MALDI-TOF MS method for differentiation of methicillin-sensitive and methicillin-resistant Staphylococcus aureus using (E)-Propyl α-cyano-4-Hydroxyl cinnamylate

Identification of Radix Bupleuri From Different Geographic Origins Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Support Vector Machine Algorithm

BACKGROUND

The geographic origin of Radix bupleuri is an important factor affecting its efficacy, which needs to be effectively identified.

OBJECTIVE

The goal is to enrich and develop the intelligent recognition technology applicable to the identification of the origin of traditional Chinese medicine.

METHOD

This article establishes an identification method of Radix bupleuri geographic origin based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and support vector machine (SVM) algorithm. The Euclidean distance method is used to measure the similarity between Radix bupleuri samples, and the quality control chart method is applied to quantitatively describe their quality fluctuation.

RESULTS

It is found that the samples from the same origin are relatively similar and mainly fluctuate within the control limit, but the fluctuation range is large, and it is impossible to distinguish the samples from different origins. The SVM algorithm can effectively eliminate the impact of intensity fluctuations and huge data dimensions by combining the normalization of MALDI-TOF MS data and the dimensionality reduction of principal components, and finally achieve efficient identification of the origin of Radix bupleuri, with an average recognition rate of 98.5%.

CONCLUSIONS

This newly established approach for identification of the geographic origin of Radix bupleuri has been realized, and it has the advantages of objectivity and intelligence, which can be used as a reference for other medical and food-related research.

HIGHLIGHTS

A new intelligent recognition method of medicinal material origin based on MALDI-TOF MS and SVM has been established.

MALDI-TOF MS Is an Effective Technique To Classify Specific Microbiota

MALDI-TOF MS is well-recognized for single microbial identification and widely used in research and clinical fields due to its specificity, speed of analysis, and low cost of consumables. Multiple commercial platforms have been developed and approved by the U.S. Food and Drug Administration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) has been used for microbial identification. However, microbes can present as a specific microbiota, and detection and classification remain a challenge. Here, we constructed several specific microbiotas and tried to classify them using MALDI-TOF MS. Different concentrations of nine bacterial strains (belonging to eight genera) constituted 20 specific microbiotas. Using MALDI-TOF MS, the overlap spectrum of each microbiota (MS spectra of nine bacterial strains with component percentages) could be classified by hierarchical clustering analysis (HCA). However, the real MS spectrum of a specific microbiota was different than that of the overlap spectrum of component bacteria. The MS spectra of specific microbiota showed excellent repeatability and were easier to classify by HCA, with an accuracy close to 90%. These results indicate that the widely used MALDI-TOF MS identification method for individual bacteria can be expanded to classification of microbiota. IMPORTANCE MALDI-TOF MS can be used to classify specific model microbiota. The actual MS spectrum of the model microbiota was not a simple superposition of every single bacterium in a certain proportion but had a specific spectral fingerprint. The specificity of this fingerprint can enhance the accuracy of microbiota classification.

The Effectiveness of MALDI ToF Mass Spectrometry in Identification of <i>Francisella tularensis</i> Strains

The aim of the study was to evaluate the effectiveness of MALDI‑ToF mass spectrometry in the identification of collection and newly isolated strains of tularemia pathogen using the database “Protein profiles of mass spectra of microorganisms belonging to I–II pathogenicity groups for the MALDI Biotyper software”.

Materials and methods. We investigated 142 strains of Francisella tularensis, including 59 collection strains and 83 newly isolated ones. Bacteriological, molecular-genetic and proteomic research methods were used to identify them. The acquisition of mass spectra, analysis, generation and expansion of reference libraries were performed on a mass analyzer “Microflex LT” using FlexControl v. 3.3, FlexAnalysis v. 3.3, and MALDI Biotyper 3.0 software packages. The cluster analysis was performed using the BioNumerics 7.6 software.

Results and discussion. The possibility of identifying tularemia pathogen has been assessed using the extended database for MALDI Biotyper 3.0 “Protein profiles of mass spectra of microorganisms belonging to I–II pathogenicity groups for the MALDI Biotyper software”. During identification to the species level, the significance of mass spectrometry results for collection strains and newly isolated ones was 91.5 % and 97.6 %, respectively. In determining the genus appurtenance, the reliability of identification was 100 %. Thus, the MALDI‑ToF mass spectrometry method allows for accurate species and genus identification of F. tularensis strains. Based on the cluster analysis of 66 F. tularensis strains in BioNumerics 7.6 software using «Pearson correlation» and the UPGMA algorithm, the possibility of subspecies differentiation has been evaluated. Due to the similarity of protein profiles of F. tularensis strains, a clear differentiation into subspecies could not be achieved. It is necessary to use other options for sample preparation, new generation devices with higher resolution, as well as apply additional approaches and analysis tools for successful subspecific differentiation.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.