Dialing down the noise. High-resolution mass spectrometry (HR-MS) has been gaining impressive traction in recent years, particularly with respect to its application in proteomics. However, with increasingly large datasets being produced, distinguishing true, quantitative peptide peaks from the background noise can be challenging, often requiring manual inspection. In a new study, researchers from Kitasato University in Japan used a combination of six machine learning algorithms to successfully extract a higher number of peptide peaks with higher accuracy and precision than conventional methods. Crucially, their strategy focused on reducing the false-positive peaks seen in other machine learning approaches by assigning unanimously selected true peaks. Link
Seeing (and predicting) resistance. Antimicrobial stewardship describes a systemic effort within healthcare settings to limit overuse and over-prescription of antibiotics. A key part of this effort is selecting the optimal treatments for a particular disease, chiefly to limit the use of broad spectrum antibiotics and to improve patient outcomes. Current antimicrobial resistance testing methods are culture-based slow, taking up to 72 hours to produce results. MALDI-TOF is already extensively used in microbiology labs to identify microbial species and has the potential to enable antimicrobial susceptibility testing; however, the lack of a comprehensive catalog of marker masses for pathogen–drug combinations has kept the approach out of the clinic. Now, a group of scientists from Switzerland have created a large-scale, publicly available database called DRIAMS, which combines mass spectra from clinical isolates with their respective laboratory-confirmed antibiotic resistance profile. Their results show that this approach can accurately predict the resistance profile within 24 hours. Link
An IMS drug test. In the world of anti-doping, MS-based methods coupled to chromatography have reigned supreme. But emerging analytical techniques like ion mobility MS (IMS) could potentially offer unique advantages to the field, particularly in identifying novel or unknown anabolic steroids (AAS) – one of the most prevalent classes of performance-enhancing drugs banned by the World Anti-Doping Agency (WADA). Analytical scientists have now established an IMS workflow that meets WADA’s minimum required performance levels for the detection of AAS in human urine. Link
Tapping ion trap potential. Hybrid quadrupole ion trap/TOF-MS instruments have the potential to offer high sensitivity, resolution and MS/MS capability in a number of fields, but analytical performance has been limited by ion trap parameters. In a recent paper, researchers studied the relationship between mass resolution, sensitivity and extraction phase angle by implementing a square waveform phase modulation strategy. By eliminating the influence of the extraction phase angle, the team were able to improve both the mass resolution and sensitivity. Link
In other news…
PreOmics, a spin-out from Matthias Mann’s lab that develops sample prep tools for MS-based proteomics analysis, has announced €13.5m of series B funding provided by Bruker Corp. Link
Waters launches new MS Quan application for Waters Xevo mass spectrometers, allowing analysis of hundreds of small molecules in food and environmental samples in a single run. Link
Researchers discover a new ionization phenomenon – triboionization – that could improve transport efficiency and signal intensity in miniature mass spectrometers. Link
Gold nanoparticles/thiol-β-cyclodextrin-functionalized TiO2 nanowires could enhance the performance of surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) and MSI for imaging natural products. Link
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