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We describe an innovative use for the recently reported fast lipid analysis technique (FLAT) that allows for the generation of MALDI tandem mass spectrometry data suitable for lipid A structure analysis directly from a single Gram-negative bacterial colony. We refer to this tandem MS version of FLAT as FLAT n . Neither technique requires sophisticated sample preparation beyond the selection of a single bacterial colony, which significantly reduces overall analysis time (∼1 h), as compared to conventional methods. Moreover, the tandem mass spectra generated by FLAT n provides comprehensive information on fragments of lipid A, for example, ester bonded acyl chain dissociations, cross-ring cleavages, and glycosidic bond dissociations, all of which allow the facile determination of novel lipid A structures or confirmation of expected structures. In addition to generating tandem mass spectra directly from single colonies, we also show that FLAT n can be used to analyze lipid A structures taken directly from a complex biological clinical sample without the need for ex vivo growth. From a urine sample from a patient with an E. coli infection, FLAT n identified the organism and demonstrated that this clinical isolate carried the mobile colistin resistance-1 gene ( mcr-1 ) that results in the addition of a phosphoethanolamine moiety and subsequently resistance to the antimicrobial, colistin (polymyxin E). Moreover, FLAT n allowed for the determination of the existence of a structural isomer in E. coli lipid A that had either a 1- or 4'-phosphate group modification by phosphoethanolamine generated by a change of bacterial culture conditions.

We developed a method to directly detect and map the Gram-negative bacterial virulence factor lipid A derived from lipopolysaccharide (LPS) by coupling acid hydrolysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). As the structure of lipid A (endotoxin) determines the innate immune outcome during infection, the ability to map its location within an infected organ or animal is needed to understand localized inflammatory responses that results during host-pathogen interactions. We previously demonstrated detection of free lipid A from infected tissue; however detection of lipid A derived from intact (smooth) LPS from host-pathogen MSI studies, proved elusive. Here, we detected LPS-derived lipid A from the Gram-negative pathogens, Escherichia coli ( Ec , m / z 1797) and Pseudomonas aeruginosa ( Pa , m / z 1446) using on-tissue acid hydrolysis to cleave the glycosidic linkage between the polysaccharide (core and O-antigen) and lipid A moieties of LPS. Using accurate mass methods, the ion corresponding to the major Ec and Pa lipid A species ( m / z 1797 and 1446, respectively) were unambiguously discriminated from complex tissue substrates. Further, we evaluated potential delocalization and signal loss of other tissue lipids and found no evidence for either, making this LPS-to-Lipid A-MSI (LLA-MSI) method, compatible with simultaneous host-pathogen lipid imaging following acid hydrolysis. This spatially sensitive technique is the first step in mapping host-influenced de novo lipid A modifications, such as those associated with antimicrobial resistance phenotypes, during Gram-negative bacterial infection and will advance our understanding of the host-pathogen interface.

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.

Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship. Current technologies employed in routine diagnostics are based on bacterial culture followed by morphological trait differentiation and biochemical testing, which can be time-consuming and labor-intensive. With advances in mass spectrometry (MS) for clinical diagnostics, the U.S. Food and Drug Administration has approved two microbial identification platforms based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of microbial proteins. We recently reported a novel and complementary approach by comparing MALDI-TOF mass spectra of microbial membrane lipid fingerprints to identify ESKAPE pathogens. However, this lipid-based approach used a sample preparation method that required more than a working day from sample collection to identification. Here, we report a new method that extracts lipids efficiently and rapidly from microbial membranes using an aqueous sodium acetate (SA) buffer that can be used to identify clinically relevant Gram-positive and -negative pathogens and fungal species in less than an hour. The SA method also has the ability to differentiate antibiotic-susceptible and antibiotic-resistant strains, directly identify microbes from biological specimens, and detect multiple pathogens in a mixed sample. These results should have positive implications for the manner in which bacteria and fungi are identified in general hospital settings and intensive care units.