A large number of FRET biosensors have been engineered to study protein cleavage, protein conformation changes, local redox and pH sensing, and determining the mechanical load on force bearing proteins 4. Genetically-encoded, unimolecular FRET biosensors are particularly useful because they are generally less-toxic than cellular dyes and they can be directed to specific regions or organelles in the cell 5. Measurements of FRET in unimolecular biosensors are simplified by the fact that donor and acceptor fluorophores are expressed in the same molecule 6.
A simple ratio image of the resolved emissions of the donor and acceptor yields a non-linear FRET index that is correlated, but not equal to the FRET transfer efficiency 7. This form of FRET imaging monitors the sensitized emission of the acceptor and quenched emission of the donor, a direct measure of the transfer of energy between the donor and acceptor molecules 8.
Measuring sensitized-emission FRET SeFRET is a common technique to monitor unimolecular sensors due to the ease of capturing ratio images and the speed at which they can be acquired 9. For studies that require increased temporal resolution, measurements of SeFRET are preferred since photo toxicity and image acquisition time is minimal when compared to photo-bleaching or fluorescent lifetime imaging FLIM respectively While uncorrected ratio-images can be used to monitor relative changes in FRET, they cannot be used to quantitatively measure the transfer efficiency of FRET.
The transfer efficiency is generally the most useful parameter in FRET experiments because it is independent of the measuring equipment and can be used to estimate the distance between fluorophores in the sensor However, one of the most difficult challenges with determining the FRET efficiency using SeFRET is accurately removing spectral bleed-through also known as cross-talk Using current methods, spectral bleed-through from the direct excitation of the acceptor fluorophore cannot be removed from SeFRET images without calibration measurements requiring donor-only or acceptor-only control samples and the implementation of correction algorithms after image capture 17 , 18 , 19 , Typically it it necessary to take these calibration measurements along with every experimental data set, as the corrections are power dependent and will therefore vary as the laser output changes over time A rapid and simple method to measure spectral bleed-through in experimental samples that does not require control samples or complicated corrections would make quantitative seFRET more attractive to researchers that need fast and quantitative measurements of FRET efficiency.
Since this method relies on a curve-fitting approach, the signal to noise ratio SNR can be estimated by computing normalized residuals on each image pixel and then correlated to the error in the FRET efficiency estimate. Using the normalized residual error as an SNR metric, SensorFRET images can be thresholded by the estimated uncertainty in each pixel depending on the precision requirements of the experiment.
This method can be implemented on any type of microscope equipped with at least two excitation wavelengths and a detector with spectral resolution. The acquisition routine of this method is substantially easier to implement than other established spectral imaging methods. SensorFRET is unique from other spectral methods since no additional calibration images are required for the bleed through correction and the laser power and gain settings for each image may be adjusted independently in order to achieve the best imaging conditions. LuxFRET requires imaging of two cell cultures expressing only donor or acceptor flurophores, sRET requires imaging of two solutions with known concentrations of the donor or acceptor fluorophore, and pFRET requires imaging of a single cell culture expressing only the acceptor fluorophore, but has additional restrictions on which excitation wavelengths can be used 17 , 19 , SensorFRET, which only requires a spectral detector and two excitation sources which are more readily available than FLIM acquisition equipment and eliminates the additional complication, time, and expense associated with calibration sample preparation, provides an easy and accessible method for accurately determining the FRET efficiency, enabling FRET analysis to be utilized by a broader range of the research community.
Since both the donor and acceptor are synthesized simultaneously, the use of uni- or bi-molecular FRET sensors greatly reduces the effect of unpaired fluorophores on the fluorescence emission. Therefore, the main challenge remaining in accurately determining the FRET efficiency is separating the acceptor emission due to FRET from the acceptor emission due to direct excitation the acceptor fluorescence in the absence of the donor.
It should be noted this analysis is valid for unpaired molecules where there is excess acceptor, however invalid for FRET experiments with unpaired donors see Supplemental Note 1 for a detailed analysis. SensorFRET takes advantage of the fact that the acceptor emission due to FRET has a different dependence on excitation wavelength than the acceptor emission due to direct excitation. This allows the calculation and removal of the acceptor direct excitation term using images of the same region of interest at two different excitation wavelengths, 1 and 2.
The results of this analysis show that there are only three key parameters needed to calculate and remove the FRET Acceptor Direct Excitation and the Unpaired Acceptor contributions. This parameter can be determined from normalized excitation spectra which are readily available in the literature for commonly used fluoroscent proteins 22 allowing the FRET efficiency to be determined without any of the standards needed by other spectral intensity approaches 17 , 18 , 19 , Besides confirming the mathematical approach of SensorFRET, another goal of these simulations was to characterize the noise dependence of this method and compare it to the other spectral intensity based methods.
In order to add realistic noise to simulated pixels of a known FRET efficiency, the variability in the signal was characterized as a function of spectra amplitude. To quantify this variability, the difference was taken between each single pixel spectra and the average emission shape scaled to have equivalent intensity. In practice this scaling is determined by a least-squares fitting of the average emission shape with each single noisy pixel spectra.
Then the standard deviation of these differences, which we call the standard deviation in signal, was calculated for each emission channel Fig. The standard deviation in signal is linearly dependent on the square root of the signal intensity Fig. This type of noise dependence is known as shot or Poisson noise and is characteristic of the photomultiplier tube PMT detector Using the fit parameters from Fig. Determination of an accurate noise model. A Signal intensity and standard deviation as a function of emission wavelength in Cerulean-Traf-Amber sample.
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B Signal standard deviation vs the square root of the intensity. C comparison of experimental and simulated noisy pixels. By simulating pixels covering a range of signal to noise ratios, we determined the expected standard deviation in the measured FRET efficiency as a function of the normalized spectra fit residual Fig.
The normalized fit residual is used as a measurable metric for the signal to noise ratio SNR in the spectra because it can be calculated on a per-pixel basis. For sensorFRET, this approach is preferable to characterizing the SNR as a function of absolute intensity by apparent photons or by photon conversion factor at a constant set of detector settings, as described by Woehler et al. This is because SensorFRET, in contrast to luxFRET, can use spectra acquired at any laser and detector settings and it is therefore preferable to use a metric for the SNR that is also independent of these parameters, such as the normalized residual, rather than try to characterize the SNR vs intensity behavior for all possible detector settings.
It should be noted that these simulations depend on the fluorophore emission shape, excitation wavelengths, and the estimated gamma parameter.
Bleed-Through in Fluorescence Imaging
The same simulated input spectra used to form Fig. These are plotted in Fig. In practice SensorFRET will likely outperform the compared methods since the laser excitation and detector gain can be adjusted on an image-by-image basis. Most biological samples exhibit large variance in the expression level of FRET sensors 27 , therefore optimizing the excitation power and gain for a given measurement will improve the signal-to-noise ratio for observed concentrations that deviate from calibration measurements.
No appreciable difference between the methods is observed. A FRET efficiency vs normalized fit residual for simulated pixels pixels at 20 signal to noise ratios. B Median estimate for each of the methods as a function of residual. C Standard deviation for each of the methods as a function of residual. The data is packaged in both matlab and python format and organized as described in the supplemental material. Furthermore, the spectra and lifetime of these dyes are well characterized, therefore any instrumental or methodological errors may be readily identified in measurements.
Donor, Acceptor, and FRET components used for linear unmixing were generated from the normalized excitation and emission spectra as shown in C , D , and E , respectively. Using linear unmixing, the normalized EEM shown in Fig. The published lifetime for Fluorescein at high pH is 4. Means of the lifetime distributions are shown as vertical dotted lines corresponding to the histogram color with averages printed in the legend.
Using spectra from the EEM of the dye mixture Fig. These paired excitation matrices reveal that all spectral FRET algorithms fail at the red-end of the spectrum where the donor magnitude is undetectable above noise at both excitation frequencies. Unexpectedly, all algorithms failed at excitation frequencies with large signal to noise ratios in a symmetric pattern with respect to the identity diagonal Fig. Excitation Pairing Matrices: Excitation-emission recordings shown in Fig. Every possible paired input spectra were used to output an estimated FRET efficiency that is color coded in each matrix.
All algorithms fail at the red-edge of the spectrum where donor signal was absent or where gamma is near unity. Finally, the normalized fit residual calculated for each pixel is used to determine the expected error in the FRET efficiency on a per pixel basis Fig. Notice that the measured FRET efficiency is uncorrelated with the measured intensity, which indicates that there is no appreciable intermolecular FRET contribution Fig. The predicted standard deviation is inversely correlated with the spectra intensity which is expected because the signal to noise ratio of the spectra increases as the intensity increases, due to the Poissonian nature of the noise.
Any acquisition parameter change which improves the signal to noise eg. Blurring was used simply as a convenient method of improving the spectral signal to noise on otherwise identical datasets. Row A shows the peak intensity of the spectra for each pixel irrespective of wavelength. Row C shows the expected standard deviation of the FRET efficiency for each pixel based on the residual. In their characterization of these same C32V standards, Koushik et al. Since the C32V FRET construct should have a spatially uniform efficiency, we are able to aggregate pixels to determine the standard deviation as a function of the normalized fit residual as shown in Fig.
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There is strong agreement with the simulated standard deviation where there are a large number of pixels at that residual level to estimate the experimental standard deviation Fig. The curves diverge at the tails of the pixel histograms where there are much fewer pixels to calculate the standard deviation. Comparison of experimental efficiency error to simulated behavior. B Comparison of experimental and simulated standard deviations showing strong agreement between the two.
The experimental standard deviation estimates are only valid where there is a significant number of pixels to estimate it with so corresponding histograms are provided on the secondary axis.
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Although biosensors based on unimolecular FRET constructs have a wide range of applications, they have been shown to be particularly useful in understanding how cellular forces affect biological processes 12 , 14 , 31 , 32 , The first of these, denoted TL for tailless, generates TV40 labeled E-cadherin proteins which cannot attach to the rest of the cytoskeleton, preventing any stress from being applied to the sensor. The second of these, denoted TS for tension sensor, function similarly to endogenous E-cadherin but transfers load through the FRET construct, allowing any decrease in FRET efficiency relative to the TL sample to be interpreted as increased force on the E-cadherin proteins.
Since the FRET efficiency is not spatially uniform in contrast to the Cerulean Venus FRET standards , in order to determine the expected standard deviation, residual vs standard deviation curves analogous to Fig. Interpolation of these curves allows the expected standard deviation to be determined for any given pixel as a function of the measured FRET efficiency and normalized fit residual, as shown in Fig. It is clear from these plots that the blurring procedure does not affect the measured FRET efficiency on average, but significantly reduces the standard deviation of the individual pixel measurements.
One of the main advantages of this approach is that it allows the user to quantify their measurement error and reduce it using blurring or other filtering methods until it reaches a level which is acceptable for their particular experimental requirements at a cost of reduced spatial resolution. In this particular application, we aimed to reduce the FRET pixel standard deviation below 0. The blurring was increased until this condition was met, finally requiring a 5 pixel gaussian blur as shown in Fig. This noise characterization is particularly useful for determining whether the pixel to pixel variance in FRET is due to measurement uncertainty derived from the instrumentation or whether the variance results from real changes in the distance or orientation of the donor-acceptor pair.
A — E show analysis for the raw data while F — J show analysis results for the same data after a 5 pixel Gaussian blur. In order to convert the measured FRET efficiency to force, the mechanical response of the peptide chain linking the two fluorophores must be known. For the particular peptide linker used in the TV40 FRET construct, the FRET efficiency vs load behavior was characterized in a previous study using optical tweezers to apply loads to single molecules 12 , Because the Cy3 and Cy5 fluorophores used by Grashoff et al.
This force estimate is in agreement with measurements of E-cadherin-TS by Borghi et al The force may also be calculated on a per pixel basis, as shown in Fig. Drawing strong conclusions about differences in force between regions within a single image is challenging when using the raw data set due to the large amount of variance, shown in the inset boxplots of Fig. In the blurred image, however, the variance in the same regions is much less than the difference in the mean observed inset boxplot of Fig.
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- Crosstalk or bleedthrough;
In this work, we demonstrate the SensorFRET analysis approach allows simultaneous measurements of spectral bleed-through and FRET efficiency on a per-pixel basis using spectral imaging microscopy. SensorFRET does not require single fluorophore references as long as the normalized excitation and emission spectra of the sensor fluorophores are known.
What does bleed-through mean?
The cell environment and cell type will affect the autofluorescence contribution to the measured spectra and any additional fluorescent labels will also contribute to the signal. As the magnitude of these contributions is variable with respect to the sensor expression, the most appropriate way to account for these effects is to measure the normalized emission spectra using a cell culture with no labels for the autofluorescence and single label controls for any additional labels and unmix these components along with the donor and acceptor.
If the emission spectra from autofluorescence or additional labels are similar to donor or acceptor spectra, this can add uncertainty to the unmixing process and reduce the accuracy of the FRET measurement. If the cellular environment pH,redox, etc. In theory, any FRET pair and excitation wavelengths could be used with this method, however, excitation wavelengths must be chosen such that sufficient donor fluorophore brightness can be achieved at both wavelengths to improve signal to noise.
We also demonstrate how this FRET method and noise model can be used to measure piconewton scale forces on the force bearing cell junction molecule E-cadherin in MDCK epithelial cells. Applying a 5-pixel radius gaussian blur reduced the standard deviation to 0. Estimating pixel-wise FRET efficiency error by use of literature spectra may bias these estimates if the spectral detector is improperly calibrated or gamma is shifted in the cellular environment.
By greatly simplifying the experimental requirements for quantitative FRET determination, the SensorFRET approach allows this nano characterization technique to be accessible to a much broader range of the research community. Simulations of FRET spectra were created using the ipython notebook. Idealized FRET spectra were simulated using parameters available in the literature. The normalized emission spectra, excitation spectra and quantum efficiencies were all readily available in the literature for both 1 photon 23 and 2 photon 20 excitation.
Values for the intensity, concentration and excitation spectra at and were chosen such that the spectral shape at a given FRET efficiency matches what is observed experimentally ie.
Image overlay background bleed through
For the luxFRET, sRET, and pFRET methods it was also necessary to simulate single fluorophore spectra for the calibration processes required by each of these analysis approaches using the following equation. Media were changed every other day. Venus plasmid was a gift from Michael Davidson Venus received from addgene depository. DNA plasmids were transfected into cells using Lipofectamine or Life Technologies per manufacturer instructions. In all experiments cells were allowed to adhere to fibronectin coated glass bottom dishes or coverslips overnight before imaging.
Live cells expressing either soluble CeruleanAmber or Venus were imaged in spectral mode using a channel spectral META detector to record spectral fingerprints of Cerulean and Venus fluorophores respectively. Working solutions were transferred to a 3. Slit widths for the excitation and emission were set to 5 and 2. All measurements were averaged for 0. All data analysis was performed in the ipython notebook where the mixed spectra were deconvolved using the non-negative least squares nnls scientific python package. To capture photons from fluorescein-only an HQ—50m dichroic filter was used.
The fitting shift had to be manually fixed to value that minimized the Chi-square statistic. The offset was manually fixed to 0. Lakowicz, J. Fluorescence anisotropy. In Principles of Fluorescence Spectroscopy , — Springer, Olenych, S. The fluorescent protein color palette. Current Protocols in Cell Biology Aoki, K. Hochreiter, B. Fluorescent proteins as genetically encoded FRET biosensors in life sciences.
Sensors 15 , — Ettinger, A. Learn more. First 10 Free. Div with being bleed through with content underneath?
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