Bacterial Whole Cell Typing by Mass Spectra Pattern Matching with Bootstrapping Assessment

MALDI-TOF Mass Spectrometry in Clinical Analysis and Research

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.

Application of MALDI-TOF MS for identification of environmental bacteria: A review

Bacteria play a variety of roles in the environment. They maintain the balance in the ecosystem and provide different ecosystem services such as in biogeochemical cycling of nutrients, biodegradation of toxic pollutants, and others. Therefore, isolation and identification of different environmental bacteria are important to most environmental research. Due to the high cost and time associated with the conventional molecular techniques, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has gained considerable attention for routine identification of bacteria. This review aims to provide an overview of the application of MALDI-TOF MS in various environmental studies through bibliometric analysis and literature review. The bibliometric analysis helped to understand the time-variable application of MALDI-TOF MS in various environmental studies. The categorical literature review covers various environmental studies comprising areas like ecology, food microbiology, environmental biotechnology, agriculture, and plant sciences, which show the application of the technique for identification and characterization of pollutant-degrading, plant-associated, disease-causing, soil-beneficial, and other environmental bacteria. Further research should focus on bridging the gap between the phylogenetic identity of bacteria and their specific environmental functions or metabolic traits that can help in rapid advancements in environmental research, thereby, improving time and cost savings.

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints

Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently based on deep learning fingerprints of plasma exosomes. Sequential size-exclusion chromatography (SSEC) was developed efficiently for separating exosomes from human plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a single sample with high purity such that the discard rates of high-density lipoproteins and low/very low-density lipoproteins were 93 and 85%, respectively. Benefitting from the rapid and high-purity isolation, the contents encapsulated in exosomes, covered by plasma proteins, were well profiled by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS). We further analyzed 220 clinical samples, including 79 breast cancer patients, 57 pancreatic cancer patients, and 84 healthy controls. After MS data pre-processing and feature selection, the extracted MS feature peaks were utilized as inputs for constructing a multi-classifier artificial neural network (denoted as Exo-ANN) model. The optimized model avoided overfitting and performed well in both training cohorts and test cohorts. For the samples in the independent test cohort, it realized a diagnosed accuracy of 80.0% with an area under the curve of 0.91 for the whole group. These results suggest that our integrated pipeline may become a generic tool for liquid biopsy based on the analysis of plasma exosomes in clinics.

SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma

Osteosarcoma is the most frequent primary bone cancer, particularly among children and adolescents. The early diagnosis of osteosarcoma is significant for timely clinical treatment to reduce the mortality of patients. Exosomes play a significant role in intercellular communication and serve as promising biomarkers in liquid biopsy for the diagnosis and monitoring of tumors. Herein, we report the utility of surface-enhanced Raman scattering (SERS) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid identification of osteosarcoma. We firstly profiled the intrinsic SERS signals and MALDI-TOF mass fingerprints of different subgroups of extracellular vesicles (EVs) and the corresponding cells, demonstrating that the SERS signals and MALDI-TOF mass spectra of exosomes from different types of cells were more discriminative compared to those of large and medium EVs and the cells themselves. Then, we characterized plasma-derived exosomes of 15 osteosarcoma patients and 15 healthy volunteers using SERS and MALDI-TOF MS, revealing distinctive biochemical differences in the spectra. We further utilized a data fusion approach to combine the two types of spectroscopic techniques, differentiating osteosarcoma patients from healthy controls with higher precision than either technique. The results reveal that the non-invasive liquid biopsy method using SERS and MALDI-TOF MS fingerprinting of exosomes has great potential for rapid diagnosis of osteosarcoma.

Matrix-assisted laser desorption ionization mass spectrometry profiling of plasma exosomes evaluates osteosarcoma metastasis

Osteosarcoma is the most common primary sarcoma of bone among adolescents, often characterized by early lung metastasis resulting in high mortality. Recently, exosomes have been used in liquid biopsy to monitor tumors. Herein, we used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile human plasma exosomes for the evaluation of osteosarcoma lung metastasis. Forty patients with osteosarcoma with (n = 20) or without (n = 20) lung metastasis as well as 12 heathy controls were recruited. Exosomes were isolated from human plasma for MALDI-TOF MS analysis. Multivariate statistical analyses were performed based on the MALDI-TOF mass spectra. The strategy can efficiently differentiate osteosarcomas from healthy controls and further discriminate osteosarcoma lung metastasis from non-lung metastasis. We identified seven exosomal proteins as potential biomarkers of osteosarcoma lung metastasis. The proposed method holds great promise to clinically diagnose osteosarcoma and monitor osteosarcoma lung metastasis.

Rapid Noninvasive Skin Monitoring by Surface Mass Recording and Data Learning

Skin problems are often overlooked due to a lack of robust and patient-friendly monitoring tools. Herein, we report a rapid, noninvasive, and high-throughput analytical chemical methodology, aiming at real-time monitoring of skin conditions and early detection of skin disorders. Within this methodology, adhesive sampling and laser desorption ionization mass spectrometry are coordinated to record skin surface molecular mass in minutes. Automated result interpretation is achieved by data learning, using similarity scoring and machine learning algorithms. Feasibility of the methodology has been demonstrated after testing a total of 117 healthy, benign-disordered, or malignant-disordered skins. Remarkably, skin malignancy, using melanoma as a proof of concept, was detected with 100% accuracy already at early stages when the lesions were submillimeter-sized, far beyond the detection limit of most existing noninvasive diagnosis tools. Moreover, the malignancy development over time has also been monitored successfully, showing the potential to predict skin disorder progression. Capable of detecting skin alterations at the molecular level in a nonsurgical and time-saving manner, this analytical chemistry platform is promising to build personalized skin care.

Ultrafast enzymatic digestion of deoxyribonucleic acid in aqueous microdroplets for sequence discrimination and identification

We report the use of aqueous microdroplets to accelerate deoxyribonucleic acid (DNA) fragmentation by deoxyribonuclease I (DNase I), and we present a simple, ultrafast approach named DNA fragment mass fingerprinting to discriminate different DNA sequences by comparing their fragment mass patterns. DNA fragmentation in tiny microdroplets, which was produced by electrosonically spraying (+3 kV) a room temperature aqueous solution containing 10 μM DNA and 10 μg ml⁻¹ DNase I from a homemade setup, takes less than 1 ms. High differentiation/identification fidelity could be obtained by applying a cosine correlation measure for similarity assessment between two fragment mass patterns, which compares both mass-to-charge ratios (m/z) with an error tolerance of 5 ppm and the peaks’ relative intensities. A single-nucleotide mutation in the sequence of bases, as exemplified by the sickle cell anemia mutation, is differentiated by setting a cutoff value of similarity at 90%. The order change of two adjacent bases in the sequence could still be well discriminated with a similarity of only 62% between the fragment mass patterns of the two similar sequences, which have the same molecular weights and thus cannot be differentiated by gel electrophoresis or direct mass detection by mass spectrometry. Compared to traditional genotyping methods, such as quantitative real-time polymerase chain reaction, the identification process with our approach could be completed within several minutes without any other expensive and complicated reagents or experimental steps. The potential of our approach for convenient and fast microbe genetic discrimination or identification is further demonstrated by differentiating the Orf1ab gene fragments of two similar coronaviruses with a very high sequence homologous rate of 96%, SARS-CoV-2 and bat-SL-CoVZC45, with a similarity of 0% between their fragment mass patterns.

Direct MALDI-TOF profiling of gingival crevicular fluid sediments for periodontitis diagnosis

Periodontitis is a widespread stomatological disease and represents one of the main causes of tooth loss in adults. Traditional diagnosis of periodontitis relies on the judgment by professional periodontists that cannot reveal its progression at the early stage. In this work, we characterized the gingival crevicular fluid (GCF) sediments of patients with periodontitis and healthy volunteers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Potential protein biomarkers were selected based on the multivariate statistical analysis of the MALDI-TOF mass spectra, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) identification. Twelve potential protein biomarkers were identified from 17 patients compared to 7 healthy volunteers, including 5 microbial proteins and 7 human proteins, indicating the microbial composition and host response components related to the etiology of periodontitis. The panel of biomarkers was then verified with the GCF samples of another 11 patients. The 12 biomarkers also showed potential value in the early diagnosis of periodontitis. This work developed a rapid assay to screen periodontitis among populations. It can be popularized to non-periodontal specialists such as community general practitioners, benefiting the early and accurate monitoring of periodontitis. The identification of the potential biomarkers can also help in the understanding of the pathogenesis of periodontitis.

Releasing bacteria from functional magnetic beads is beneficial to MALDI-TOF MS based identification

Bacterial infections are the key cause of morbidity and mortality worldwide. Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS)-based bacterial identification has been widely accepted in the clinic. Functional material, such as rabbit immunoglobulin G-modified Fe₃O₄ (IgG@Fe₃O₄) and fragment crystallizable mannose binding lectin-modified Fe₃O₄ (FcMBL@Fe₃O₄), is used to capture bacteria from biological samples for MALDI-TOF MS identification, and the bacteria MS signals are usually obtained by directly smearing enriched bacteria on a MALDI target with MALDI matrix solution. However, the accuracy of identification based on MALDI-TOF MS may be affected by the presence of functional molecules, especially proteins, resulting in errors in the comparison with the standard bacterial spectra in the database. Moreover, the long-term presence of the magnetic beads on the MALDI-TOF target may reduce the instrument service life. In this study, we constructed FcMBL@Fe₃O₄ and used it to capture bacteria from both aqueous solution and bovine blood, and the bacterial identification accuracy based on different target preparation methods was compared. In the presence of Ca²⁺, the similarity scores for bacteria identified with FcMBL@Fe₃O₄ were ~88% and ~82% for Staphylococcus. aureus and Escherichia coli, respectively. In the presence of ethylenediaminetetraacetic acid (EDTA), bacteria separate from FcMBL@Fe₃O₄, resulting in similarity scores of ~96% and ~92% for S. aureus and E. coli, respectively. These results indicate that the functional proteins on the surface of nanoparticles affect the accuracy of identification accuracy based on the MALDI-TOF MS database. Thus, the release of bacteria from the functional material could increase the identification accuracy and be beneficial for maintaining the instrument.

Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry

INTRODUCTION

Marine planktonic communities are complex microbial consortia often dominated by microscopic algae. The taxonomic identification of individual phytoplankton cells usually relies on their morphology and demands expert knowledge. Recently, a live single-cell mass spectrometry (LSC-MS) pipeline was developed to generate metabolic profiles of microalgae.

OBJECTIVE

Taxonomic identification of diverse microalgal single cells from collection strains and plankton samples based on the metabolic fingerprints analyzed with matrix-free laser desorption/ionization high-resolution mass spectrometry.

METHODS

Matrix-free atmospheric pressure laser-desorption ionization mass spectrometry was performed to acquire single-cell mass spectra from collection strains and prior identified environmental isolates. The computational identification of microalgal species was performed by spectral pattern matching (SPM). Three similarity scores and a bootstrap-derived confidence score were evaluated in terms of their classification performance. The effects of high and low-mass resolutions on the classification success were evaluated.

RESULTS

Several hundred single-cell mass spectra from nine genera and nine species of marine microalgae were obtained. SPM enabled the identification of single cells at the genus and species level with high accuracies. The receiver operating characteristic (ROC) curves indicated a good performance of the similarity measures but were outperformed by the bootstrap-derived confidence scores.

CONCLUSION

This is the first study to solve taxonomic identification of microalgae based on the metabolic fingerprints of the individual cell using an SPM approach.

Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.

Electrostatic Spray Ionization-Mass Spectrometry for Direct and Fast Wine Characterization

Due to the globally existed and economically motivated adulteration including mislabeling and/or blending, fast wine characterization is important in wine industry. Herein, we developed an electrostatic spray ionization-mass spectrometry (ESTASI-MS)-based method to distinguish wines. Wine samples were directly analyzed by ESTASI-MS without any pretreatment. Microdroplets of wine were deposited on a plastic plate for analysis. The collection of each mass spectrometric datum can be accomplished in 1-2 min without any need of pretreatment to the sample, followed by principle component analysis to discriminate wines with different labels and vintages. Long-term storage of wine was simulated and characterized by utilizing the method. High-performance liquid chromatography-MS was further applied to identify the distinctive compounds in wines to indicate their difference. We found that the method can offer a strategy for quick wine analysis, which is of practical value in wine industry for wine classification and aging control.

Direct MALDI-TOF MS Identification of Bacterial Mixtures

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is now widely used to characterize bacterial samples for clinical diagnosis, food safety control, environmental monitoring, and so on. However, existing standard approaches are only applied to analyze single colonies purified by plate culture, which limits the approaches to cultivable bacteria and makes the whole approaches time-consuming. In this work, we propose a new framework to analyze MALDI-TOF spectra of bacterial mixtures and to directly characterize each component without purification procedures. The framework is a combination of a synthetic mixture model based on a non-negative linear combination of candidate reference spectra and a statistical assessment by in silico generated spectra via a jackknife resampling. Ninety-seven model bacterial mixture samples and 8 cocultured blind-coded bacterial mixture samples, containing up to 6 strains in varied ratios in each sample, together with a reference database containing the mass spectra of 1081 strains, were used to validate the framework. High sensitivity (>80%, with error rate <5%) was achieved for balanced binary and ternary mixtures. The sensitivity was >60% for balanced quaternary and pentabasic mixtures, and 48%-71% for asymmetric situation, with error rate <5%. The work can facilitate rapid and reliable characterization of bacterial mixtures without purification procedures, which is of practical value in clinical diagnosis, food safety control, environmental monitoring, and so on. The framework can be further applied to many other spectroscopy-based analytics to interpret spectra from mixed samples.