HistoSnap: A Novel Software Tool to Extract m/z-Specific Images from Large MSHC Datasets.

We describe an informatics tool for comfortable browsing through highly complex, multi-gigabyte mass spectrometry histochemistry (MSHC) datasets, via clever ion-specific image extraction.The package is developed particularly for the untargeted localization/discovery of biomolecules such as endogenous (neuro)secretory peptides on histological sections of biobanked formaldehyde-fixed paraffin-embedded (FFPE) samples straight from tissue banks.Atmospheric pressure-MALDI-Orbitrap MSHC data of sections through human pituitary adenomas in which two well-known human neuropeptides are detected are used as an example to demonstrate the key features of the novel software, named HistoSnap.

MS1-Based Data Analysis Approaches for FFPE Tissue Imaging of Endogenous Peptide Ions by Mass Spectrometry Histochemistry (MSHC).

Ambiguous reports in the literature exist regarding the use and usefulness of formalin-fixed paraffin-embedded (FFPE) tissues in mass spectrometry imaging (MSI). Especially for the study of endogenous (non-tryptic) peptides, several studies have concluded that MSI on archived FFPE tissue bank samples is virtually impossible. We here illustrate that by employing a variant of MSI, called mass spectrometry histochemistry (MSHC), biomolecular tissue localization data are obtained that unequivocally comprise endogenous peptides. We here discuss different informatics steps in a data analysis workflow to help filter peptide-related features out of large and complex datasets generated by atmospheric pressure matrix-assisted laser desorption/ionization high-resolution (Orbitrap mass analyzer) MSHC. These include, in addition to accurate mass measurements, Kendrick mass defect filtering and isotopic distribution scrutiny.

Rapid and Quantitative Analysis of Aflatoxin M1 From Milk Using Atmospheric Pressure-Matrix Assisted Laser Desorption/Ionization (AP-MALDI)-Triple Quadrupole Selected Reaction Monitoring.

BACKGROUND: Aflatoxin M1 (AFM1) is a carcinogenic hydroxylated metabolite commonly found in milk. It is relatively stable toward decontamination procedures posing a major health risk, and it requires an international regulatory mandate of detection at trace levels. OBJECTIVE: To develop a high-throughput, reliable, and compliant method for the identification of AFM1 in milk samples using atmospheric pressure-matrix assisted laser desorption/ionization (AP-MALDI) selected reaction monitoring (SRM) quantitation. METHOD: The milk sample was diluted in water and cleaned with immunoaffinity chromatography (IAC), followed by analysis using AP-MALDI hyphenated with a triple quadrupole mass spectrometer for SRM. RESULTS: A fast and reliable AP-MALDI SRM quantitative method was developed for the determination of AFM1 with analysis time of 1 min per sample. The diagnostic product ions of AFM1 at 273.1 u and 229.2 u were monitored during the SRM. The calibration curves yielded excellent linearity (R2 = 0.99) with good recoveries for quality control samples (97-106%). The ion ratios of the qualifier to quantifier displayed excellent RSD (1-7.8%) for n = 3. CONCLUSIONS: The developed method provided rapid quantification for AFM1. The fast AP-MALDI SRM method can allow analysis of AFM1 in a large number of milk samples. Given the time required for analysis, cost-effectiveness, and superior analytical performance, this method can be adopted in commercial food testing laboratories. HIGHLIGHTS: Aflatoxins (AF) are a major health risk. Speedy analysis of large sample sizes from food is a risk mitigation strategy but remains an unmet need. Quantitative, chromatography-free, and internal standard-free AP-MALDI SRM based analysis of AF is a high-throughput and cost-efficient alternative. Satisfactory performance was achieved for quantitative AP-MALDI SRM analysis of AFM1 in milk subsequent to a simple sample clean-up step.

Plausible diagnostic value of urinary isomeric dimethylarginine ratio for diabetic nephropathy.

Altered circulatory asymmetric and symmetric dimethylarginines have been independently reported in patients with end-stage renal failure suggesting their potential role as mediators and early biomarkers of nephropathy. These alterations can also be reflected in urine. Herein, we aimed to evaluate urinary asymmetric to symmetric dimethylarginine ratio (ASR) for early prediction of diabetic nephropathy (DN). In this cross-sectional study, individuals with impaired glucose tolerance (IGT), newly diagnosed diabetes (NDD), diabetic microalbuminuria (MIC), macroalbuminuria (MAC), and normal glucose tolerance (NGT) were recruited from Dr. Mohans' Diabetes Specialties centre, India. Urinary ASR was measured using a validated high-throughput MALDI-MS/MS method. Significantly lower ASR was observed in MIC (0.909) and MAC (0.741) in comparison to the NGT and NDD groups. On regression models, ASR was associated with MIC [OR: 0.256; 95% CI: 0.158-0.491] and MAC [OR 0.146; 95% CI: 0.071-0.292] controlled for all the available confounding factors. ROC analysis revealed ASR cut-point of 0.95 had C-statistic of 0.691 (95% CI: 0.627-0.755) to discriminate MIC from NDD with 72% sensitivity. Whereas, an ASR cut-point of 0.82 had C-statistic of 0.846 (95% CI: 0.800 - 0.893) had 91% sensitivity for identifying MAC. Our results suggest ASR as a potential early diagnostic biomarker for DN among the Asian Indians.

A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD+/NADH+ imbalance.

BACKGROUND: The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites. RESULTS: Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance. CONCLUSIONS: The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.