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Quantitative Brightness Analysis of Fluorescence Intensity Fluctuations in E. Coli.

Hur KH, Mueller JD - PLoS ONE (2015)

Bottom Line: Photobleaching leads to a depletion of fluorophores and a reduction of the brightness of protein complexes.We applied MSQ to measure the brightness of EGFP in E. coli and compared it to solution measurements.The results obtained demonstrate the feasibility of quantifying the stoichiometry of proteins by brightness analysis in a prokaryotic cell.

View Article: PubMed Central - PubMed

Affiliation: School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota, United States of America.

ABSTRACT
The brightness measured by fluorescence fluctuation spectroscopy specifies the average stoichiometry of a labeled protein in a sample. Here we extended brightness analysis, which has been mainly applied in eukaryotic cells, to prokaryotic cells with E. coli serving as a model system. The small size of the E. coli cell introduces unique challenges for applying brightness analysis that are addressed in this work. Photobleaching leads to a depletion of fluorophores and a reduction of the brightness of protein complexes. In addition, the E. coli cell and the point spread function of the instrument only partially overlap, which influences intensity fluctuations. To address these challenges we developed MSQ analysis, which is based on the mean Q-value of segmented photon count data, and combined it with the analysis of axial scans through the E. coli cell. The MSQ method recovers brightness, concentration, and diffusion time of soluble proteins in E. coli. We applied MSQ to measure the brightness of EGFP in E. coli and compared it to solution measurements. We further used MSQ analysis to determine the oligomeric state of nuclear transport factor 2 labeled with EGFP expressed in E. coli cells. The results obtained demonstrate the feasibility of quantifying the stoichiometry of proteins by brightness analysis in a prokaryotic cell.

No MeSH data available.


Related in: MedlinePlus

Schematic representation of MSQ analysis procedure.(A) The decaying fluorescence intensity trace is divided into M segments. Each segment has a length of TS. (B) The Q-value  is calculated from the photon count data of each segment, followed by the calculation of the mean of the segmented Q-values MSQ(TS). (C) The above steps are repeated for different segment lengths to calculate MSQ as a function of TS. Conventional FFS theory predicts that MSQ is independent of the segment length (solid line). The presence of photodepletion and estimator bias introduces curvature into the MSQ-curve (triangles).
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pone.0130063.g002: Schematic representation of MSQ analysis procedure.(A) The decaying fluorescence intensity trace is divided into M segments. Each segment has a length of TS. (B) The Q-value is calculated from the photon count data of each segment, followed by the calculation of the mean of the segmented Q-values MSQ(TS). (C) The above steps are repeated for different segment lengths to calculate MSQ as a function of TS. Conventional FFS theory predicts that MSQ is independent of the segment length (solid line). The presence of photodepletion and estimator bias introduces curvature into the MSQ-curve (triangles).

Mentions: When we performed an FFS experiment in E. coli expressing EGFP, the fluorescence intensity F(t) was not stationary as in the U2OS cell, but decayed exponentially (Fig 1A) from an initial intensity F0,F(t)=F0exp(−kDt),(5)because photobleaching within the very small volume of the bacterium leads to photodepletion with a rate coefficient kD. Because the decaying signal is non-stationary, applying Eq 3, which is based on conventional FFS theory, can result in strongly biased brightness values [8]. SBA theory was introduced to circumvent this bias by dividing the intensity trace into segments (Fig 2A) short enough that the intensity decay per segment is negligible [8]. This process leads to quasistationary data within a segment provided that the segment time TS does not exceed a limit TS,limit, which is determined by SBA theory from the intensity decay curve. SBA calculates the unbiased brightness λ from the segmented FFS data as previously demonstrated [8]. Applying SBA analysis to the E. coli data of Fig 1A determined a very short limit (TS,limit = 0.2 s), which reflects the relatively fast intensity decay within the bacterium. To test the SBA model for such short data sections, we calculated the brightness for segment times of 0.2 s, 0.05 s, and 0.025 s and recovered 1.78, 1.49, and 1.21 kcps, respectively. Instead of recovering the same value as expected from SBA theory, we observed a decrease in brightness at shorter segment times. This result demonstrated that SBA analysis is not suitable for E. coli samples.


Quantitative Brightness Analysis of Fluorescence Intensity Fluctuations in E. Coli.

Hur KH, Mueller JD - PLoS ONE (2015)

Schematic representation of MSQ analysis procedure.(A) The decaying fluorescence intensity trace is divided into M segments. Each segment has a length of TS. (B) The Q-value  is calculated from the photon count data of each segment, followed by the calculation of the mean of the segmented Q-values MSQ(TS). (C) The above steps are repeated for different segment lengths to calculate MSQ as a function of TS. Conventional FFS theory predicts that MSQ is independent of the segment length (solid line). The presence of photodepletion and estimator bias introduces curvature into the MSQ-curve (triangles).
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4476568&req=5

pone.0130063.g002: Schematic representation of MSQ analysis procedure.(A) The decaying fluorescence intensity trace is divided into M segments. Each segment has a length of TS. (B) The Q-value is calculated from the photon count data of each segment, followed by the calculation of the mean of the segmented Q-values MSQ(TS). (C) The above steps are repeated for different segment lengths to calculate MSQ as a function of TS. Conventional FFS theory predicts that MSQ is independent of the segment length (solid line). The presence of photodepletion and estimator bias introduces curvature into the MSQ-curve (triangles).
Mentions: When we performed an FFS experiment in E. coli expressing EGFP, the fluorescence intensity F(t) was not stationary as in the U2OS cell, but decayed exponentially (Fig 1A) from an initial intensity F0,F(t)=F0exp(−kDt),(5)because photobleaching within the very small volume of the bacterium leads to photodepletion with a rate coefficient kD. Because the decaying signal is non-stationary, applying Eq 3, which is based on conventional FFS theory, can result in strongly biased brightness values [8]. SBA theory was introduced to circumvent this bias by dividing the intensity trace into segments (Fig 2A) short enough that the intensity decay per segment is negligible [8]. This process leads to quasistationary data within a segment provided that the segment time TS does not exceed a limit TS,limit, which is determined by SBA theory from the intensity decay curve. SBA calculates the unbiased brightness λ from the segmented FFS data as previously demonstrated [8]. Applying SBA analysis to the E. coli data of Fig 1A determined a very short limit (TS,limit = 0.2 s), which reflects the relatively fast intensity decay within the bacterium. To test the SBA model for such short data sections, we calculated the brightness for segment times of 0.2 s, 0.05 s, and 0.025 s and recovered 1.78, 1.49, and 1.21 kcps, respectively. Instead of recovering the same value as expected from SBA theory, we observed a decrease in brightness at shorter segment times. This result demonstrated that SBA analysis is not suitable for E. coli samples.

Bottom Line: Photobleaching leads to a depletion of fluorophores and a reduction of the brightness of protein complexes.We applied MSQ to measure the brightness of EGFP in E. coli and compared it to solution measurements.The results obtained demonstrate the feasibility of quantifying the stoichiometry of proteins by brightness analysis in a prokaryotic cell.

View Article: PubMed Central - PubMed

Affiliation: School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota, United States of America.

ABSTRACT
The brightness measured by fluorescence fluctuation spectroscopy specifies the average stoichiometry of a labeled protein in a sample. Here we extended brightness analysis, which has been mainly applied in eukaryotic cells, to prokaryotic cells with E. coli serving as a model system. The small size of the E. coli cell introduces unique challenges for applying brightness analysis that are addressed in this work. Photobleaching leads to a depletion of fluorophores and a reduction of the brightness of protein complexes. In addition, the E. coli cell and the point spread function of the instrument only partially overlap, which influences intensity fluctuations. To address these challenges we developed MSQ analysis, which is based on the mean Q-value of segmented photon count data, and combined it with the analysis of axial scans through the E. coli cell. The MSQ method recovers brightness, concentration, and diffusion time of soluble proteins in E. coli. We applied MSQ to measure the brightness of EGFP in E. coli and compared it to solution measurements. We further used MSQ analysis to determine the oligomeric state of nuclear transport factor 2 labeled with EGFP expressed in E. coli cells. The results obtained demonstrate the feasibility of quantifying the stoichiometry of proteins by brightness analysis in a prokaryotic cell.

No MeSH data available.


Related in: MedlinePlus