Literature DB >> 25123296

Maximum likelihood estimation of FRET efficiency and its implications for distortions in pixelwise calculation of FRET in microscopy.

Peter Nagy1, Agnes Szabó, Tímea Váradi, Tamás Kovács, Gyula Batta, János Szöllősi.   

Abstract

Ratiometric determination of the efficiency of fluorescence or Förster resonance energy transfer (FRET) is one of the most widespread methods for the characterization of protein clustering and conformation. Low photon numbers, often present in pixel-by-pixel determination of FRET efficiency in digital microscopy, result in large uncertainties in the derived FRET parameter. Here, we propose a method based on maximum likelihood estimation (MLE) of FRET efficiency using photon counting detectors to overcome this limitation. Intensities measured in the donor, FRET, and acceptor channels were all assumed to follow Poisson statistics as a result of detector shot noise. The joint probability of photon numbers detected in the donor, FRET, and acceptor channels was derived using an equation describing the relationship between the three measured intensities. The FRET efficiency generating the measured photon numbers with the largest likelihood was determined iteratively providing a single FRET value for all pixels in the calculation. Since as few as 100 pixels are sufficient to provide a maximum likelihood estimate for FRET, biological variability in FRET values can be revealed by performing the analysis for regions of interests in an image. Since the algorithm provides the probability of a combination of donor, FRET, and acceptor intensities observed in each individual pixel given a certain FRET efficiency, outlier pixels with low probabilities could be excluded from the analysis. Simulations carried out with low photon numbers in the presence and absence of outlier pixels revealed that the proposed approach can reliably and reproducibly estimate FRET efficiency. In addition, systematic evaluation of the simulation results showed that the distribution of pixel-by-pixel FRET efficiencies is skewed, and the mean of these FRET values is a biased and unreliable estimate of the FRET efficiency. In the absence of outlier pixels, FRET calculated from summed donor, FRET, and acceptor intensities proved to be as reliable as MLE. We conclude that MLE of FRET outperforms calculations using summed and pixel-by-pixel intensities in biologically relevant situations involving low photon numbers and outlier pixels.
© 2014 International Society for Advancement of Cytometry. © 2014 International Society for Advancement of Cytometry.

Entities:  

Keywords:  FRET; Poisson statistics; error propagation; maximum likelihood estimation (MLE); microscopy

Mesh:

Year:  2014        PMID: 25123296     DOI: 10.1002/cyto.a.22518

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  7 in total

1.  Overcoming limitations of FRET measurements.

Authors:  Silas J Leavesley; Thomas C Rich
Journal:  Cytometry A       Date:  2016-04       Impact factor: 4.355

Review 2.  Milestones in the development and implementation of FRET-based sensors of intracellular signals: A biological perspective of the history of FRET.

Authors:  J Deal; D J Pleshinger; S C Johnson; S J Leavesley; T C Rich
Journal:  Cell Signal       Date:  2020-09-06       Impact factor: 4.315

3.  Improving Quality, Reproducibility, and Usability of FRET-Based Tension Sensors.

Authors:  Evan M Gates; Andrew S LaCroix; Katheryn E Rothenberg; Brenton D Hoffman
Journal:  Cytometry A       Date:  2018-12-06       Impact factor: 4.355

4.  FRET: signals hidden within the noise.

Authors:  Silas J Leavesley; Thomas C Rich
Journal:  Cytometry A       Date:  2014-09-19       Impact factor: 4.355

5.  Minimum degree of overlap between IL-9R and IL-2R on human T lymphoma cells: A quantitative CLSM and FRET analysis.

Authors:  Enikő Nizsalóczki; Péter Nagy; Gábor Mocsár; Ágnes Szabó; István Csomós; Thomas A Waldmann; György Vámosi; László Mátyus; Andrea Bodnár
Journal:  Cytometry A       Date:  2018-10-31       Impact factor: 4.355

Review 6.  Understanding FRET as a research tool for cellular studies.

Authors:  Dilip Shrestha; Attila Jenei; Péter Nagy; György Vereb; János Szöllősi
Journal:  Int J Mol Sci       Date:  2015-03-25       Impact factor: 5.923

7.  Detecting stoichiometry of macromolecular complexes in live cells using FRET.

Authors:  Manu Ben-Johny; Daniel N Yue; David T Yue
Journal:  Nat Commun       Date:  2016-12-06       Impact factor: 14.919

  7 in total

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