MALDI-TOF Mass Spectrometry in Clinical Analysis and Research.
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Abstract
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MALDI-TOF Mass Spectrometry in Clinical Analysis and Research
Abstract
In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of “pathogen identification”, “disease diagnosis”, “nucleic acids analysis”, and “small molecules analysis” were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.
1. Background
In 1985, the concept of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) was originally proposed by Karas et al.1 They adopted organic molecules as matrices to assist the desorption/ionization of molecules under UV laser irradiation. In 1988, mass analyzer time-of-flight (TOF) was coupled to MALDI by Koichi Tanaka and colleagues for the analyses of macromolecules, especially proteins.2 After that, MALDI-TOF MS was developed for generating and analyzing ions from a variety of molecules, particularly large, nonvolatile, and thermally labile compounds such as proteins, polymers, and oligonucleotides, which provides the basis for the use of MS in biomedical research. In the decade since being awarded the Nobel Prize in Chemistry in 2002, MALDI-TOF MS has been largely developed for proteomics/metabolomics studies through the qualitative and quantitative analysis of proteins/metabolites.
Modern life sciences have been greatly facilitated by the MALDI-TOF MS technology. Due to the advantages including easy operation, high throughput, and high tolerance to contamination (i.e., salts, buffers, detergents), MALDI-TOF MS-based instruments have been widely used in many clinical scenarios to help clinical specialists make medical decisions. For instance, MALDI-TOF has been developed for the diagnosis of bloodstream infections and neurodegenerative diseases. As technology matures, MALDI-TOF MS-based systems, e.g., the MALDI Biotyper by Bruker, the VitekMS by bioMérieux Clinical Diagnostics, the MassARRAY System by Agena Bioscience, and the Clin-TOF from Bioyong Technology, have been registered as medical devices for clinical uses.
In recent years, a considerable number of review articles have been published to summarize MALDI-TOF MS clinical applications. On the basis of a search of the Web of Science database, reviews with MALDI-TOF as a theme in the last five years (2018–2022) mostly focused on the applications of microbiology such as microbial species identification,3,4 antimicrobial susceptibility testing,5,6 and bloodstream infection diagnosis.7 Emphasis is often placed on specific clinically relevant application scenarios that are accompanied by in-depth discussions with very few reviews covering different clinical application directions. This review incorporates several clinically relevant applications of MALDI-TOF with high significance. Four themes of MALDI-TOF MS are summarized through a systematic analysis of keywords from published MALDI MS-related reviews collected in the Web of Science database. For a deeper understanding, the application implications, analytical methods, and systems as well as the limitations that need to be addressed are outlined and discussed. This review aims to provide a general guide for MALDI-TOF MS users to develop potential MALDI-TOF MS-based analysis methods in the field of clinical analysis and research.
2. Basis of MALDI-TOF MS
2.1. Instrumental Basis of MALDI-TOF MS
During MALDI MS detection of biomolecules, the sample to be analyzed is placed on a conductive plate together with an organic matrix (Figure Figure11). Under laser irradiation by an nS-class pulsed laser (typically a nitrogen laser at a wavelength of 337 nm or a Nd:YAG laser at a wavelength of 355 nm), the matrix molecules absorb the laser energy and convert it to electronically excited energy, instantly transforming the solid mixture of the matrix and analyte into a gaseous state. After sample desorption, charge transfer occurs, causing ionization of the analyte in collisions between the uncharged neutral molecules, the matrix ions, the protons, the electrons, and the metal cations. Then, ions produced by photoablation and photoionization are accelerated by an electric field into the mass analyzer for mass-to-charge ratio (m/z) analysis. Although the ionization mechanism of the MALDI process remains a subject of ongoing debate, it is generally recognized that the matrix plays a crucial role. The matrix used in MALDI is usually an organic molecule capable of absorbing laser energy and dispersing sample molecules. Common matrices include 2,5-dihydroxybenzoic acid (DHB), α-cyano-4-hydroxy-trans-cinnamic acid (HCCA), sinapinic acid (SA), etc. These matrixes are suitable for nitrogen and Nd:YAG lasers.8 The analyte is usually dissolved in a solvent containing the matrix and deposited on a conductive plate for drying and crystallization. It I believed that the analyte ions are carried in the matrix crystal and then desorbed into the gas phase as matrix–analyte ion pairs under laser irradiation.9−12 Active species like electrons and radicals are produced during the process as well.10 As a result, secondary reactions between the analyte, the matrix, and the active species take place in the plume, ultimately producing analyte ions. MALDI is characterized to generate mainly singly charged ions.
MALDI-TOF mass spectrometer. Target plate is placed in the vacuum chamber of a mass spectrometer. Spots to be analyzed are shot by a laser to desorb and ionize the sample and matrix molecules from the target plate. Cloud of ionized molecules is accelerated into the TOF mass analyzer, toward the detector. Lighter molecules travel faster, followed by progressively heavier analytes. Mass spectrum is generated, representing the number of ions hitting the detector over time. Separation is by the mass-to-charge ratio, but because the charge is typically single, separation is effectively by molecular weight.
Generally, TOF is selected as a suitable mass analyzer to couple with the MALDI ion source (Figure Figure11). The high-throughput characteristic of the TOF mass analyzer makes it a perfect match for the MALDI ion source, a pulsed ion source at high frequency. For mass spectrometers equipped with a TOF mass analyzer, sample ions are extracted by a delayed potential pulse (delayed ion extraction) into the TOF tube, which is a field-free region, and the counterions are driven back to the target plate. The extracted sample ions will pass through the TOF tube at speeds inversely proportional to the square root of their m/z values. By recording the time of the flight pass, the m/z of the ions can be calculated. With the principle, the TOF mass analyzer is not limited in mass range, which also makes it an ideal mass analyzer to couple with MALDI, which generates singly charged ions with large m/z. The resolution of a TOF mass analyzer depends on the length of the flight path and can be optimized with techniques of delayed ion extraction and reflectron. In reflectron TOF, a contrary electric field is placed at the end of the TOF tube to push the ions back but with a single angle from the original axial direction. In such a way, ions with the same m/z but different kinetic energies due to the initial energy distribution generated during the MALDI process can be corrected to reach a much higher analysis resolution than the linear TOF. When one TOF mass analyzer is not sufficient to analyze the target analytes in a complex mixture, coupling a second TOF mass analyzer helps to perform tandem MS analysis. The first TOF mass analyzer is then used to select the precursor ions, which are fragmented in a collision cell and then analyzed by the second TOF mass analyzer to obtain a tandem mass spectrum.
2.2. Common Analytical Strategies by MALDI-TOF MS
The experimental method of MALDI-TOF MS can be summarized as follows. First, the sample is mixed with a suitable matrix material and dropped onto a clean MALDI sample plate. For surface-assisted laser desorption/ionization (SALDI), a variant of the typical MALDI, solid nanomaterial is used as the matrix with a more homogeneous sample distribution (Figure Figure22).13 After drying, samples were analyzed by MALDI-TOF MS. Under laser irradiation, the analyte molecules are ionized by protonation or deprotonation in a thermal plume of ablation gas and accelerated into the mass analyzers or hybrid mass analyzers for separation and detection.14,15
Typical workflow of sample preparations for analyses by MALDI-TOF MS and SALDI-TOF MS. In conventional MALDI-TOF MS, the analytes and chemical matrices are mixed and dried to form cocrystals, whereas analytes are coated homogeneously and distributed evenly on a layer of solid matrix in SALDI-TOF MS. Reprinted with permission from ref (16). Copyright 2013 Springer Nature.
MALDI-TOF MS has been widely used in many biochemical analyses, such as bacterial typing, proteomics, and MS imaging, and in polymer science as well as inorganic chemistry, e.g., nanomaterials characterization. Due to its high speed and sensitivity, it is often used to confirm the existence of a target molecule,16 to discriminate or type species based on their mass fingerprint,17 to image the distribution of molecules in biological samples (e.g., cells and tissues),18 and to identify compounds in a nontargeted manner by coupling with separation methods, such as gel electrophoresis, capillary electrophoresis, and liquid chromatography via an automatic sample depositing system, and tandem MS strategies.19 MS imaging is based on the mapping of the corresponding ion intensities while determining the spatial distribution of many molecules in a sample. Because organic matrices deposited on the sample can cause diffusion of the analytes, altering their original distribution and reducing the spatial resolution, several matrix-free ionization platforms have been developed to address this problem, including inorganic matrix and nanophotonic platforms.20
Surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) is an extension of MALDI-TOF MS. Compared with MALDI, the main difference of SELDI is that the proteins of interest can be sequestered and purified before laser ionization by interacting with premodified substances on the surface of ProteinChip based on biological or chemical affinities.13 For SELDI, samples such as serum and urine can be spotted without extra treatment. Followed by surface extraction by ProteinChip, nonspecific substances and contaminants can be removed by subsequent on-spot washing and better crystallization of the matrix and target molecules can be achieved, which delivers higher specificity and sensitivity in subsequent analysis.
3. Keyword Co-Occurrence of MALDI MS-Associated Review Articles
Developed applications of MALDI MS with the highest level of interests were summarized by analyzing keywords in published reviews related to MALDI. The Web of Science database was selected for the research work, and VOSviewer (The Centre for Science and Technology Studies, CWTS), software for the construction of relationships between the structure, the evolution, and the collaboration of knowledge domains, was used for keyword co-occurrence analysis.
3.1. Methods of Keywords Co-Occurrence Analysis
The steps of keyword co-occurrence analysis mainly include reference searching, keyword cleaning, data importing, and analysis of included keywords. First, MALDI-associated reviews were screened from the Web of Science database. The searching method was “Topic = MALDI, Document Type = Review Articles, Database = Web of Science Core Collection”. A total of 2401 records were represented on the Web site and added to a marked list. The information was exported in the form of plain text followed by keyword co-occurrence analysis using VOSviewer on 18 October 2021. It should be noted that some critical words need to be “cleaned” before co-occurrence analysis because the same object may be expressed differently in different articles with the usage of abbreviations, symbols, and so on. On the basis of preliminary co-occurrence analysis, the following substitutions were adopted. “Matrix-assisted laser desorption/ionization (maldi), matrix-assisted laser desorption, assisted-laser-desorption/ionization, laser-desorption-ionization, laser-desorption ionization, desorption-ionization, desorption ionization, maldi-tof-ms, maldi-tof ms, maldi-tof, maldi-ms, maldi, laser-desorption/ionization-time, laser-desorption/ionization-, laser-laser-desorption/ionization, laser-desorption/ionization, and assisted laser-desorption” were replaced with “l-d/i”. ‘High performance liquid chromatography, high-performance liquid chromatography, performance liquid chromatography, liquid-chromatography” were replaced with “liquid chromatography”. “Tandem mass-spectrometry” and “mass-spectrometry” were replaced with “mass spectrometry”. “Imaging mass spectrometry” was replaced with “mass spectrometry imaging”. “Breast-cancer” was replaced with “breast cancer”. “Alzheimers-disease, Alzheimer’s disease, Alzheimer disease” were replaced with “Alzheimer”, and “lung-cancer, lung cancer” were replaced with “lung disease”. For keyword co-occurrence analysis, we created a map based on the bibliographic area and read data from bibliographic database files. All cleansed texts were imported, and the minimum occurrence threshold of keywords was set as 20. A total of 177 keywords that met the criteria among 12 480 keywords were obtained. According to the results of keyword co-occurrence analysis, the most important clinical applications of laser desorption/ionization were summarized.
3.2. Network Visualization of Keywords in MALDI MS-Associated Review Articles
The results of the network visualization are shown in Figure Figure33. In the network visualization, a circle represents a keyword label. The size of the circle is proportional to the number of occurrences. The lines between the keywords represent the relevance of the keyword labels. In general, the closer two keyword labels are located to each other, the stronger their correlation is. Depending on the results of the cluster analysis, neighboring keywords share a common color.
Network visualization of keyword co-occurrence analysis using the keywords with the highest co-occurrence among all MALDI MS-related reviews exported from the Web of Science Core Collection. Size of the circles is proportional to the number of occurrences. Distance between different circles is inversely proportional to their relationship. Closely related keywords are marked with the same color according to the cluster analysis. Web of Science search covers review articles published from January 1, 1994 to October 18, 2021.
As shown in Figure Figure33, relatively significant keywords among all 177 keywords appear in the network. Through cluster analysis, the most important MALDI-related keywords were divided into six main sections with different color labels. The red section occupies the largest area. The keyword “identification” dominates the red section, and most of the remaining keywords including “microbial or microbiology”, “infections”, “antibiotics”, and “resistance” are related to clinical microbiology. Keywords such as “diagnosis” and “discrimination” are mostly used to represent research purposes ranging from pathogen species identification to bloodstream infection diagnosis. Most of the themes in the blue section focus on disease diagnosis such as “biomarker discovery”, “lung disease”, “ovarian-cancer”, “breast cancer”, and “Alzheimer”. In the blue-green section, the clinically relevant keyword “nucleic acid” can be observed with few surrounding branches. In the purple section, the keyword “small molecules” can be found. The keywords in the green section are mostly related to techniques, such as electrophoresis, chromatography, mass analyzer, and fragmentation. The keywords in the yellow section are mostly related to biological research, like proteomics and metabolomics. Above all, the four most important themes of MALDI in clinical applications can be summarized. They are “pathogen identification”, “disease diagnosis”, “nucleic acids analysis”, and “small molecule analysis”.
3.3. Dynamic Changes of Research Focus on MALDI MS
The results of dynamic changes of research focus on MALDI MS are represented in the form of overlay visualization. The keyword distribution is the same as the network visualization except that the items are colored differently. A color bar is represented in the bottom right of the visualization. The color indicates the publication year of the review. For instance, reviews in yellow were published around or after 2016, while reviews in dark blue were published around or before 2010. A lighter color means a more recent publication, and a dark color represents an older publication. By overlaying the visualizations, Figure Figure44 shows a timeline of MALDI MS-based developments and the main periods of research on each of the important keywords associated with MALDI MS.
Overlay visualization of keyword co-occurrence network with publication year. Most frequently occurring keywords in all MALDI MS-related reviews were exported from the Web of Science Core Collection. Size of the circles is proportional to the number of occurrences. Distance between different circles is inversely proportional to their relationship. All circles are colored according to the timeline bar. Web of Science search timeline runs from January 1, 1994 to October 18, 2021.
In Figure Figure44, the color changing from purple to green to yellow is proportional to the proximity of the review in which the review was published. The yellow-filled “pathogen identification” section has attracted intensive attention from researchers in the last five years. The green-filled “small molecule” has been a popular theme since 2015. “Cancer” is a green keyword accompanied by some nearby keywords describing specific cancer types such as breast cancer and ovarian cancer in a darker color, demonstrating a rise of the MALDI-based biomarker discovery and disease diagnosis over the past decade. “Small molecule” in light green is also a relatively young theme surrounded by many light green topics over the last 10 years. The purple-decorated “nucleic acid” indicates the relatively early use of MALDI in the analysis of nucleic acids at least 10 years ago. From the timeline of MALDI MS-based developments, we can observe a clear change of the application development focus on the technique from biological analysis/research to clinical analysis/research.
3.4. Conclusions of Keywords Co-Occurrence Analysis on MALDI MS-Associated Review Articles
After the keyword clustering analysis of over 2401 review articles, four of the most important clinical applications of MALDI MS were summarized consisting of “pathogen identification”, “disease diagnosis”, “nucleic acids analysis”, and “small molecules analysis”. In combination with the results of the overlay visualization, their respective years of popular development were concluded. Before 2012, the development of MALDI MS in “nucleic acids analysis” wa emphasized, but there have been relatively fewer studies related to this theme in the following years. From 2011 to 2021, the themes of “disease diagnosis” and “small molecules analysis” attracted much attention, with the former being a more extended theme. Since 2015, “pathogen identification” with the use of MALDI MS is the most focused theme, with many recent derivative topics. However, as many individual studies were not included in the published review articles, the results of overlay visualization only reflect the relative temporal distribution density of the focused applications. Nevertheless, because of the high number of review articles included in the analysis, the directions of representative clinical applications of MALDI were mostly covered.
4. Clinical Application and Potential of MALDI-TOF MS
4.1. Pathogen Identification by MALDI-TOF MS
Through keyword co-occurrence analysis (Figure Figure33), “pathogen identification” can be extracted as a dominant theme in the microbiology applications of MALDI-TOF MS. The red section of Figure Figure33 is led by “identification”, with the remaining keywords relating to “microorganism or microbiology”, “infection”, “antibiotic”, and “resistance”, all of which are related to clinical microbiology. Verb keywords like “identify” and “diagnose” were used most frequently from the identification of pathogen species to the diagnosis of bloodstream infections. Pathogen detection based on MALDI-TOF MS nucleic acid analysis is not discussed in this section and will be presented in section 4.3.
In clinical practice, many diseases are caused by pathogenic infections, such as bloodstream infections. Some bacteria are resistant to antibiotics, and the level of resistance increases over time, which can pose a significant threat to infection control in patients, particularly in surgery, hemato-oncology, and intensive care. The clinical microbiology laboratory plays a key role in patient care. It not only provides definitive knowledge of the cause of infection but also provides antimicrobial susceptibility data to the physicians. Hospitals or routine microbiology laboratories need rapid and reliable methods to identify pathogens or detect antimicrobial resistance of bacteria to reduce mortality caused by inappropriate or delayed treatment.
In the past, bacterial culture and biochemical tests were the dominant methods for identifying pathogens with different phenotypic characteristics, including bacteria, yeasts, and fungi. Methods based on classical morphology and staining were also used for bacterial identification. Due to long turnaround times and cumbersome steps, these methods cannot satisfy the requirement of fast and accurate identification of pathogenic bacteria by considering the current requirement of clinical microbiology laboratories. Since the 1960s, molecular diagnostic methods, such as 16S or 18S rRNA (rRNA) gene sequencing, real-time polymerase chain reaction (PCR) assays, multilocus sequencing typing (MLST), etc.,21 have been used for bacterial identification. Although molecular technology-based methods have largely reduced the detection time, there is a need to reduce the cost of detection with simpler operations for large-scale clinical applications. With such motivation and the technique characteristics of MALDI-TOF MS, bacterial cell molecular mass fingerprinting by MALDI-TOF MS has been developed during the past years to realize fast and accurate identification of bacteria and has been widely used in clinical microbiology laboratories. Pathogen identification by MALDI-TOF MS is one of the most successful applications of the technique in the clinical scene.
4.1.1. Bacteria and Fungi Identification by MALDI-TOF MS
The first attempts to identify bacteria by mass spectrometry were made in 1975 by Anhalt et al.22 In the 1990s, MALDI-TOF mass spectrometry was introduced for the identification of bacteria. Since then, studies assessed the feasibility of MALDI-TOF MS in identifying bacterial species.23−25 In 1994, Cain et al. found that bacteria could be differentiated using water-soluble protein MALDI-TOF MS fingerprints.26 In 1996, Liang et al. reported that bacteria could be distinguished at the species level by MALDI-TOF MS.26 In the same year, Holland et al. suggested that intact Gram-negative bacteria could be detected in their overall state by MALDI-TOF MS without the need for protein extraction.27 Inspired by this work, Claydon et al. subsequently demonstrated that MALDI-TOF MS could generate characteristic spectra of Gram-negative and Gram-positive bacteria in the intact state within minutes.28 Since then, MALDI-TOF MS-based bacterial identification has been fully developed in terms of sample preparation, database optimization, and software improvements. Until recently, bacterial mass fingerprinting systems were incorporated into commercial instruments for routine clinical applications, including VitekMS developed by BioMérieux, MALDI Biotyper developed by Bruker Daltonics Corp., etc. During MALDI-TOF MS-based bacterial identification, whole bacterial cells or whole cell extracts are deposited on a sample spot of the MALDI target plate, overlayered with a matrix, and then subjected to MS analysis. Cellular molecular mass fingerprints are obtained, mainly from the highly abundant small proteins or polypeptides, especially ribosomal proteins. It was found that different species could have a distinctive mass pattern. By pattern matching, it is possible to identify bacteria at the species level.
With the initial focus of MALDI-TOF MS-based microorganism identification on bacteria, the application was subsequently extended to the identification of yeasts and fungi, such as filamentous fungi, in a similar whole cell mass fingerprinting way. Currently, MALDI-TOF MS has been applied in yeast identification and developed as a routine tool for filamentous and dimorphic fungi identification. VitekMS and MALDI BiotyperCA systems are two systems with licenses awarded by the Food and Drug Administration (FDA) for the identification of yeasts and filamentous fungi. For both systems, extensive application in yeast identification is claimed based on individual established databases.29 The performance of the two systems is similar but not identical; thus, many comparative studies have been conducted focusing on species with the growths of databases. The correct identification, misidentification, and cutoff of isolates have frequently been assessed at the species, genus, or group level since 2010. For instance, VitekMS Knowledge Base v3.0 was used for the identification of 319 mold isolates (43 genera), and an identification accuracy of 67% was obtained. The identification accuracy increased to 77% with the addition of a modified SARAMIS database from Shimadzu Scientific Instruments.30 The selection of isolates, sample sources, and sample preparation methods may correspond to the differences in the assessment results of the FDA-cleared systems.31−34 For the following up-gradation of databases, the focus is on extending the range of species, subspecies, or strains and the reference quality of all clinically relevant fungi. In addition to optimizing databases, commercial databases, databases modified by personals, and user-developed databases can be used together, alternatively, or additionally to meet individual requirements.29
Three main approaches for microbial identification in clinical laboratories were summarized by Hou et al. regarding the sample preparation methods.35 The sample can be prepared by bacterial culture, where bacterial single colonies on solid agar plates were transferred on a MALDI target plate for analysis. In an alternative strategy, liquid samples such as positive blood culture and body fluids (blood, urine, sputum) can be directly identified after multistep sample preparation and extraction protocols without plate culture. For fungi, e.g., mycobacterium spp., they can be identified after specific extraction methods. The prepared samples are dropped on the MALDI target plate and then analyzed by MALDI-TOF MS for the spectral acquisition of samples to be identified. Then, commercial databases or self-built databases that contain the spectra of standard strains are used to match with the sample spectra for microbial identification. The final identification results are determined according to the spectral similarity between standard strains and analytes (Figure Figure55).
MALDI-TOF MS-based workflow for microbial identification.
4.1.2. Antimicrobial Resistance Detection by MALDI-TOF MS
In addition to pathogen identification, it is also necessary to assess the antimicrobial resistance of a pathogen to guide the clinical treatment of infectious cases. Antimicrobial resistance can be caused by various mechanisms: the formation of enzymes that can blunt or catabolize antibiotics,36 the generation of novel efflux pumps and the alteration of the cell membrane,37 the generation of protective proteins on the action site of antibiotics,38 the alteration of the enzymes that can decrease the sensitivity of bacteria against antibiotics, and the alteration of metabolic pathways related to the response against antibiotics.39 MALDI-TOF MS has the potential to detect different antimicrobial resistance mechanisms by analyzing antibiotic molecules, modification products, the component of bacterial cells, ribosomal methylation, mutations, etc.
One important class of antimicrobial-resistant bacteria is β-lactamase-producing bacteria that can hydrolyze β-lactam antibiotics. MALDI-TOF MS has been used to detect the β-lactamase activity.40 The degradation products after hydrolysis show a difference in molecular mass compared to the native antibiotic molecule, which can be represented by the peaks on MALDI-TOF mass spectra (Figure Figure66). Freshly cultured bacteria are washed by buffer, and the pellet after centrifugation is resuspended in buffer containing β-lactam and then incubated at 35 °C for 1–3 h. During incubation, some β-lactam-type molecules, such as ampicillin and piperacillin, can be degraded spontaneously. After incubation, the supernatant is extracted and analyzed by MALDI-TOF MS. The obtained peaks associated with β-lactam, corresponding salts, and its degradation products are used to assess the β-lactamase activity. Bacterial extracts can also be used for antimicrobial resistance detection using MALDI-TOF MS. Camara et al. in 2007 innovated the detection of a specific lactamase peak in ampicillin-resistant E. coli strains. After cocultivation of the resistant strain and ampicillin in Luria–Bertani broth, protein extraction with a formic acid–isopropyl alcohol–water solution, and sample spotting with SA as the matrix, a β-lactamase peak at approximately 29 kDa was detected by MALDI-TOF MS.41 The result was also confirmed by the analysis based on sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and liquid chromatography mass spectrometry (LC-MS).41
MALDI-TOF MS detection of β-lactam hydrolysis by antimicrobial-resistant bacteria.
Apart from the β-lactamase-producing bacteria, MALDI-TOF MS has also been used to detect various antimicrobial-resistance-associated proteins for the identification of many different antimicrobial-resistant bacteria. In 2011, Lenka et al. pointed out the possibility of SELDI-TOF MS in discriminating Escherichia coli strains carrying different resistance genes.42 In 2018, Zhu et al. utilized a TiO2-modified target plate as the substrate for MALDI-TOF MS analysis of antimicrobial-resistant bacteria.43 With the method, the mass range of MALDI-TOF MS-based bacterial mass fingerprinting was extended to 80 000 Da for the detection of antimicrobial-resistance-associated proteins from intact bacteria. They applied the method to the detection of extended-spectrum β-lactamase-producing E. coli (ESBL-E. coli), multidrug-resistant Pseudomonas aeruginosa (MDR-P. aeruginosa), and methicillin-resistant Staphylococcus aureus (MRSA).
In view of the diverse drug resistance mechanism and the varied sequences of antimicrobial-resistance-associated proteins, only monitoring the hydrolysis of β-lactam and the detection of known antimicrobial-resistance-associated proteins cannot satisfy the clinical requirement of antimicrobial-resistance detection. Wu et al. used LDI MS to assess the bacterial viability and antimicrobial resistance by tracing the redox of resazurin (RS) by viable bacteria.44 Bacterial cells were incubated with various antibiotic drugs for a period and then incubated them with RS. By monitoring the reduction of RS by bacteria, the viability of the microbes after antibiotic drug treatment could be obtained for the assessment of antimicrobial resistance.
4.1.3. Limitations of Pathogen Identification by MALDI-TOF MS
A major limitation of MALDI-TOF MS in pathogen identification is the coverage of the spectral database, which can only be experimentally acquired to date. The coverage of microbial species in the reference database determines the range of applications for pathogen identification by MALDI-TOF MS as spectral matching to the database is the final and critical step for successful identification. Many kinds of databases or software have been developed by manufacturers and granted access for clinical use, such as Bruker MALDI BioTyper, Shimadzu SampleStations and AuraSolution, and BioMérieux Andromas systems.45 In 2009, the Bruker IVD (in vitro diagnostic products) MALDI Biotyper CA system obtained the CE mark referring to the European IVD directive EC/98/79. In 2013, the BioMérieux VITEK MS system received U.S. FDA 510(k) de novo clearance. In the same year, the Bruker MALDI Biotyper CA system was granted U.S. FDA clearance under Section 510(k) for the identification of Gram-negative bacteria. The MALDI Biotyper CA system includes a MALDI-TOF MS, IVD-labeled reagents, a 48-spot target, supporting software, and a microorganism reference library. In 2014, the China Food and Drug Administration (CFDA) also approved the access of the IVD MALDI Biotyper system into the Chinese market as a medical device to identify microorganisms isolated from human specimens. The reference library covering thousands of microbial species is expanded continuously with the appearance of new infection crises. In 2017, the U.S. FDA authorized the expanded application of the BioMérieux VITEK MS system in identifying mycobacteria, Nocardia, and molds by granting 510(k) clearance. With the emerging Candida auris (C. auris) pathogen causing bloodstream infections in hospitalized patients with high drug resistance, the U.S. FDA approved the use of the Bruker MALDI Biotyper CA system in C. auris identification in 2018, enlarging the licensed uses of identifying clinically relevant bacteria or yeast species. As an alternative to upgrading the database experimentally, there are also methods developed to match experimental spectra to public protein sequence database. Cheng et al. identified 10 genes to encode proteins most often observed by MALD-TOF MS from bacteria. Using the 10 genes to annotate peaks on the MALDI-TOF spectra of bacteria, genus-level identification with an accuracy of 84.1% was achieved.46
In addition to the coverage of microbial species in the reference database, there are some other limitations in the use of MALDI-TOF MS-based systems as routine diagnostic tools for bacteria or fungi identification. Purified single colonies obtained by bacterial culture are required for MALDI-TOF MS analysis to reach high accuracy. The overall positive rate of bacterial culture from infected clinical samples is in general low. To solve the limitation, specific sample pretreatment can be adopted, including bacterial enrichment by magnetic beads47 or microfluidic techniques48 to shorten or avoid bacterial culture, developing new matrixes or surface enhancement methods to obtain more molecular information43 for higher identification accuracy, coupling with high-resolution mass analyzers for higher specificity in identification, or developing new algorithms or data analysis frameworks49 for deep data mining to enable direct identification of bacterial mixtures,50 reorganization of antimicrobial-resistant bacteria, and virulence factors.
For the detection of antimicrobial resistance, it is difficult to discriminate subspecies and strains that are drug resistant or not, even though bacteria can be confidently identified at the species level based on MALDI-TOF fingerprinting. Therefore, antibiotic susceptibility testing (AST) needs to be applied after MALDI-TOF identification. Besides, there is room for improvement in the detection of the enzymes that degrade antibiotics, detection of the commonly shared resistance mechanism by constructing a protein fingerprint database of antibiotic-resistant isolates, analysis of the modification of target points, and quantitative analysis of antibiotics influx and efflux.40 More efforts are needed to enable the extension of MALDI TOF-based pathogen antimicrobial-resistance analysis to routine clinics.
4.2. Disease Diagnosis by MALDI-TOF MS-Based Peptidome and Proteome Profiling of Body Fluid
The results of the keyword co-occurrence analysis show that MALDI MS has been widely developed for disease diagnosis. As shown in the network diagram (Figure Figure33), blue keywords of “cancer”, “breast cancer”, “ovarian cancer”, “Alzheimer”, and “lung disease” can be seen, indicating that these diseases are focused on very frequently in MALDI-related reviews and may attract a high level of interest in the development of MALDI MS-based clinical diagnostics. The keywords of “peptidomes”, “serum”, and “plasma” in the blue section imply that proteins and peptides in human body fluids, especially blood, are often used as research objects in MALDI MS-based disease diagnostic studies. The keyword of “model” in the blue section indicates that disease diagnostic models are often established. The keywords of “proteomics” and “lc-ms-ms” can also be found in the blue section, based on which it can be found that LC-MS/MS-based proteomics is commonly used for protein identification together with MALDI MS.
As the pathogenesis and phenotype of different diseases vary, so do the diagnostic modalities. Here is a summary of some common diagnostic options for different diseases. For a physical test, doctors usually start by searching for visible abnormalities such as enlarged organs that may indicate the presence of disease. For laboratory tests, samples such as blood and urine are usually collected and tested to help identify abnormalities. Imaging tests allow doctors to examine internal organs in a noninvasive way, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and X-ray. In most cases, a biopsy is the only way to definitively diagnose cancer. In view of the long cycle, late course of disease, low sensitivity, and strong invasiveness of many clinical diagnostic methods as well as the complexity of the occurrence and development of the diseases themselves, it is necessary to develop new detection methods to detect diseases and help doctors make medical decisions.
Anomalies of protein expression such as disruption, mutation, and misfolding are closely linked to various diseases. Some proteins change significantly as disease progresses, resulting in abnormal levels of protein concentrations in the blood. The changed proteins are then potential disease biomarkers. For example, prostate cancer-specific antigen (PSA) is used clinically as a serum marker for prostate cancer and is recommended by the American Cancer Society for early detection of cancer. However, PSA is also related to other cancers; therefore, diagnosis based on a single protein marker normally suffers from low specificity. Monitoring a panel of distinctive proteins can greatly improve the sensitivity and specificity of disease diagnosis and help doctors make more accurate clinical decisions.
Searching for specific disease biomarkers has been tough work all along. One widely used method is detecting and quantifying specific biomolecules in the body fluids of patients and controls, particularly accessible body fluids including serum, urine, or saliva, which are sources of protein-rich information.51 Of these, serum is the most commonly analyzed specimen.52 In addition, in recent years, plasma exosomes have been increasingly focused on in diagnostic studies of disease. Exosomes are nanoscale cellular multivesicular vesicles that encase intracellular substances such as lipids, metabolites, proteins, and genetic materials.53 Secreted by all types of human cells and circulating in the body, exosomes are associated with many physiological and pathological processes. Cancer-derived exosomes can contribute to the development of cancer and have been shown to be a target for research in cancer biology and clinical laboratories.54
Since the concentration of circulating biomolecules is highly variable, it is essential to analyze the biomolecule information in body fluids accurately with a reasonable sample grouping and sample size. Before the appearance of biomass spectrometry, there was no means to analyze proteins in a rapid, sensitive, and high-throughput manner. The analysis and identification of unknown proteins by traditional methods need to isolate and purify the proteins first, which requires a long experimental cycle. The invention of MALDI MS in the late 1980s allowed the accurate measurement of endogenous biomolecules in a large scale with high throughput. After simple pretreatment, samples on target plates can be analyzed by MALDI-TOF MS in less than 1 min per spot. By analyzing protein changes at different stages of disease onset, protein biomarkers can be identified at different times of the disease, which can not only provide important diagnostic indicators for early disease diagnosis and detection but also serve as targets for drug screening and guidance for drug discovery. Compared to traditional biochemical assays, MALDI-TOF MS shows higher sensitivity. It also has other characteristics such as low cost and easy operation. With the introduction of MALDI-TOF MS, the discovery of disease-related biomarkers was largely facilitated by monitoring a panel of features, driving the development of disease diagnosis and personalized treatment.
The main processes for developing disease diagnosis models based on MALDI-TOF MS are usually similar. Clinical samples from the case and control groups, generally human body fluids (i.e., blood, urine, saliva, sweat, tissue fluid), were collected and divided into a training group and a testing group. Pretreatment was performed for all samples (i.e., desalinization, enrichment, purification) followed by the analysis of MALDI-TOF MS. It should be noted that matrix selection and spotting methods need to be selected according to the target mass range, actual sample peaks, and even results of the pre-experimental analysis. Appropriate machine-learning methods (e.g., partial least-squares discriminant analysis, logistic regression, support vector machine, random forest, etc.) are used to extract one or more sets of MALDI distinctive peaks between the case and the control groups in the training samples. The proteomic methods based on LC-MS/MS can be used to identify the selected MALDI-TOF MS distinctive peaks as proteins. The step of feature extraction can be considered as “disease biomarker discovery”. With the selected biomarkers, a model for diagnosing the disease can be established based on specific machine-learning methods. The established model can be assessed among testing samples in terms of precision, accuracy, sensitivity, specificity, and an area under receiver operating characteristic curve (AUC). A receiver operating characteristic (ROC) curve, discussing the measure of AUC, is a plot of the sensitivity versus 1—the specificity of a diagnostic test. It provides a useful summary of the overall diagnostic accuracy of the test.55 On the basis of these evaluation indicators, a MALDI-TOF-based diagnosis method with the relative best performance for the prediction of specific diseases can be determined. On the other hand, when known protein biomarkers exist, it is also possible to extract and enrich the biomarkers using chemical or biological affinity methods followed by MALDI-TOF MS analysis. Here, in this section, we introduce the representative application examples of MALDI-TOF MS in the diagnosis of cancer, infectious disease, and neurodegenerative disease.
4.2.1. Cancer Diagnosis and Prognosis
Cancer diagnosis and prognosis accounts for a large proportion of MALDI-based clinical disease diagnosis. Take ovarian cancer as an example. Many studies have reported the use of MALDI-TOF MS in distinguishing ovarian cancer (OC) from healthy controls. Cancer antigen 125 (CA125) is one of the two biomarkers approved by the FDA for the diagnosis of recurrence and treatment response in ovarian cancer.56 CA125 alone can predict OC up to 9 months before diagnosis. However, not all early-stage tumors produce CA125. It might be regulated by other benign gynecological diseases as well. Other biomarkers are required for early OC diagnosis.57 Timms et al. found that the combined use of CA125 and two MALDI-TOF MS feature peaks, identified as connective tissue-activating peptide III and platelet factor 4, could detect OC about 6 months earlier than using CA125 alone.58 Addition of the cancer progression or tumor development-related serum peptides can advance the diagnosis of OC with a high degree of confidence.58 Adoption of specific sample pretreatment methods can improve the MALDI-TOF MS-based diagnosis of OC. One study reported that analysis of ions with low mass from serum performed better in differentiating ovarian cancer from controls after removing high levels of proteins and peptides.59 More discussion on small molecule studies by MALDI-TOF MS is presented in section 4.4. There are studies extracting the low-abundance proteins or peptides through enrichment technology such as magnetic beads before MALDI-TOF MS analysis.60 Periyasamy et al. suggested that solid-phase extraction before MALDI-TOF MS analysis can improve the sensitivity of a diagnosis model to differentiate serous adenocarcinoma (a common type of epithelial OC) and healthy controls.61 Swiatly et al. proposed that the combined usage of iTRAQ (isobaric Tags for Relative and Absolute Quantification)-based quantitative proteomic analysis and MALDI-TOF MS can improve the differentiation of benign and malignant tumors in OC.62
MALDI-TOF MS has also been applied to the early diagnosis and prognosis of many other cancers, such as prostate cancer,60,63 liver cancer,64 and multiple myeloma.16,65 In 2019, Long et al. developed a MALDI-TOF MS-based methodology to diagnose multiple myeloma by detecting Bence–Jones proteins in human urine samples.16 The study included 21 positive urine samples and 27 negative urine samples. The urine proteins were first enriched by macroporous ordered silica foams (MOSF) and then analyzed by MALDI-TOF MS. In the study, a rapid method with high sensitivity (95.24%) and specificity (100%) was obtained for the diagnosis of multiple myeloma, which outperformed immunofixation electrophoresis-based66 and immunonephelometry-based methods.67 In 2020, Sun et al. applied the technology of MALDI-TOF MS serum peptide fingerprinting to prediagnose prostate cancer (PCa) involving 100 PCa patients and 47 non-PCa controls (20 healthy people and 27 inflammatory hyperplasia people) (Figure Figure77b).60 First, the authors enriched the target low molecular wight proteins or peptides using synthetic hydrophilic interaction chromatography nanoparticles (HICNPs) to attenuate interference in human serum (Figure Figure77a). These functionalized mesoporous silica materials with a large pore surface area, highly ordered pore structure, homogeneous mesoporous hydrophilic effect, and some chemical and mechanical stability can be used as good substrates for selective protein/peptide adsorption. After coincubation with HICNPs magnetic beads, washing, and elution, the serum MALDI-TOF MS signals of the samples were simplified with more valuable fingerprint-like patterns. Machine learning was eventually used to establish a diagnostic model of PCa. Recently, MALDI-TOF MS fingerprinting was further applied to discover lipid PCa biomarkers in urine samples from 121 PCa patients and 18 healthy people.63 In this study, the diagnostic accuracy ranged from 83.3% to 100.0%. It is worth noting that although the example cited here is being developed to diagnose PCa cancer, it could be subsumed in section 4.4 in this review because urinary lipids belong to small molecule metabolites. The MALDI-based cancer diagnosis in section 4.2 mostly focuses on biological macromolecules such as proteins and peptides. The main difference between the analytical protocols for large molecules (e.g., m/z 2000–20 000) and that of small molecules (e.g., smaller than m/z 1000) is the choice of the target analytical mass range and hence the suitable matrices or materials to assist the ionization.
On the mature foundation of MALDI-TOF MS fingerprinting technology, many other samples have been used for biomarker discovery and cancer diagnosis, especially exosomes isolated from body fluids.65,68−70 In 2019, Zhu et al. used MALDI-TOF MS to analyze exosomes extracted from serum of melanoma patients and healthy donors and demonstrated that the mass fingerprinting of bloodstream-circulating exosomes can be used for cancer diagnosis and monitoring.69 In 2021, Han et al. used MALDI-TOF MS to analyze serum exosomes from 12 healthy donors, 20 osteosarcoma patients without lung metastasis, and 20 osteosarcoma patients with lung metastasis. It was found that the MALDI-TOF MS-based serum exosome mass fingerprinting can not only identify osteosarcoma but also differentiate osteosarcoma patients with lung metastasis from those without lung metastasis.68 Seven protein biomarkers of osteosarcoma with lung metastasis were identified, including immunoglobulin lambda variable 2-23 (IGLV2-23), immunoglobulin lambda variable 4-3 (IGLV4-3), immunoglobulin lambda variable 1-51 (IGLV1-51), immunoglobulin kappa variable 3-15 (IGKV3-15), immunoglobulin heavy variable 4-4 (IGHV4-4), immunoglobulin lambda variable 4-60 (IGLV4-60), and hemoglobin subunit alpha (HBA1). In 2021, cells collected from the surface of suspected skin areas via sterile adhesive sampling discs were used by Zhu et al. to detect melanoma and predict skin disorder progression.71 Noninvasive sampling is another advantage of this study considering the benign method of obtaining cells from the skin surface.
4.2.2. Infectious Disease Diagnosis
In addition to the direct identification of a pathogen, which is detailed in section 4.1, MALDI-TOF MS-based profiling of human body fluids was also used for the diagnosis of infectious diseases. Mycobacterium tuberculosis (MTB) is a common “lung disease” and can also be described as an infectious disease. Initial methods for diagnosing MTB are usually based on microbiologic techniques such as acid-fast bacillus smear microscopy and MTB culture. These methods require a long analysis time and are not sensitive or specific enough.72 Liu et al. developed a rapid diagnosis of active MTB infections by monitoring serum CFP-10 (TDAATLAQEAGNFER; m/z 1593.75) and ESAT-6 (WDATATELNNALQNLAR; m/z 1900.95). These biomarkers could theoretically be used to diagnose all MTB infections based on MALDI-TOF MS. The analysis time was shortened to 4 h with high sensitivity and specificity.73 In addition, techniques can be combined to aid MALDI-TOF MS analysis for the diagnosis of MTB infection, including microwave irradiation for CFP-10 and ESAT-6 digestion, stable isotope-labeled internal standard peptides for CFP-10, and ESAT-6 quantification and antibody-conjugated nanodisks for target peptide enrichment and MALDI signal enhancement.73
Since 2019, a novel coronavirus infectious disease COVID-19 caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become massively prevalent worldwide. The enormous demand for disease diagnosis has put enormous pressure on traditional molecular detection methods such as reverse transcription (RT) polymerase chain reaction (PCR).75 Apart from viral nucleic acids, viral proteins, antibodies specific to the virus, and host-induced molecular changes can also be used for the diagnosis of COVID-19 or progression mornitoring.76,77 In 2020, Nachtigall et al. developed a novel method to detect SARS-CoV-2 infection based on MALDI-TOF MS analysis of nasal swab samples using 362 samples (211 RT-PCR positive samples and 151 RT-PCR negative samples), achieving an identification accuracy of 93.9%.75 In 2021, Yan et al. used MALDI-TOF MS to analyze the serum of COVID-19 patients and controls to collect the serum peptides/proteins mass fingerprinting involving 146 COVID-19 patients, 73 non-COVID-19 patients with similar clinical symptoms, and 46 healthy controls (Figure Figure88). The samples were collected in very early 2020, reflecting the immune response to the very original SARS-CoV-2 strains in mainland China. Various machine-learning methods were applied for feature selection from the MALDI-TOF mass spectra of serum peptidome, and different classification models were built for the diagnosis of COVID-19. Eventually, a rapid diagnosis model was established to detect COVID-19 infections with high accuracy (99%), sensitivity (98%), and specificity (100%).74 Fifteen serum protein/peptide biomarkers were identified for COVID-19.
4.2.3. Neurodegenerative Disease Diagnosis
Alzheimer’s disease (AD) is a neurodegenerative disease. In the early phase of discovering AD biomarkers, CSF, a proximal fluid, was often studied. As a review summarized, the most characteristic AD biomarkers in CSF are β-amyloid (βA), tau protein, and phospho-tau.78 As early as 1993, the first study of targeted βA proteomics detected multiple βA isoforms in CSF using MALDI-TOF MS.79 On the basis of initial efforts to explore markers, more accessible fluids, particularly blood, were focused on reducing the invasiveness of sampling.78 In 2018, Nakamura et al. found a new composite biomarker, βA precursor protein (APP) 669-711/Aß42 and Aß40/Aß42 ratios in plasma, to predict positive or negative brain βA based on MALDI-TOF MS analysis, illustrating the high performance of plasma biomarkers in brain βA burden prediction and AD diagnosis.80 Last year, Shimadzu Japan released a MALDI-based Amyloid MS CL system for testing the levels of amyloid peptides in blood that are associated with AD. The product was licensed for manufacture and sale by the Japanese Ministry of Health, Labor, and Welfare in December 2020.
4.2.4. Limitations of Disease Diagnosis by MALDI-TOF MS-Based Peptidome and Proteome Profiling
Although many advantages of MALDI MS, such as high throughput, short time analysis, easy operation, and low cost, have been repeatedly reported in the literature for different disease diagnostic applications, there are still some common application drawbacks of the technique. Unfavorable repeatability is a major issue to the clinical spread of MALDI-TOF MS, limiting the application of the technique when considering quantitative analysis. The issue is mainly caused by the random procedure of matrix–analyte cocrystal formation and the random sampling by a laser on the sample spot. Various techniques have been adopted to enhance the repeatability of MALDI-TOF MS in view of quantification, including the development of a new matrix than can form a homogeneous crystal on the target plate, new sample and matrix deposition methods, optimization of internal standards, ionization process optimization, and postdata acquisition processing.78 In future work, the focus should be on validation of a MALDI-TOF MS-based diagnosis model in many different centers over a long period. Besides, there is still uncertainty in the results of protein identification based on proteomics techniques for MALDI MS signature peaks. Because of the complex composition of clinical samples, the differences between the experimental techniques, such as pretreatment, mass analyzer, ion separation, and detection, and artificially introduced differences, such as data processing and protein matching rules, the identification of potential protein markers needs to be further confirmed by other methods like biological experiments, etc.
4.3. Analysis of Nucleic Acids with MALDI-TOF MS
Keyword “nucleic acid” can be found in Figure Figure33. From Figures Figures33 and and4,4, it can be found that the application of MALDI-TOF MS in the analysis of “nucleic acid” was developed earlier than the other themes but with fewer continuous efforts in the following years compared to many other applications of MALDI-TOF MS, e.g., pathogen identification and cancer diagnosis. Nevertheless, nucleic acid analysis is indeed one of the most successful applications of MALDI-TOF MS in the clinical scene. DNA and RNA are two common classes of nucleic acids. DNA can store the genetic information and play key roles in maintaining the proper function of all living organisms. RNA is similar to DNA in structure but is single-stranded and with uracil (U) as one of the four bases instead of thymine (T) in DNA. A major class of RNA is the mRNA that can direct the synthesis of proteins based on genetic information. For DNA and RNA, the sequence of their nucleotides determines their uniqueness. During tumorigeneses, breaks and recombination at the genomic level often occur. When two genes are broken in half and misaligned, it is possible to form a new gene fragment, which is known as a fusion gene. In most cases, fusion genes can lead to the production of abnormal sequences or functional proteins or to the dysregulation of the expression of certain genes, which can cause or promote the development of tumors.
With the development of matrices that can assist the ionization of DNA and RNA, the technology of MALDI-TOF MS has been applied to the analysis of nucleic acids. In 1990, it was first reported that MALDI-TOF MS can be applied to the analysis of oligonucleotides.81 Subsequently, various matrices (e.g., 2,4,6-trihydroxyacetophenone82 and glycerol83) and matrix additives (e.g., various sugars84 and spermine85) were developed to enhance the ionization of nucleic acids. Since the common matrices in nucleic acid analysis can generate large crystals, which would hinder the ionization efficiency and reproducibility, a sample preparation method was developed to miniaturize sample spot sizes to improve the homogeneity of matrix–analyte crystals and hence the reproducibility of mass spectra.86 As nucleic acid samples are normally in the presence of high concentrations of salts and salts can inference the ionization of analytes during MALDI, cation exchange resins are normally used to remove salts in nucleic acid samples.87 MALDI-TOF MS is often combined with PCR for nucleic acid analysis. The main workflow is composed of gene locus selection, primer design, and quality control, PCR (primer extension and base-specific cleavage) amplification, Shrimp Alkaline Phosphatase (SAP) reaction and purification, single-base extension, sampling, desalting, and MALDI-TOF MS analysis. On the basis of the method, a commercial system, MassARRAY, was developed by Agena Bioscience.
4.3.1. Screening of SNP, DNA Methylation, and Inherited Genetic Diseases
The screening of nucleic acid fragments associated with distinct genotypes or mutants by MALDI-TOF MS has been developed mainly for single-nucleotide polymorphism (SNP) genotyping, DNA methylation identification, and inherited genetic disease screening. Single-nucleotide polymorphism (SNP) often occurs in human DNA and is the most common type of genetic variation in human beings. The presence of SNP can be used to predict the personal risk of developing specific diseases or track the inheritance of disease genes within families. The screening of SNP can be directly realized with the PCR-MALDI-TOF MS workflow using a commercial solution of MassARRAY (Agena Bioscience, California, United States). Kenji et al. applied MALDI-TOF MS to screen SNPs and demonstrated ethnic differences in coronary artery disease-associated SNPs among two healthy Israeli populations (Ashkenazi Jews and Yemenite Jews) based on 15 SNPs determined from 14 candidate genes.88
DNA methylation is a potential biomarker for several cancers with a close association with tumorigenesis, development, and cell carcinogenesis. MassCLEAVETM chemical methods (Sequenom, California, United States) based on the MassARRAY system (Agena Bioscience, California, United States) have shown high sensitivity in the detection of methylated DNA, allowing high-resolution quantitative methylation analysis by base-specific cleavage on bisulfite-converted DNA. Ehrich et al. used the method to quantify methylation differences between normal and neoplastic lung cancer tissue samples.89 Gao et al. detected a higher level of methylation status in gastric cancer tissues than normal tissues by comparing the hypermethylation levels of Nell-1 in tumor tissue, paraneoplastic tissue, and normal tissue from gastric cancer patients.90
The molecular diagnosis of inherited genetic diseases or genetic disorders was also developed based on MALDI-TOF MS genotyping. In 2011, Lambros et al. established a novel method to identify recurrent fusion genes in cancers using the MassARRAY platform (Figure Figure99).91 The increasing number of genomes undergoing massively parallel sequencing will undoubtedly require scalable platforms to validate the fusion genes identified. This study shows the feasibility of it. However, its false-positive rate is unsatisfactory, which remains to be solved. In 2016, Tian et al. established a method to detect the multiplex mutations in lung cancer cell lines with high sensitivity (100%) and specificity (96.3%) using the MassARRAY platform.92 The authors pointed out the limitation that the developed method relied on a group of preselected sites, so that it cannot comprehensively detect the highly random mutational patterns in tumor suppressors.92 MALDI-TOF MS-based nucleic acid analysis has also been applied in genetic diseases, such as trisomy 21 (Down syndrome), a genetic disorder caused by the presence of all or part of the third copy of chromosome 21,93 genetic deafness inherited from parental generation with genetic and chromosomal abnormalities,94 and familial adenomatous polyposis (FAP) caused by APC germline mutations, an autosomal dominant colorectal cancer susceptibility syndrome.95
Illustration of the MALDI-TOF MS-based detection of recurrent fusion genes. PCR amplification of an amplicon from the breakpoint region (a) was followed by several steps of sample preparation and hybridization with custom-designed extension primers (b). Only in the presence of a fusion gene was a PCR product generated for the extension reaction to take place. Samples were spotted onto a SpectroChip after cation removal (c) and then analyzed by the MALDI-TOF MS system (d). If the predicted alleles were detected by the probe combination, a fusion gene was present (red arrow), whereas if peaks were only seen representing unextended primers (black arrow) with no alleles detected, no fusion gene was present (e). Reprinted with permission from ref (91). Copyright 2011 Springer Nature.
4.3.2. Screening of SARS-CoV-2 Variants
MALDI-TOF MS-based nucleic acid detection can also be used for pathogen detection. In view of the recent COVID-19 pandemic and the continuous evolution of SARS-CoV-2 variants, we present here the application of MALDI-TOF MS-based nucleic acid analysis in the identification of SARS-CoV-2 virus strains. In 2020, Wang et al. applied PCR-MALDI-TOF MS to detect SARS-CoV-2 nucleic acids.96 In their study, sputum and pharyngeal swab samples collected from multicenters were analyzed. The open reading frame 1ab (ORF1ab) gene and nucleocapsid protein (N) gene of SARS-CoV-2 were the focus. The study revealed that SARS-CoV-2 detection by PCR-MALDI-TOF MS had high accuracy, sensitivity, and specificity and could be used in clinical settings to improve SARS-CoV-2 nucleic acids testing efficiency. At the end of 2021, Zhao et al. developed a novel strategy of a MALDI TOF-based multiplex PCR mini-sequencing technique to identify SARS-CoV-2 variants, including SARS-CoV-2 and the variants of B.1.1.7 (Alpha), B.1.351 (Beta), B.1.429 (Epsilon), B.1.526 (Iota), P.1 (Gamma), and B.1.617.2 (Delta).97 RNA was extracted from clinical oropharyngeal swabs. Genes containing targets of SNPs were amplified using multiplex PCR. SNP sites were extended using an extension mass probe. MALDI-TOF MS was performed to identify the m/z of the extended mass probe, based on which SARS-CoV-2 variants can be detected. Nine mutant types of SARS-CoV-2 variants at seven mutation sites in the spike receptor binding domain (HV6970del, N501Y, K417N, P681H, D614G, E484 K, L452R, E484Q, and P681R) were detected, and high specificity and an accuracy of 100% were achieved among 20 clinical verification samples.97 In 2022, Han et al. reported a Y-structure-induced rolling loop amplification method combined with MALDI-TOF MS for nucleic acid detection of SARS-CoV-2. The method enabled the simultaneous detection of the SARS-CoV-2 N gene and orf1ab gene in a single reaction tube within 30 min at 55 °C with high specificity compared to the SARS-CoV, MERS, and bat-SL-CoVZC45 coronaviruses.98 The MassARRAY system achieved the CE-IVD mark in Europe for the qualitative detection of SARS-CoV-2 nucleic acids. Compared with the real-time PCR test, the gold standard of SARS-CoV-2 early diagnosis approved by the WHO, PCR-MALDI-TOF MS-based assay had superior performance in the discrimination of SARS-CoV-2 variants.99
4.4. Analysis of Small Molecules by MALDI-TOF MS and Its Potential in Clinical Analysis
In the purple section of Figure Figure33 associated with “small molecules analysis”, the keyword of “matrix” is highly prominent. Most of its nearby keywords, including “porous silicon”, “carbon nanotubes”, “affinity probes”, and ‘metal-organic frameworks”, are related to the matrix or materials. Therefore, the focus of laser desorption ionization (LDI)-based small molecule analysis is on the development of suitable matrices for a long time. In the yellow section of Figure Figure33, the keyword “metabolites” can be classified as small molecules. “Metabolites” can be regarded as endogenous compounds such as amino acids, lipids, sugars, short peptides, alcohols, organic acids, etc.100 Usually metabolites can be found in organelle cells, organs, biological fluids, and organisms.100
MALDI-TOF MS has been widely applied for analyzing many kinds of large molecules as described above. The application of it for the analysis of small molecules is mostly limited by the selection of a suitable matrix.101 Conventional matrices used for the analysis of large molecules are often unsuitable for the analysis of low molecular weight compounds (m/z < 1000 Da) mainly due to the matrix background interference, ion suppression, and nonuniformity of crystallization by traditional organic matrices. For the analysis of small molecules, many inorganic materials have been developed to assist the ionization process, such as nanoparticles (NPs) and nanotubes. Compared to an organic matrix, inorganic nanomaterials can provide a clean ground in the low-mass region and homogeneous surface for good reproducibility. The original use of inorganic materials as the matrix for LDI MS analysis was in 1988 when Koichi Tanaka used cobalt powder to desorb and ionize proteins.2 With the success of nanostructure-based surface ionization and advances in nanomaterial synthesis techniques, the use of NPs for LDI MS analysis flourished around 2010.102 For a long time, gold and silver NPs synthesized by different methods were the most widely adopted matrices for LDI MS-based small molecule analysis.103 In the last two decades, a wide variety of NPs have emerged, including metal oxides (TiO2104), carbon-based NPs (colloidal graphite105), metal NPs (e.g., platinum106), and semiconductor quantum dots (HgTe107), and many of them have been used to assist ionization initiated by a laser. In 2021, a kind of carbon nanomaterial matrix graphdiyne was developed for the analysis of small molecules such as amino acids, fatty acids, and peptides.108 It can enhance the desorption and ionization efficiency with a low background that has been used for discovering fatty acid biomarkers for liver cancer diagnosis.108 A multishelled hollow Cr2O3 sphere material was developed to enhance ionization with strong photoresponse and to stabilize the effective LDI of metabolites. Its high sensitivity and selectivity were demonstrated in discriminating schizophrenia patients from healthy controls.109 An advanced mesoporous material PdPtAu was developed to extract metabolites and assist in LDI. Its high sensitivity and selectivity were demonstrated in differentiating gastric cancer cases from normal controls.110 In addition, cation induction and fragmentation of small metabolites and successful control of metabolite fragmentation with the help of nanomaterials were reported to enhance metabolite identification by enlarging atomic/fragmentation coverage, which can help simulate the different physiological or pathological processes of specific diseases and explore more metabolic pathways.111
There are many types of matrices for the analysis of various small molecules, and the selection mainly depends on the target analytes. A review published in 2016 demonstrates the different efficiency of NPs for LDI MS analysis of a wide range of small molecules with the addition of two widely used organic matrixes in positive and negative modes (Figure Figure1010).103 A thermal desorption model was established in the study with the normalization by the strongest ion signal for each analyte. The model can provide significant reference information for the selection of NPs in the analysis of small molecules based on MALDI-TOF MS. In 2021, Kulkarni et al. summarized the recently published (from 2016 to 2021) materials-based matrices for small molecule analysis in the biomedical field.112 Three types of matrices (inorganic, organic, and hybrid) and 32 materials were included for the analysis of low molecular weight compounds, especially amino acids, lipids, and metabolites.
Summary of nanoparticles as matrices for small molecule analysis by MALDI-TOF MS: DHB, 2,5-dihydroxybenzoic acid; 9AA, 9-aminoacridine; DAN, 1,5-diaminonaphthalene. DHB and DAN were used for positive-ion mode, and 9AA and DAN were used for negative-ion mode. Reprinted with permission from ref (103). Copyright 2016 American Chemical Society.
The application of MALDI-TOF MS in small molecule analysis can be divided into three main directions: tissue fingerprinting, biofluid profiling, and cellular typing. For tissue fingerprinting, LDI-based tissue fingerprinting allowed the in situ analysis of relative molecular concentrations in organ tissues. For instance, Zhou et al. used graphene oxide (GO) to analyze small molecules in mouse brain tissue. The authors also used the method to characterize the difference in the spatial molecular distribution between surviving and necrotic tumor regions of breast cancer mice.113 Palermo et al. used fluorinated gold NPs (f-AuNPs) to analyze the metabolites in mouse colon comprehensively. LDI MS analysis based on f-AuNPs requires low laser energy to induce the release of fluorocarbon chains with a low background signal and sensitive detection of sample molecules, avoiding metabolite endogenous fragmentation and maintaining the integrity of molecular ions.114 Considering the contribution in terms of signal enhancement and biocompatibility, Kulkarni et al. proposed that LDI MS will be a promising tool for in vivo monitoring of biological tissue dynamically.112
MALDI-TOF MS is also used to analyze the low molecular weight compounds in biofluids for biomarker discovery and disease diagnosis.112 Vedarethinam et al. developed vanadium core–shell nanorods to profile molecular variation in the plasma of diabetic retinopathy patients, based on which a disease diagnosis model of diabetic retinopathy was established with high sensitivity (94%) and specificity (90%) (Figure Figure1111).115 In the presence of high concentrations of co-occurring peptides and proteins, only Na+- and K+-coupled metabolites can be detected using the vanadium core–shell nanorods. More recently, the same group also reported the high performance of nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) in differentiating between breast and nonbreast cancers with an area under the receiver operating characteristic curve of 0.948.116 Wang et al. assembled gold nanoparticle arrays at the liquid–liquid interface to assist LDI MS with high desorption efficacy and reproducibility in quantification (RSD < 5%) that can be used to quantify glucose in the cerebrospinal fluid, allowing rapid identification of patients with brain infections.117 The LDI MS-based small molecule applications in biofluid profiling have similarities with section 4.2 of this review. Both are about biomarker discovery and disease diagnosis, but here we focus on low molecular weight compounds, while section 4.2 mainly concerns macromolecules (i.e., proteins and peptides).
(a) Schematic workflow for the extraction of plasma metabolic fingerprints by vanadium core–shell nanorod-assisted laser desorption/ionization mass spectrometry (LDI MS). Only Na+- and K+-adducted metabolite ions can be detected with the coexistence of high concentrations of peptides and proteins, consuming only 500 nL of native plasma. (b) Schematic outline for the diabetic retinopathy (DR) differentiation from non-DR (NDR) diabetic control and progression evaluation by machine learning of plasma molecular fingerprints. Reprinted with permission from ref (115). Copyright 2020 John Wiley & Sons, Inc.
Cell metabolism involves the dynamic changes of low molecular weight compounds associated with the intrinsic and extrinsic states of cells.118 Since individual cells have their unique metabolic behaviors, the cell metabolites detected by MALDI-TOF MS can be used for cell typing. For example, Zhang et al. used TiO2-assisted LDI MS to profile the metabolites of intact bacterial cells to detect the strains with antimicrobial resistance.119 Walker et al. detected 24 metabolites in single yeast cells using LDI MS.120
As mentioned above, the main obstacle that limits the application of MALDI in the analysis of small molecules is the selection of the matrix. Although great efforts have been made by researchers to develop suitable matrices, more efforts are necessary to promote the extension of LDI-based techniques to clinical use for the analysis of small molecules or metabolites. For this purpose, more studies should be focused on developing matrices that can enhance the detection sensitivity of specific types of molecules, show reliable quantification performance among multicenters, and remain consistent from batch-to-batch for massive production.
5. Conclusions
Through keyword co-occurrence analysis of more than 2000 previously published reviews with “MALDI” as the keyword in the Web of Science database, the four most important MALDI clinical applications, “pathogen identification”, “disease diagnosis”, “nucleic acids analysis”, and “small molecules analysis”, are summarized. On the basis of the dynamic changes of research focus on MALDI, an overlay visualization of the keywords with publication years is also presented. Around 2010, MALDI was mainly used for the analysis of nucleic acids with few related extension topics and late-stage research. Considering that MALDI-based nucleic acid analysis was developed earlier, the technology is relatively mature. The decade from 2011 to 2021 has seen a focus on MALDI-TOF MS-based research in “disease diagnosis “ and “small molecules analysis”, with the former having a relatively wider range of clinical applications than the latter that mostly focuses on matrix development. The most significant applications of MALDI in disease diagnosis or prognosis mainly include “breast cancer”, “ovarian cancer”, “Alzheimer”, and “lung disease”. For the technology of MALDI-TOF MS-based disease diagnostics, the difficulties in transferring it to the clinic include biological verification of the biomarkers and validation of the established methods by multicenter trials. Since 2015, the theme of “pathogen identification” has attracted considerable attention. MALDI-TOF MS-based bacterial identification mostly focuses on techniques for the isolation and purification of pathogens from clinical samples, the expansion of spectral libraries, and the upgrading of software. As technology advances, many MALDI-based microbial identification databases and systems have been licensed and put into clinical use. Nevertheless, it is still necessary to develop MALDI-TOF MS-based antimicrobial-resistance analysis for comprehensive clinical microbiology characterization. The important applications of MALDI in clinical research, including specific application categories, common analytes, main methods, limitations and solutions, have been summarized in Table 1.
Table 1
| application types | common analytes | principal methods | main limitations | potential solutions | |
|---|---|---|---|---|---|
| pathogen identification | (1) bacterial, yeasts, and fungi identification35 | proteins or polypeptides | (1) sample profiling | coverage of spectral database is limited | (1) upgrade databases including expand species or strain coverage30 and improve current spectral quality |
| (2) pattern matching | high levels of interfering components and low concentrations of target pathogens in clinical samples resulted in a low positive test rate | (2) enrich bacteria by magnetic beads,47 microfluidic techniques,48 etc. | |||
| (3) develop matrixes or signal enlargement methods43 | |||||
| (2) antimicrobial resistance detection40 | antibiotic molecules, modification products, component of bacterial cells, ribosomal methylation, mutations, etc. | (1) sample profiling | low accuracy in bacterial antimicrobial-resistance detection directly from MALDI-TOF MS profiling | develop algorithms or data analysis frameworks49 for deep data mining50 | |
| (2) feature extraction | |||||
| (3) model establishment and assessment | |||||
| disease diagnosis by peptidome | (1) cancer diagnosis and prognosis65 | proteins and peptides | (1) biofluid profiling | unfavorable repeatability in quantitative analysis limits clinical application | (1) develop matrix, deposition methods, and signal enlargement methods73 |
| (2) infectious disease74 | (2) feature extraction and/or biomarker discovery | (2) improve internal standard, ionization process, and postdata acquisition processing78 | |||
| (3) neurodegenerative disease diagnosis80 | (3) model establishment and assessment | (3) develop multicenter clinical validation work over a long period | |||
| nucleic acids analysis | (1) single-nucleotide polymorphism (SNP) genotyping88 | DNA and RNA | (1) gene locus selection, primer design, and quality control | developed method relies on a group of preselected sites, so that it cannot comprehensively detect the highly random mutational patterns in tumor suppressors | determining the target sites before MALDI-TOF MS analysis is necessary |
| (2) DNA methylation identification89 | (2) PCR amplification | ||||
| (3) inherited genetic disease screening93 | (3) single-base extension | ||||
| (4) model establishment and assessment | |||||
| small molecules analysis | (1) tissue fingerprinting114 | amino acids, lipids, sugars, short peptides, alcohols, organic acids, etc. | (1) biofluid profiling | lack of suitable matrix for different kinds of small molecules | develop matrix to enhance the detection sensitivity of specific types of molecules103 |
| (2) biofluid profiling116 | (2) feature extraction and/or biomarker discovery | ||||
| (3) cellular typing119 | (3) model establishment and assessment |
In summary, the development of clinical applications of MALDI-TOF MS has been dominated by methodological innovations, from sample pretreatment (e.g., desalting, sample spotting, matrix selection, etc.) and development of highly sensitive methods (e.g., enrichment, signal amplification, etc.) to data analysis (e.g., data process, algorithm development, etc.). Besides, there could be some innovations at the front end of the study design, such as delineating clinical staging and typing, as well as selecting sample types and molecular mass analysis ranges. Upon completion of a study, multidimensional validation may be considered to improve the reliability of the findings. On the basis of the achievements accumulated by previous studies, MALDI-TOF MS could be further promoted for more clinical applications.
Glossary
Vocabulary
| MALDI-TOF | MALDI (matrix-assisted laser desorption/ionization) is an ionization method that uses laser energy to generate ions from molecules with minimal fragmentation with the assistance of a matrix that can absorb the laser energy; TOF (time-of-flight) is a common high-throughput and middle–high-resolution mass analyzer to couple with MALDI |
| bloodstream infections | bloodstream infections are infectious diseases defined by the presence of viable pathogenic microorganisms in the bloodstream |
| keyword co-occurrence | keyword co-occurrence is a cluster method to display the keywords with the strongest citation bursts, i.e., using bibliometric and visualization methods |
| SNP genotyping | SNP (single-nucleotide polymorphism) genotyping is the measurement of genetic variations of SNPs between members of a species |
| DNA methylation | DNA methylation is a biological process by which methyl groups are added to DNA molecules |
| VOSviewer | VOSviewer is software for the construction of relationships between the structure, the evolution, and the collaboration of knowledge domains |
Author Contributions
D.L. wrote the draft of the manuscript. J.Y., G.H., and L.Q. revised and finalized the manuscript.
Notes
This work was supported by the National Natural Science Foundation of China (NSFC, 22022401, 22074022, and 21934001) and the Ministry of Science and Technology of China (2020YFF0304502).
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Funding
Funders who supported this work.
Ministry of Science and Technology of the People's Republic of China (1)
Grant ID: 2020YFF0304502
National Natural Science Foundation of China (3)
Grant ID: 22074022
Grant ID: 21934001
Grant ID: 22022401