No abstract

Gold nanoparticles functionalized with water-soluble zwitterionic ligands form kinetically stable complexes with hydrophobic drugs and dyes. These drugs and dyes are efficiently released into cells, as demonstrated through fluorescence microscopy and cytotoxicity assays. Significantly, there is little or no cellular uptake of particle, making these low toxicity particles promising for delivery applications.

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.

Untargeted metabolomics provides a comprehensive platform to identify metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-ToF-MS), hundreds to thousands of peaks with a unique m/z and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time, and tandem mass spectral fragmentation pattern. Here a 7-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-ToF-MS data that has been analyzed with the bioinformatic program XCMS, and describes a strategy for selecting interesting features as well as performing subsequent targeted tandem mass spectrometry. The 7 steps described will require 2-4 hours to complete per feature, depending on the compound.