Make Profiling Easier
Mass Spectrometry Metabolite Library of Standards (MSMLSTM) supplied with software tool MSMLSDiscoveryTM
Take the hassle out of sourcing, preparing compounds and building a library! MSMLS contains over 600 high quality primary metabolite standards provided in convenient 96 well format. The library of standards are most commonly used to provide retention times and spectra for key metabolic compounds, help optimize mass spectrometry analytical protocols, and qualify and quantify mass spectrometry sensitivity and limit of detection. MSMLSDiscovery is a software tool designed to streamline and simplify the tedious process of building authentic standard libraries for mass spectrometry and supports the extraction, manipulation, and stoarge of the date generated when using MSMLS.
IROA Biochemical Quantitation Kit Catalog #300-250 (for Mammalian cells)
Like SILAC, IROA utilizes full metabolic labeling to distinguish between two mammalian cell populations but uses both 5% 13C (experimental samples) and 95% 13C (control samples) to remove artifacts/noise, calculate carbon number and molecular formula to enable metabolite identification.
News & Events
Chaevien S. Clendinen 1,2, Gregory S. Stupp 3, Ramadan Ajredini 1,2, Brittany Lee-McMullen 1,2, Chris Beecher 1,2,4 and Arthur S. Edison 1,2*
1 Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA, 2 Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA, 3The Scripps Research Institute, La Jolla, CA, USA, 4IROA Technologies, Ann Arbor, MI, USA
ABSTRACT: Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward.
We have designed and implemented several NMR and LC-MS approaches that utilize 13C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) 13C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two 13Cbased
approaches. For samples at natural abundance, we have developed a workflow to obtain 13C–13C and 13C–1H statistical correlations using 1D 13C and 1H NMR spectra.
For samples that can be isotopically labeled, we describe another NMR approach to obtain direct 13C–13C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which 13C NMR can be used to identify unknown compounds from IROA experiments.