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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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Comparing methods of fitting photobleaching step size distributions to extract unitary step size. Histograms represent step size distributions from Tdetector2 applied to simulated photobleaching data with copy number 12 and SNR = 2. The distribution is made up of 570 detected steps. (A) Fit of two Gaussian functions to the data using a bin size of 50. Fit parameters are μ1 = 510 a.u., σ1 = 55, μ2 = 836 a.u., and σ2 = 335. (B) Fit of two Gaussian functions to the data using a bin size of 150. Fit parameters are μ1 = 568 a.u., σ1 = 67, μ2 = 873 a.u., and σ2 = 342. In both cases fits to more than two Gaussians did not converge. (C) Identifying modes by KDE. A histogram with bin size 50 is plotted for the purpose of visual comparison but is not used for fitting. Smooth curve is the estimation of multiple Gaussians (kernels) by KDE.
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Figure 5: Comparing methods of fitting photobleaching step size distributions to extract unitary step size. Histograms represent step size distributions from Tdetector2 applied to simulated photobleaching data with copy number 12 and SNR = 2. The distribution is made up of 570 detected steps. (A) Fit of two Gaussian functions to the data using a bin size of 50. Fit parameters are μ1 = 510 a.u., σ1 = 55, μ2 = 836 a.u., and σ2 = 335. (B) Fit of two Gaussian functions to the data using a bin size of 150. Fit parameters are μ1 = 568 a.u., σ1 = 67, μ2 = 873 a.u., and σ2 = 342. In both cases fits to more than two Gaussians did not converge. (C) Identifying modes by KDE. A histogram with bin size 50 is plotted for the purpose of visual comparison but is not used for fitting. Smooth curve is the estimation of multiple Gaussians (kernels) by KDE.

Mentions: After identifying steps, the next task in analyzing photobleaching data is to use the identified step amplitudes to extract the amplitude of a unitary photobleaching event. The total subunit number is subsequently estimated by dividing the initial (high) fluorescence amplitudes by this quantal unit. We initially focused on results from the simulated data set shown in Figure 4A having SNR = 2 and a GFP copy number of 12. A histogram of step amplitudes predicted by the Tdetector2 algorithm suggests the presence of at least two modes (Figure 5A). The simplest method of estimating the unitary step size is to fit the binned histogram data with multiple Gaussian functions corresponding to the different modes. However, estimation by this method is strongly dependent on bin size (Figure 5, A and B), and there are no existing objective methods for identifying the optimal bin size.


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)

Comparing methods of fitting photobleaching step size distributions to extract unitary step size. Histograms represent step size distributions from Tdetector2 applied to simulated photobleaching data with copy number 12 and SNR = 2. The distribution is made up of 570 detected steps. (A) Fit of two Gaussian functions to the data using a bin size of 50. Fit parameters are μ1 = 510 a.u., σ1 = 55, μ2 = 836 a.u., and σ2 = 335. (B) Fit of two Gaussian functions to the data using a bin size of 150. Fit parameters are μ1 = 568 a.u., σ1 = 67, μ2 = 873 a.u., and σ2 = 342. In both cases fits to more than two Gaussians did not converge. (C) Identifying modes by KDE. A histogram with bin size 50 is plotted for the purpose of visual comparison but is not used for fitting. Smooth curve is the estimation of multiple Gaussians (kernels) by KDE.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 5: Comparing methods of fitting photobleaching step size distributions to extract unitary step size. Histograms represent step size distributions from Tdetector2 applied to simulated photobleaching data with copy number 12 and SNR = 2. The distribution is made up of 570 detected steps. (A) Fit of two Gaussian functions to the data using a bin size of 50. Fit parameters are μ1 = 510 a.u., σ1 = 55, μ2 = 836 a.u., and σ2 = 335. (B) Fit of two Gaussian functions to the data using a bin size of 150. Fit parameters are μ1 = 568 a.u., σ1 = 67, μ2 = 873 a.u., and σ2 = 342. In both cases fits to more than two Gaussians did not converge. (C) Identifying modes by KDE. A histogram with bin size 50 is plotted for the purpose of visual comparison but is not used for fitting. Smooth curve is the estimation of multiple Gaussians (kernels) by KDE.
Mentions: After identifying steps, the next task in analyzing photobleaching data is to use the identified step amplitudes to extract the amplitude of a unitary photobleaching event. The total subunit number is subsequently estimated by dividing the initial (high) fluorescence amplitudes by this quantal unit. We initially focused on results from the simulated data set shown in Figure 4A having SNR = 2 and a GFP copy number of 12. A histogram of step amplitudes predicted by the Tdetector2 algorithm suggests the presence of at least two modes (Figure 5A). The simplest method of estimating the unitary step size is to fit the binned histogram data with multiple Gaussian functions corresponding to the different modes. However, estimation by this method is strongly dependent on bin size (Figure 5, A and B), and there are no existing objective methods for identifying the optimal bin size.

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