Sample preparation and culture condition effects on MALDI-TOF MS identification of bacteria: A review

Contribution of MALDI-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains

Clostridioides difficile (CD) infections are defined by toxins A (TcdA) and B (TcdB) along with the binary toxin (CDT). The emergence of the 'hypervirulent' (Hv) strain PR 027, along with PR 176 and 181, two decades ago, reshaped CD infection epidemiology in Europe. This study assessed MALDI-TOF mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) and Deep Learning (DL) to identify toxigenic strains (producing TcdA, TcdB with or without CDT) and Hv strains. In total, 201 CD strains were analysed, comprising 151 toxigenic (24 ToxA+B+CDT+, 22 ToxA+B+CDT+ Hv+ and 105 ToxA+B+CDT-) and 50 non-toxigenic (ToxA-B-) strains. The DL-based classifier exhibited a 0.95 negative predictive value for excluding ToxA-B- strains, showcasing accuracy in identifying this strain category. Sensitivity in correctly identifying ToxA+B+CDT- strains ranged from 0.68 to 0.91. Additionally, all classifiers consistently demonstrated high specificity (>0.96) in detecting ToxA+B+CDT+ strains. The classifiers' performances for Hv strain detection were linked to high specificity (≥0.96). This study highlights MALDI-TOF MS enhanced by ML techniques as a rapid and cost-effective tool for identifying CD strain virulence factors. Our results brought a proof-of-concept concerning the ability of MALDI-TOF MS coupled with ML techniques to detect virulence factor and potentially improve the outbreak's management.

Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing

Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures.

Mass spectrometric analysis of Odonthobuthus Doriae scorpion venom and its non-neutralized fractions after interaction with commercial antivenom

It is believed that antivenoms play a crucial role in neutralizing venoms. However, uncontrolled clinical effects appear in patients stung by scorpions after the injection of antivenom. In this research, non-neutralized components of the venom of the Iranian scorpion Odonthobuthus doriae were analyzed after interacting with the commercial antivenom available in the market. The venom and antivenom interaction was performed, then centrifuged, and the supernatant was analyzed by high-performance liquid chromatography (HPLC). Two peaks of Odonthobuthus doriae venom were observed in the chromatogram of the supernatant. Two components were isolated by HPLC and analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) instruments. Peptide sequencing was done by Liquid Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry (LC-Q-TOF MS/MS). Results indicate that the components of scorpion venom mainly have a molecular weight below 10 kDa, consisting of toxic peptides that disrupt the function of sodium and potassium channels. The MALDI-TOF MS results show that two toxic peptides with molecular masses of 6941 Da and 6396 Da were not neutralized by the antivenom. According to the MS/MS sequencing data, the components have been related to peptides A0A5P8U2Q6_MESEU and A0A0U4FP89_ODODO, which belong to the sodium and potassium channels toxins family, respectively.

Detection accuracy and clinical applications of DP-TOF mass spectrometry

OBJECTIVE

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently used in clinical microbiology laboratories. This study aimed to determine whether dual-polarity time-of-flight mass spectrometry (DP-TOF MS) could be applied to clinical nucleotide detection.

METHODS

This prospective study included 40 healthy individuals and 110 patients diagnosed with cardiovascular diseases. We used DP-TOF MS and Sanger sequencing to evaluate 17 loci across 11 genes associated with cardiovascular drug responses. In addition, we used DP-TOF MS to test 998 retrospectively collected clinical DNA samples with known results.

RESULTS

A, T, and G nucleotide detection by DP-TOF MS and Sanger sequencing revealed 100% concordance, whereas the C nucleotide concordance was 99.86%. Genotyping based on the results of the two methods showed 99.96% concordance. Regarding clinical applications, DP-TOF MS yielded a 99.91% concordance rate for known loci. The minimum detection limit for DNA was 0.4 ng; the inter-assay and intra-assay precision rates were both 100%. Anti-interference analysis showed that aerosol contamination greater than 1013 copies/µL in the laboratory environment could influence the results of DP-TOF MS.

CONCLUSIONS

The DP-TOF MS platform displayed good detection performance, as demonstrated by its 99.96% concordance rate with Sanger sequencing. Thus, it may be applied to clinical nucleotide detection.

Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe3O4 magnetic nanoparticle-based affinity mass spectrometry with a machine learning strategy

Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe3O4 MNPs were used as affinity probes for sample enrichment to eliminate the overnight culture and reduce the time in sample preparation. The limit of detection of the MNP-based affinity approach toward S. aureus combined with our machine learning strategy was as low as ~ 8 × 103 CFU mL-1. The feasibility of using the current approach for the identification of S. aureus in juice samples was also demonstrated.

Exploiting the role of plant growth promoting rhizobacteria in reducing heavy metal toxicity of pepper (Capsicum annuum L.)

Microorganisms are cost-effective and eco-friendly alternative methods for removing heavy metals (HM) from contaminated agricultural soils. Therefore, this study aims to identify and characterize HM-tolerant (HMT) plant growth-promoting rhizobacteria (PGPR) isolated from industry-contaminated soils to determine their impact as bioremediators on HM-stressed pepper plants. Four isolates [Pseudomonas azotoformans (Pa), Serratia rubidaea (Sr), Paenibacillus pabuli (Pp) and Bacillus velezensis (Bv)] were identified based on their remarkable levels of HM tolerance in vitro. Field studies were conducted to evaluate the growth promotion and tolerance to HM toxicity of pepper plants grown in HM-polluted soils. Plants exposed to HM stress showed improved growth, physio-biochemistry, and antioxidant defense system components when treated with any of the individual isolates, in contrast to the control group that did not receive PGPR. The combined treatment of the tested HMT PGPR was, however, relatively superior to other treatments. Compared to no or single PGPR treatment, the consortia (Pa+Sr+Pp+Bv) increased the photosynthetic pigment contents, relative water content, and membrane stability index but lowered the electrolyte leakage and contents of malondialdehyde and hydrogen peroxide by suppressing the (non) enzymatic antioxidants in plant tissues. In pepper, Cd, Cu, Pb, and Ni contents decreased by 88.0-88.5, 63.8-66.5, 66.2-67.0, and 90.2-90.9% in leaves, and 87.2-88.1, 69.4-70.0%, 80.0-81.3, and 92.3%% in fruits, respectively. Thus, these PGPR are highly effective at immobilizing HM and reducing translocation in planta. These findings indicate that the application of HMT PGPR could be a promising "bioremediation" strategy to enhance growth and productivity of crops cultivated in soils contaminated with HM for sustainable agricultural practices.

Correlation between antimicrobial resistance, biofilm formation, and virulence determinants in uropathogenic Escherichia coli from Egyptian hospital

BACKGROUND

Uropathogenic Escherichia coli (UPEC) is the main etiological agent behind community-acquired and hospital-acquired urinary tract infections (UTIs), which are among the most prevalent human infections. The management of UPEC infections is becoming increasingly difficult owing to multi-drug resistance, biofilm formation, and the possession of an extensive virulence arsenal. This study aims to characterize UPEC isolates in Tanta, Egypt, with regard to their antimicrobial resistance, phylogenetic profile, biofilm formation, and virulence, as well as the potential associations among these factors.

METHODS

One hundred UPEC isolates were obtained from UTI patients in Tanta, Egypt. Antimicrobial susceptibility was assessed using the Kirby-Bauer method. Extended-spectrum β-lactamases (ESBLs) production was screened using the double disk synergy test and confirmed with PCR. Biofilm formation was evaluated using the microtiter-plate assay and microscopy-based techniques. The phylogenetic groups of the isolates were determined. The hemolytic activity, motility, siderophore production, and serum resistance of the isolates were also evaluated. The clonal relatedness of the isolates was assessed using ERIC-PCR.

RESULTS

Isolates displayed elevated resistance to cephalosporins (90-43%), sulfamethoxazole-trimethoprim (63%), and ciprofloxacin (53%). Ninety percent of the isolates were multidrug-resistant (MDR)/ extensively drug-resistant (XDR) and 67% produced ESBLs. Notably, there was an inverse correlation between biofilm formation and antimicrobial resistance, and 31%, 29%, 32%, and 8% of the isolates were strong, moderate, weak, and non-biofilm producers, respectively. Beta-hemolysis, motility, siderophore production, and serum resistance were detected in 64%, 84%, 65%, and 11% of the isolates, respectively. Siderophore production was correlated to resistance to multiple antibiotics, while hemolysis was more prevalent in susceptible isolates and associated with stronger biofilms. Phylogroups B2 and D predominated, with lower resistance and stronger biofilms in group B2. ERIC-PCR revealed considerable diversity among the isolates.

CONCLUSION

This research highlights the dissemination of resistance in UPEC in Tanta, Egypt. The evident correlation between biofilm and resistance suggests a resistance cost on bacterial cells; and that isolates with lower resistance may rely on biofilms to enhance their survival. This emphasizes the importance of considering biofilm formation ability during the treatment of UPEC infections to avoid therapeutic failure and/or infection recurrence.

Comparison of the Bruker Microflex LT and Zybio EXS2600 MALDI TOF MS systems for the identification of clinical microorganisms

The emergence of Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI TOF MS) technology has expanded the capabilities for identifying microorganisms in clinical labs, replacing traditional biochemical testing with a proteomic approach. In the present study, we compared results between the two commercial MALDI TOF MS systems, Bruker Microflex LT Biotyper and Zybio EXS2600 Ex-Accuspec, for the identification of 1979 urinary isolates by direct extraction method. Current study found that both systems identified a high percentage of isolates to at least the genus level - Bruker 95.6 % of isolates, Zybio 92.4 %. In the case of 89.5 % of all analyzed spectra, the identification results were consistent between the used devices. The highest score values and the highest percentage of spectra identified to species were obtained for gram-negative bacteria. The results show that both systems are equally good choices in terms of analytical performance for routine microbiological diagnostic procedures.

Present and future perspectives on mass spectrometry for clinical microbiology

In the last decade, MALDI-TOF Mass Spectrometry (MALDI-TOF MS) has been introduced and broadly accepted by clinical laboratory laboratories throughout the world as a powerful and efficient tool for rapid microbial identification. During the MALDI-TOF MS process, microbes are identified using either intact cells or cell extracts. The process is rapid, sensitive, and economical in terms of both labor and costs involved. Whilst MALDI-TOF MS is currently the gold-standard, it suffers from several shortcomings such as lack of direct information on antibiotic resistance, poor depth of analysis and insufficient discriminatory power for the distinction of closely related bacterial species or for reliably sub-differentiating isolates to the level of clones or strains. Thus, new approaches targeting proteins and allowing a better characterization of bacterial strains are strongly needed, if possible, on a very short time scale after sample collection in the hospital. Bottom-up proteomics (BUP) is a nice alternative to MALDI-TOF MS, offering the possibility for in-depth proteome analysis. Top-down proteomics (TDP) provides the highest molecular precision in proteomics, allowing the characterization of proteins at the proteoform level. A number of studies have already demonstrated the potential of these techniques in clinical microbiology. In this review, we will discuss the current state-of-the-art of MALDI-TOF MS for the rapid microbial identification and detection of resistance to antibiotics and describe emerging approaches, including bottom-up and top-down proteomics as well as ambient MS technologies.

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

Effect of Three Different Preservatives on the Microbiota of Shalgam, a Traditional Lactic Acid Fermented Beverage

Shalgam is a traditional Turkish beverage derived from the natural fermentation of purple carrots (Daucus carota) that boasts valuable antioxidant and prebiotic properties. These features of shalgam increase efforts to enhance its shelf life and ensure safe consumption. In this study, the effects of three different preservatives (sodium benzoate, potassium sorbate, or natamycin) on the physicochemical and microbiological properties of shalgam produced at laboratory scale and stored at room temperature for six months were investigated. Each preservative was used in four different concentrations (25, 100, 400, and 800 mg/L) to assess their impacts on the population of lactic acid bacteria (LAB) and yeast. After determining the total acidity and pH of the samples, colorimetric measurements were performed. The isolated LAB were defined using the matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/TOF MS) method. The addition of preservatives did not significantly affect the pH of the shalgam samples (3.44-3.52) compared to the control sample (3.43). However, a slight increase was observed in the total acidity of preservative-treated samples, with the highest level (5.61 g/L lactic acid) recorded in samples containing 100 mg/L sodium benzoate. Lacticaseibacillus paracasei subsp. paracasei, which has the potential to impart probiotic properties to shalgam, was the predominant LAB species in both non-treated and preservative-treated samples. The use of preservatives significantly reduced the total number of yeasts, which may cause spoilage in shalgam. The results indicate that using sodium benzoate at a concentration of 100 mg/L is the optimum method for shalgam production, resulting in the highest total acidity value obtained. Overall, the findings provide a significant contribution to prolonging the shelf life of shalgam, a beverage with immense production and consumption potential worldwide.

An easy adjustment of instrument settings ('Peak MALDI') improves identification of organisms by MALDI-ToF mass spectrometry

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a mature technolaogy with 'auto-execute' instrument settings and peak processing parameters tailored for rapid bacterial identification. Adoption for other organisms has been problematic, with optimisation efforts focusing on sample preparation. Using the Bruker MALDI Biotyper, we demonstrate 'Peak MALDI': easily-applied settings that immediately enhance sensitivity, improve spectrum quality, and increase identification confidence for any target, establishing its potential value for all MALDI-ToF MS systems.

Toxin and phage production from pathogenic E. coli by antibiotic induction analyzed by chemical reduction, MALDI-TOF-TOF mass spectrometry and top-down proteomic analysis

RATIONALE

Shiga toxin-producing Escherichia coli (STEC) are an ongoing threat to public health and agriculture. Our laboratory has developed a rapid method for identification of Shiga toxin (Stx), bacteriophage, and host proteins produced from STEC. We demonstrate this technique on two genomically sequenced STEC O145:H28 strains linked to two major outbreaks of foodborne illness occurring in 2007 (Belgium) and 2010 (Arizona).

METHODS

Our approach was to induce expression of stx, prophage, and host genes by antibiotic exposure, chemically reduce samples, and identify protein biomarkers from unfractionated samples using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, tandem mass spectrometry (MS/MS), and post-source decay (PSD). The protein mass and prominent fragment ions were used to identify protein sequences using top-down proteomic software developed in-house. Prominent fragment ions are the result of polypeptide backbone cleavage resulting from the aspartic acid effect fragmentation mechanism.

RESULTS

The B-subunit of Stx and acid-stress proteins HdeA and HdeB were identified in both STEC strains in their intramolecular disulfide bond-intact and reduced states. In addition, two cysteine-containing phage tail proteins were detected and identified from the Arizona strain but only under reducing conditions, which suggests that bacteriophage complexes are bound by intermolecular disulfide bonds. An acyl carrier protein (ACP) and a phosphocarrier protein were also identified from the Belgium strain. ACP was post-translationally modified with attachment of a phosphopantetheine linker at residue S36. The abundance of ACP (plus linker) was significantly increased on chemical reduction, suggesting the release of fatty acids bound to the ACP + linker at a thioester bond. MS/MS-PSD revealed dissociative loss of the linker from the precursor ion as well as fragment ions with and without the attached linker consistent with its attachment at S36.

CONCLUSIONS

This study demonstrates the advantages of chemical reduction in facilitating the detection and top-down identification of protein biomarkers of pathogenic bacteria.

Identification of Francisella tularensis Subspecies in a Clinical Setting Using MALDI-TOF MS: An In-House Francisella Library and Biomarkers

Francisella tularensis is a zoonotic bacterium that is endemic in large parts of the world. It is absent in the standard library of the most applied matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) systems: the Vitek MS and the Bruker Biotyper system. The additional Bruker MALDI Biotyper Security library contains F. tularensis without subspecies differentiation. The virulence of F. tularensis differs between the subspecies. The F. tularensis subspecies (ssp.) tularensis is highly pathogenic, whereas the subspecies holarctica displays lower virulence and subspecies novicida and F. tularensis ssp. mediasiatica are hardly virulent. To differentiate the Francisellaceae and the F. tularensis-subspecies, an in-house Francisella library was built with the Bruker Biotyper system and validated together with the existing Bruker databases. In addition, specific biomarkers were defined based on the main spectra of the Francisella strains supplemented with in silico genome data. Our in-house Francisella library accurately differentiates the F. tularensis subspecies and the other Francisellaceae. The biomarkers correctly differentiate the various species within the genus Francisella and the F. tularensis subspecies. These MALDI-TOF MS strategies can successfully be applied in a clinical laboratory setting as a fast and specific method to identify F. tularensis to subspecies level.

Infrared pulsed fiber laser-produced silver-109 nanoparticles for laser desorption/ionization mass spectrometry of 3-hydroxycarboxylic acids

RATIONALE

3-Hydroxycarboxylic acids are one of the major components of bacterial lipopolysaccharides (LPS), also known as endotoxins. Endotoxins pose a serious health risk and can seriously damage the internal organs of humans and animals. 3-Hydroxycarboxylic acids can be used as environmental markers to determine endotoxin levels. At the time of preparation of this manuscript no studies on laser mass spectrometry (MS) and analysis with silver nanoparticles (NP) for 3-hydroxycarboxylic acids have been published in literature.

METHODS

Six acids, 3-hydroxyoctanoic (3-OH-C8:0), 3-hydroxydecanoic (3-OH-C10:0), 3-hydroxydodecanoic (3-OH-C12:0), 3-hydroxytetradecanoic (3-OH-C14:0), 3-hydroxyhexadecanoic (3-OH-C16:0), and 3-hydroxyoctadecanoic (3-OH-C18:0) acids, were used as test compounds on the target containing silver-109 NPs for quantification using matrix-assisted laser desorption/ionization (MALDI)-type mass spectrometer. Methods were also tested on spiked human blood serum samples to quantify 3-hydroxycarboxylic acids and verify the influence of the biological matrix on the measurement.

RESULTS

Analyzed acids were directly tested in 1 000 000-fold concentration change conditions ranging from 1 mg/mL to 1 ng/mL. The semi-automatic MSI (MS imaging) method allowed us to obtain two to five times lower limit of detection (LOD) and lower limit of quantitation (LLOQ) values than common LDI (Bruker Daltonics, Bremen, Germany) method for analyzed acids. For almost all results of 3-hydroxycarboxylic acids, the trendline fit was better for the semi-automatic MSI method than the manual LDI method.

CONCLUSION

For the first time, the use of laser MS for the quantification of 3-hydroxycarboxylic acids has been demonstrated, and it has been proven that it can be used in the quantitative analysis of such compounds over a wide range of concentrations. In addition, a comparison of two methods-manual LDI-MS and semi-automatic MSI-is presented.

Multimodal analysis of south-eastern Black Sea sediment bacterial population diversity

This study focused on marine sediments from the Black Sea, mainly due to bacterial diversity-induced public health / biotechnology application value. Sediment samples were gathered from 14 locations at differing depths across Turkish shores on a seasonal basis over 10 months, with bacterial identifications performed through using multimodal analytical platforms. Overall, 26 differing, predominantly Gram-positive (57.5 %) bacterial species were identified for this region, including Bacillaceae (50.0 %) and Pseudomonadaceae (15.0 %). The most dominant classes were identified as Bacilli (52.5 %) and Gammaproteobacteria (40.0 %). Ten isolates (25 %) to the species level and thirty-six isolates (90 %) to the genus level were identified using VITEK® MS and Bruker Microflex® LT/SH, in comparison to 16S rRNA sequencing results. Identified species - particularly, novel reported species - can contribute to the knowledge of microbial life dwelling upon sediments of the south-eastern regions of the Black Sea.

MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification

The term “proteome” refers to the total of all proteins expressed in an organism. The term “proteomics” refers to the field of research that includes not only information on the expression levels of individual proteins, but also their higher-order structures, intermolecular interactions, and post-translational modifications. The core technology, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), is available for protein analysis thanks to the work of Koichi Tanaka and John Fenn, who were awarded the Nobel Prize in Chemistry in 2002. The most successful proteome analysis in clinical practice is rapid microbial identification. This method determines the bacterial species by comparing the proteome profile of the bacteria obtained by matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) with a database. MS is superior in simplicity, speed, and accuracy to classic speciation by staining and phenotyping. In clinical microbiology, MS has had a large impact on the diagnosis and treatment of infectious disease. Early diagnosis and treatment of infectious disease are important, and rapid identification by MALDI-TOF MS has made a major contribution to this field.