MALDI-TOF 질량 분석 및 사용자 지정 데이터베이스의 사용은 고유 동굴 환경 (Kartchner 동굴, AZ, USA)에 토착 박테리아의 특성을
Summary
This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented.
Abstract
MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, strain levels. Commercially available and open source software tools have been developed to facilitate identification; however, no universal/standardized data analysis pipeline has been described in the literature. Here, we provide a comprehensive and detailed demonstration of bacterial identification procedures using a MALDI-TOF mass spectrometer. Mass spectra were collected from 15 diverse bacteria isolated from Kartchner Caverns, AZ, USA, and identified by 16S rDNA sequencing. Databases were constructed in BioNumerics 7.1. Follow-up analyses of mass spectra were performed, including cluster analyses, peak matching, and statistical analyses. Identification was performed using blind-coded samples randomly selected from these 15 bacteria. Two identification methods are presented: similarity coefficient-based and biomarker-based methods. Results show that both identification methods can identify the bacteria to the species level.
Introduction
Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been shown to be a rapid and reliable tool for identification of bacteria at the genus, species, and in some cases, strain levels1-4. MALDI-TOF MS ionizes biological molecules (typically proteins) that originate from cell surfaces, intracellular membranes, and ribosomes from bacterial whole cells or protein extracts1,5. The resulting peaks form characteristic patterns or “fingerprints” of the bacteria analyzed1. Identification of bacteria is based on these mass-to-charge “fingerprints”.
Two of the most commonly used identification strategies are library-based and bioinformatics-based strategies1. Library-based approaches involve comparing the mass spectra of unknowns to previously collected mass spectra of known bacteria in databases/libraries for identification. Commercially available software, such as BioNumerics, Biotyper, and SARAMIS software packages, as well as open source software tools, such as SpectraBank6, are available to facilitate the comparison and quantification of similarity between mass spectra of unknowns and reference bacteria. Bioinformatics-based approaches usually rely on fully sequenced genomes of bacteria for identification. In contrast to library-based approaches which do not involve identification of the biological nature of particular peaks, bioinformatics-based approaches involve protein identification1.
The majority of recent MALDI fingerprint-based studies have used library-based approaches to identify bacteria1. Library-based approaches require construction of databases and comparison of the similarity between mass spectra. Studies show that many experimental procedures, such as medium3,7, cultivation time8, sample preparation method3, and matrix used9, affect the mass spectra obtained. Furthermore, some closely-related species and strains generate spectra with only subtle differences. Thus, library-based approaches require rigorously standardized procedures to generate highly reproducible mass spectra between replicates. Minor variations in protocols may compromise the efficacy of identification, especially at the subspecies and strain levels1,3,10. However, neither manufacturer-provided reference databases nor reported custom databases include visually documented procedures for database construction and/or application of a data analysis pipeline. For this reason, the objective of this work was to develop, apply, and demonstrate a comprehensive and detailed procedure for library-based bacterial identification using MALDI-TOF MS.
In this demonstration, mass spectra of 15 bacteria isolated from a karstic environment (Kartchner Cavern, AZ, USA) were collected and imported into software to construct a model database. Data processing and the analysis pipeline were detailed using the model database. Finally, mass spectra of blind-coded bacteria which were randomly selected from these 15 bacteria were collected again and compared to the reference spectra in the model database for identification. Results show that bacteria can be correctly identified either based on similarity coefficients or potential biomarkers/peak classes.
Protocol
Representative Results
Discussion
이 데모는 특성화 및 MALDI-TOF MS와 사용자 지정 데이터베이스를 사용하여 세균의 식별의 세부 절차를 보여 주었다. 예를 들어 전통적인 분자 방법과 비교하여, 16S rDNA의 염기 서열, MALDI-TOF MS 기반 지문 방법은 다양한 박테리아의보다 신속한 식별을 용이하게한다. 견고성으로 인해,이 기술은 널리 임상 환경에서 설정 1,14-16에서 박테리아, 바이러스, 곰팡이 및 효모를 특성화하기 위해 사용된…
Disclosures
The authors have nothing to disclose.
Acknowledgements
This work was supported by the New College of Interdisciplinary Arts and Sciences at Arizona State University, Applied Maths NV, and by the National Science Foundation (ROA Supplement to Award No. MCB0604300). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Materials
| α-cyano-4-hydroxy-cinnamic acid | ACROS Organics | 163440050 | ≥ 97%, CAS 28168-41-8 |
| MALDI calibration kit | Aigma-Aldrich | MSCAL1-1KT | This kit also contains acetonitrile, trifluoroacetic acid , sinapinic acid, etc. |
| MALDI target plate | Bruker Daltonics | 280800 | Polished Steel |
| Bruker Microflex LRF MALDI-TOF mass spectrometer | Bruker Daltonics | ||
| Bruker FlexControl software | Bruker Daltonics | version 3.0 | |
| Bruker FlexAnalysis software | Bruker Daltonics | version 3.0 | |
| Bionumerics software | Applied Maths | version 7.1 |
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