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Many different laboratories are currently developing mass-spectrometric techniques to analyze and identify microorganisms. However, minimal work has been done with mixtures of bacteria. To demonstrate that microbial mixtures could be analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), mixed bacterial cultures were analyzed in a double-blind fashion. Nine different bacterial species currently in our MALDI-MS fingerprint library were used to generate 50 different simulated mixed bacterial cultures similar to that done for an initial blind study previously reported (Jarman, K. H.; Cebula, S. T.; Saenz, A. J.; Petersen, C. E.; Valentine, N. B.; Kingsley, M. T.; Wahl, K. L. Anal. Chem. 2000, 72, 1217-1223). The samples were analyzed by MALDI-MS with automated data extraction and analysis algorithms developed in our laboratory. The components present in the sample were identified correctly to the species level in all but one of the samples. However, correctly eliminating closely related organisms was challenging for the current algorithms, especially in differentiating Serratia marcescens, Escherichia coli, and Yersinia enterocolitica, which have some similarities in their MALDI-MS fingerprints. Efforts to improve the specificity of the algorithms are in progress.

Bacterial analysis by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry has been demonstrated in numerous laboratories, and a few attempts have been made to compare results from different laboratories on the same organism. It has been difficult to understand the causes behind the observed differences between laboratories when different instruments, matrices, solvents, etc. are used. In order to establish this technique as a useful tool for bacterial identification, additional efforts in standardizing the methods by which MALDI mass spectra are obtained and comparisons of spectra from different instruments with different operators are needed. Presented here is an extension of our previous single-laboratory reproducibility study with three different laboratories in a controlled experiment with aliquots of the same bacterial culture, matrix stock solution, and calibrant standards. Using automated spectral collection of whole-cell bacteria and automated data processing and analysis algorithms, fingerprints from three different laboratories were constructed and compared. Nine of the ions appeared reproducibly within all three laboratories, with additional unique ions observed within each of the laboratories. An initial evaluation of the ability to use a fingerprint generated within one laboratory for bacterial identification of a sample from another laboratory is presented, and strategies for improving identification rates between laboratories is discussed. has been used to analyze intact, cultured microorganisms with minimal sample handling. Two recent review articles, which include the capabilities and current limitations that need to be addressed, provide an excellent overview of this emerging research field [1,2]. The MALDI-TOF MS technique for identifying biomolecules provides rapid analysis time (Ͻ1 min per sample analysis), low sample-volume requirements (Ͻ1 L fluid), and the highly selective nature of mass-spectrometric analysis based on relative molecular masses. The m/z values for mass spectral peaks and the patterns with which they are observed provide very specific and unbiased analysis, as they indicate molecular weights of true components of the sample. Bacterial cells have been identified by comparing MALDI-TOF spectra obtained from cultured bacterial cells and simple microbial mixtures against a library of known MALDI-TOF spectral fingerprints obtained from intact bacterial cells [3,4] or from comparison with masses predicted from a proteomic database [5,6]. The proteomic approach has been demonstrated to correctly identify bacteria from spectra originating at different laboratories [5], however, this approach is currently suffering from an incomplete protein database for many of the organisms that are of interest. As the proteomic database becomes more populated with organisms of concern, this approach will become more feasible for bacterial identification, at

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.The need to rapidly detect specific microorganisms is both varied and extensive, encompassing basic biochemical, genetic, and ecological research and numerous applications in the genetic identification and tracking of pathogenic microorganisms. Current epidemiological investigations of pathogenic microorganisms use fairly standard techniques for DNA fingerprinting or discriminating between closely related isolates. These include pulsed-field gel electrophoresis (2), variations on Southern hybridization (43), and PCR-based techniques such as randomly amplified polymorphic DNA PCR (39), repetitive element PCR (18, 24), analysis of restriction fragment length polymorphisms (20, 30), single-stranded conformation polymorphisms (26), denaturing gradient gel electrophoresis (29), and combinations thereof (40). In most cases, current DNA-typing methods access a limited complement of genetic information and the fingerprint is based on DNA fragment sizing technology (i.e., gels) that requires parallel processing with many independent restriction enzymes or probes to achieve statistical rigor and confidence in the resulting pattern of DNA fragments.Despite the widespread acceptance of gel-based DNA fingerprinting techniques, they frequently fail to answer fundamental epidemiological questions. For example, Hancock et al. identified multiple sources of Escherichia coli O157:H7 in feedlots and dairy farms but were un...

We report on a genome-independent microbial fingerprinting method using nucleic acid microarrays for microbial forensics and epidemiology applications and demonstrate that the microarray method provides high resolution differentiation between closely related microorganisms, using Salmonella enterica strains as the test case. In replicate trials we used a simple 192 probe nonamer array to construct a fingerprint library of 25 closely related Salmonella isolates. Controlling false discovery rate for multiple testing at alpha = 0.05, at least 295 of 300 pairs of S.enterica isolate fingerprints were found to be statistically distinct using a modified Hotelling T2 test. Although most pairs of Salmonella fingerprints are found to be distinct, forensic applications will also require a protocol for library construction and reliable microbial classification against a fingerprint library. We outline additional steps required to produce such a protocol.

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Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype 'fingerprints'. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.

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