Classification and Identification of Bacteria by Mass Spectrometry and Computational Analysis Sascha Sauer1*, Anja Freiwald1, Thomas Maier2, Michael Kube1, Richard Reinhardt1, Markus Kostrzewa 2 , Klaus Geider3 1 Max Planck Institute for Molecular Genetics, Berlin, Germany, 2 Bruker Daltonik, Leipzig, Germany, 3 Julius Kuehn Institute for Plant Protection in Fruit Crops and Viticulture, Dossenheim, Germany Abstract Background: In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications. Methodology: We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection. Conclusions: With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome. Citation: Sauer S, Freiwald A, Maier T, Kube M, Reinhardt R, et al. (2008) Classification and Identification of Bacteria by Mass Spectrometry and Computational Analysis. PLoS ONE 3(7): e2843. doi:10.1371/journal.pone.0002843 Editor: Ching-Hong Yang, University of Wisconsin-Milwaukee, United States of America Received April 25, 2008; Accepted July 3, 2008; Published July 30, 2008 Copyright: 2008 Sauer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the German Ministry of Education and Research, the European Union and the Max-Planck Society. Competing Interests: The authors T.M. and M.Ko. declare competing financial interests. T.M. and M.Ko. are employed by the mass spectrometry company Bruker Daltonik. * E-mail: sauer@molgen.mpg.de Introduction In general, new technologies for accurate and rapid identifica- tion of bacteria are essential to epidemiological surveillance, i.e. the recognition of early outbreak, the analysis of cross-transmis- sion, and the monitoring of treatment programs including application of antagonistic bacteria. For classifying and identifying bacterial species, cumbersome physiological, serological, biochem- ical, chemotaxonomic, and more recently genomic methods have been routinely applied in microbiology [1]. For example, genetic approaches using digital genomic information for the detection of 16S rRNA genes provide specific tools for classification of bacteria. The analysis of DNA sequence similarities of housekeeping genes is also being used for multilocus sequence typing (MLST) approaches [2], which can however be tedious in mass screening. PCR-based methods to detect pathogens are available but cannot be used for classification, especially in the case of unknown bacterial samples. Moreover, the analysis of bacteria such as the highly conserved fire blight pathogen E. amylovora requires special efforts to differentiate strains by pulsed field gel electrophoresis analysis or by sequencing of virulence genes [3]. Mass spectrometric approaches that use molecular biological sample preparation have been shown recently in microbial typing [4,5]. These methods comprise highly sophisticated instrumenta- tion, and the associated costs from sample preparation make the operation prohibitive for general use. Alternatively, MALDI time- of-flight (TOF) mass spectrometry protein profiling of whole bacterial cells can be applied for detection of bacteria [6]. However, most of these procedures have so far not exceeded proof-of-principle level and were applied only to a limited number of bacterial species [7,8]. Moreover, all these procedures do not have proven maturity for easy and systematic application in microbiology. Consequently, biologists have not consistently utilized these approaches despite their great potential. In the exemplary study of this article we focused on the mass spectrometry analysis of bacteria of the genus Erwinia and related (phytopathogenic) bacteria. The genus Erwinia comprises several bacterial species, many of them connected to plant diseases [9]. The Erwinia species belong to the family of Enterobacteriaceae, which also include Escherichia coli, Yersinia spp., Shigella spp., and Salmonella spp. Erwinia amylovora causes the devastating fire blight disease of rosaceous plants, such as apple and pear trees and some PLoS ONE | www.plosone.org 1 July 2008 | Volume 3 | Issue 7 | e2843