Mass Spectrometry at the Forefront of the COVID-19 Pandemic
MS has emerged as a powerful tool for SARS-CoV-2 and COVID-19 research
Over the last decade, mass spectrometry (MS) has arisen as an essential tool in biomedical research and clinical laboratories. Over the past year, the emergence of the SARS-CoV-2 pandemic has accelerated that progress and pushed MS to the forefront of clinical research and diagnostics.
SARS-CoV-2 is quickly transmitted from person to person through aerosols and respiratory droplets. This rapid spread has demanded an equally rapid response from the scientific community in order to understand the virus and its pathogenesis. In the process, MS has emerged as a powerful tool in SARS-CoV-2 and COVID-19 basic and applied research. This has led to the creation of the COVID-19 MS Coalition, a collaborative effort from scientists to share protocols and data, and to promote collaborations within the field.
Diagnosing SARS-CoV-2 infections
Diagnosing SARS-CoV-2 infections quickly became a key component of the pandemic control strategy by enabling the identification and containment of people infected with the virus. ?Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the standard method for SARS-COV-2 diagnosis; however, the high demand for testing and the shortage of reagents and instruments resulted in major testing delays, and researchers needed to develop alternatives.
Although MS is not routinely used for viral diagnostics, the wide availability of MS instruments in clinical laboratories makes it a good alternative. MS can be used as a faster, high-throughput method that does not depend on reagent supply and only requires minimal sample preparation. Hence, many research groups have developed protocols for MS-based SARS-CoV-2 diagnosis.
In a July 2020 study, researchers used matrix assisted laser desorption ionization MS (MALDI-MS) to compare and match the ?spectral pattern obtained from unpurified nasal swabs to those of confirmed SARS-CoV-2 positive and negative samples. This allowed them to classify the samples as infected or noninfected with a reported accuracy of 93.9 percent, with 7 percent false positives and 5 percent false negatives.
A more recent study used a similar approach—MALDI-time of flight (TOF) MS—for SARS-CoV-2 screening. Here, researchers used positive and negative nasal swab samples to develop a machine learning model to identify SARS-CoV-2 positive samples. This method had a higher accuracy (98.3 percent) with fewer false positives (1.7 percent) and no false negatives. Additionally, 46 samples can be analyzed per run in an hour using this approach, which translates to a potential throughput of 1,104 samples per day per instrument.
Another group of researchers tackled SARS-CoV-2 diagnosis using a targeted proteomics approach—an MS-based technique for detection and quantification of a protein of interest. They first selected peptides from different viral proteins in SARS-CoV-2 positive respiratory tract samples that could serve as biomarkers for infection. Then, they examined new samples by turbulent flow chromatography coupled to tandem MS (TFC-MS/MS) for the presence of the selected peptides. This method detected up to 84 percent of positive cases with 97 percent specificity, proving to be highly specific and robust. Also, the addition of automated sample preparation enabled the analysis of more than 500 samples per day.
Together these studies show that MS-based methods can be a rapid, accurate, and large-scale alternative to RT-PCR for SARS-CoV-2 diagnosis, without depending on reagent supply and with minimal sample preparation.
Predicting COVID-19 severity
As SARS-CoV-2 positive patients may deteriorate quickly, one of the major challenges of the pandemic is predicting the course of the disease. Hence, it is important to identify biomarkers that can predict disease severity.
A research group developed a proteomics approach for this purpose. First, using liquid chromatography tandem MS (LC-MS/MS), the researchers obtained the proteomics profile of plasma samples from healthy patients and patients with different degrees of disease severity (mild, severe, or fatal). Then, they identified a set of 11 proteins and combinations of proteins that could be used as biomarkers to distinguish between different levels of COVID-19 severity and outcomes. The identified biomarkers, which were mostly linked to immune cell migration and platelet/neutrophil degranulation, were then validated using a new cohort of patients.
Later in a January 2021 study, another group took a multi-omics approach, exploring not only proteins but also metabolites, lipids, and transcripts as biomarkers for COVID-19 prognosis. The researchers analyzed plasma from 102 patients with different levels of COVID-19 severity and 26 noninfected patients and identified 219 biomarkers that were strongly associated with disease severity. The identified biomarkers suggest that there is a dysregulation of processes related to lipid transport, blood coagulation, acute phase response, endotheliopathy, and neutrophil degranulation in this disease. The results of this study are freely accessible in an interactive webtool.
Together, these studies demonstrate the power of MS-based methods as unbiased high-throughput platforms to identify biomarkers of COVID-19 severity, which can reveal new insights into the mechanisms of viral pathogenesis.
Characterization of SARS-CoV-2 interactomics and drug discovery
In the early stages of the pandemic, there were no known therapeutics for COVID-19. This required a fast response from the scientific community to develop or repurpose existing drugs to treat the disease. The search for new therapies depends on understanding virus–host interactions (referred to as an interactome) , which can be potential targets for antiviral drugs. MS has been widely employed to map virus–host interactions, as it allows the unbiased characterization of interactions between viral and host proteins through a combination of proteomics and binding assays.
The SARS-CoV-2 interactome was first characterized by cloning and expressing 26 of the 29 viral proteins as baits and analyzing their associated proteins through affinity-purification–MS (AP–MS) to identify host factors that interact with the viral proteins. Then, researchers screened for drugs, either FDA-approved or in development, with the ability to bind to and inhibit those host factors, resulting in antiviral activity. In total, the researchers identified 69 potential antivirals. Published in Nature last April, these results were produced at a striking speed, demonstrating the significant advantages of using MS for this type of research.
Among the 69 potential antiviral therapies was plitidepsin, a drug used to treat myeloma. Plitidepsin works by inhibiting eEF1A, a human protein that interacts with SARS-CoV-2. This past February, plitidepsin was shown to have potent antiviral activity in vivo and in vitro and is now in Phase 3 clinical trials for use in COVID-19.
Similar studies have followed, providing further clues to the viral mechanisms underlying the pathogenesis of COVID-19, as well as suggestions for potential drugs for treating the disease.
An essential tool
The COVID-19 pandemic required fast, accurate, and easily scalable high-throughput methods to use in research and the clinic. MS proved to be an essential tool to answer the need for diagnosis, disease characterization, and drug discovery. In the future, new advances in MS and new ways to combine it with other methods will emerge further increasing the potential of this technology.
