaminated clinical tissue samples. Twelve pairs of carcinoma and para-carcinoma tissue samples from lung cancer patients were used for proteomics assays separately by DIA and DDA, and the blood contamination level in samples was evaluated by contamination index (CI). Compared with the DDA strategy, DIA in whole exhibited much better analytical capabilities in proteomics analysis of these samples with more identified protein groups and a higher discovery of differential proteins. With CI value increasing, whether DIA or DDA showed decreasing analysis ability. However, for samples with high CI values, the DIA strategy still shows acceptable analytical capability and indicates better blood pollution resistance than the DDA strategy. Our results implied that for clinical tissue samples, particularly for those contaminated with blood, DIA strategy should be a preferred method in proteomics studies.

Recognizing Contamination Fragment Ions in Liquid Chromatography-Tandem Mass Spectrometry Data

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not. To assess coelution, we performed a parallel LC-MS/MS analysis in data-independent acquisition (DIA) with all-ion-fragmentation (AIF) mode. Using the DIA (AIF) data, peak-peak correlation (PPC) score is calculated between the extracted ion chromatogram (EIC) of the fragment ion using the MS/MS scans and the EIC of the precursor ion using the MS1 scans. A high PPC score is an indication of TFIs, and a low PPC score is an indication of CFIs. Tested using metabolomics data generated by high resolution QTOF and Orbitrap MS from various vendors in different LC-MS configurations, we found that more than 70% of the fragment ions have PPC scores < 0.8 and identified three common sources of CFIs, including (1) solvent contamination, (2) adjacent chemical contamination, and (3) undetermined signals from artifacts and noise. Combining PPC scores with other precursor and fragment ion information, we further developed a machine learning model that can robustly and conservatively predict CFIs. Incorporating the machine learning model, we created an R program, MS2Purifier, to automatically recognize CFIs and clean MS/MS spectra of metabolic features in LC-MS/MS data with high sensitivity and specificity.

Quantitative Proteomics and Relative Enzymatic Activities Reveal Different Mechanisms in Two Peanut Cultivars (Arachis hypogaea L.) Under Waterlogging Conditions

Peanut is an important oil and economic crop in China. The rainy season (April-June) in the downstream Yangtze River in China always leads to waterlogging, which seriously affects plant growth and development. Therefore, understanding the metabolic mechanisms under waterlogging stress is important for future waterlogging tolerance breeding in peanut. In this study, waterlogging treatment was carried out in two different peanut cultivars [Zhonghua 4 (ZH4) and Xianghua08 (XH08)] with different waterlogging tolerance. The data-independent acquisition (DIA) technique was used to quantitatively identify the differentially accumulated proteins (DAPs) between two different cultivars. Meanwhile, the functions of DAPs were predicted, and the interactions between the hub DAPs were analyzed. As a result, a total of 6,441 DAPs were identified in ZH4 and its control, of which 49 and 88 DAPs were upregulated and downregulated under waterlogging stress, respectively, while in XH08, a total of 6,285 DAPs were identified, including 123 upregulated and 114 downregulated proteins, respectively. The hub DAPs unique to the waterlogging-tolerant cultivar XH08 were related to malate metabolism and synthesis, and the utilization of the glyoxylic acid cycle, such as L-lactate dehydrogenase, NAD+-dependent malic enzyme, aspartate aminotransferase, and glutamate dehydrogenase. In agreement with the DIA results, the alcohol dehydrogenase and malate dehydrogenase activities in XH08 were more active than ZH4 under waterlogging stress, and lactate dehydrogenase activity in XH08 was prolonged, suggesting that XH08 could better tolerate waterlogging stress by using various carbon sources to obtain energy, such as enhancing the activity of anaerobic respiration enzymes, catalyzing malate metabolism and the glyoxylic acid cycle, and thus alleviating the accumulation of toxic substances. This study provides insight into the mechanisms in response to waterlogging stress in peanuts and lays a foundation for future molecular breeding targeting in the improvement of peanut waterlogging tolerance, especially in rainy area, and will enhance the sustainable development in the entire peanut industry.

Sensitive Screening of New Psychoactive Substances in Serum Using Liquid-Chromatography Quadrupole Time-of-Flight Mass Spectrometry

Analysis of new psychoactive substances (NPS) still pose a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study we developed a qualitative screening approach for serum samples by means of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were measured in data-dependent auto-MS/MS mode and identified by fragment spectra comparison, retention time and accurate mass. Approximately 500 NPS, including 195 synthetic cannabinoids, 180 stimulants, 86 hallucinogens, 26 benzodiazepines and 7 others were investigated. Serum samples were fortified to 1 ng/mL and 10 ng/mL concentrations to estimate approximate limits of identification. Samples were extracted using solid-phase extraction with non-endcapped C18 material and elution in two consecutive steps. Benzodiazepines were eluted in the first step, while substances of other NPS subclasses were distributed among both extracts. To determine limits of identification, both extracts were combined. 96 % (470/492) of investigated NPS were detected in 10 ng/mL samples and 88 % (432/492) were detected in 1 ng/mL samples. Stimulants stood out with higher limits of identification, possibly due to instability of certain methcathinone derivatives. However, considering relevant blood concentrations, the method provided sufficient sensitivity for stimulants as well as other NPS subclasses. Data-dependent acquisition was proven to provide high sensitivity and reliability when combined with an information-dependent preferred list, without losing its untargeted operation principle. Summarizing, the developed method fulfilled its purpose as a sensitive untargeted screening for serum samples and allows uncomplicated expansion of the spectral library to include thousands of targets.

Synergistic optimization of Liquid Chromatography and Mass Spectrometry parameters on Orbitrap Tribrid mass spectrometer for high efficient data-dependent proteomics

Steady improvement in Orbitrap-based mass spectrometry (MS) technologies has greatly advanced the peptide sequencing speed and depth. In-depth analysis of the performance of state-of-the-art MS and optimization of key parameters can improve sequencing efficiency. In this study, we first systematically compared the performance of two popular data-dependent acquisition approaches, with Orbitrap as the first-stage (MS1) mass analyzer and the same Orbitrap (high-high approach) or ion trap (high-low approach) as the second-stage (MS2) mass analyzer, on the Orbitrap Fusion mass spectrometer. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. However, regardless of the acquisition method, there are still more than 60% of peptide features untargeted for MS2 scan. We then systematically optimized the MS parameters using the high-high approach. Increasing the isolation window in the high-high approach could facilitate faster scan speed, but decreased MS2 identification rate. On the contrary, increasing the injection time of MS2 scan could increase identification rate but decrease scan speed and the number of identified MS2 spectra. Dynamic exclusion time should be set properly according to the chromatography peak width. Furthermore, we found that the Orbitrap analyzer, rather than the analytical column, was easily saturated with higher loading amount, thus limited the dynamic range of MS1-based quantification. By using optimized parameters, 10 000 proteins and 110 000 unique peptides were identified by using 20 h of effective liquid chromatography (LC) gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor ion targeting, fragment ion detection, and chromatographic separation for high efficient data-dependent proteomics.

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

Mass spectrometry-based proteomics technologies are prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm, MealTime-MS, that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS' efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Direct and Fast Screening of New Psychoactive Substances Using Medical Swabs and Atmospheric Solids Analysis Probe Triple Quadrupole with Data-Dependent Acquisition

New psychoactive substances (NPS) have become a serious public health problem, as they are continuously changing their structures and modifying their potency and effects on humans, and therefore, novel compounds are unceasingly appearing. One of the major challenges in forensic analysis, particularly related to the problem of NPS, is the development of fast screening methodologies that allow the detection of a wide variety of compounds in a single analysis. In this study, a novel application of the atmospheric solids analysis probe (ASAP) using medical swabs has been developed. The swab-ASAP was coupled to a triple quadrupole mass analyzer working under a data-dependent acquisition mode in order to perform a suspect screening of NPS in different types of samples as well as on surfaces. The compounds were automatically identified based on the observed fragmentation spectra using an in-house built MS/MS spectra library. The developed methodology was applied for the identification of psychoactive substances in research chemicals and herbal blends. The sensitivity of the method, as well as its applicability for surface analysis, was also assessed by identifying down to 1 μg of compound impregnated onto a laboratory table. Another remarkable application was the identification of cathinones and synthetic cannabinoids on the fingers of potential consumers. Interestingly, our data showed that NPS could be identified on the fingers after being in contact with the product and even after cleaning their hands by shaking off with a cloth. The methodology proposed in this paper can be applied for routine analyses of NPS in different matrix samples without the need to establish a list of target compounds prior to analysis.

Data Dependent-Independent Acquisition (DDIA) Proteomics

Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy data dependent-independent acquisition proteomics, or DDIA for short. Peptides identified from DDA scans by a conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously, can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including the modules for iRT vs RT calibration curve generation, DIA extraction classifier training, and false discovery rate control, has been developed. Compared to another spectral library-free method, DIA-Umpire, the DDIA method produced a similar number of peptide identifications, but nearly twice as many protein group identifications. The primary advantage of the DDIA method is that it requires minimal information for processing its data.

Proper imputation of missing values in proteomics datasets for differential expression analysis

Label-free shotgun proteomics is an important tool in biomedical research, where tandem mass spectrometry with data-dependent acquisition (DDA) is frequently used for protein identification and quantification. However, the DDA datasets contain a significant number of missing values (MVs) that severely hinders proper analysis. Existing literature suggests that different imputation methods should be used for the two types of MVs: missing completely at random or missing not at random. However, the simulated or biased datasets utilized by most of such studies offer few clues about the composition and thus proper imputation of MVs in real-life proteomic datasets. Moreover, the impact of imputation methods on downstream differential expression analysis-a critical goal for many biomedical projects-is largely undetermined. In this study, we investigated public DDA datasets of various tissue/sample types to determine the composition of MVs in them. We then developed simulated datasets that imitate the MV profile of real-life datasets. Using such datasets, we compared the impact of various popular imputation methods on the analysis of differentially expressed proteins. Finally, we make recommendations on which imputation method(s) to use for proteomic data beyond just DDA datasets.

Comprehensive Map of the Artemisia annua Proteome and Quantification of Differential Protein Expression in Chemotypes Producing High versus Low Content of Artemisinin

Artemisia annua is well known for biosynthesizing the antimalarial drug artemisinin. Here, a global proteomic profiling of A. annua is conducted with identification of a total of 13 403 proteins based on the genome sequence annotation database. Furthermore, a spectral library is generated to perform quantitative proteomic analysis using data independent acquisition mass spectrometry. Specifically, proteins between two chemotypes that produce high (HAP) and low (LAP) artemisinin content, respectively, are comprehensively quantified and compared. 182 proteins are identified with abundance significantly different between these two chemotypes means after the statistic use the p-value and fold change it is found 182 proteins can reach the demand conditions which represent the expression are significantly different between the high artemisnin content plants (HAPs) and the low artemisnin content plants (LAPs). Data are available via ProteomeXchange with identifier PXD015547. Overall, this current study globally identifies the proteome of A. annua and quantitatively compares the targeted sub-proteomes between the two cultivars of HAP and LAP, providing systematic information on metabolic pathways of A. annua.

Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

Performance enhancement and sample throughput increase of a multiresidue pesticides method in fruits and vegetables using Data-Dependent MS acquisition

Due to the growing number of analysed pesticide residues, analytical strategies have evolved for the data processing of 100s of pesticides in a single analysis. We present herein a LC-MS/MS method based on triple quadrupole technology capable of detecting concentrations at 5 ng/g and confirming 381 pesticides in a single injection. Confirmatory analysis is performed using data-dependent acquisition that compares full MS/MS spectra of candidates to a fast library interrogation within the same injection. A comparison on more than 200 samples of fruits and vegetables (representing principal types: normal, pigmented, and fatty) with pre-existing workflow based on single MRM analysis per compound was performed to validate this approach. A fast turnaround time was demonstrated due to more-unambiguous identification suppressing the need for reinjection to confirm candidates. The automated library searching and confirmation only of putative hits also allowed focusing on the manual verification and validation steps just for putative candidates which hence also increased overall throughput and results quality. Superior robustness of the method due partially to a reduced volume injected was also one of the key points achieved using this methodology. An interesting feature is also the capability to enrich the library and the number of pesticides screened with ease.

Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species

The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

Data-Dependent Acquisition and Database-Driven Efficient Peak Annotation for the Comprehensive Profiling and Characterization of the Multicomponents from Compound Xueshuantong Capsule by UHPLC/IM-QTOF-MS

The state of the art ion mobility quadrupole time of flight (IM-QTOF) mass spectrometer coupled with ultra-high performance liquid chromatography (UHPLC) can offer four-dimensional information supporting the comprehensive multicomponent characterization of traditional Chinese medicine (TCM). Compound Xueshuantong Capsule (CXC) is a four-component Chinese patent medicine prescribed to treat ophthalmic disease and angina. However, research systematically elucidating its chemical composition is not available. An approach was established by integrating reversed-phase UHPLC separation, IM-QTOF-MS operating in both the negative and positive electrospray ionization modes, and a “Component Knockout” strategy. An in-house ginsenoside library and the incorporated TCM library of UNIFI^TM drove automated peak annotation. With the aid of 85 reference compounds, we could separate and characterize 230 components from CXC, including 155 ginsenosides, six astragalosides, 16 phenolic acids, 16 tanshinones, 13 flavonoids, six iridoids, ten phenylpropanoid, and eight others. Major components of CXC were from the monarch drug, Notoginseng Radix et Rhizoma. This study first clarifies the chemical complexity of CXC and the results obtained can assist to unveil the bioactive components and improve its quality control.

Liquid Chromatography-Tandem Mass Spectrometry Analysis of Acetaminophen Covalent Binding to Glutathione S-Transferases

Acetaminophen (APAP)-induced hepatotoxicity is the most common cause of acute liver failure in the Western world. APAP is bioactivated to N-acetyl p-benzoquinone imine (NAPQI), a reactive metabolite, which can subsequently covalently bind to glutathione and protein thiols. In this study, we have used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to characterize NAPQI binding to human glutathione S-transferases (GSTs) in vitro. GSTs play a crucial role in the detoxification of reactive metabolites and therefore are interesting target proteins to study in the context of APAP covalent binding. Recombinantly-expressed and purified GSTs were used to assess NAPQI binding in vitro. APAP biotransformation to NAPQI was achieved using rat liver microsomes or human cytochrome P450 Supersomes in the presence of GSTA1, M1, M2, or P1. Resulting adducts were analyzed using bottom-up proteomics, with or without LC fractionation prior to LC-MS/MS analysis on a quadrupole-time-of-flight instrument with data-dependent acquisition (DDA). Targeted methods using multiple reaction monitoring (MRM) on a triple quadrupole platform were also developed by quantitatively labeling all available cysteine residues with a labeling reagent yielding isomerically-modified peptides following enzymatic digestion. Seven modified cysteine sites were confirmed, including Cys112 in GSTA1, Cys78 in GSTM1, Cys115 and 174 in GSTM2, as well as Cys15, 48, and 170 in GSTP1. Most modified peptides could be detected using both untargeted (DDA) and targeted (MRM) approaches, however the latter yielded better detection sensitivity with higher signal-to-noise and two sites were uniquely found by MRM.

Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data

Peptides detected by tandem mass spectrometry (MS/MS) in bottom-up proteomics serve as proxies for the proteins expressed in the sample. Protein inference is a process routinely applied to these peptides to generate a plausible list of candidate protein identifications. The use of multiple proteases for parallel protein digestions expands sequence coverage, provides additional peptide identifications, and increases the probability of identifying peptides that are unique to a single protein, which are all valuable for protein inference. We have developed and implemented a multi-protease protein inference algorithm in MetaMorpheus, a bottom-up search software program, which incorporates the calculation of protease-specific q-values and preserves the association of peptide sequences and their protease of origin. This integrated multi-protease protein inference algorithm provides more accurate results than either the aggregation of results from the separate analysis of the peptide identifications produced by each protease (separate approach) in MetaMorpheus, or results that are obtained using Fido, ProteinProphet, or DTASelect2. MetaMorpheus' integrated multi-protease data analysis decreases the ambiguity of the protein group list, reduces the frequency of erroneous identifications, and increases the number of post-translational modifications identified, while combining multi-protease search and protein inference into a single software program.

Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

The identification of nearly all proteins in a biological system using data-dependent acquisition (DDA) tandem mass spectrometry has become routine for organisms with relatively small genomes such as bacteria and yeast. Still, the quantification of the identified proteins may be a complex process and often requires multiple different software packages. In this protocol, I describe a flexible strategy for the identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans and may be used to quantify proteome remodeling in response to drug treatment or a gene knockout. Notably, the method is statistically rigorous, uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with a small number of data files from the analysis of yeast.

Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

Probing the Sensitivity of the Orbitrap Lumos Mass Spectrometer Using a Standard Reference Protein in a Complex Background

The use of mass spectrometry as a tool to detect proteins of biological interest has become a cornerstone of proteomics. The popularity of mass spectrometry-based methods has increased along with instrument improvements in detection and speed. The Orbitrap Fusion Lumos mass spectrometer has recently been shown to have better fragmentation and detection than its predecessors. Here, we determined the sensitivity of the Lumos using the NIST monoclonal antibody reference material at various concentrations to detect its peptides in a background of S. cerevisiae whole cell lysate, which was kept at a constant concentration. The data collected by data-dependent acquisition showed that the spiked protein could be detected at 10 pg by an average of 4 peptides in 250 ng of whole cell lysate when the instrument was operated by detecting the peptide masses in the Orbitrap and the fragment masses in the ion trap (FTIT mode). In contrast, when the peptides and fragments were both detected in the Orbitrap on either the Lumos or Q-Exactive Plus (FTFT mode), the lowest concentration of NIST monoclonal antibody detected was 50 pg. The Lumos can detect a single protein at a level 2500 times lower than the whole cell background and the combination of detecting ions in the Orbitrap and ion trap can improve the identification of low abundance proteins. Furthermore, the total number of proteins identified from decreasing starting amounts of whole cell extracts was determined. The Lumos, when operated in FTIT mode, was able to identify twice as many proteins compared to the Q-Exactive+ at 5 ng of whole cell lysate. Similar numbers of proteins were identified on both platforms at higher concentrations of starting material. Therefore, the Lumos mass spectrometer is especially useful for detecting proteins of low abundance in complex backgrounds or samples that have limited starting material.

Expanding lipidome coverage using MS/MS-aided untargeted data-independent RP-UPLC-TOF-MSE acquisition

Lipid function and importance in disease are being rediscovered due to modern advancements in chemical analysis. RP-UPLC-TOF-MS^E is now the lipidomics tool of choice and can provide the demanded specificity for detecting the great diversity of the lipidome. It can offer simplicity, rapidity, robustness and high throughputness, without the need for further optimization in current sample preparation protocols. This method can cover the major lipid categories with the ability to detect several corresponding subclasses. It can deliver adequate information for deciphering fatty chain length, unsaturation and regioisomerism. It has enabled the detection of a vast number of lipids, of which more than 250 are reported here. These lipids were detected from applications in a variety of biological matrices and species.

Advancing Top-down Analysis of the Human Proteome Using a Benchtop Quadrupole-Orbitrap Mass Spectrometer

Over the past decade, developments in high resolution mass spectrometry have enabled the high throughput analysis of intact proteins from complex proteomes, leading to the identification of thousands of proteoforms. Several previous reports on top-down proteomics (TDP) relied on hybrid ion trap-Fourier transform mass spectrometers combined with data-dependent acquisition strategies. To further reduce TDP to practice, we use a quadrupole-Orbitrap instrument coupled with software for proteoform-dependent data acquisition to identify and characterize nearly 2000 proteoforms at a 1% false discovery rate from human fibroblasts. By combining a 3 m/z isolation window with short transients to improve specificity and signal-to-noise for proteoforms >30 kDa, we demonstrate improving proteome coverage by capturing 439 proteoforms in the 30-60 kDa range. Three different data acquisition strategies were compared and resulted in the identification of many proteoforms not observed in replicate data-dependent experiments. Notably, the data set is reported with updated metrics and tools including a new viewer and assignment of permanent proteoform record identifiers for inclusion of highly characterized proteoforms (i.e., those with C-scores >40) in a repository curated by the Consortium for Top-Down Proteomics.

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.

Mass Spectrometry Applied to Bottom-Up Proteomics: Entering the High-Throughput Era for Hypothesis Testing

Proteins constitute a key class of molecular components that perform essential biochemical reactions in living cells. Whether the aim is to extensively characterize a given protein or to perform high-throughput qualitative and quantitative analysis of the proteome content of a sample, liquid chromatography coupled to tandem mass spectrometry has become the technology of choice. In this review, we summarize the current state of mass spectrometry applied to bottom-up proteomics, the approach that focuses on analyzing peptides obtained from proteolytic digestion of proteins. With the recent advances in instrumentation and methodology, we show that the field is moving away from providing qualitative identification of long lists of proteins to delivering highly consistent and accurate quantification values for large numbers of proteins across large numbers of samples. We believe that this shift will have a profound impact for the field of proteomics and life science research in general.

A laser ablation ICP-MS based method for multiplexed immunoblot analysis: applications to manganese-dependent protein dynamics of photosystem II in barley (Hordeum vulgare L.)

Manganese (Mn) constitutes an essential co-factor in the oxygen-evolving complex of photosystem II (PSII). Consequently, Mn deficiency reduces photosynthetic efficiency and leads to changes in PSII composition. In order to study these changes, multiplexed protein assays are advantageous. Here, we developed a multiplexed antibody-based assay and analysed selected PSII subunits in barley (Hordeum vulgare L.). A selection of antibodies were labelled with specific lanthanides and immunoreacted with thylakoids exposed to Mn deficiency after western blotting. Subsequently, western blot membranes were analysed by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), which allowed selective and relative quantitative analysis via the different lanthanides. The method was evaluated against established liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) methods, based on data-dependent acquisition (DDA) and selected reaction monitoring (SRM). Manganese deficiency resulted in a general decrease in PSII protein abundances, an effect that was shown to be reversible upon Mn re-supplementation. Specifically, the extrinsic proteins PsbP and PsbQ showed Mn-dependent changes in abundances. Similar trends in the response to Mn deficiency at the protein level were observed when comparing DDA, SRM and LA-ICP-MS results. A biologically important exception to this trend was the loss of PsbO in the SRM analysis, which highlights the necessity of validating protein changes by more than one technique. The developed method enables a higher number of proteins to be multiplexed in comparison to existing immunoassays. Furthermore, multiplexed protein analysis by LA-ICP-MS provides an analytical platform with high throughput appropriate for screening large collections of plants.