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. 2019 Mar 15:157:28-41.
doi: 10.1016/j.ymeth.2018.09.008. Epub 2018 Sep 28.

Protein oligomerization and mobility within the nuclear envelope evaluated by the time-shifted mean-segmented Q factor

Jared Hennen  1 , Kwang-Ho Hur  2 , Siddarth Reddy Karuka  1 , G W Gant Luxton  3 , Joachim D Mueller  4
Affiliations

Protein oligomerization and mobility within the nuclear envelope evaluated by the time-shifted mean-segmented Q factor

Jared Hennen et al. Methods. .

Abstract

Analysis of fluorescence fluctuation experiments by the mean-segmented Q (MSQ) method was recently used to successfully characterize the oligomeric state and mobility of proteins within the nuclear envelope (NE) of living cells. However, two significant shortcomings of MSQ were recognized. Non-ideal detector behavior due to dead-time and afterpulsing as well as the lack of error analysis currently limit the potential of MSQ. This paper presents time-shifted MSQ (tsMSQ), a new formulation of MSQ that is robust with respect to dead-time and afterpulsing. In addition, a protocol for performing error analysis on tsMSQ data is introduced to assess the quality of fit models and estimate the uncertainties of fit parameters. Together, these developments significantly simplify and improve the analysis of fluorescence fluctuation data taken within the NE. To demonstrate these new developments, tsMSQ was used to characterize the oligomeric state and mobility of the luminal domains of two inner nuclear membrane SUN proteins. The results for the luminal domain of SUN2 obtained through tsMSQ without correction for non-ideal detector effects agree with a recent study that was conducted using the original MSQ formulation. Finally, tsMSQ was applied to characterize the oligomeric state and mobility of the luminal domain of the germline-restricted SUN3.

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Conflict of interest statement

Declarations of interest: none

Figures

Fig. 1.
Fig. 1.. Brightness b identifies the average oligomeric state of a fluorescently labeled protein complex.
Monomers and dimers are characterized by b of 1 and 2, respectively. The brightness of a mixture of monomers and dimers is between 1 and 2 depending on the composition of the mixture.
Fig. 2.
Fig. 2.. Illustration of FFS in the cytoplasm.
A) The PSF (red oval) is completely embedded within the cytoplasm. B) The entire volume of the PSF is accessible to the fluorescent proteins. Therefore the OV is equivalent to the PSF volume. C) The fluorescence signal fluctuates in time reflecting the passage of proteins through the OV. D) The brightness titration curve of a FP-labeled monomer is independent of concentration (dashed line). A labeled protein that forms dimers leads to a concentration-dependent brightness titration curve (solid line) that increases from 1 to 2. The x-axis depicts either molar concentration c or the occupation number N.
Fig. 3.
Fig. 3.. FFS within the NE.
A) The PSF (red oval) is not fully embedded within the NE. B) The OV (hatched area) reflects the overlap between the PSF and the NE.
Fig. 4.
Fig. 4.. Construction of MSQ and tsMSQ curves.
A) The recorded fluorescence intensity signal is divided into segments of period T (top panel). Q or tsQ1 is calculated for each of these segments (bottom panel). B) The Q-values for the segment time T are converted by an algorithm into an MSQ(T) value. Similarly, the tsQ1 values are converted into a tsMSQ(T) value. Repeating this procedure for a range of segment times identifies the experimental MSQ or tsMSQ curve.
Fig. 5.
Fig. 5.. Results of MSQ and tsMSQ analysis of FFS data collected for EGFP and SS-EGFP within the cytoplasm and NE, respectively.
A – D) Results based on MSQ analysis include biases. E – H) Results based on reanalysis by tsMSQ removes bias. A) MSQ curves from EGFP expressing cells with low (black circles), medium (red squares), and high (blue triangles) intensities with fits (dashed lines). B) Biased b from MSQ vs. intensity for EGFP expressing cells (n = 17) with a linear fit (red line) representing first-order non-ideal detector effects (Eq. (53)). C) Biased brightness from MSQ vs. intensity for SS-EGFP expressing cells (n = 13). D) Biased diffusion time from MSQ for SS-EGFP vs. intensity. E) tsMSQ curves from EGFP expressing cells with low (black circles), medium (red squares) and high (blue triangles) intensities with fits (dashed lines). F) b from tsMSQ for EGFP vs. intensity and the average brightness (grey line). G) Brightness from tsMSQ for SS-EGFP vs. intensity. H) Diffusion time from tsMSQ for SS-EGFP vs. intensity.
Fig. 6.
Fig. 6.. χ2curve fitting of tsMSQ data.
A) Illustration of tsMSQ curve construction using the same experimental data set to determine each tsMSQ point along the curve. B) Experimental tsMSQ curve from cytoplasmic EGFP with fit to Eq. (17) (red line) and residuals (bottom panel). C) Illustration of the construction of a decorrelated tsMSQ curve, where a long photon count record is split into separate experiments (top) and each point on the tsMSQ curve is calculated from a unique experiment (bottom). D) Experimental decorrelated tsMSQ curve for cytoplasmic EGFP, constructed as described in the previous panel, with fit (red) and residuals. E) Experimental decorrelated tsMSQ curve for cytoplasmic EGFP, constructed by randomly selecting from 10 experiments for each data point, with fit (red) and residuals. F) The dependence of χν2 on the number of experiments used to construct the decorrelated tsMSQ. Four cells expressing cytoplasmic EGFP were used and the average over the four cells was calculated (black circles).
Fig. 7.
Fig. 7.. tsMSQ analysis of SS-EGFP-SUN2261−731 within the NE.
A) Decorrelated tsMSQ curve with a fit to a two species diffusion model (Eq. (18)) with residuals calculated from experimental uncertainty. B) Plot of b vs. N of SS-EGFP-SUN2261−731 in the NE (n = 23 cells) with a fit to a monomer / trimer binding model (red curve). C) Diffusion times from tsMSQ fits identify a fast (black circles) and a slow (red squares) diffusing species. D) Plot of relative amplitude of the fast species to the slow species vs. b with a model of a transition from fast monomers to slow trimers (red line).
Fig. 8.
Fig. 8.. tsMSQ analysis of EGFP tagged SUN330−320 within the NE and cytoplasm.
A-C) Results from fitting tsMSQ data from SS-EGFP-SUN330−320 within the NE to a two species diffusion model. A) Plot of b vs. N for SS-EGFP-SUN330−320 measured within the NE (n = 41 cells) together with a linear fit to data (red dashed line). B) Diffusion times from two species fits of MSQ curves showing both a fast (black circles) and slow (red squares) component. C) Relative amplitude of the fast component to the slow component vs. brightness. The lines represent a monomer / trimer (solid red), monomer / tetramer (dashed green), and monomer / hexamer (dashed-dotted blue) transition. D) Plot of b vs. N for EGFP-SUN330−320 within the cytoplasm (n = 32 cells) with best fit line from NE (red dashed line) after converting N from the NE to its equivalent cytoplasmic value.
Fig. 9.
Fig. 9.. Working models for the observed behavior of the luminal domains of SUN2 and SUN3.
A) SS-EGFP-SUN2261−731 (grey) exists as either freely diffusing, luminal monomers or membrane-associated trimers, potentially due to interactions with endogenous nesprins (red lines) at the ONM. B) SS-EGFP-SUN330−320 (tan) exists as either freely diffusing, luminal monomers or membrane-associated oligomers. The size of these membrane associated oligomers has yet to be determined.
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