Senior Researcher @ Hospital Gregorio Marañón and Institute of Health Research Gregorio Marañón, Department of Clinical Microbiology and Infectious Diseases


BSc in Biology and Biochemistry from the University of Salamanca (Spain), specialized in cellular and molecular biology. Focused on the implementation of MALDI-TOF MS in the clinical microbiolgy laboratory since 2011. Apart from the identification of a wide range of microorganisms with MALDI-TOF, in the last years her group has applied machine learning techniques for the classification of microbial isolates resistant to antibiotics/antifungals or belonging to belonging to specific sequence types.

MALDI-TOF throughout the times: from identification to bacterial typing

In the last ten years, MALDI-TOF has evolved from a high-tech lab instrument to an essential tool in the clinical microbiology laboratory. Many conventional identification methods based on biochemical are no longer in use due to the use-friendliness, reliability and cost-effectiveness of MALDI-TOF. Both Gram positive- and Gram negative bacteria can be easily identified from agar plates in a routine manner. Besides, direct identification of pathogens from clinical samples (blood cultures, urine or cerebrospinal fluid) has been demonstrated to be a clinically impacting application of MALDI-TOF since the identification of pathogens can be achieved in an early manner, allowing the prompt initiation of directed treatment. The detection of resistant mechanisms (beta-lactamases, carbapenemases) almost simultaneously to the identification of the host microorganisms has been another important milestone of MALDI-TOF, showing that the information contained in the protein spectra can provide much more information than the mere name of the microorganism analyzed.

Therefore, in the last couple of years our MALDI-TOF-based research group has started collaborations with bioinformaticians and biomedicine engineers in order to model the protein spectra and classify microbial isolates according to the presence of antibiotic/antifungal resistance mechanisms, the sequence type (ST) or the serotype/ribotype they belong to or their connection with hospital outbreaks. The algorithms applied so far have allowed the correct classification of azole-resistant Aspergillus fumigatus, vancomycin-resistant Enteroccocus faecium or high-risk clones of Pseudomonas aeruginosa. Although some of our studies are still in a preliminary stage, the robustness of the results show that MALDI-TOF is a promising tool for rapid screening of highly important strains and that its implementation may allow to implement control and treatment measures in a quick and effective way.

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