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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.

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Related in: MedlinePlus

Relative bias in the Q-value introduced by diffusion.Correlations in the photon counts introduced by the diffusion time τD give rise to a relative bias in the Q-estimator that depends on the segment period TS. The relative bias for diffusion times of 1 ms, 10 ms, and 100 ms is shown.
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pone.0130063.g004: Relative bias in the Q-value introduced by diffusion.Correlations in the photon counts introduced by the diffusion time τD give rise to a relative bias in the Q-estimator that depends on the segment period TS. The relative bias for diffusion times of 1 ms, 10 ms, and 100 ms is shown.

Mentions: The relative bias between the MSQ-value and the true Q-value depends on the second and third term of Eq 8. The influence of the second term on the MSQ-value is mostly negligible, since N ≥ 500 for all data shown in Fig 3, which results in a maximum relative bias of ~10% at the shortest segment length. The third term, which arises from the correlation in the photon counts, becomes more important as the ratio TS/τD decreases. The relative bias in MSQ due to the third term is and exceeds 10% once TS < 50τD (Fig 4). This result demonstrates that slow diffusing species are more prone to estimator bias than fast diffusing species. The bias in the MSQ decreases with increasing TS and disappears in the limit TS → ∞, which demonstrates that Eq 8 describes an asymptotically unbiased estimator.


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

Hur KH, Mueller JD - PLoS ONE (2015)

Relative bias in the Q-value introduced by diffusion.Correlations in the photon counts introduced by the diffusion time τD give rise to a relative bias in the Q-estimator that depends on the segment period TS. The relative bias for diffusion times of 1 ms, 10 ms, and 100 ms is shown.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130063.g004: Relative bias in the Q-value introduced by diffusion.Correlations in the photon counts introduced by the diffusion time τD give rise to a relative bias in the Q-estimator that depends on the segment period TS. The relative bias for diffusion times of 1 ms, 10 ms, and 100 ms is shown.
Mentions: The relative bias between the MSQ-value and the true Q-value depends on the second and third term of Eq 8. The influence of the second term on the MSQ-value is mostly negligible, since N ≥ 500 for all data shown in Fig 3, which results in a maximum relative bias of ~10% at the shortest segment length. The third term, which arises from the correlation in the photon counts, becomes more important as the ratio TS/τD decreases. The relative bias in MSQ due to the third term is and exceeds 10% once TS < 50τD (Fig 4). This result demonstrates that slow diffusing species are more prone to estimator bias than fast diffusing species. The bias in the MSQ decreases with increasing TS and disappears in the limit TS → ∞, which demonstrates that Eq 8 describes an asymptotically unbiased estimator.

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