Spectral imaging has become a reliable solution for identification and separation of multiple fluorescence signals in a single sample and can readily distinguish signals of interest from background or autofluorescence. Excitation-scanning hyperspectral imaging improves on this technique by decreasing the necessary image acquisition time while simultaneously increasing the signal-to-noise ratio.
Several techniques rely on detection of fluorescence signals to identify or study phenomena or to elucidate functions. Separation of these fluorescence signals were proven cumbersome until the advent of hyperspectral imaging, in which fluorescence sources can be separated from each other as well as from background signals and autofluorescence (given knowledge of their spectral signatures). However, traditional, emission-scanning hyperspectral imaging suffers from slow acquisition times and low signal-to-noise ratios due to the necessary filtering of both excitation and emission light. It has been previously shown that excitation-scanning hyperspectral imaging reduces the necessary acquisition time while simultaneously increasing the signal-to-noise ratio of acquired data. Using commercially available equipment, this protocol describes how to assemble, calibrate, and use an excitation-scanning hyperspectral imaging microscopy system for separation of signals from several fluorescence sources in a single sample. While highly applicable to microscopic imaging of cells and tissues, this technique may also be useful for any type of experiment utilizing fluorescence in which it is possible to vary excitation wavelengths, including but not limited to: chemical imaging, environmental applications, eye care, food science, forensic science, medical science, and mineralogy.
Spectral imaging may be performed in a variety of ways and is referred to by several terms1,2,3,4. In general, spectral imaging refers to data acquired in at least two spatial dimensions and one spectral dimension. Multispectral and hyperspectral imaging are most often distinguished by the number of wavelength bands or whether the spectral bands are contiguous1. For this application, hyperspectral data is defined as spectral data acquired with contiguous wavelength bands achieved by spacing of center wavelengths no less than half the full width at half maximum (FWHM) of each bandpass filter used for excitation (i.e., 5 nm center wavelength spacing for bandpass filters with 14-20 nm bandwidths). The contiguous nature of the data bands allows for an oversampling of the dataset, ensuring that Nyquist criteria are satisfied when sampling the spectral domain.
Hyperspectral imaging was developed by NASA in the 1970s and 1980s in conjunction with the first Landsat satellite5,6. Collecting data from several contiguous spectral bands allowed the generation of a radiance spectrum of each pixel. Identifying and defining the radiance spectrum of individual components made it possible to not only detect surface materials by their characteristic spectra, but it also allowed for the removal of intervening signals, such as variations in the signal due to atmospheric conditions. The concept of detecting materials using their characteristic spectra was applied to biological systems in 1996 when Schröck et al. used combinations of five different fluorophores and their known spectra to distinguish labeled chromosomes in a process termed spectral karyotyping7. This technique was elaborated upon in 2000 by Tsurui et al. for fluorescence imaging of tissue samples, using seven fluorescent dyes and singular value decomposition to achieve spectral separation of each pixel into linear combinations of spectra in the reference library8. Similar to their remote sensing counterparts, the contribution of each known fluorophore can be calculated from the hyperspectral image, given a priori information of the spectrum of each fluorophore.
Hyperspectral imaging has also been used in the areas of agriculture9, astronomy10, biomedicine11, chemical imaging12, environmental applications13, eye care14, food science15, forensic science16,17, medical science18, mineralogy19, and surveillance20. A key limitation of current fluorescence microscope hyperspectral imaging systems is that the standard hyperspectral imaging technology isolates fluorescence signals in narrow bands by 1) first filtering the excitation light to control sample excitation, then 2) further filtering emitted light to separate the fluorescence emission into narrow bands that can later be separated mathematically21. Filtering both the excitation illumination and emitted fluorescence reduces the amount of available signal, which lowers the signal-to-noise ratio and necessitates lengthy acquisition times. The low signal and lengthy acquisition times limit the applicability of hyperspectral imaging as a diagnostic tool.
An imaging modality has been developed that makes use of hyperspectral imaging but boosts the available signal, thereby reducing the necessary acquisition time21,22. This new modality, called excitation-scanning hyperspectral imaging, acquires spectral image data by varying the excitation wavelength and collecting a broad range of emitted light. It has been previously shown that this technique yields orders of magnitude increases in signal-to-noise ratio compared to emission scanning techniques21,22. The increase in signal-to-noise ratio is largely due to the wide bandpass (~600 nm) of emission light detected, while specificity is provided by filtering only the excitation light instead of the fluorescence emission. This allows all emitted light (for every excitation wavelength) to reach the detector21. Additionally, this technique can be used to discriminate autofluorescence from exogenous labels. Furthermore, the ability to reduce acquisition time due to increased detectable signal reduces the danger of photobleaching as well as allows spectral scans at an acquisition rate that is acceptable for spectral video imaging.
The goal of this protocol is to serve as a data acquisition guide for excitation-scanning hyperspectral imaging microscopy. In addition, descriptions are included that help to understand the light path and hardware. Also described is the implementation of open-source software for an excitation-scanning hyperspectral imaging microscope. Finally, descriptions are provided for how to calibrate the system to a NIST-traceable standard, adjust software and hardware settings for accurate results, and unmix the detected signal into contributions from individual components.
1. Device set-up
2. Acquisition software
3. Spectral response correction (optional):
4. Sample preparation
5. Data acquisition:
6. Image analysis
Several important steps from this protocol are necessary to ensure the collection of data that is both accurate and devoid of imaging and spectral artifacts. Skipping these steps may result in data that appear significant but cannot be verified or reproduced with any other spectral imaging system, thereby effectively nullifying any conclusions made with said data. Chief among these important steps is proper spectral output correction (section 3). The correction factor compensates for wavelength-dependent variations in the spectral output of the tunable excitation system. This is accomplished by scaling wavelengths with high power excitation such that the optical illumination power is comparable to wavelengths with a low power excitation, achieving a flat spectral excitation profile. An example of an inappropriate correction factor is one that contains very low values (e.g., <0.001) at one or more wavelengths, indicating that the intensity values measured at those wavelengths must be greatly attenuated to achieve a flat spectral response.
Calibration of the system to a NIST-traceable standard ensures that data collected with the excitation-scanning spectral imaging system is comparable to other systems also calibrated to NIST-traceable standards. Therefore, it is imperative to ensure that any correction factor adjusts the collected data appropriately. Accuracy of a correction factor can be verified with the use of a fluorescence standard, such as NIST-traceable fluorescein. Figure 1 illustrates an appropriate and inappropriate correction factor, visualized through graphical and image data. In this case, the spectral output at 340 nm was virtually nonexistent compared to the rest of the spectral range, resulting in a near-zero (<0.001) value for virtually every wavelength. Applied to the image stack, this results in near-zero values through most of the image stack for most pixels.
As stated in section 5, the excitation range, selected fluorophores, and acquisition settings may create the potential that one or many excitation wavelength image bands contain oversaturated pixels. Figure 2 illustrates an example in which the exposure time was set too long for several of the excitation wavelengths, causing the subsequent images to contain oversaturated pixels. This is important to note because, as the figure shows, both the individual images and the false-colored images may visually appear to be within acceptable intensity ranges.
Appropriate selection of a background region for subtraction is also important for data comparison among systems, as it removes elements of camera noise or stray light prior to NIST-traceable spectral correction (Figure 3). It is often helpful for subsequent image processing and data analysis steps to generate an RGB false-colored image of the spectral image stack in order to visualize spectral features within the image. Figure 4 shows an RGB false-colored image generated by merging three selected wavelength bands (370 nm = blue, 420 nm = green, 470 nm = red).
Spectral unmixing requires knowledge of the spectral profile of each individual fluorescence source. In the case of excitation-scanning hyperspectral imaging, this is done by acquiring spectral image stacks for each fluorophore (and autofluorescence). Figure 5 is included as an example for choosing regions from single-label controls to generate a spectral library. It should be noted that each measurement has been normalized to its peak wavelength.
When performed correctly, the spectral unmixing process allows separation of a spectral image stack into respective contributions from each fluorescence label. Figure 6 shows an example unmixed spectral image set, with individual images for each of the following signals: airway smooth muscle cell autofluorescence, a GCaMP probe, and a mitochondrial label. The error associated with spectral unmixing is also shown and can be examined to compare intensity levels of the error to those of the unmixed signals. Calculation and interpretation of this error has been discussed previously37. As shown in Figure 6, 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. One potential source of error may be that the single-label controls for GCaMP and the mitochondrial label were prepared using airway smooth muscle cells, which have a high native autofluorescence. Hence, the GCaMP and the mitochondrial label may not represent pure endmember spectra. In addition, the autofluorescence signal may influence the library spectra for the two other labels in a way that was not properly accounted for, resulting in a less accurate fitting than if the labels had been acquired using a cell line with little to no autofluorescence. Additionally, examples are included for when too few or too many components of a spectral library are available, resulting in underfitting and overfitting of the spectral image data, respectively (Figure 6, Figure 7, Figure 8, Figure 9, and Table 1).
As noted in section 6.2 and specifically step 6.2.4, "pure" spectra collected from labeled cells that are also autofluorescent will likely contaminate the spectral profile for the pure component. As such, care should be taken to separate the pure spectra of the labels of interest from their autofluorescent hosts. Figure 10 shows the difference of unmixing with "pure" spectra contaminated with autofluorescence (mixed) versus pure spectra calculated by the method described in step 6.2.4. The difference occurs mainly in unmixing the autofluorescence signal. Without proper signal separation (e.g., subtraction of autofluorescence contamination from the GCaMP spectrum), the autofluorescence and GCaMP library components compete for the same spectral data in each pixel, resulting in characteristic holes or dark spots in the autofluorescence image.
Figure 1: Example applications of inappropriate and appropriate correction factors used to correct images to a flat spectral response. (A) Plotted inappropriate and appropriate correction factors. (B) An RGB image generated with use of the inappropriate correction factor in (A). (C) An RGB image generated with the same field of view as (B), except with the use of an appropriate correction factor. Please click here to view a larger version of this figure.
Figure 2: Examples that illustrate the importance of appropriate acquisition settings. (A,B) RGB images generated by an appropriate acquisition time (A, 100 ms) vs. the same field of view with a saturating acquisition time (B, 500 ms). It should be noted that the RGB images appear identical when false-colored. (C) The region of interest selected to survey intensity values from the most intense regions of (A) and (B). (D) Plotted intensity values per wavelength from the 100 ms exposure time image (A, black line) and 500 ms exposure time (B, red line). It should be noted that pixel intensities for the 500 ms exposure time have reached the limit of the dynamic range of the detector (65,535 AU) at 370 nm and do not decrease until 525 nm, resulting in spectral artifact. Please click here to view a larger version of this figure.
Figure 3: Selection of a region of interest for background subtraction. (A) A raw, wavelength-summed intensity image. (B) Regions of the image selected to determine the pixel-averaged background spectrum for background subtraction shown in red. (C) The background-subtracted, corrected, and summed intensity image. (D) An RGB coloring of the corrected image (C). The process of generating an RGB false-colored image is shown in Figure 4. Please click here to view a larger version of this figure.
Figure 4: Process of generating an RGB false-colored image. (A–C) Three wavelength bands spaced evenly throughout the spectral acquisition range were selected for false-coloring (blue = 370 nm, green = 420 nm, red = 470 nm). (D) The summed intensity image generated by adding pixel intensities from all wavelength bands in the image cube. (E–G) The images in panels (A-C) with their respective false-color look-up tables applied. (H) The resultant merged image of (E-G). Please click here to view a larger version of this figure.
Figure 5: Region selection of single-label controls for spectral library generation. (A) RGB false-colored image of airway smooth muscle (ASM) cell autofluorescence. (B) Shown in red, regions of (A) chosen for the autofluorescence component of the spectral library. (C) RGB false-colored ASM cells labeled with the mitochondrial label. (D) Shown in red, regions of (C) chosen for the mitochondiral label component of the spectral library. Due to the autofluorescence of the ASM cells localized near the nucleus, small regions were selected far from the nuclei to identify the mitochondrial label spectrum. (E) RGB false-colored ASM cells transfected with the GCaMP probe. (F) Regions of (E) selected for the GCaMP component of the spectral library are shown in red. Similar to (D), regions away from the nuclei were selected. (G) The spectral library obtained from A-F, normalized to a value of unity at the wavelength with the strongest signal. Please click here to view a larger version of this figure.
Figure 6: Example of unmixed image data in which unmixed relative signal contributions from each library component can be visualized. (A–D) The unmixed abundance of GCaMP, mitochondrial label, autofluorescence, and error term. (E–G) The images in panels (A-C) with their respective false-color look-up tables applied. (H) The composite, merged, false-colored image. Please click here to view a larger version of this figure.
Figure 7: Unmixed relative signal contributions, including mean intensity and percent of total fluorescence, from a properly defined spectral library. (A–D) The unmixed abundance for autofluorescence, GCaMP, mitochondrial label, and error term. It should be noted that the error term comprises less than 10% of the total fluorescence signal measured. Please click here to view a larger version of this figure.
Figure 8: Unmixed relative signal contributions, including mean intensity and percent of total fluorescence, from a spectral library missing a component known to be included in the sample (i.e., an underdefined spectral library). (A–C) The unmixed abundance of autofluorescence, mitochondrial label, and error term. Note that the omission of GCaMP from the spectral library has increased the calculated relative signal contributions from the library components as well as the error term, when compared to Figure 7. Please click here to view a larger version of this figure.
Figure 9: Unmixed relative signal contributions, including mean intensity and percent of total fluorescence, from a spectral library containing an additional component known to be missing in the sample (i.e., an overdefined spectral library). (A–E) The unmixed abundance of autofluorescence, GCaMP, mitochondrial label, nuclear label, and error term. Note that the addition of a nuclear label to the spectral library has decreased the calculated relative signal contributions from autofluorescence, when compared to Figure 7. Furthermore, the error term is decreased below the percent error specified by the properly defined spectral library. This is because an overdefined library will almost always allow a better fit to the experimental data than a properly defined library, even though abundance signals for components known to be absent from the sample are (in reality) artifacts of overdefining the spectral library. Please click here to view a larger version of this figure.
Figure 10: Comparison of unmixed images when using a library before and after proper autofluorescence contamination signal subtraction. (A) The original "pure" spectra derived from the single-label controls in highly autofluorescent airway smooth muscle cells before the scaled subtraction detailed in step 6.2.4. (B–D) The unmixed autofluorescence, GCaMP, and nuclear label images generated using (A) as the library. (E) The corrected pure spectra derived from single-label controls in highly autofluorescent airway smooth muscle cells after scaled subtraction. (F–H) The unmixed autofluorescence, GCaMP, and nuclear label images generated using (E) as the library. Please click here to view a larger version of this figure.
Mean Intensity (AU) | |||
Proper Fitting | Underfitting | Overfitting | |
Autofluorescence | 187 | 299 | 164 |
GCaMP | 139 | – | 140 |
Mitochondrial Label | 246 | 318 | 248 |
Nuclear Label | – | – | 26 |
RMS Error | 53 | 126 | 43 |
Standard Deviation (AU) | |||
Proper Fitting | Underfitting | Overfitting | |
Autofluorescence | 362 | 442 | 315 |
GCaMP | 168 | 168 | |
Mitochondrial Label | 344 | 388 | 345 |
Nuclear Label | – | – | 93 |
RMS Error | 62 | 126 | 44 |
Maximum Intensity (AU) | |||
Proper Fitting | Underfitting | Overfitting | |
Autofluorescence | 6738 | 7409 | 6738 |
GCaMP | 1336 | – | 1336 |
Mitochondrial Label | 5098 | 5194 | 5098 |
Nuclear Label | – | – | 1257 |
RMS Error | 1050 | 1286 | 910 |
% of Total Fluorescence | |||
Proper Fitting | Underfitting | Overfitting | |
Autofluorescence | 30% | 40% | 26% |
GCaMP | 22% | 43% | 23% |
Mitochondrial Label | 39% | – | 40% |
Nuclear Label | – | – | 4% |
RMS Error | 8% | 17% | 7% |
Table 1: A table comparing the average, standard deviation, and maximum unmixed abundance intensity values per unmixed abundance image from a proper spectral library and from underdefined or overdefined spectral libraries, as well as the percent of total fluorescence per unmixed abundance image (the minimum unmixed abundance intensity for each image was always zero). Data taken from Figure 7, Figure 8, and Figure 9.
The optimal use of an excitation-scanning hyperspectral imaging set-up begins with construction of the light path. In particular, choice of light source, filters (tunable and dichroic), filter switching method, and camera determine the available spectral range, possible scan speed, detector sensitivity, and spatial sampling. Mercury arc lamps offer many excitation wavelength peaks but do not provide a flat spectral output and will require significant signal reduction at the output peaks to correct the spectral image data back to a NIST-traceable response38. Alternative light sources, such as Xe arc lamps and white light supercontinuum lasers, may provide a more uniform spectral output that is better suited for excitation-scanning hyperspectral imaging38,39,40. Choice of light source, tunable filters, and dichroic filters determine the available spectral range. This range should be chosen with careful consideration for the desired spectral information of the experimental sample.
Additionally, consideration should be given to the switching mechanism used for the tunable filters, as well as various camera factors such as quantum efficiency, pixel size, and available frame rates, as these factors will affect potential sampling rates41,42,43. However, all other factors being constant, utilization of the excitation-scanning approach should provide increased sensitivity and the ability for faster imaging, compared to most emission-scanning spectral imaging approaches21.
As noted in the introduction, hyperspectral imaging here refers to the contiguous and spectrally overlapping nature of the acquired data. As such, the capabilities of the system need to be able to collect data using spacing of excitation center wavelengths less than half the distance of the FWHM of the filters. As reported previously, a carefully chosen array of thin-film tunable filters allows data acquisition with center wavelengths spaced 5 nm apart, a distance sufficient to oversample the excitation spectrum given filters with FWHM of between 14-20 nm. Such a spacing gives slight redundancy in spectral data collection with likely increases the accuracy of the unmixing process. Consideration of both spectral ranges and necessary minimum number of wavelength channels for accurate unmixing have been discussed previously44,45,46. To this end, given the bandwidth of these filters, the excitation range should be selected to end at a wavelength that is somewhat lower (5-10 nm lower) than the cut-off wavelength of the dichroic beamsplitter. This will ensure that the entire bandwidth of the excitation illumination is below the cutoff wavelength of the dichroic beamsplitter (e.g., 360-485 nm for the 495 dichroic filter) to avoid excitation-emission cross-talk.
Software to independently control each component of the hardware to achieve high-speed spectral imaging scans is required. The software should be able to operate the shutter, select excitation wavelengths, and acquire images at sufficiently high speeds to meet experimental conditions (exact sample rate requirements will vary experiment, but an example goal might be to acquire four complete spectral image stacks per minute). More complex experiments may make use of multiple dichroic mirrors, objectives, or XYZ locations. There are a number of software packages available for data acquisition47,48. Micro-Manager is a free open-source software for microscope automation that offers a variety of customization options. Furthermore, Micro-Manager includes a scripting panel for additional customization not available within the main user interface. For example, it is possible to use a custom script to reduce the 250 ms delay of the tunable filter switcher to 10 ms at all excitation wavelengths except the wavelength transitions in which the filter wheel rotates to a new tunable filter, reducing the effective imaging time by 240 ms per wavelength for most wavelengths. This customization has allowed acquisition of up to 30 wavelengths in under 4 s. Finally, Micro-Manager can be operated alongside other environments, such as MATLAB, to further customize microscope device control.
Determining the proper spectral excitation range, acquisition time, and initial wavelength for sample viewing and subsequent data acquisition is very important. However, incorrect operation of individual components of the system may require further troubleshooting. Each component of the optical path contributes to the image data acquired. Hence, it is important to verify the spectral response and optical transmission of optical components within the light path, especially if trying to optimize overall system response. Light sources often have variable intensity settings and may have reduction in power over the lifetime of the bulb40. If a sample appears dim, the cause may be reduced output from the light source. The autoshutter function in Micro-Manager, in our experience, does not operate 100% of the time. Lack of signal may indicate a closed shutter. The initial excitation wavelength saved within Micro-Manager's configuration file may default to a wavelength with little or no spectral output, such as the 340 nm example shown in Figure 1.
Additionally, it is not uncommon to mistakenly choose an excitation wavelength that is above the cutoff wavelength of the long-pass dichroic beamsplitter, resulting in additional cross-talk and/or excitation light being shunted directly to the camera which may potentially damage the camera sensor. Similarly, adjusting the light path for transmission imaging may result in loss of signal to the camera, depending on the configuration of the microscope. Furthermore, as indicated in Figure 2, choice of initial excitation wavelength and exposure time for sample viewing may appear appropriate but actually result in oversaturated pixels at other excitation wavelengths. This fact will not be apparent unless pixel saturation is checked for each image, so it is often prudent to perform a test image stack on an area of the sample that appears to contain the most intense fluorescence.
It is also worth noting that variable intensities may be present in images chosen for background regions. Care should be taken to ensure that these regions do not actually contain relevant image data, as subsequent data correction steps will then subtract this signal from the image data acquired at other regions in the sample. It is sometimes necessary to use the transmission settings and/or drastically increase the exposure time to check that the region selected for measuring sample background does indeed contain no sample. Similarly, unmixed images may present with dark spots or holes in the autofluorescence image. This may be due to an improper library caused by autofluorescence "contamination" of the "pure" spectral signals from single-labeled cells. This is because any "pure" spectral signal collected from a sample containing autofluorescence will invariably contain some of the autofluorescence spectra itself, even if in trace amounts. Care should be taken to subtract out autofluorescence signals from the single-labeled controls to ensure a proper spectral library, as described on several occasions by Mansfield et al.28,29,30,31,32
Though not demonstrated in this article, the excitation-scanning spectral imaging system can also be used for time-lapse imaging and imaging over multiple XY locations (or a large field of view due to image stitching) in multiple focal planes. If these options are desired for a single experiment, careful thought should be given to the importance of acquisition order and potential effects of photobleaching. It is also worth noting that if the actual time taken exceeds the estimated time (e.g., an image stack takes 11 s to acquire rather than the estimated 10 s), Micro-Manager will continue to acquire the selected number of timepoints and/or positions. This miscalculation can compound with multiple timepoints and alter calculated temporal sampling, potentially skewing any conclusions made from the data.
This example demonstrates the ability to separate three sources of fluorescence within a 145 nm excitation scanning range. Previous experiments using this system have been able to separate up to five sources of fluorescence within the same range. The time required for this image acquisition is less than 1 min, which is significantly faster than most emission-scanning spectral imaging systems. Improvements in the light path, such as higher speed excitation wavelength tuning, may further advance this imaging technology to speeds that are sufficient for video-rate spectral imaging.
The authors have nothing to disclose.
The authors would like to acknowledge support from NSF 1725937, NIH P01HL066299, NIH R01HL058506, NIH S10OD020149, NIH UL1 TR001417, NIH R01HL137030, AHA 18PRE34060163, and the Abraham Mitchell Cancer Research Fund.
Airway Smooth Muscle Cells | National Disease Research Interchange (NDRI) | Isolated from human lung tissues obtained from NDRI | Highly autofluorescent, calcium sensitive cells |
Automated Shutter | Thorlabs Inc. | SHB1 | Remote-controllable shutter to minimize photobleaching |
Automated Stage | Prior Scientific | H177P1T4 | Remote-controllable stage for automated multiple field of view or stitched image collection. |
Automated Stage Controller (XY) | Prior Scientific | Proscan III (H31XYZE-US) | For interfacing automated stage with computer and joystick |
Buffer | Made in-house | Made in-house | 145 mM NaCl, 4 mM KCl, 20 mM HEPES, 10 mM D-glucose, 1 mM MgCl2, and 1mM CaCl2, at pH 7.3 |
Cell Chamber | ThermoFisher Scientific | Attofluor Cell Chamber, A7816 | Coverslip holder composed of surgical stainless steel and a rubber O-ring to seal in media and prevent sample and/or objective contamination |
Excitation Filters | Semrock Inc. | TBP01-378/16 | Center wavelength range (340-378 nm), Bandwidth (Minimum 16 nm, nominal FWHM 20 nm), Refractive index (1.88) |
Semrock Inc. | TBP01-402/16 | Center wavelength range (360-400 nm), Bandwidth (Minimum 16 nm, nominal FWHM 20 nm), Refractive index (1.8) | |
Semrock Inc. | TBP01-449/15 | Center wavelength range (400-448.8 nm), Bandwidth (Minimum 15 nm, nominal FWHM 20 nm), Refractive index (1.8) | |
Semrock Inc. | TBP01-501/15 | Center wavelength range (448.8-501.5 nm), Bandwidth (Minimum 15 nm, nominal FWHM 20 nm), Refractive index (1.84) | |
Semrock Inc. | TBP01-561/14 | Center wavelength range (501.5-561 nm), Bandwidth (Minimum 14 nm, nominal FWHM 20 nm), Refractive index (1.83) | |
Fluorescence Filter Cube Dichroic Beamsplitter | Semrock Inc. | FF495-Di03 | Separates excitation and emission light at 495 nm (>98% reflection between 350-488 nm, >93% transmission between 502-950 nm), Filter effective index (1.78) |
Fluorescence Filter Cube Longpass Filter | Semrock Inc. | FF01 496/LP-25 | Allows passage of light longer than 496 nm ( >93% average transmission between 503.2-1100 nm), Refractive index (1.86) |
GCaMP Probe | Addgene | G-CaMP3; Plasmid #22692 | A single-wavelength GCaMP2-based genetically encoded calcium indicator |
Integrating Sphere | Ocean Optics | FOIS-1 | Used for accurate measurement of wide-angle illumination |
Inverted Fluorescence Microscope | Nikon Instruments | TE2000 | Inverted microscopes allow direct excitation of sample without the need to penetrate layers of media and/or tissue. |
Mitotracker Green FM | ThermoFisher Scientific | M7514 | Labels mitochondria |
NIST-Traceable Calibration Lamp | Ocean Optics | LS-1-CAL-INT | A lamp with a known spectrum for use as a standard |
NIST-Traceable Fluorescein | ThermoFisher Scientific | F36915 | For verifying appropriate spectral response of the system |
NucBlue | ThermoFisher Scientific | R37605 | Labels cell nuclei |
Objective (10X) | Nikon Instruments | Plan Apo λ 10X/0.45 ∞/0.17 MRD00105 | Useful for large fields of view |
Objective (20X) | Nikon Instruments | Plan Apo λ 20X/0.75 ∞/0.17 MRD00205 | Most often used for tissue samples |
Objective (60X) | Nikon Instruments | Plan Apo VC 60X/1.2 WI ∞/0.15-0.18 WD 0.27 | Most often used for cell samples |
sCMOS Camera | Photometrics | Prime 95B (Rev A8-062802018) | For acquiring high-sensitivity digital images |
Spectrometer | Ocean Optics | QE65000 | Used to measure spectral output of excitation-scanning spectral system |
Tunable Filter Changer | Sutter Instrument | Lambda VF-5 | Motorized unit for automated excitation filter tuning/switching |
Xenon Arc Lamp | Sunoptic Technologies | Titan 300HP Lightsource | Light source with relatively uniform spectral output |