An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules.

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Liquid Chromatography-Mass Spectrometry-Based Qualitative Profiling of mRNA Therapeutic Reagents Using Stable Isotope-Labeled Standards Followed by the Automatic Quantitation Software Ariadne.

mRNA-based medicines are a promising modality for preventing virus-caused illnesses, including COVID-19, and treating various types of cancer and genetic diseases. To develop such medicines, methods to characterize long mRNA molecules are needed for quality control and metabolic analysis. Here, we developed an analytical platform based on isotope-dilution liquid chromatography-mass spectrometry (LC-MS) that quantitatively characterizes long, modified mRNAs by comparing them to a stable isotope-labeled reference with an identical sequence to that of the target medicine. This platform also includes database searching using the mass spectra as a query, which allowed us to confirm the primary structures of 200 to 4300 nt mRNAs including chemical modifications, with sequence coverage at 100%, to detect/identify defects in the sequences, and to define the efficiencies of the 5'-capping and integrity of the polyadenylated tail. Our findings indicated that this platform should be valuable for quantitatively characterizing mRNA vaccines and other mRNA medicines.

A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment.

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

A feasibility study of Covid-19 detection using breath analysis by high-pressure photon ionization time-of-flight mass spectrometry.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a tremendous threat to global health. polymerase chain reaction (PCR) and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, the food and drug administration (FDA) emergency approved volatile organic component (VOC) as an alternative test for COVID-19 detection. In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by high-pressure photon ionization time-of-flight mass spectrometry for VOCs. Machine learning algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Phytometabolite profiling of Coronil, a herbal medicine for COVID-19, its identification by mass-spectroscopy and quality validation on liquid chromatographic platforms.

Coronil is a tri-herbal medicine consisting of immunomodulatory herbs, Withania somnifera, Tinospora cordifolia, and Ocimum sanctum. The formulation has been developed specifically as the supporting measure for COVID-19. Current investigation is aimed to identify the phytoconstituents in Coronil utilizing ultra-performance liquid chromatography-mass spectrometry coupled with quadrapole time of flight and to establish its quality standardization using high-performance liquid chromatography and high performance thin layer chromatography. Out of 52 identified compounds, cordifolioside A, magnoflorine, rosmarinic acid, palmatine, withanoside IV, withanoside V, withanone, betulinic acid, and ursolic acid were quantified in 15 different batches of Coronil on validated high-performance liquid chromatography method. Similarly, withanoside IV, withaferin A, magnoflorine, palmatine, rosmarinic acid, and ursolic acid were analyzed on high performance thin layer chromatography. Methods were validated as per the International Council for Harmonization guidelines. These methods were specific, reproducible, accurate, precise, linear (r2 > 0.99), and percent recoveries were within the prescribed limits. The content uniformity of Coronil was ascertained using Fourier transform infrared spectroscopy. Results indicated that, validated methods were fit for their intended use and the analytical quality of Coronil was consistent across the batches. Taken together, these developed methods could drive the analytical quality control of herbal medicines such as Coronil, and other formulations containing similar chemical profiles.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2.

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

SARS-CoV-2 S protein:ACE2 interaction reveals novel allosteric targets.

The spike (S) protein is the main handle for SARS-CoV-2 to enter host cells via surface angiotensin-converting enzyme 2 (ACE2) receptors. How ACE2 binding activates proteolysis of S protein is unknown. Here, using amide hydrogen-deuterium exchange mass spectrometry and molecular dynamics simulations, we have mapped the S:ACE2 interaction interface and uncovered long-range allosteric propagation of ACE2 binding to sites necessary for host-mediated proteolysis of S protein, critical for viral host entry. Unexpectedly, ACE2 binding enhances dynamics at a distal S1/S2 cleavage site and flanking protease docking site ~27 Å away while dampening dynamics of the stalk hinge (central helix and heptad repeat [HR]) regions ~130 Å away. This highlights that the stalk and proteolysis sites of the S protein are dynamic hotspots in the prefusion state. Our findings provide a dynamics map of the S:ACE2 interface in solution and also offer mechanistic insights into how ACE2 binding is allosterically coupled to distal proteolytic processing sites and viral-host membrane fusion. Thus, protease docking sites flanking the S1/S2 cleavage site represent alternate allosteric hotspot targets for potential therapeutic development.

Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a pandemic infection in March 2020. As of December 2020, two COVID-19 vaccines have been authorized for emergency use by the U.S. Food and Drug Administration, but there are no effective drugs to treat COVID-19, and pandemic mitigation efforts like physical distancing have had acute social and economic consequences. In this perspective, we discuss how the proteomic research community can leverage technologies and expertise to address the pandemic by investigating four key areas of study in SARS-CoV-2 biology. Specifically, we discuss how (1) mass spectrometry-based structural techniques can overcome limitations and complement traditional structural approaches to inform the dynamic structure of SARS-CoV-2 proteins, complexes, and virions; (2) virus-host protein-protein interaction mapping can identify the cellular machinery required for SARS-CoV-2 replication; (3) global protein abundance and post-translational modification profiling can characterize signaling pathways that are rewired during infection; and (4) proteomic technologies can aid in biomarker identification, diagnostics, and drug development in order to monitor COVID-19 pathology and investigate treatment strategies. Systems-level high-throughput capabilities of proteomic technologies can yield important insights into SARS-CoV-2 biology that are urgently needed during the pandemic, and more broadly, can inform coronavirus virology and host biology.

Mass spectrometry reveals potential of ß-lactams as SARS-CoV-2 Mpro inhibitors.

The main viral protease (Mpro) of SARS-CoV-2 is a nucleophilic cysteine hydrolase and a current target for anti-viral chemotherapy. We describe a high-throughput solid phase extraction coupled to mass spectrometry Mpro assay. The results reveal some ß-lactams, including penicillin esters, are active site reacting Mpro inhibitors, thus highlighting the potential of acylating agents for Mpro inhibition.

Mass spectrometry reveals potential of ß-lactams as SARS-CoV-2 Mpro inhibitors.

The main viral protease (Mpro) of SARS-CoV-2 is a nucleophilic cysteine hydrolase and a current target for anti-viral chemotherapy. We describe a high-throughput solid phase extraction coupled to mass spectrometry Mpro assay. The results reveal some ß-lactams, including penicillin esters, are active site reacting Mpro inhibitors, thus highlighting the potential of acylating agents for Mpro inhibition.

Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms.

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins.

Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.

Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts.

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pressing public health systems around the world, and large population testing is a key step to control this pandemic disease. Here, we develop a high-throughput targeted proteomics assay to detect SARS-CoV-2 nucleoprotein peptides directly from nasopharyngeal and oropharyngeal swabs. A modified magnetic particle-based proteomics approach implemented on a robotic liquid handler enables fully automated preparation of 96 samples within 4 hours. A TFC-MS system allows multiplexed analysis of 4 samples within 10 min, enabling the processing of more than 500 samples per day. We validate this method qualitatively (Tier 3) and quantitatively (Tier 1) using 985 specimens previously analyzed by real-time RT-PCR, and detect up to 84% of the positive cases with up to 97% specificity. The presented strategy has high sample stability and should be considered as an option for SARS-CoV-2 testing in large populations.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.

Development of a Parallel Reaction Monitoring Mass Spectrometry Assay for the Detection of SARS-CoV-2 Spike Glycoprotein and Nucleoprotein.

There is an urgent need for robust and high-throughput methods for SARS-CoV-2 detection in suspected patient samples to facilitate disease management, surveillance, and control. Although nucleic acid detection methods such as reverse transcription polymerase chain reaction (RT-PCR) are the gold standard, during the current pandemic, the deployment of RT-PCR tests has been extremely slow, and key reagents such as PCR primers and RNA extraction kits are at critical shortages. Rapid point-of-care viral antigen detection methods have been previously employed for the diagnosis of respiratory viruses such as influenza and respiratory syncytial viruses. Therefore, the direct detection of SARS-CoV-2 viral antigens in patient samples could also be used for diagnosis of active infection, and alternative methodologies for specific and sensitive viral protein detection should be explored. Targeted mass spectrometry techniques have enabled the identification and quantitation of a defined subset of proteins/peptides at single amino acid resolution with attomole level sensitivity and high reproducibility. Herein, we report a targeted mass spectrometry assay for the detection of SARS-CoV-2 spike protein and nucleoprotein in a relevant biological matrix. Recombinant full-length spike protein and nucleoprotein were digested and proteotypic peptides were selected for parallel reaction monitoring (PRM) quantitation using a high-resolution Orbitrap instrument. A spectral library, which contained seven proteotypic peptides (four from spike protein and three from nucleoprotein) and the top three to four transitions, was generated and evaluated. From the original spectral library, we selected two best performing peptides for the final PRM assay. The assay was evaluated using mock test samples containing inactivated SARS-CoV-2 virions, added to in vitro derived mucus. The PRM assay provided a limit of detection of ∼200 attomoles and a limit of quantitation of ∼ 390 attomoles. Extrapolating from the test samples, the projected titer of virus particles necessary for the detection of SARS-CoV-2 spike and nucleoprotein detection was approximately 2 × 105 viral particles/mL, making it an attractive alternative to RT-PCR assays. Potentially, mass spectrometry-based methods for viral antigen detection may deliver higher throughput and could serve as a complementary diagnostic tool to RT-PCR. Furthermore, this assay could be used to evaluate the presence of SARS-CoV-2 in archived or recently collected biological fluids, in vitro-derived research materials, and wastewater samples.