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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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Step detection algorithms. (A–C) Bdetector algorithm. (A) To fit the first step, Bdetector scans all possible change points and calculates a corresponding BIC value at each position (blue line). If the minimum BIC is lower than the BIC value for not adding a step (green line), a step is added (red line) at the position where the minimum BIC occurs. (B) Keeping the first step, Bdetector rescans all possible change points, calculates new corresponding BIC values (blue line), and adds a second step at the position of the minimum BIC (red line). This process is iteratively repeated. (C) When the minimum BIC value for adding an additional step (blue line) is not lower than the current BIC value (green line), the program terminates. (D–F) Tdetector algorithm in which, in contrast to the BIC, a higher significance for the t test indicates a better fit. (D) To add the first step, the significance at each possible change point is calculated (blue line) and is compared with the threshold (green line). Provided it is above the significance threshold, a step is added at the point of maximum significance (red line). (E) The data are split into two segments at the detected change point, and the procedure is repeated for each segment (splitting the right segment into two in this case). This process is repeated for each new segment until adding a step does result in a significance value greater than the threshold. The algorithm then moves on to another segment. (F) When adding a change point fails to raise the significance above the threshold for every segment, the program terminates.
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Figure 2: Step detection algorithms. (A–C) Bdetector algorithm. (A) To fit the first step, Bdetector scans all possible change points and calculates a corresponding BIC value at each position (blue line). If the minimum BIC is lower than the BIC value for not adding a step (green line), a step is added (red line) at the position where the minimum BIC occurs. (B) Keeping the first step, Bdetector rescans all possible change points, calculates new corresponding BIC values (blue line), and adds a second step at the position of the minimum BIC (red line). This process is iteratively repeated. (C) When the minimum BIC value for adding an additional step (blue line) is not lower than the current BIC value (green line), the program terminates. (D–F) Tdetector algorithm in which, in contrast to the BIC, a higher significance for the t test indicates a better fit. (D) To add the first step, the significance at each possible change point is calculated (blue line) and is compared with the threshold (green line). Provided it is above the significance threshold, a step is added at the point of maximum significance (red line). (E) The data are split into two segments at the detected change point, and the procedure is repeated for each segment (splitting the right segment into two in this case). This process is repeated for each new segment until adding a step does result in a significance value greater than the threshold. The algorithm then moves on to another segment. (F) When adding a change point fails to raise the significance above the threshold for every segment, the program terminates.

Mentions: Both pairs of algorithms use a conceptually similar step detection approach of iteratively searching for change points until no statistically significant step can be added (Figure 2 and Supplemental Movie S2). The algorithms are summarized as follows:


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)

Step detection algorithms. (A–C) Bdetector algorithm. (A) To fit the first step, Bdetector scans all possible change points and calculates a corresponding BIC value at each position (blue line). If the minimum BIC is lower than the BIC value for not adding a step (green line), a step is added (red line) at the position where the minimum BIC occurs. (B) Keeping the first step, Bdetector rescans all possible change points, calculates new corresponding BIC values (blue line), and adds a second step at the position of the minimum BIC (red line). This process is iteratively repeated. (C) When the minimum BIC value for adding an additional step (blue line) is not lower than the current BIC value (green line), the program terminates. (D–F) Tdetector algorithm in which, in contrast to the BIC, a higher significance for the t test indicates a better fit. (D) To add the first step, the significance at each possible change point is calculated (blue line) and is compared with the threshold (green line). Provided it is above the significance threshold, a step is added at the point of maximum significance (red line). (E) The data are split into two segments at the detected change point, and the procedure is repeated for each segment (splitting the right segment into two in this case). This process is repeated for each new segment until adding a step does result in a significance value greater than the threshold. The algorithm then moves on to another segment. (F) When adding a change point fails to raise the significance above the threshold for every segment, the program terminates.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 2: Step detection algorithms. (A–C) Bdetector algorithm. (A) To fit the first step, Bdetector scans all possible change points and calculates a corresponding BIC value at each position (blue line). If the minimum BIC is lower than the BIC value for not adding a step (green line), a step is added (red line) at the position where the minimum BIC occurs. (B) Keeping the first step, Bdetector rescans all possible change points, calculates new corresponding BIC values (blue line), and adds a second step at the position of the minimum BIC (red line). This process is iteratively repeated. (C) When the minimum BIC value for adding an additional step (blue line) is not lower than the current BIC value (green line), the program terminates. (D–F) Tdetector algorithm in which, in contrast to the BIC, a higher significance for the t test indicates a better fit. (D) To add the first step, the significance at each possible change point is calculated (blue line) and is compared with the threshold (green line). Provided it is above the significance threshold, a step is added at the point of maximum significance (red line). (E) The data are split into two segments at the detected change point, and the procedure is repeated for each segment (splitting the right segment into two in this case). This process is repeated for each new segment until adding a step does result in a significance value greater than the threshold. The algorithm then moves on to another segment. (F) When adding a change point fails to raise the significance above the threshold for every segment, the program terminates.
Mentions: Both pairs of algorithms use a conceptually similar step detection approach of iteratively searching for change points until no statistically significant step can be added (Figure 2 and Supplemental Movie S2). The algorithms are summarized as follows:

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
Related in: MedlinePlus