This protocol is significant as it describes a method which can be used to separate several fluorescent signals in a single sample. Compared to traditional methods, excitation-scanning can increase the speed of the experiment and improve the sensitivity of the data. By using different illumination sources such as a supercontinuum laser, this method may be applicable to spinning disk or laser scanning confocal microscope systems.
When performing excitation-scannings, spectral imaging microscopy, it is important to check that the excitation peak of each fluorescent label occurs at a different wavelength. To begin, set up an inverted fluorescence microscope and camera as described in the text protocol. Then load the sample on the stage.
From the drop-down menu of the micromanager main window, select 475 nanometers for initial sample viewing. Then double-click at the box next to exposure and enter 100 to set the exposure time at 100 milliseconds. Finally, click live to view the sample.
Now click on Auto. The automatic intensity viewing range button to bring the minimum and maximum values into meaningful, visual ranges. Next, use the microscope's focus knobs to focus the sample.
Once in focus, adjust the position of the sample so that it's edge is in the center of the field of view. Then fine-tune the focus. The sample is in focus when the edge features in the image appear sharp.
Now, open the multi-dimensional acquisition tool. In the menu, click the load button and choose a preset setting containing an appropriate wavelength range for spectral acquisition. Once the acquisition settings are confirmed, move to a blank region of the sample and click acquire to collect a spectral image stack containing background and noise to use for subtraction later.
Then, move about the sample to find the brightest regions. Samples are likely to have more intense fluorescence away from the edge of the sample. Once the brightest region is located, take a single image stack.
Load the acquired image into Image J and use Image J's measure function to confirm that no wavelengths contain overexposed pixels. If a reduced exposure time is necessary due to overexposure, the background image should be retaken with a new exposure time as well. If any of the images were found to contain the upward detectional limit of the camera, adjust the exposure time of the spectral scan to ensure that the maximum signal throughout the spectral range does not exceed the dynamic range of the camera.
With an appropriate exposure time now determined, place an unlabeled sample onto the stage. Acquire spectral image data from the unlabeled sample to determine if there is any underlying autofluoresence. Then, place a single-labeled sample on the stage.
Acquire spectral image data from each of the single-labeled samples to use as spectral controls to build a spectral library. Perform the scan for each control sample using an identical wavelength range exposure time and camera settings. Next, place a slide containing the experimental sample on the microscope.
Select a field of view that has appropriately labeled cells and acquire the spectral image data from the sample using the determined acquisition settings. Correct images to a flat spectral response by subtracting the background spectrum from the sample image and multiplying the image by the correction coefficient. Here, a MATLAB script is used to quickly process the images.
Next, generate the spectral library. For best results, normalize each end member to the wavelength band of the greatest intensity by determining which wavelength band has the most intense value and dividing the measurement at each wavelength band by that maximum value. Then save the spectral data as a dat file.
When complete, unmix the spectral image data. The unmixing step will generate an abundance image for each fluorescent label, where abundance is the amount of relative fluorescent signal in the image from the prospective label. Next, open each unmixed image to visually inspect the distribution of pure components.
Compare the magnitude of the error image to those of the unmixed images. If the magnitude of the error image is similar to the unmixed abundance images, signals in the spectral image data may not be accurately accounted for. Finally, check that there are no unidentified residual structures by comparing the error image to the unmixed images.
When performed correctly, the spectral unmixing process allows separation of a spectral image stack into respective contributions from each fluorescence label. Here, an unmixed spectral image set with individual images highlighting airway smooth muscle cell autofluorescence, a GCaMP probe and a mitochondrial label. There is high error associated with the nuclear and perinuclear regions of the cells indicating that the measured spectra of those regions are not well accounted for by the spectra in the spectral library.
Pure spectra collected from labeled cells that are also autofluorescent will likely contaminate the spectral profile for the pure component. Here, one can see the difference of unmixing with pure spectra contaminated with autofluorescence, versus the corrected pure spectra after scaled subtraction. Proper data acquisition is essential when imaging sensitive samples as they may only survive a single imaging session unlike the background and controls which may be reimaged.
Ensure that the proper hardware and acquisition settings are predetermined as much as possible. As a next step, videos created from time-lapse data acquired using this system can be used to track fluorescent sources in discreet locations over time such as second messenger signal localization. Take care when using powerful light sources to avoid eye exposure.
Also, be sure to wear proper gloves when handling biological labels.
