Due to inherent low signal-to-noise ratio (SNR) of Fӧrster resonance energy transfer (FRET) based sensors, measurement of cAMP signals has been challenging, especially in three spatial dimensions. Here, we describe a hyperspectral FRET imaging and analysis methodology that allows measurement of cAMP distribution in three spatial dimensions.
Cyclic AMP is a second messenger that is involved in a wide range of cellular and physiological activities. Several studies suggest that cAMP signals are compartmentalized, and that compartmentalization contributes to signaling specificity within the cAMP signaling pathway. The development of Fӧrster resonance energy transfer (FRET) based biosensors has furthered the ability to measure and visualize cAMP signals in cells. However, these measurements are often confined to two spatial dimensions, which may result in misinterpretation of data. To date, there have been only very limited measurements of cAMP signals in three spatial dimensions (x, y, and z), due to the technical limitations in using FRET sensors that inherently exhibit low signal to noise ratio (SNR). In addition, traditional filter-based imaging approaches are often ineffective for accurate measurement of cAMP signals in localized subcellular regions due to a range of factors, including spectral crosstalk, limited signal strength, and autofluorescence. To overcome these limitations and allow FRET-based biosensors to be used with multiple fluorophores, we have developed hyperspectral FRET imaging and analysis approaches that provide spectral specificity for calculating FRET efficiencies and the ability to spectrally separate FRET signals from confounding autofluorescence and/or signals from additional fluorescent labels. Here, we present the methodology for implementing hyperspectral FRET imaging as well as the need to construct an appropriate spectral library that is neither undersampled nor oversampled to perform spectral unmixing. While we present this methodology for measurement of three-dimensional cAMP distributions in pulmonary microvascular endothelial cells (PMVECs), this methodology could be used to study spatial distributions of cAMP in a range of cell types.
Cyclic adenosine monophosphate (cAMP) is a second messenger involved in key cellular and physiological processes including cell division, calcium influx, gene transcription, and signal transduction. A growing body of evidence suggests the existence of cAMP compartments in the cell through which signaling specificity is achieved1,2,3,4,5,6,7. Until recently, cAMP compartmentalization was inferred based upon distinct physiological or cellular effects induced by different G-coupled receptor agonists8,9,10,11. More recently, FRET based fluorescence imaging probes have provided new approaches for the direct measurement and observation of cAMP signals in a cell12,13,14.
Förster resonance energy transfer (FRET) is a physical phenomenon in which energy transfer occurs between donor and acceptor molecules in a non-radiative fashion when the molecules are in close proximity15,16. With the development of FRET based fluorescent indicators, this physical phenomenon has been used in biological applications to study protein-protein interactions17, protein co-localization18, Ca+2 signaling19, gene expression20, cell division21 and cyclic nucleotide signaling. FRET based cAMP indicators typically consist of a cAMP binding domain, a donor fluorophore and an acceptor fluorophore22. For example, the H188 cAMP sensor12,22 used in this methodology consists of a cAMP binding domain obtained from Epac, sandwiched between Turquoise (donor) and Venus (acceptor) fluorophores. At basal conditions (unbound), Turquoise and Venus are in an orientation such that FRET occurs between the fluorophores. Upon binding of cAMP to the binding domain, a conformational change occurs such that Turquoise and Venus move apart resulting in a decrease in FRET.
FRET based imaging approaches offer a promising tool for investigating and visualizing cAMP signals within a cell. However, current FRET based microscopic imaging techniques are often only partially successful in achieving sufficient signal strength to measure FRET with subcellular spatial clarity. This is due to several factors, including the limited signal strength of many FRET reporters, the high level of precision required to accurately quantify changes in FRET efficiency, and the presence of confounding factors, such as cellular autofluorescence23,24. The result is often a FRET image that is plagued by weak SNR, making visualization of subcellular changes in FRET very difficult. In addition, investigation of spatially localized cAMP signals has been almost exclusively performed in only two spatial dimensions and the axial cAMP distribution has been rarely considered25. This is likely because low SNR impeded the ability to measure and visualize cAMP gradients in three spatial dimensions. To overcome limitations of using FRET sensors with low SNR, we have implemented hyperspectral imaging and analysis approaches to measure FRET in single cells25,26,27.
Hyperspectral imaging approaches were developed by NASA to differentiate terrestrial objects present in satellite images28,29. These techniques have since been translated to the fluorescence microscopy field30, with several commercial confocal microscope systems offering spectral detectors. In traditional (non-spectral) fluorescence imaging, the sample is excited using a band-pass filter or a laser line, and the emission is collected using a second band-pass filter, often selected to match the peak emission wavelength of the fluorophore(s). By contrast, hyperspectral imaging approaches seek to sample a complete spectral profile of either the fluorescence emission26,31,32 or excitation33,34 at specific wavelength intervals. In our previous studies, we showed that hyperspectral imaging and analysis approaches can offer improved quantification of FRET signals in cells when compared to traditional filter-based FRET imaging techniques26. Here, we present a methodology for performing 4-dimensional (x, y, z, and λ) hyperspectral FRET imaging and analysis to measure and visualize cAMP distributions in three spatial dimensions. These approaches have allowed visualization of agonist-induced cAMP spatial gradients in single cells25. Interestingly, depending on the agonist, cAMP gradients may be apparent in cells. The methodology presented here utilizes spectral unmixing of non-uniform background and cellular autofluorescence to improve the accuracy of the FRET measurements. While this methodology is demonstrated in pulmonary microvascular endothelial cells (PMVECs) using a cAMP FRET biosensor, the methodology could easily be modified for use with alternative FRET reporters or alternative cell lines.
This protocol follows procedures approved by the University of South Alabama Institutional Animal Care and Use Committee.
1. Cell, sample, and reagent preparation for imaging
2. Image acquisition
3. Image analysis
NOTE: These images will be used to construct a spectral library containing the pure spectra of all individual endmembers present in the study. The endmembers in the spectral library might vary from study to study if different fluorophores are used. A detailed procedure to construct the spectral library is provided in a supplemental information file named “Supplemental File_Spectral Library”. Here, we describe exporting data to .tiff files, linear spectral unmixing, FRET efficiency measurements, three-dimensional reconstruction, and cAMP levels estimation. Image analysis can be performed using different image analysis and programming platforms such as ImageJ, Python, MATLAB, or CellProfiler. In these studies, MATLAB scripts were used.
4. Mapping FRET efficiency to cAMP levels
This protocol describes the use of hyperspectral FRET imaging and analysis approaches to measure cAMP gradients in three spatial dimensions in living cells. There are several key steps involved in generating these results, for which careful attention is required while analyzing and quantifying the data. These key steps include construction of an appropriate spectral library, background spectral unmixing, thresholding to identify cell borders, and FRET efficiency calculations. Figure 1 illustrates the schematic flow of all the steps involved in measuring FRET efficiency and cAMP levels in living cells. When performed properly, these imaging and analysis steps will allow measurement of FRET efficiency and estimation of cAMP spatial gradients in 3 dimensions in a cell, while accounting for non-uniform background signals.
Figure 2 depicts 3-dimensional views of false-colored raw hyperspectral image data acquired using a Nikon A1R confocal microscope at baseline conditions (Figure 2A) and 10 minutes after (Figure 2B) forskolin treatment. Note that similar detector and system parameters were used to acquire before and after treatment image stacks to maintain consistency for quantitative analysis. Also note that changes in FRET are not obvious in this image, as this is purely a visualization of raw data, before calculating the FRET efficiency.
A spectral library with pure spectra of all end members is needed to further analyze raw spectral image data. Constructing an appropriate spectral library is one of the key steps to ensure appropriate measurements of FRET efficiency. Figure 3 demonstrates the construction of a spectral library containing the pure spectra of endmembers (in these current studies, the endmembers are Turquoise, Venus, and DRAQ5). To measure FRET efficiency, it is important to obtain the spectra of Turquoise and Venus using a sample with 1:1 stoichiometry. Here, we have provided an approach where the acceptor fluorophore is completely photo-destructed, allowing spectral signatures of the donor and acceptor with 1:1 stoichiometry to be obtained (Figure 3A-F). In addition, linear power relationships among the lasers (Supplemental Figure 1) were applied to calculate the acceptor spectrum with an intensity that would be expected if it were excited using the donor excitation laser, in this case 405 nm for Turquoise (Figure 3G). This ensures that unmixed donor and acceptor signals are comparable in absolute intensity when FRET is excited with a single 405 nm laser line. Non-transfected cells labeled with the nuclear dye, DRAQ5 (Figure 3H) were utilized to obtain the pure spectrum of DRAQ5 (Figure 3I). Combining the spectra of the donor, Turquoise (Figure 3F), acceptor, Venus (Figure 3G), and DRAQ5 (Figure 3I), a 3-component library was created (Figure 3J).
Signals from sources other than the fluorescent labels may also be present in a sample. To account for these, three different spectral signatures with peaks that occur at 424 nm, 504 nm, and 574 nm were identified within unlabeled cellular samples. We believe that these spectral signatures correspond to coverslip reflectance and cell matrix or cellular autofluorescence. Figure 4 depicts the sources of these three background spectral signatures. It is important to note that these signals are distributed non-uniformly within the sample and hence cannot simply be subtracted out. However, adding the spectral signatures of these signals to the spectral library and using linear unmixing to separate the signals presents an approach for removing these confounding signals from the donor and acceptor signals prior to calculating FRET efficiencies. To achieve this, the three background spectral signatures were added to the 3-component spectral library, forming a new 6-component library consisting of donor (Turquoise), acceptor (Venus), DRAQ5 and three background spectral signatures.
A custom programming script was written to unmix the spectral image data into individual endmembers, and a separate script was written to perform subsequent FRET efficiency calculations. Linear spectral unmixing (illustrated in Figure 5) was performed using the 6-component spectral library. Unmixing was performed for each slice in the axial image stack, also referred to as z-stack (refer to the 3-dimensional visualization of raw spectral image data in Figure 5A). This resulted in separate unmixed images for each endmember, for each z-slice in the z-stack (Figure 5C–E, background unmixed images are not shown). If desired, the unmixed signals may be false-colored for visualization with a different color assigned to each unmixed signal (Figure 5F-H).
Figure 6 illustrates the steps involved in the FRET efficiency calculations, as well as the steps for mapping FRET efficiency to cAMP levels. A FRET efficiency image was generated using smoothed unmixed donor and acceptor images. A binary mask image was obtained using unmixed donor, acceptor, and nuclear images. The mask was applied to the FRET efficiency image to remove contributions from pixels outside of the cell. Though unmixed images from single z-slices were used for the pictorial demonstration of FRET efficiency measurements, these calculations were performed on each slice within the 3-dimensional image stack. The 3-dimensional FRET image data set was then resliced in three orthogonal planes to visualize spatial gradients of cAMP signals in different directions.
Visualizing agonist-induced changes in FRET efficiency and cAMP levels
The steps described above provide a method for calculating FRET efficiency and cAMP levels from hyperspectral image data in three spatial dimensions. These steps can be applied to cellular preparations before and after treatment with compounds that elicit a cAMP response, such as forskolin. Here, we provide an example of using this approach to observe changes in FRET and cAMP distribution in PMVECs following treatment with 50 µM forskolin. Figure 7 illustrates the changes in FRET efficiency and cAMP levels in different XY plane slices (z slices), from apical to basal, allowing comparison of baseline conditions (before forskolin treatment) and 10-minutes post-treatment. In this illustrative example, FRET efficiency decreased (columns 1 and 3 in Figure 7) upon forskolin treatment, correlating to an increase in cAMP levels (columns 2 and 4 in Figure 7). Minimal spatial variation of cAMP within a single XY plane was observed. However, axial (apical-to-basal) cAMP spatial gradients were observed, as can be surmised by noting that the apical slice has a deeper red color (indicating higher cAMP levels through the color lookup table that was applied) than the basal slice after forskolin treatment (column 4 in Figure 7). Axial FRET or cAMP distributions can often be better visualized using images obtained from the two orthogonal spatial planes: Figure 8 depicts the FRET efficiency and cAMP levels in the YZ plane at baseline (columns 1 and 2) and 10 minutes after forskolin treatment (columns 3 and 4), while Supplemental Figure 2 depicts the FRET efficiency and cAMP level changes in the XZ plane. These results demonstrate the feasibility of measuring FRET and estimating cAMP levels from 3-dimensional hyperspectral image data and also demonstrate the importance of visualizing axial distributions of FRET or cAMP. While beyond the scope of this methodological paper, it may be that axial spatial distributions of cyclic nucleotides contribute to specificity within cyclic nucleotide signaling pathways.
When performing the methods described above, it is important to meticulously check the accuracy of the steps and to run appropriate experimental and vehicle controls to ensure that changes in FRET (and corresponding cAMP) are due to actual changes in donor and acceptor signals and are not imaging artifacts. For example, important steps to consider include:
Using an appropriate spectral library that contains all of the needed spectral components
As mentioned, there can be significant contribution of background signals from cellular autofluorescence, deposited matrix, or reflected light from the coverslip (excitation-emission bleed through). Figure 9 represents an example data set illustrating that the background signal was retained within the images when a 3-component (Turquoise, Venus, and DRAQ5) library was used to unmix the spectral data. By contrast, the background signal was effectively removed when a more complete (and appropriate) 6-component spectral library was used.
Using an optimal threshold value to generate a cell mask
In many of the experiments that we have performed, the background signal intensity is approximately 50-60% of the FRET signal intensity that is present within the raw spectral image data (i.e., when visualizing unprocessed spectral image data the peak of the FRET signal is only ~2X higher than that of the surrounding background signal). Thus, separation of background signal from foreground signal using a threshold value to obtain a binary mask is a sensitive step and must be carefully performed in order to avoid analysis artifacts. Figure 10 illustrates the effect of different threshold values applied for cell segmentation to create a binary mask of the cell. A low threshold value may include background signal as part of the expressing cell. On other hand, an overly high threshold value may preclude measurement of FRET in low-expressing cells or regions of a cell with low donor+acceptor signal (either very thin portions of the cell or portions that may have a lower regional concentration of the FRET probe).
Selecting a transfected cell with donor+acceptor signal intensity ≥ background signal
Figure 11 illustrates an example data set where FRET efficiencies and corresponding levels were measured from unmixed images obtained using a 6-component spectral library for linear spectral unmixing. Despite using the 6-component library to unmix spectral image data and selecting a high threshold for creating the cell border and nuclear mask, FRET efficiency images were still prone to high background noise signal near the basal side of the cell. In this case, the presence of background noise was due to selection of a cell that was only weakly expressing the FRET probe, and the donor+acceptor signal strength was approximately equal to the background signal strength, even after unmixing. Thus, in addition to applying the sophisticated analysis steps described above, it is also important to select a cell with sufficient expression of the FRET probe and correspondingly sufficient FRET signal (donor and acceptor signal at least equal or above the noise signal) during acquisition in order to ensure high-quality results.
Figure 1: Flowchart depicting the steps involved in FRET efficiency and level measurements in three spatial dimensions using hyperspectral FRET imaging and analysis. Please click here to view a larger version of this figure.
Figure 2: A 3-dimensional visualization of a cell expressing the cAMP FRET reporter (green) and nuclei from the cell and surrounding non-expressing cells (red). Raw spectral image data acquired using a Nikon A1R spectral confocal microscope have been false colored according to wavelength and visualized in 3 dimensions using NIS Elements software. A) 3-dimensional image data at baseline and B) 10 minutes after 50 μM forskolin treatment. Please click here to view a larger version of this figure.
Figure 3: Construction of a spectral library containing the pure spectra of fluorescent labels in the study (referred to as endmembers by the remote sensing field). A) A false-colored image of cells expressing the FRET biosensor acquired at 405 nm excitation (donor excitation). B) Spectrum corresponding to FRET signal: 4 different regions of interest (ROIs) were drawn on image A shown by red, yellow, blue and green rectangles. Average intensity at each wavelength for each ROI selected was exported. The average intensity from these 4 ROIs was plotted at each emission wavelength. The emission peaks of the spectrum correspond to both the donor (Turquoise) and the acceptor (Venus) fluorophores. C) A false-colored image of cells expressing the FRET biosensor acquired at 488 nm excitation (acceptor excitation). D) The average spectrum was estimated as explained in B from several ROIs in C (the same regions as in A) with emission peak due only to the acceptor. E) A false-colored image of cells expressing the FRET biosensor acquired at 405 nm excitation after photo-destruction of the acceptor fluorophore by 514 nm irradiation. F) The average spectrum was estimated as explained in B from several ROIs in E (the same regions as A) with emission peak due only to the donor. NOTE that the donor intensity is increased in F when compared to that of the original FRET signal, indicating that after photobleaching of the acceptor, donor excitation is leading to direct donor emission. G) The acceptor spectrum as would be expected if obtained using 405 nm excitation. This was estimated by utilizing the facts that the 405 nm and 488 nm excitation lasers have a linear response which can be characterized using a spectrometer and integrating sphere (Supplemental Figure 1) and the wavelength-dependent extinction coefficient of Venus. Hence, the Venus spectrum obtained at 488 nm excitation can be converted into the Venus spectrum that would be expected if obtained at 405 nm excitation H) A false-colored image of cells labeled with the nuclear label, DRAQ5. I) The average spectrum from several regions of interest in H. J) The resultant spectral library containing the normalized pure spectra of the donor and DRAQ5 and the excitation wavelength–corrected spectrum of the acceptor. Note that Turquoise and Venus spectra were normalized to the maximum value of combined Turquoise + Venus spectral data while DRAQ5 was simply normalized to unity at the value of the peak emission wavelength. Please click here to view a larger version of this figure.
Figure 4: Background spectral signatures were identified and included into the spectral library to account for background fluorescence signals during the unmixing process. A, B) Two background fluorescence spectral signatures were observed when characterizing a sample blank (unlabeled coverslip). These spectral signatures were named based on their peak wavelengths – one at 424 nm (from coverslip fluorescence) and the other at 504 nm (likely from reflectance or back scatter). C) A third background spectral signature was observed with a peak emission wavelength of 574 nm when non-labeled cells were analyzed, potentially from cellular autofluorescence or fluorescence of the underlying matrix. D) Background spectra extracted from images A, B, and C. These three background spectra were added to the existing 3-component library (Figure 3J) to develop a 6-component spectral library. Please click here to view a larger version of this figure.
Figure 5: Linear spectral unmixing using a 6-component spectral library that accounts for background signals. A) Raw spectral image data acquired as an axial z-stack. B) 6-component spectral library used to linearly unmix raw spectral data. C, D, and E) Gray scale unmixed images of DRAQ5, Turquoise, and Venus, respectively, resulted from linear spectral unmixing. F, G, and H) False colored unmixed images of DRAQ5, Turquoise, and Venus respectively. Unmixed images of background signals were also calculated (data not shown here as only the fluorescent labels are of interest for this methodology – refer to Figure 4 in Annamdevula, et al. for examples of unmixed background signals25). Please click here to view a larger version of this figure.
Figure 6: Flowchart depicting the steps involved in calculating FRET and cAMP levels from unmixed spectral image data. A) Representative unmixed image of donor, Turquoise. B) Representative unmixed image of acceptor, Venus. C) Representative unmixed image of nuclei, DRAQ5. D) Binary cell mask is generated by thresholding unmixed donor+acceptor summed image. E) A threshold is applied to the nuclear image to create a binary mask of the nucleus. F) The pixel wise FRET efficiency was calculated from unmixed donor and acceptor images. G) A composite binary mask was created from cell border and nuclear masks. H) Masked FRET efficiency image: composite cell mask was applied to FRET efficiency image to exclude non-specific FRET signals outside the cell and within the nucleus. I) A colormap was applied to the masked FRET efficiency image to better visualize spatial changes in FRET. J) The cAMP levels image that was estimated from the FRET efficiency image. K) Colormap was applied to better visualize spatial changes in cAMP. Colorbars shown on the right side were used to visualize FRET efficiency (top panel) and cAMP levels (bottom panel). Images shown in this figure are representative of one single axial z-slice, but the analysis operation described in this figure is performed on the entire axial z-stack. Please click here to view a larger version of this figure.
Figure 7: Forskolin-induced FRET efficiency and cAMP spatial gradients visualized in PMVECs. XY plane images were generated by reslicing 3-dimensional reconstructed FRET and cAMP image data in the axial (Z) direction from the apical to the basal side of the cell. Five contiguous XY slices are shown. Columns 1 and 3 represent the FRET efficiency at baseline and 10 minutes after treatment with 50 μM forskolin, respectively. Columns 2 and 4 indicate the levels at baseline and 10 minutes after forskolin treatment. The colorbars were used to relate changes in the colormap to FRET efficiency (top) and cAMP levels (bottom). White lines shown on images in column 2 and column 4 are used to generate the intensity profile (line scan profile) of cAMP signals across this selected line. Intensity profile plot obtained from the mid-slice of the cell at baseline (blue profile) and after forskolin treatment (green profile) demonstrates the spatial distribution of cAMP signals across the line scan. Scale bar indicates 20 µm. Please click here to view a larger version of this figure.
Figure 8: Forskolin-induced FRET efficiency and cAMP spatial gradients visualized in the axial direction. YZ plane images were generated by reslicing 3-dimensional reconstructed FRET and cAMP image data in the lateral (X) direction (from left to right side of the cell). Columns 1 and 3 represent the FRET efficiency at baseline and at 10 minutes after 50 μM forskolin treatment, respectively. Columns 2 and 4 represent the cAMP levels at baseline and at 10 minutes after forskolin treatment. The colorbars at right were used to relate changes in the colormap to FRET efficiency (top) and cAMP levels (bottom). Please click here to view a larger version of this figure.
Figure 9: A comparison of the effectiveness of utilizing either a 3-component or a 6-component spectral library for calculating FRET and cAMP levels images. Nonspecific background signals were observed in images calculated using a 3-component library, which did not account for background spectral signatures. This was especially true for images near the basal side of the cell following 50 μM forskolin treatment (column 2). By contrast, background signal artifacts were effectively removed when using a 6-component library that included background spectral signatures, improving the ability to segment the cell and analyze FRET and cAMP signals. Please click here to view a larger version of this figure.
Figure 10: Image analysis artifacts can be introduced if an appropriate threshold value is not selected to delineate the cell and nuclear borders. Unmixed donor and acceptor images were used to create a mask with three different threshold values: 0.35 (columns 1 as 2), 0.65 (columns 3 and 4) and 0.9 (columns 5 and 6). Columns 1, 3, and 5 display the FRET efficiency at baseline. Columns 2, 4, and 6 display the FRET efficiency at 10 minutes after 50 μM forskolin treatment. Selecting a threshold that was too low resulted in portions of the background, or extracellular region, being included with the cell for analysis, while selecting a threshold that is too high resulted in the loss of part of the cell (see the apical slice in columns 4-5 when compared to columns 3-4). Please click here to view a larger version of this figure.
Figure 11: Background signal artifacts may still persist even after background unmixing and selecting an appropriate threshold during creation of a binary mask. Column 1 shows representative apical, middle, and basal slices at baseline and column 2 shows the same at 10 minutes after 50 μM forskolin treatment. Despite using a 6-component library for unmixing that accounted for background signals and utilizing a higher threshold of 0.85 for binary mask generation, background regions were still identified as being part of the cell, especially after treatment with forskolin. If this occurs, a possible explanation may be that a cell with weak expression of the FRET reporter was selected for imaging. Please click here to view a larger version of this figure.
Supplemental Figure 1: Measurements of laser line intensity as a function of laser setting in the software. A fiber-coupled spectrometer and integrating sphere were calibrated to a NIST-traceable lamp to measure laser intensity for both the 405 nm laser line and the 488 nm laser line. A) Total number of photons measured at the microscope stage corresponding to different laser intensities of the 405 nm laser. B) Total number of photons measured at the microscope stage corresponding to different laser intensities of 488 nm laser. Linear response in laser intensity was observed for both the lasers as measured at the microscope stage. A linear trendline was fit and the trendline equation for each laser line was used to calculate the acceptor spectrum that would be expected if excited with the 405nm laser line (donor excitation). Please click here to download this figure.
Supplemental Figure 2: Forskolin-induced FRET efficiency and cAMP spatial gradients visualized in the axial direction. XZ plane images were generated by reslicing 3-dimensional reconstructed FRET and cAMP image data in the lateral (Y) direction (from front to back of the cell). Columns 1 and 3 represent the FRET efficiency at baseline and at 10 minutes after 50 μM forskolin treatment, respectively. Columns 2 and 4 represent the cAMP levels at baseline and at 10 minutes after forskolin treatment. The color bars at right were used to relate changes in the colormap to FRET efficiency (top) and cAMP levels (bottom). Similar to results shown for the YZ planes (Figure 8), apical to basal spatial gradients in FRET efficiency and cAMP levels may be visualized as changes in color from the top to bottom of a single YZ slice, both at baseline conditions (any given slice in columns 1-2) and after 50 µM forskolin treatment (columns 3-4). Please click here to download this figure.
Supplemental Files. Please click here to download these files.
The development of FRET biosensors has allowed the measurement and visualization of cyclic nucleotide signals in single cells, and there is great promise for visualizing subcellular signaling events13,22,37,38. However, the use of FRET biosensors presents several limitations, including the low signal-to-noise characteristics of many fluorescent protein-based FRET reporters and the weak transfection or expression efficiencies of the FRET reporters (this may be especially challenging in certain cell lines, such as PMVECs)23,24. Imaging of weakly expressed fluorescent proteins, combined with ratiometric FRET calculations, often results in a high degree of uncertainty or fluctuation in the calculated FRET efficiency. In an effort to improve the reliability of FRET measurements, we and others have previously demonstrated the use of hyperspectral imaging and analysis to measure FRET signals and to reduce crosstalk or bleed through of fluorescence signals between fluorescent labels and autofluorescence effects26,31,32,39. Due to limitations in signal strength, these spectral FRET studies were limited to two (X and Y) spatial dimensions until very recently. Hence, they provided a single-slice view of FRET changes within a cell.
In recent studies, we noted that FRET (and corresponding cAMP levels) appeared to be spatially distributed not just in the XY plane, but also across the XZ and YZ planes25. The hyperspectral FRET imaging approach described here extends our ability to visualize and measure FRET and cyclic nucleotide (cAMP) changes into three spatial dimensions, opening new possibilities for assessing signal compartmentalization. This 4-dimensional (X, Y, Z, and λ) hyperspectral imaging and analysis approach allows the measurement and visualization of cAMP gradients in three spatial dimensions while accounting for non-uniform background signals. Here, we have demonstrated how to implement this approach through the example of forskolin-induced cAMP spatial gradients. In the post-treatment image data, cAMP spatial gradients can be observed from the apical to the basal side of the cell (Figure 7 and Figure 8). The cAMP produced upon treatment with forskolin did not appear to reach cell-cell junctions (Figure 7 and Figure 8).
In utilizing the methodology described here, it was important to account for different sources of background and autofluorescence signals to allow accurate estimation of FRET efficiencies. Linear unmixing provides a potential avenue for accounting for unique background spectra, if their signatures are different than the fluorescent probes of interest. In particular, linear unmixing is better suited for separating background and autofluorescence signals than standard background subtraction.40,41,42 In the example shown here, three different spectral signatures were measured and assigned a name based on the peak emission wavelength of the signature: the 424 nm background spectrum (possibly from the coverslip fluorescence), the 504 nm background spectrum (likely due to reflected or back-scattered light), and the 574 nm background spectrum (possibly due to cell or cellular matrix autofluorescence). To illustrate the effects of failing to account for these signatures, two spectral libraries were created, and unmixed images compared. First, a spectral library containing just the fluorescent labels in the sample, Turquoise, Venus, and DRAQ5, was created and labeled the 3-component library. Second, a spectral library that additionally contained the three background spectral signatures was created and labeled the 6-component library. As shown above, unmixing with the 6-component library (background unmixing) allowed removal of background signals, whereas unmixing with the 3-component library did not (Figure 9). In previous work, we have shown that the root mean squared error (RMSE) associated with linear unmixing is decreased when using a spectral library that accounts for both the fluorescent labels and background signatures. It should be noted that the axial distribution of background fluorescence is often non-uniform and hence, a simple background subtraction will not work to correct the image data. Thus, the spectral unmixing approach is needed to provide accurate background removal and reliable FRET measurements.
It is important to optimize system parameters to select the best possible system and camera settings when acquiring spectral images. The overall goal should be to optimize SNR while minimizing acquisition time and preventing photobleaching.43 Thus, when optimizing an imaging system, a compromise between spatial, temporal, and spectral resolutions is often needed. In these studies, optimal values were selected for the system and camera settings including pinhole size, laser power, scan speed, scan size, and frame averaging as described in the protocol section. Utilizing these settings, the spatial sampling of 80 nm/pixel, temporal sampling of ~3 minutes per spectral z-stack (~10 seconds/XY image) and spectral sampling of 10 nm interval is achieved with negligible or minimal photobleaching.
To ensure accurate estimates of FRET efficiency, it is necessary to measure donor and acceptor spectra as they would be expected with 1:1 stoichiometry and identical excitation wavelengths (Figure 3). Turquoise and Venus spectra were normalized with respect to the Turquoise peak emission wavelength. The FRET efficiency that was estimated using this spectral library produced values similar to those reported in literature12,22. Typically, endmember spectra in the library are normalized to unity. However, to accurately calculate FRET efficiency, the acceptor spectrum must be acquired with respect to the donor spectrum (i.e., at equimolar concentration or 1:1 stoichiometry and referenced to the same excitation wavelength). In addition to the use of a properly constructed library, several steps are involved in appropriate estimation of FRET efficiency and avoidance of analysis artifacts. These include selecting an expressing cell with adequate signal intensity (Figure 11), smoothing of unmixed images before estimating FRET efficiency (Gaussian blur was applied in this example), using an appropriate threshold value to create the mask (Figure 10), and estimation of a correction factor using extinction coefficients of donor and acceptor (Supplemental File_Spectral Library). When these steps are followed, 3-dimensional distributions of FRET efficiency and underlying cAMP levels can be accurately visualized in single cells.
Localized cAMP signals have been estimated in 2-spatial dimensions using targeted FRET biosensors13,37. However, targeting FRET probes to specific subcellular compartments (for example, plasma membrane or lipid rafts) typically results in an increased local concentration of probe. This may result in measurement artifacts introduced due to intermolecular or bimolecular FRET. In addition, the results presented here and in Annamdevula et.al., 2018, Cytometry A25 demonstrate the importance of 3-dimensional measurements of FRET from either soluble or targeted probes.
Despite the importance of measuring cAMP signals in three spatial dimensions, there are also limitations to this approach. The most restrictive limitation is the long acquisition times required – approximately 3 minutes per spectral z-stack. This acquisition rate precludes using this approach for detecting anything other than quasi – steady state cAMP distributions in cells. That said, the results presented demonstrate the importance of including quasi – steady state 3-dimensional (x, y, z) cAMP measurements as critical complements to standard 2-dimensional (x, y) measurements. In the future it will be interesting to incorporate labeled proteins and cellular structures into the experimental design. Careful choice of fluorophores used to label proteins (and/or structures) would allow assessment of the 3-dimensional distributions of the labeled proteins and FRET without additional loss of acquisition speed. This would in turn allow measurement of localized cAMP signals and cAMP signals near the labeled proteins in three spatial dimensions, thus offering an important experimental complement to targeted cAMP probes and further expanding the utility of hyperspectral measurements of 3-dimensional cAMP distributions in living cells.
The authors have nothing to disclose.
The authors would like to acknowledge Dr. Kees Jalink (The Netherlands Cancer Institute and van Leeuwenhoek Center for Advanced Microscopy, Amsterdam, the Netherlands) for providing us with the H188 cAMP FRET biosensor and Kenny Trinh (College of Engineering, University of South Alabama) for technical help in reducing the time taken to run our custom developed programming scripts.
The authors would like to acknowledge the funding sources: American Heart Association (16PRE27130004), National Science Foundation; (1725937) NIH, S100D020149, S10RR027535, R01HL058506, P01HL066299).
Attofluor Cell Chamber | Invitrogen | A7816 | Attofluor contains steel cell chambers and a rubber O-ring. Cell chamber holds the coverslip and O-ring provides a lock in mechanism to hold the buffer in cell chamber with out leakage |
Dimethyl Sulfoxide (DMSO) | Fisher Scientific | BP231-100 | Solvent used to prepare stock solution forskolin. |
DRAQ5 Fluoroscent Probe Solution | Thermo Scientific | 62252 | Nuclear label |
Dulbecco Modified Eagle Medium (DMEM) | Gibco | 11965-092 | Contains nutrients and growth factors for the cells to grow and divide in the culture dishes. |
Fetal Bovine Serum (FBS) | Sigma | F6178 | Growth factor suppliment that is added to culture medium, DMEM |
Forskolin | Sigma | F3917 | Adenyly cyclase activator. |
H188 Cyclic AMP FRET biosensor | Netherland Cancer Institute, Dr. K. Jalink | Gift | Plasmid encoding Turquoise (donor fluorophore), Venus (acceptor fluorophore), and binding domain obtained from Epac. |
Image J | image.net | Free download | Another image processing platform used to extact spectral information and image processing. |
Integrating Sphere | Ocean Optics | FOIS-1 | Used to measure illumination intensity of the laser line at different laser intensities (?). |
Laminin Mouse Protein, Natural | Invitrogen | 23017-015 | Coverslips are coated with laminin and this helps in cell attachment, growth and motility of the cell. |
Lipofectamine 3000 Transfection Kit | Invitrogen | L3000-015 | Transfection reagent used to transfect cells with H188 FRET biosensor |
MATLAB | Mathworks | R2019a | Image processing operations (linear unmixing and FRET efficiency calculations) are performed by writing custom programs in MATLAB programming environment |
Nikon A1R confocal microscope | Nikon Instruments | Nikon A1R | Spectral image acquisition is performed using confocal microscope. |
Nikon Elements Software | Nikon Instruments | Software dongle | used to export and handle nd2 image files (multidimensional image files) that are aquired using Nikon A1R |
NIST-Traceable Calibration Lamp | Ocean Optics | LS-1-CAL-INT | A lamp with a known spectrum for use as a standard |
PBS pH 7.4 (1X) | Gibco | 10010-023 | coomonly used buffer suring cell culture |
Pulmonary Microvascular Endothelial Cells (PMVECs) | In house – Cell culture core, Univeristy of South Alabama | Isolated from Rat pulmonary microvasculature | PMVECs form inner lining of a blood vessel. |
Penicillin-Streptomycin (10,000 U/ml) | Gibco | 15140-122 | antibiotics are added to culture medum to prevent contamination of the cells. |
Pre-Cleaned Gold Seal Micro Slides | Clay Adams | 3010 | Microscope slides used for cell fixation |
ProLong Diamond Antifade Mounting Media | Invitrogen | P36961 | If samples are fixed using antifade mountant, then the later protects fluoroscent dyes and chromophores from fading. |
Spectrometer | Ocean Optics | QE65000 | Used to measure spectral response of the light source (?) |
Trypsin-EDTA (0.25%) | Gibco | 25200-056 | Digests the protein-protein bond between the cell and cell matrix and helps to disscociate and lift the cells during cell plating. |
Tyrodes Buffer | Made in-house | Made in-house | Tyrodes buffer is used to make working solutions and to maintain cells in aqueous solution during image acquisition. |
6 Well Cell Culture Plate | Corning | 3506 | Laminin coated coverslips are placed in 6-well culture dish (one coverlisps/well). Cells along with medium is added into each well. |
25 mm Round Microscope Cover Slips | Fisher Scientific | 12545102 | Cells were grown on round glass coverslips |
60X Ojective | Nikon Instruments | Plan Apo VC 60X/1.2 WI ∞/0.15-0.18 WD 0.27 | water immersion and commonly used objective for cells |