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Molecular counting by photobleaching in protein complexes with many subunits: best practices and application to the cellulose synthesis complex.

Chen Y, Deffenbaugh NC, Anderson CT, Hancock WO - Mol. Biol. Cell (2014)

Bottom Line: The step detection algorithms account for changes in signal variance due to changing numbers of fluorophores, and the subsequent analysis avoids common problems associated with fitting multiple Gaussian functions to binned histogram data.The analysis indicates that at least 10 GFP-AtCESA3 molecules can exist in each particle.These procedures can be applied to photobleaching data for any protein complex with large numbers of fluorescently tagged subunits, providing a new analytical tool with which to probe complex composition and stoichiometry.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Huck Institutes of the Life Sciences, University Park, PA 16802 Interdisciplinary Graduate Degree Program in Cell and Developmental Biology, Huck Institutes of the Life Sciences, University Park, PA 16802.

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Detecting steps in simulated photobleaching data. (A) Simulated photobleaching data (black) with step detection by the Tdetector2 (red) and Bdetector2 (blue) algorithms. (B, C) Precision and sensitivity plots for the four algorithms. The two algorithms not assuming equal variance (Bdetector2 and Tdetector2) gave better precision but missed events, whereas Bdetector1 and Tdetector1 gave better sensitivity but led to false positives.
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Figure 4: Detecting steps in simulated photobleaching data. (A) Simulated photobleaching data (black) with step detection by the Tdetector2 (red) and Bdetector2 (blue) algorithms. (B, C) Precision and sensitivity plots for the four algorithms. The two algorithms not assuming equal variance (Bdetector2 and Tdetector2) gave better precision but missed events, whereas Bdetector1 and Tdetector1 gave better sensitivity but led to false positives.

Mentions: After benchmarking the step detection algorithms on the stepping data, we used the algorithms to detect unitary steps in the simulated photobleaching data. For ease of comparison, the step size was fixed at 500 a.u. for all simulated data, and the variance was altered to achieve different SNR values. As seen in Figure 4A, both algorithms identified similar steps in the simulated photobleaching data with SNR = 2. Considering the performance at different SNR values, the methods assuming unequal variance (Bdetector2 and Tdetector2) resulted in higher precision but lower sensitivity than the methods assuming equal variance (Bdetector1 and Tdetector1; Figure 4, B and C). For estimating subunit numbers from photobleaching data, the most important factor is properly estimating the amplitude of a quantal photobleaching event (the first mode). Hence a loss in sensitivity corresponding to missed steps (resulting in higher modes) is acceptable. In contrast, the falsely identified steps corresponding to low precision can lead to underestimating the quantal photobleaching amplitude. On the basis of these considerations, the two methods assuming constant variance were inferior to the methods assuming unequal variance. The Tdetector2 algorithm performed the best overall and was chosen for the subsequent analyses described later.


Molecular counting by photobleaching in protein complexes with many subunits: best practices and application to the cellulose synthesis complex.

Chen Y, Deffenbaugh NC, Anderson CT, Hancock WO - Mol. Biol. Cell (2014)

Detecting steps in simulated photobleaching data. (A) Simulated photobleaching data (black) with step detection by the Tdetector2 (red) and Bdetector2 (blue) algorithms. (B, C) Precision and sensitivity plots for the four algorithms. The two algorithms not assuming equal variance (Bdetector2 and Tdetector2) gave better precision but missed events, whereas Bdetector1 and Tdetector1 gave better sensitivity but led to false positives.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 4: Detecting steps in simulated photobleaching data. (A) Simulated photobleaching data (black) with step detection by the Tdetector2 (red) and Bdetector2 (blue) algorithms. (B, C) Precision and sensitivity plots for the four algorithms. The two algorithms not assuming equal variance (Bdetector2 and Tdetector2) gave better precision but missed events, whereas Bdetector1 and Tdetector1 gave better sensitivity but led to false positives.
Mentions: After benchmarking the step detection algorithms on the stepping data, we used the algorithms to detect unitary steps in the simulated photobleaching data. For ease of comparison, the step size was fixed at 500 a.u. for all simulated data, and the variance was altered to achieve different SNR values. As seen in Figure 4A, both algorithms identified similar steps in the simulated photobleaching data with SNR = 2. Considering the performance at different SNR values, the methods assuming unequal variance (Bdetector2 and Tdetector2) resulted in higher precision but lower sensitivity than the methods assuming equal variance (Bdetector1 and Tdetector1; Figure 4, B and C). For estimating subunit numbers from photobleaching data, the most important factor is properly estimating the amplitude of a quantal photobleaching event (the first mode). Hence a loss in sensitivity corresponding to missed steps (resulting in higher modes) is acceptable. In contrast, the falsely identified steps corresponding to low precision can lead to underestimating the quantal photobleaching amplitude. On the basis of these considerations, the two methods assuming constant variance were inferior to the methods assuming unequal variance. The Tdetector2 algorithm performed the best overall and was chosen for the subsequent analyses described later.

Bottom Line: The step detection algorithms account for changes in signal variance due to changing numbers of fluorophores, and the subsequent analysis avoids common problems associated with fitting multiple Gaussian functions to binned histogram data.The analysis indicates that at least 10 GFP-AtCESA3 molecules can exist in each particle.These procedures can be applied to photobleaching data for any protein complex with large numbers of fluorescently tagged subunits, providing a new analytical tool with which to probe complex composition and stoichiometry.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Huck Institutes of the Life Sciences, University Park, PA 16802 Interdisciplinary Graduate Degree Program in Cell and Developmental Biology, Huck Institutes of the Life Sciences, University Park, PA 16802.

Show MeSH