Evaluation of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for Molecular Typing of Acinetobacter baumannii in Comparison with Orthogonal Methods.

Colonization and subsequent health care-associated infection (HCAI) with Acinetobacter baumannii are a concern for vulnerable patient groups within the hospital setting. Outbreaks involving multidrug-resistant strains are associated with increased patient morbidity and mortality and poorer overall outcomes. Reliable molecular typing methods can help to trace transmission routes and manage outbreaks. In addition to methods deployed by reference laboratories, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) may assist by making initial in-house judgments on strain relatedness. However, limited studies on method reproducibility exist for this application. We applied MALDI-TOF MS typing to A. baumannii isolates associated with a nosocomial outbreak and evaluated different methods for data analysis. In addition, we compared MALDI-TOF MS with whole-genome sequencing (WGS) and Fourier transform infrared spectroscopy (FTIR) as orthogonal methods to further explore their resolution for bacterial strain typing. A related subgroup of isolates consistently clustered separately from the main outbreak group by all investigated methods. This finding, combined with epidemiological data from the outbreak, indicates that these methods identified a separate transmission event unrelated to the main outbreak. However, the MALDI-TOF MS upstream approach introduced measurement variability impacting method reproducibility and limiting its reliability as a standalone typing method. Availability of in-house typing methods with well-characterized sources of measurement uncertainty could assist with rapid and dependable confirmation (or denial) of suspected transmission events. This work highlights some of the steps to be improved before such tools can be fully integrated into routine diagnostic service workflows for strain typing. IMPORTANCE Managing the transmission of antimicrobial resistance necessitates reliable methods for tracking outbreaks. We compared the performance of MALDI-TOF MS with orthogonal approaches for strain typing, including WGS and FTIR, for Acinetobacter baumannii isolates correlated with a health care-associated infection (HCAI) event. Combined with epidemiological data, all methods investigated identified a group of isolates that were temporally and spatially linked to the outbreak, yet potentially attributed to a separate transmission event. This may have implications for guiding infection control strategies during an outbreak. However, the technical reproducibility of MALDI-TOF MS needs to be improved for it to be employed as a standalone typing method, as different stages of the experimental workflow introduced bias influencing interpretation of biomarker peak data. Availability of in-house methods for strain typing of bacteria could improve infection control practices following increased reports of outbreaks of antimicrobial-resistant organisms during the COVID-19 pandemic, related to sessional usage of personal protective equipment (PPE).

Changes in the Protein Profile in Staphylococcal Strains from Patients Infected with the SARS-CoV-2 Virus.

Staphylococcus aureus strains are particularly often isolated from patients with SARS-CoV-2 infection. The aim of the current research was to determine whether the SARS-CoV-2 virus infection affects the protein profile of S. aureus. Bacteria were isolated from the forty swabs collected from the patients in the hospitals of the Pomeranian region. MALDI-TOF MS spectra were obtained using a Microflex LT instrument. Twenty-nine peaks were identified. The peak (2,430) is described here for the first time and was unique for the isolates from patients infected with the SARS-CoV-2 virus. These results support the hypothesis of bacterial adaptation to the conditions caused by viral infection.

Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles.

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry.

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

MALDI-TOF Mass Spectrometric Detection of SARS-CoV-2 Using Cellulose Sulfate Ester Enrichment and Hot Acid Treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Here we report a novel strategy for the rapid detection of SARS-CoV-2 based on an enrichment approach exploiting the affinity between the virus and cellulose sulfate ester functional groups, hot acid hydrolysis, and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Virus samples were enriched using cellulose sulfate ester microcolumns. Virus peptides were prepared using the hot acid aspartate-selective hydrolysis and characterized by MALDI-TOF MS. Collected spectra were processed with a peptide fingerprint algorithm, and searching parameters were optimized for the detection of SARS-CoV-2. These peptides provide high sequence coverage for nucleocapsid (N protein) and allow confident identification of SARS-CoV-2. Peptide markers contributing to the detection were rigorously identified using bottom-up proteomics. The approach demonstrated in this study holds the potential for developing a rapid assay for COVID-19 diagnosis and detecting virus variants from a variety of sources, such as sewage and nasal swabs.

Use of MALDI-TOF mass spectrometry for virus identification: a review.

The coronavirus pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) directly or indirectly affects every individual worldwide. The fight against SARS-CoV-2 is based on the rapid and accurate diagnosis and subsequent isolation of infected individuals. Therefore, the demands for the scientific development of diagnostic methods for the confirmation of SARS-CoV-2 are enormous. Currently, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) is the main method used for detecting viruses, including SARS-CoV-2, and is considered the gold standard for coronavirus disease 2019 (COVID-19) identification. However, various alternatives have been investigated due to the time and cost demands of this method or to shortages of reagents. In this review, we focus on matrix-assisted laser desorption and ionisation with time-of-flight analyser mass spectrometry (MALDI-TOF MS) techniques as potential tools for the diagnosis of viruses with an emphasis on SARS-CoV-2. MALDI-TOF is commonly used in clinical laboratories for bacterial characterization and identification, but in the field of clinical virology, MALDI-TOF remains only a promising technology for routine diagnosis. This review provides an overview of the development of clinical virology from the point of view of using MALDI-TOF for virus identification and as a possible diagnostic tool for SARS-CoV-2 detection. In addition, this review summarizes the current state of standard methods for virus diagnostics including the preparation of clinical samples.

Case report of Curtobacterium isolated from a catheter tip sample misidentified as Cronobacter.

The Curtobacterium genus is a member of the family Microbacteriaceae, and Curtobacterium species are recognized as plant pathogens. The aim of this study was to investigate a dubious result of species identification for an infection located on a catheter tip of a patient with Covid-19. A strain isolated from a catheter tip sample, identified by VITEK® 2 as Cronobacter spp., was submitted to polyphasic analysis: Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) using VITEK® MS, real-time polymerase chain reaction targeting dnaG gene, and 16S rRNA full gene Sanger sequencing analysis for confirmation. The strain presented negative result using qPCR and could not identified by MALDI-TOF MS. 16S rRNA full gene Sanger sequencing analysis identified the strain as Curtobacterium spp. The Gram-variable characteristic (Gram-negative instead of Gram-positive) of the isolated strain was the responsible for the misidentification by VITEK® 2 and VITEK® MS did not identify the strain. 16S rRNA full gene sequencing analysis identified the strain as Curtobacterium genus, but other complementary techniques are necessary to identify at species level.

Staphylococcus massiliensis isolated from human blood cultures, Germany, 2017-2020.

Clinical and laboratory data on newly described staphylococcal species is rare, which hampers decision-making when such pathogens are detected in clinical specimens. Here, we describe Staphylococcus massiliensis detected in three patients at a university hospital in southwest Germany. We report the discrepancy of microbiological findings between matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, 16S-rRNA polymerase chain reaction, and whole-genome sequencing for all three isolates. Our findings highlight the diagnostic pitfalls pertinent to novel and non-model organisms in daily microbiological practice, in whom the correct identification is dependent on database accuracy.

Matrix-Assisted Laser Desorption and Ionization Time-of-Flight Mass Spectrometry Analysis for the Direct Detection of SARS-CoV-2 in Nasopharyngeal Swabs.

The most common diagnostic method used for coronavirus disease-2019 (COVID-19) is real-time reverse transcription polymerase chain reaction (PCR). However, it requires complex and labor-intensive procedures and involves excessive positive results derived from viral debris. We developed a method for the direct detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in nasopharyngeal swabs, which uses matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-ToF MS) to identify specific peptides from the SARS-CoV-2 nucleocapsid phosphoprotein (NP). SARS-CoV-2 viral particles were separated from biological molecules in nasopharyngeal swabs by an ultrafiltration cartridge. Further purification was performed by an anion exchange resin, and purified NP was digested into peptides using trypsin. The peptides from SARS-CoV-2 that were inoculated into nasopharyngeal swabs were detected by MALDI-ToF MS, and the limit of detection was 106.7 viral copies. This value equates to 107.9 viral copies per swab and is approximately equivalent to the viral load of contagious patients. Seven NP-derived peptides were selected as the target molecules for the detection of SARS-CoV-2 in clinical specimens. The method detected between two and seven NP-derived peptides in 19 nasopharyngeal swab specimens from contagious COVID-19 patients. These peptides were not detected in four specimens in which SARS-CoV-2 RNA was not detected by PCR. Mutated NP-derived peptides were found in some specimens, and their patterns of amino acid replacement were estimated by accurate mass. Our results provide evidence that the developed MALDI-ToF MS-based method in a combination of straightforward purification steps and a rapid detection step directly detect SARS-CoV-2-specific peptides in nasopharyngeal swabs and can be a reliable high-throughput diagnostic method for COVID-19.

Detection and evolution of SARS-CoV-2 coronavirus variants of concern with mass spectrometry.

Mass mapping using high-resolution mass spectrometry has been applied to identify and rapidly distinguish SARS-CoV-2 coronavirus strains across five major variants of concern. Deletions or mutations within the surface spike protein across these variants, which originated in the UK, South Africa, Brazil and India (known as the alpha, beta, gamma and delta variants respectively), lead to associated mass differences in the mass maps. Peptides of unique mass have thus been determined that can be used to identify and distinguish the variants. The same mass map profiles are also utilized to construct phylogenetic trees, without the need for protein (or gene) sequences or their alignment, in order to chart and study viral evolution. The combined strategy offers advantages over conventional PCR-based gene-based approaches exploiting the ease with which protein mass maps can be generated and the speed and sensitivity of mass spectrometric analysis.

RT-PCR/MALDI-TOF mass spectrometry-based detection of SARS-CoV-2 in saliva specimens.

As severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections continue, there is a substantial need for cost-effective and large-scale testing that utilizes specimens that can be readily collected from both symptomatic and asymptomatic individuals in various community settings. Although multiple diagnostic methods utilize nasopharyngeal specimens, saliva specimens represent an attractive alternative as they can rapidly and safely be collected from different populations. While saliva has been described as an acceptable clinical matrix for the detection of SARS-CoV-2, evaluations of analytic performance across platforms for this specimen type are limited. Here, we used a novel sensitive RT-PCR/MALDI-TOF mass spectrometry-based assay (Agena MassARRAY®) to detect SARS-CoV-2 in saliva specimens. The platform demonstrated high diagnostic sensitivity and specificity when compared to matched patient upper respiratory specimens. We also evaluated the analytical sensitivity of the platform and determined the limit of detection of the assay to be 1562.5 copies/ml. Furthermore, across the five individual target components of this assay, there was a range in analytic sensitivities for each target with the N2 target being the most sensitive. Overall, this system also demonstrated comparable performance when compared to the detection of SARS-CoV-2 RNA in saliva by the cobas® 6800/8800 SARS-CoV-2 real-time RT-PCR Test (Roche). Together, we demonstrate that saliva represents an appropriate matrix for SARS-CoV-2 detection on the novel Agena system as well as on a conventional real-time RT-PCR assay. We conclude that the MassARRAY® system is a sensitive and reliable platform for SARS-CoV-2 detection in saliva, offering scalable throughput in a large variety of clinical laboratory settings.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Superiority of MALDI-TOF Mass Spectrometry over Real-Time PCR for SARS-CoV-2 RNA Detection.

At present, the RT-PCR test remains the gold standard for early diagnosis of SARS-CoV-2. Nevertheless, there is growing evidence demonstrating that this technique may generate false-negative results. Here, we aimed to compare the new mass spectrometry-based assay MassARRAY® SARS-CoV-2 Panel with the RT-PCR diagnostic test approved for clinical use. The study group consisted of 168 suspected patients with symptoms of a respiratory infection. After simultaneous analysis by RT-PCR and mass spectrometry methods, we obtained discordant results for 17 samples (10.12%). Within fifteen samples officially reported as presumptive positive, 13 were positive according to the MS-based assay. Moreover, four samples reported by the officially approved RT-PCR as negative were positive in at least one MS assay. We have successfully demonstrated superior sensitivity of the MS-based assay in SARS-CoV-2 detection, showing that MALDI-TOF MS seems to be ideal for the detection as well as discrimination of mutations within the viral genome.

Label-Free Screening of SARS-CoV-2 NSP14 Exonuclease Activity Using SAMDI Mass Spectrometry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for the global COVID-19 pandemic. Nonstructural protein 14 (NSP14), which features exonuclease (ExoN) and guanine N7 methyltransferase activity, is a critical player in SARS-CoV-2 replication and fidelity and represents an attractive antiviral target. Initiating drug discovery efforts for nucleases such as NSP14 remains a challenge due to a lack of suitable high-throughput assay methodologies. This report describes the combination of self-assembled monolayers and matrix-assisted laser desorption ionization mass spectrometry to enable the first label-free and high-throughput assay for NSP14 ExoN activity. The assay was used to measure NSP14 activity and gain insight into substrate specificity and the reaction mechanism. Next, the assay was optimized for kinetically balanced conditions and miniaturized, while achieving a robust assay (Z factor > 0.8) and a significant assay window (signal-to-background ratio > 200). Screening 10,240 small molecules from a diverse library revealed candidate inhibitors, which were counterscreened for NSP14 selectivity and RNA intercalation. The assay methodology described here will enable, for the first time, a label-free and high-throughput assay for NSP14 ExoN activity to accelerate drug discovery efforts and, due to the assay flexibility, can be more broadly applicable for measuring other enzyme activities from other viruses or implicated in various pathologies.

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

Detection, Mapping, and Proteotyping of SARS-CoV-2 Coronavirus with High Resolution Mass Spectrometry.

A high resolution mass spectrometry approach has been applied for the first time to detect and characterize SARS-CoV-2 coronavirus in cell cultured and nasopharyngeal swab specimens. Peptide ions for three of the most abundant structural viral proteins (membrane, nucleocapid, and spike) are detected and assigned directly, by virtue of the high resolution and mass accuracy within the mass maps of whole virus digests, without the need for tandem mass spectrometry (MS/MS). MALDI-MS based approaches offer high sample throughput and speed, compared with those of LC-MS strategies, and detection limits at some 105 copies, or orders of magnitude less with selected ion monitoring, that compete favorably with conventional reverse transcription polymerase chain reaction (RT-PCR) strategies. The detection of signature peptides unique to SARS-CoV-2 coronavirus over those from the influenza virus allows for its unambiguous detection.