Rapid diagnostics that enable identification of infectious agents improve patient outcomes, antimicrobial stewardship, and length of hospital stay. Current methods for pathogen detection in the clinical laboratory include biological culture, nucleic acid amplification, ribosomal protein characterization, and genome sequencing. Pathogen identification from single colonies by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of high abundance proteins is gaining popularity in clinical laboratories. Here, we present a novel and complementary approach that utilizes essential microbial glycolipids as chemical fingerprints for identification of individual bacterial species. Gram-positive and negative bacterial glycolipids were extracted using a single optimized protocol. Extracts of the clinically significant ESKAPE pathogens: E
nterococcus faecium, S
taphylococcus aureus, K
lebsiella pneumoniae, A
cinetobacter baumannii, P
seudomonas aeruginosa, and E
nterobacter spp. were analyzed by MALDI-TOF-MS in negative ion mode to obtain glycolipid mass spectra. A library of glycolipid mass spectra from 50 microbial entries was developed that allowed bacterial speciation of the ESKAPE pathogens, as well as identification of pathogens directly from blood bottles without culture on solid medium and determination of antimicrobial peptide resistance. These results demonstrate that bacterial glycolipid mass spectra represent chemical barcodes that identify pathogens, potentially providing a useful alternative to existing diagnostics.
Proteomics studies
rely on the accurate assignment of peptides
to the acquired tandem mass spectra—a task where machine learning
algorithms have proven invaluable. We describe mokapot, which provides
a flexible semisupervised learning algorithm that allows for highly
customized analyses. We demonstrate some of the unique features of
mokapot by improving the detection of RNA-cross-linked peptides from
an analysis of RNA-binding proteins and increasing the consistency
of peptide detection in a single-cell proteomics study.
Exosomes are microvesicles of endocytic origin constitutively released by multiple cell types into the extracellular environment. With evidence that exosomes can be detected in the blood of patients with various malignancies, the development of a platform that uses exosomes as a diagnostic tool has been proposed. However, it has been difficult to truly define the exosome proteome due to the challenge of discerning contaminant proteins that may be identified via mass spectrometry using various exosome enrichment strategies. To better define the exosome proteome in breast cancer, we incorporated a combination of Tandem-Mass-Tag (TMT) quantitative proteomics approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived fractions corresponding to a 10 000g cellular debris pellet, a 100 000g crude exosome pellet, and an Optiprep enriched exosome pellet. The quantitative analysis identified 2 179 proteins in all three fractions, with known exosomal cargo proteins displaying at least a 2-fold enrichment in the exosome fraction based on the TMT protein ratios. Employing SVM cluster analysis allowed for the classification 251 proteins as "true" exosomal cargo proteins. This study provides a robust and vigorous framework for the future development of using exosomes as a potential multiprotein marker phenotyping tool that could be useful in breast cancer diagnosis and monitoring disease progression.
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.
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