Technology Bundle ID

Use of Detector Response Curves to Optimize Settings for Mass Spectrometry

Linked ID
Lead Inventors
Brian Gurbaxani (CDC)
Vincent Emanuele (CDC)
Development Stages
Pre-Clinical (in vitro)
Development Status
In vitro data available
This CDC developed optimization technology allows one to characterize the behavior of the coefficient of variation (CV) for a range of mass spectrometer machine settings. Surface-enhanced laser desorption/ionization (SELDI) and matrix-assisted laser desorption/ionization (MALDI) are used for the early detection of numerous diseases, for example cervical cancer . A critical step in the analytical process is the optimization of experiment and machine settings to ensure the best possible reproducibility of results, as measured by the CV. The high cost of this procedure includes man hours spent optimizing the machine, opportunity cost, materials used, and spent biological samples used in the optimization process.

This technology can be used to optimize the CV with the following advantages over conventional methods: 1) no need to use biological samples, 2) fewer materials are consumed in the process, 3) improved CV and thus more reproducible results, 4) fewer man hours required to find ideal machine settings, and 5) potential full-automation of the process of optimizing CV. This idea is beneficial to all scientists and clinicians that use MALDI/SELDI for biomarker discovery and clinical diagnostics. Further, manufacturers of MALDI/SELDI mass spectrometer devices would find incorporation of this technology quite beneficial.

Commercial Applications
  • MALDI/SELDI mass spectrometer calibration improvement
  • Biomarker discovery studies
  • Quality control techniques
  • Automated coefficient of variation (CV) optimization of mass spectrometer devices
Competitive Advantages
  • Lower resource input requirement
  • Increased cost efficiency
  • Simplifies SELDI/MALDI setup, reducing technician man-hours and need for extensive training
  • Improves experimental optimization providing greater reproducibility
  • Potential for automation of CV optimization

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