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eagle-i ID


Resource Type

  1. Liquid chromatography mass spectrometry platform


  1. Related Software
  2. Resource Description
    "The Applied BioSystems 4000 QTRAP triple-quadrupole, linear ion trap hybrid MS provides maximum quantitative and qualitative performance with UPLC capability available from an in-line Shimadzu Prominence system. A second set of Shimadzu pumps can be used for automated on line sample extraction using solid phase extraction. The system has a TurboV ionization source with an electrospray or atmospheric pressure chemical ionization probe capable of positive and negative ionization mode, allowing detection of a wide range of molecules. A photodiode array detector is also available prior to mass spectral analysis. Sample analysis and instrument control will be conducted with Analyst software. The combination of true triple-quadrupole and sensitive ion trap scan modes in a single LC/MS/MS run by the QTRAP allows for results that would otherwise require multiple analyses on multiple MS platforms. The system also has LightSight™ Software that is excellent for finding drug metabolites. The software automatically creates highly optimized methods that improve metabolite detection by constructing compound specific multiple reaction monitoring-information dependent acquisition methods (pMRM-IDA) where the survey scan will provide MS/MS data for potential metabolites. The MS/MS spectra generated will be analyzed for structural elucidation by opening MS/MS spectra in ACD/MS Processor software directly from LightSight™."
  3. Resource Description
    With Shimadzu Prominence XR HPLC and UV/VIS Detector
  4. Manufacturer
    Applied Biosystems
  5. Model Number
    4000 Q TRAP
  6. Website(s)
  7. Website(s)
  8. Related Technique
    Liquid chromatography-tandem mass spectrometry
  9. Location
    Bioanalytical Shared Resource/Pharmacokinetics Core Laboratory
Provenance Metadata About This Resource Record
Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016