The standard way to identify a microorganism in the clinic is to grow cultures in the laboratory and look for characteristic physical properties, a process that can take days. In recent years, culture-free methods have emerged that identify microbes based on their characteristic 16S ribosomal RNA sequences. However, these approaches often require extensive sample preparation and still sometimes fail to make an unequivocal designation, limiting their usefulness.
Mass spectrometry could be a quick alternative to gene sequencing, says Zoltan Takats of the Imperial College London. He and his colleagues envisioned using the technique to obtain profiles of the lipids in different microbes and then using those molecular signatures to identify species. Such profiles may also provide additional information, such as the capacity of a microorganism to cause disease, he says.
To test their idea, Takats’s team used a method called rapid evaporative ionization mass spectrometry (REIMS) to analyze 28 species of bacteria grown under a variety of conditions. They picked up colonies they wanted to analyze with a device that looks like a pair of forceps. The tip of the forceps is wired: When the researchers flip a switch, current pulses through the device, generating a cloud of mostly lipids from the selected colony. The airborne lipids are then drawn into a fluid line embedded in the body of the forceps, directing the molecules to the spectrometer for detection. Takats says the forceps could be adapted to gather samples taken from a patient’s mucosal membranes.
A computer program analyzed the spectra and compared the colonies’ lipid profiles, allowing Takats’s team to successfully distinguish all 28 species of bacteria and five species of Candida fungi. They could make the identifications regardless of growth conditions and often at greater than 95% accuracy. The identification process, from picking the colony to running the program, took as little as three to five seconds. To see if they could identify different strains of the same types of bacteria, the researchers analyzed seven strains of Escherichia coli and found they could distinguish them with around 90% accuracy.
The technique’s ability to distinguish microorganisms based on their lipid profiles is surprising and exciting, says Charles Y. Chiu of the University of California, San Francisco. However, he points out that the study was done with pure colonies, which have a high concentration of organisms. “In clinical samples, it’s more of a needle and a haystack,” Chiu says.
Chiu also wonders if the method will be able to distinguish between dangerous and harmless versions of the same microorganism. Takats says that is his goal, and he is currently working to discriminate between pathogenic and innocuous strains of Clostridium difficile.
The reference is provided below, and a full PDF is available for download.
Rapid Characterization and Identification of Clinically Relevant Microorganisms Using Rapid Evaporative Ionization Mass Spectrometry
Nicole Strittmatter , Monica Rebec , Emrys A Jones , Ottmar M.H. Golf , Alireza Abdolrasouli , Julia Balog , Volker D Behrends , Kirill A. Veselkov , and Zoltan Takats.
Anal. Chem., Just Accepted Manuscript
DOI: 10.1021/ac501075f
Publication Date (Web): June 4, 2014
Abstract:
Rapid evaporative ionization mass spectrometry was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.9%, 97.8% and 100% on species-, genus- and Gram-stain-level, respectively. These results were not affected by the resolution of the mass spectral data. Blind identification tests were performed for strains cultured on different culture media and analyzed using different instrumental platforms which led to 97.8-100% correct identification. Seven different Escherichia coli strains were subjected to different culture conditions and were distinguishable with 88% accuracy. In addition, the technique proved suitable to distinguish five pathogenic Candida species with 98.8% accuracy without any further modification to the experimental workflow. These results prove that REIMS is sufficiently specific to serve as a culture condition-independent tool for the identification and characterization of microorganisms.