Signal/noise analysis of FRET-based sensors

Biophys J. 2010 Oct 6;99(7):2344-54. doi: 10.1016/j.bpj.2010.07.053.

Abstract

Molecular sensors based on intramolecular Förster resonance energy transfer (FRET) have become versatile tools to monitor regulatory molecules in living tissue. However, their use is often compromised by low signal strength and excessive noise. We analyzed signal/noise (SNR) aspects of spectral FRET analysis methods, with the following conclusions: The most commonly used method (measurement of the emission ratio after a single short wavelength excitation) is optimal in terms of signal/noise, if only relative changes of this uncalibrated ratio are of interest. In the case that quantitative data on FRET efficiencies are required, these can be calculated from the emission ratio and some calibration parameters, but at reduced SNR. Lux-FRET, a recently described method for spectral analysis of FRET data, allows one to do so in three different ways, each based on a ratio of two out of three measured fluorescence signals (the donor and acceptor signal during a short-wavelength excitation and the acceptor signal during long wavelength excitation). Lux-FRET also allows for calculation of the total abundance of donor and acceptor fluorophores. The SNR for all these quantities is lower than that of the plain emission ratio due to unfavorable error propagation. However, if ligand concentration is calculated either from lux-FRET values or else, after its calibration, from the emission ratio, SNR for both analysis modes is very similar. Likewise, SNR values are similar, if the noise of these quantities is related to the expected dynamic range. We demonstrate these relationships based on data from an Epac-based cAMP sensor and discuss how the SNR changes with the FRET efficiency and the number of photons collected.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biosensing Techniques / instrumentation*
  • Cyclic AMP / metabolism
  • Fluorescence Resonance Energy Transfer / instrumentation*
  • Guanine Nucleotide Exchange Factors / metabolism
  • HEK293 Cells
  • Humans
  • Image Processing, Computer-Assisted
  • Photobleaching
  • Photons
  • Time Factors

Substances

  • Guanine Nucleotide Exchange Factors
  • Cyclic AMP