Discrimination of intact mycobacteria at the strain level: A combined MALDI-TOF MS and biostatistical analysis

Abstract

New methodologies for surveillance and identification of Mycobacterium tuberculosis are required to stem the spread of disease worldwide. In addition, the ability to discriminate mycobacteria at the strain level may be important to contact or source case investigations. To this end, we are developing MALDI-TOF MS methods for the identification of M. tuberculosis in culture. In this report, we describe the application of MALDI-TOF MS, as well as statistical analysis including linear discriminant and random forest analysis, to 16 medically relevant strains from four species of mycobacteria, M. tuberculosis, M. avium, M. intracellulare, and M. kansasii. Although species discrimination can be accomplished on the basis of unique m/z values observed in the MS fingerprint spectrum, discrimination at the strain level is predicted on the relative abundance of shared m/z values among strains within a species. For the 16 mycobacterial strains investigated in the present study, it is possible to unambiguously identify strains within a species on the basis of MALDI-TOF MS data. The error rate for classification of individual strains using linear discriminant analysis was 0.053 using 37 m/z variables, whereas the error rate for classification of individual strains using random forest analysis was 0.023 using only 18 m/z variables. In addition, using random forest analysis of MALDI-TOF MS data, it was possible to correctly classify bacterial strains as either M. tuberculosis or non-tuberculous with 100% accuracy.

MALDI imaging MS reveals candidate lipid markers of polycystic kidney disease

“…Most available computing processes and algorithms, such as principal component analysis or hierarchical clustering, enable the classifi cation of different tissues, e.g., healthy versus diseased, but fail to pinpoint discriminating m/z values that are needed for identifi cation of the corresponding biomolecules. Recently, nonimaging MALDI-TOF MS has been combined with linear discriminant analysis to achieve that goal ( 29 ). In microarray data sets even larger than MALDI images, Fisher discriminant analysis (FDA), a related discriminant analysis method, has successfully been applied for gene selection ( 30 ); it has previously been used for discrimination of magnetic resonance or positron emission tomography images (31)(32)(33).…”

MALDI-TOF MS: Its Application in the Clinical Laboratory and a Paradigm Shift in Clinical Microbiology

“…Several studies supported the observation that IC MALDI-TOF MS was sufficiently sensitive to differentiate closely related microorganisms [8], and perhaps even discriminate between different strains of the same microorganism or phenotypically similar microorganisms [9][10][11], providing new avenues for genus-, species-, and strain-level identification.…”

Salmonella Türlerinin Hızlı Tanısında Kromojenik Besiyeri İle Birlikte “Matrix-Assisted Laser Desorption Ionization - Time of Flight Mass Spectrometry” Yönteminin Phoenix Otomatize Sistemi ile Kıyaslanması

“…Son yıllarda, "Matrix-Assisted Laser Desorption Ionization (MALDI)-Time of Flight (TOF) mass spectrometry (MS) yön-temi ile mikroorganizmaların tanımlanmaları uygunluğu açısından gittikçe daha fazla uygulanan bir yöntem olmaya başlamıştır 9,10 . MALDI-TOF MS yönteminde tipik "parmak izi" görüntüsü tüm hücre kullanımı ile gerçekleşmekte ve önceden biyobelirteç ile ayırım, parçalama, farklı şekillerde ayırma ya da temizlik gerektirmeksizin elde edilebilmektedir.…”