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Local and global analysis of endocytic patch dynamics in fission yeast using a new "temporal superresolution" realignment method.

Berro J, Pollard TD - Mol. Biol. Cell (2014)

Bottom Line: These methods allowed us to extract new information about endocytic actin patches in wild-type cells from measurements of the fluorescence of fimbrin-mEGFP.We show that the time course of actin assembly and disassembly varies <600 ms between patches.Our methods also show that the number of patches in fission yeast is proportional to cell length and that the variability in the repartition of patches between the tips of interphase cells has been underestimated.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular, Cellular and Developmental Biology Department of Molecular Biophysics and Biochemistry Nanobiology Institute, Yale University, New Haven, CT 06520-8103 Institut Camille Jordan, UMR CNRS 5208, Université de Lyon, 69622 Villeurbanne-Cedex, France Centre de Génétique et de Physiologie Moléculaire et Cellulaire, UMR CNRS 5534, Université de Lyon, 69622 Villeurbanne-Cedex, France.

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Example of application of the continuous-alignment method. (A and B) A sinusoidal signal is measured and the data sets are realigned with (A) the discrete-alignment method on peak values or (B) the continuous-alignment method. Dots of the same color are from the same data set. (B) Inset, comparison of offsets in the original data sets with offsets estimated by the continuous-alignment method. The estimates are accurate and allow reconstruction of the original signal with a higher temporal precision than the sampling time. (C and D) Noise representing biological variability (40% Gaussian noise proportional to the data) and the measurement variability (20% white noise) was added to the sinusoidal signal used in A and B. Data were collected in 20 independent simulated experiments with sampling times of 1 s. Data are realigned with (C) the discrete-alignment method or (D) the continuous-alignment method and then averaged. (C) Discrete alignment gives average values (blue dots) and their SDs (blue lines) different from the true average (black line) and SD (gray lines) of the original signal. (D) Continuous alignment gives average values (red dots) and SDs (pink points) close to the true average (black line) and SDs (gray lines). (D) Inset, comparison of offsets in the original data sets with offsets estimate by the continuous-alignment method. The agreement is good even in the presence of a fairly large noise in the original signal and/or in its measurement. Each dot represents the offset for one data set.
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Figure 2: Example of application of the continuous-alignment method. (A and B) A sinusoidal signal is measured and the data sets are realigned with (A) the discrete-alignment method on peak values or (B) the continuous-alignment method. Dots of the same color are from the same data set. (B) Inset, comparison of offsets in the original data sets with offsets estimated by the continuous-alignment method. The estimates are accurate and allow reconstruction of the original signal with a higher temporal precision than the sampling time. (C and D) Noise representing biological variability (40% Gaussian noise proportional to the data) and the measurement variability (20% white noise) was added to the sinusoidal signal used in A and B. Data were collected in 20 independent simulated experiments with sampling times of 1 s. Data are realigned with (C) the discrete-alignment method or (D) the continuous-alignment method and then averaged. (C) Discrete alignment gives average values (blue dots) and their SDs (blue lines) different from the true average (black line) and SD (gray lines) of the original signal. (D) Continuous alignment gives average values (red dots) and SDs (pink points) close to the true average (black line) and SDs (gray lines). (D) Inset, comparison of offsets in the original data sets with offsets estimate by the continuous-alignment method. The agreement is good even in the presence of a fairly large noise in the original signal and/or in its measurement. Each dot represents the offset for one data set.

Mentions: During a typical quantitative microscopy experiment, the investigator measures several examples of similar events, aligns all the data sets, and averages them before further analysis. For example, one measures the fluorescence intensity of several independent endocytic patches over time, realigns these intensities on the same time­scale, and calculates the average and SD of these temporal data. However, in most cases, an absolute and objective time reference is lacking, and the alignment of the experimental data on one timescale can be challenging, as illustrated by sampling the wave in Figure 1A. Commonly, the experimentalist aligns the data subjectively (such that they “superimpose”) or on one characteristic time point, such as the first measured data point or the data point with the largest (or smallest) value (Figure 1B). However, these choices strongly depend on the quality of the measured fluorescence signal. These alignment methods have a temporal resolution no better than the sampling interval, at best, and introduce artificial variability in the estimated mean and the SD that does not exist in the original data (Figures 1B and 2, A and C). Indeed, the average of the aligned data includes errors from the misalignment of the data that are independent of the biological and experimental variability. To illustrate this point, we simulated 20 noisy data sets and sampled them every 1 s (Figure 2C). When we realigned these data sets on the maximum value with a 1-s resolution, the average value (blue dots) and the SD (blue lines) differed significantly from the true averages and SDs of the original data (black and gray curves).


Local and global analysis of endocytic patch dynamics in fission yeast using a new "temporal superresolution" realignment method.

Berro J, Pollard TD - Mol. Biol. Cell (2014)

Example of application of the continuous-alignment method. (A and B) A sinusoidal signal is measured and the data sets are realigned with (A) the discrete-alignment method on peak values or (B) the continuous-alignment method. Dots of the same color are from the same data set. (B) Inset, comparison of offsets in the original data sets with offsets estimated by the continuous-alignment method. The estimates are accurate and allow reconstruction of the original signal with a higher temporal precision than the sampling time. (C and D) Noise representing biological variability (40% Gaussian noise proportional to the data) and the measurement variability (20% white noise) was added to the sinusoidal signal used in A and B. Data were collected in 20 independent simulated experiments with sampling times of 1 s. Data are realigned with (C) the discrete-alignment method or (D) the continuous-alignment method and then averaged. (C) Discrete alignment gives average values (blue dots) and their SDs (blue lines) different from the true average (black line) and SD (gray lines) of the original signal. (D) Continuous alignment gives average values (red dots) and SDs (pink points) close to the true average (black line) and SDs (gray lines). (D) Inset, comparison of offsets in the original data sets with offsets estimate by the continuous-alignment method. The agreement is good even in the presence of a fairly large noise in the original signal and/or in its measurement. Each dot represents the offset for one data set.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 2: Example of application of the continuous-alignment method. (A and B) A sinusoidal signal is measured and the data sets are realigned with (A) the discrete-alignment method on peak values or (B) the continuous-alignment method. Dots of the same color are from the same data set. (B) Inset, comparison of offsets in the original data sets with offsets estimated by the continuous-alignment method. The estimates are accurate and allow reconstruction of the original signal with a higher temporal precision than the sampling time. (C and D) Noise representing biological variability (40% Gaussian noise proportional to the data) and the measurement variability (20% white noise) was added to the sinusoidal signal used in A and B. Data were collected in 20 independent simulated experiments with sampling times of 1 s. Data are realigned with (C) the discrete-alignment method or (D) the continuous-alignment method and then averaged. (C) Discrete alignment gives average values (blue dots) and their SDs (blue lines) different from the true average (black line) and SD (gray lines) of the original signal. (D) Continuous alignment gives average values (red dots) and SDs (pink points) close to the true average (black line) and SDs (gray lines). (D) Inset, comparison of offsets in the original data sets with offsets estimate by the continuous-alignment method. The agreement is good even in the presence of a fairly large noise in the original signal and/or in its measurement. Each dot represents the offset for one data set.
Mentions: During a typical quantitative microscopy experiment, the investigator measures several examples of similar events, aligns all the data sets, and averages them before further analysis. For example, one measures the fluorescence intensity of several independent endocytic patches over time, realigns these intensities on the same time­scale, and calculates the average and SD of these temporal data. However, in most cases, an absolute and objective time reference is lacking, and the alignment of the experimental data on one timescale can be challenging, as illustrated by sampling the wave in Figure 1A. Commonly, the experimentalist aligns the data subjectively (such that they “superimpose”) or on one characteristic time point, such as the first measured data point or the data point with the largest (or smallest) value (Figure 1B). However, these choices strongly depend on the quality of the measured fluorescence signal. These alignment methods have a temporal resolution no better than the sampling interval, at best, and introduce artificial variability in the estimated mean and the SD that does not exist in the original data (Figures 1B and 2, A and C). Indeed, the average of the aligned data includes errors from the misalignment of the data that are independent of the biological and experimental variability. To illustrate this point, we simulated 20 noisy data sets and sampled them every 1 s (Figure 2C). When we realigned these data sets on the maximum value with a 1-s resolution, the average value (blue dots) and the SD (blue lines) differed significantly from the true averages and SDs of the original data (black and gray curves).

Bottom Line: These methods allowed us to extract new information about endocytic actin patches in wild-type cells from measurements of the fluorescence of fimbrin-mEGFP.We show that the time course of actin assembly and disassembly varies <600 ms between patches.Our methods also show that the number of patches in fission yeast is proportional to cell length and that the variability in the repartition of patches between the tips of interphase cells has been underestimated.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular, Cellular and Developmental Biology Department of Molecular Biophysics and Biochemistry Nanobiology Institute, Yale University, New Haven, CT 06520-8103 Institut Camille Jordan, UMR CNRS 5208, Université de Lyon, 69622 Villeurbanne-Cedex, France Centre de Génétique et de Physiologie Moléculaire et Cellulaire, UMR CNRS 5534, Université de Lyon, 69622 Villeurbanne-Cedex, France.

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Related in: MedlinePlus