Bacteria/Mass Spectrometry Figure 3. Determination of experimental variation of the procedure. E. amylovora sub-species 1/79 (German strain) was grown in LB-glucose in quintuplicate and analyzed. E. pyrifoliae 16/96 was used as an outlier for clustering. Distance levels below ,30–50 could not be resolved any further by our clustering approach due to experimental noise. The experimental variation was presumably caused mainly by slight changes in matrix- protein co-crystallization such as room temperature, pressure, and humidity. In this exemplary experiment, the coefficient of variation (CV) for intra run was 0.31 and for inter run 0.35. Similar CVs were observed for the identification experiments presented in the paper. As shown in Table 1, the robust analysis algorithms applied can easily handle the experimental variation that is associated with our approach. doi:10.1371/journal.pone.0002843.g003 procedure. The settings used in this study are summarized in Table 3. The integration of data from different biological levels refines the analysis, which has so far rarely been exploited in bioanalytics [1]. As shown in Figure 6 and Table 4, MALDI mass spectrometry can be used for integrating the analysis of data derived from detection of protein mass patterns and genomic markers such as SNPs [13]. We made use of a novel SNP that we discovered by re- sequencing of the galE gene of several E. amylovora strains (Figure S1). Genotyping of this informative SNP by mass spectrometry [13] allows us to distinguish E. amylovora strains of North American from European origin, which was impossible by merely analyzing protein mass patterns. Discussion As compared to protein identification based mass spectrometry methods [14], our procedure is robust against slight mass deviations detected by the mass spectrometer. Moreover, in many cases in microbiology - such as for many species of Erwinia - complete genomic or protein sequence databases are not available, which inhibits the use of identification-based mass spectrometry approaches. In practice, such approaches could rarely generate additional information for bacterial identification to the finger- printing method shown here. Mainly ribosomal proteins as in the case of E. coli and other Enterobacteriaceae would be identifed with enormous cost and time commitment. Other recently published mass spectrometry procedures for microbial typing rely on the detection of fragments of nucleic acids [4,5]. By using a template amplification procedure these approaches are potentially very sensitive. However, the analyte fragments were produced in a sequence of parallel molecular biological reactions, which significantly contribute to the costs of these procedures. DNA sequencing is one of the gold standards for characteriza- tion of bacteria, but this approach cannot be easily applied for fast classification and identification. In general, DNA-based methods such as PCR require optimization for setting up specific assays for each bacterial strain. Once they are being set up, conventionally applied procedures such as real-time PCR might be more specific and faster for bacterial identification than our method. However, our procedure can be readily applied and does not require reference sequences. Moreover, culturing of bacteria on agar plates and later on in liquid cultures reduces the sample complexity and improves the control of environmental samples. As it is shown for several Erwinia species, our method is largely independent from culturing conditions. Even when minimal media have to be used, however, potential culture-dependency of some mass signals could be excluded from the software analysis. The protein mass pattern detection approach presented in this study is independent from DNA sequence information and complementary to DNA sequencing or PCR-based approaches. As we have shown here, digital information encoded in informative regions of bacterial genomes, such as a single SNP, can provide additional resolution in the mass spectrometry data analysis [14]. Data integration from different biological levels as demonstrated in this paper has so far rarely been applied in bioanalytics. Particularly mass spectrometry allows integrating biological information as this technique can be used to detect both, nucleic acids and proteins. In general such an integrated approach would certainly improve the accuracy and reliability in many diagnostic life sciences applications. Due to the specificity, speed of analysis and low costs for consumables, our method for classifying and identifying bacterial species can replace a number of conventionally applied but cumbersome physiological, serological, biochemical, and chemo- taxonomic procedures. The standardized bacterial detection procedure presented herein is facile and reproducible. It fulfils biological safety requirements and can be easily scaled up. At least 107 bacterial cells are required for our preparation procedure to generate reliable results, which can be easily produced by cell culturing. Excluding the time for bacterial culturing, our approach takes 90 minutes from sample preparation to identification. The method presented requires only a simple time-of-flight (TOF) mass spectrometer. Due to the software support demon- strated in this study, the method can be used for bacterial identification in a variety of applications without (extensively) analyzing mass spectra. The procedure can be easily applied by a microbiologist with essential knowledge in mass spectrometry. To provide the basis for large-scale longitudinal evaluation studies that PLoS ONE | www.plosone.org 4 July 2008 | Volume 3 | Issue 7 | e2843