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In-Vivo Multispectral Fluorescent Imaging
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Introduction Conventional in vivo optical fluorescence imaging (FLI) employs a single excitation filter and a single emission filter. This has limitations for distinguishing fluorescent signal of a desired target signal, alternative reporter signals that may be present, and autofluorescent tissue signal. Multispectral (MS) FLI employs multiple excitation filters and a single emission filter, or a single excitation filter and multiple emission filters to generate a distinct spectral profile of fluorescent regions/materials. (¹) With this, the contribution of each fluorescent component can be determined for every pixel of the image (Fig. 1). Why In Vivo Multispectral Fluorescent Imaging? There are two primary motivations for in vivo MS FLI: When imaging lower wavelength reporters, within the typical range of tissue autofluorescence. This allows for suppression of autofluorescent background signal. When imaging multiple longer wavelength reporters (i.e. NIR) with overlapping spectrum. This allows for cross-talk suppression and distinct detection of the unique reporters. In-Vivo Multispectral Fluorescent Imaging Anna Yudina¹, Todd A Sasser² Author Information: ¹Bruker BioSpin, 34 rue de l’Industrie, 67166 Wissembourg, France, ²Bruker Preclinical Imaging., 44 Manning Rd, Billerica, MA, 01821 Figure 1 Fig. 1. Conventional (top) and MS FLI (bottom) of the mouse injected with Alexa 680 and Alexa 700. Top: ‘Cross-talk’ is produced when dyes with closely overlapping specta are imaged using conventional FLI. Bottom: signal is successfully “unmixed” using MS FLI.
Transcript of In-Vivo Multispectral Fluorescent Imaging
Introduction
Conventional in vivo optical fluorescence imaging (FLI) employs a single excitation filter and a single emission filter. This has limitations for distinguishing fluorescent signal of a desired target signal, alternative reporter signals that may be present, and autofluorescent tissue signal. Multispectral (MS) FLI employs multiple excitation filters and a single emission filter, or a single excitation filter and multiple emission filters to generate a distinct spectral profile of fluorescent regions/materials.(¹) With this, the contribution of each fluorescent component can be determined for every pixel of the image (Fig. 1). Why In Vivo Multispectral Fluorescent Imaging?
There are two primary motivations for in vivo MS FLI:
�� When imaging lower wavelength reporters, within the typical range of tissue autofluorescence. This allows for suppression of autofluorescent background signal.�� When imaging multiple longer wavelength reporters (i.e. NIR) with overlapping spectrum. This allows for cross-talk suppression and distinct detection of the unique reporters.
In-Vivo Multispectral Fluorescent Imaging
Anna Yudina¹, Todd A Sasser²
Author Information: ¹Bruker BioSpin, 34 rue de l’Industrie, 67166 Wissembourg, France, ²Bruker Preclinical Imaging., 44 Manning Rd, Billerica, MA, 01821
Figure 1
Fig. 1. Conventional (top) and MS FLI (bottom) of the mouse injected with Alexa 680 and Alexa 700. Top: ‘Cross-talk’ is produced when dyes with closely overlapping specta are imaged using conventional FLI. Bottom: signal is successfully “unmixed” using MS FLI.
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Figure 3
Fig. 3. Excitation and emission spectra of fluorescent protein DsRed. The excitation spectrum for most dyes and reporters used for in vivo imaging provide more information than emission spectrum because of the presence of secondary peaks (indicated by arrows), allowing for better signal separations.
To understand these motivations, it is necessary to first understand the basics of mouse tissue autofluorescence, imaging within different ranges of the fluorescence spectrum. Figure 2 below demonstrates the typical natural autofluorescent properties of mouse tissue using green/red, blue/green and NIR filter pairs(²). Notice that while green/red and blue/green pairs exhibit a high degree of tissue autofluorescence, NIR pair imaging results in relatively little tissue autofluorescence. MS FLI can be useful for subtracting tissue autofluorescence using reporters such as green fluorescent protein (GFP), red fluorescent protein (RFP), fluorescein isothiocyanate (FITC), and Alexa 600. As an alternative, far red (650-700 nm) or near-infrared (NIR; 700-900 nm) fluorophores can be used where the contribution of skin autofluorescence is minimal, but multiplexing is desired(³). In this way, multiple molecular markers can be simultaneously detected, which might include a FL cell tracking reporter and a FL protease activity reporter for example. Additionally, rodent chow commonly contains chlorophyll containing plant materials which produces strong autofluorescence in the low NIR spectrum. Multispectral imaging is commonly used to subtract this GI autofluorescence. This avoids the need to switch feeds to special alfalfa-free rodent pellets for imaging studies. Getting Started and Acquisitions
Bruker systems utilize an ‘excitation-based” approach for multispectral imaging. Here, multiple excitation filters and a single emission filter are used to capture an “image stack”. For most organic fluorophores, including genetic fluorescent protein reporters, the excitation spectra provide more information compared to the emission spectra (Fig. 3), and excitation based multispectral imaging better captures this distinct information compared to emission based multispectral imaging.
Systems are equipped with 28 excitation filters. The optimum excitation filters should be selected for a given imaging experiment prior to defining the emission filter. To facilitate filter selection, it can be useful to align the excitation profiles for reporters used to determine the points where the fluorescent spectra differ. In the example shown below (Fig. 4) for Alexa 680 and Alexa 700, there are distinct differences in the excitation spectra between 520 and 720 nm. (For the complete details of the Alexa 680 and Alexa 700 filter selection and modeling, readers are directed to the following recorded webinar: https://youtu.be/iEyLl3qAzpA).
Systems are equipped with 6 emission filters (535 nm, 600 nm, 700 nm, 750 nm, 790 nm, and 830 nm). Typically, an emission filter that does not overlap within 60 nm of the longest wavelength excitation filter is selected. The emission filter selected need not be aligned with the peak of the fluorescent emission(s), and the priority for filter selection should be to use excitation filters that cover the range of distinct fluorescence spectrums. In the continuing example for Alexa 680 and 700 imaging, the 790 nm filter would be selected because the 535, 600, 700, and 750 nm filter bandpass range overlaps with the selected excitation filter range (Fig. 5).
Figure 2
Fig. 2. Natural fluorescent properties of mouse tissue using green/red, blue/green, and NIR filters. Top left: white light image. Bottom right: green (Ex/Em = 480/535 nm) filter set is applied. Strong autofluorescence signal is observed from the skin and gastrointestinal (GI) tract. Top right: red (Ex/Em=540/600 nm) filter set is applied. Moderate autofluorescence signal is observed from the skin and strong autofluorescence signal is observed from GI tract. Bottom right: NIR (Ex/Em = 700/780 nm) filter set is applied. Autofluorescence signal is minimized. Figure reproduced Frangioni (2003).
Figure 4
Fig. 4. Selection of excitation filters for multispectral imaging of Alexa 680 and Alexa 700, based on distinct points comparing the overlaid spectra. Black arrows denote regions of distinct signal for Alexa 680 and Alexa 700. Excitation filters that are recommended to use for MS FLI are marked by red arrows. (Overlay produced using Invitrogen).
Figure 5
Fig. 5. Selection of the emission filter for spectral unmixing (example of Alexa 680 and Alexa 700) considering previously selected (red region) excitation filter range. Considering the possible 750 nm and 790 nm emission filters, the 790 nm filter is selected to avoid overlap with the longest wavelength excitation filters used.
Multiple excitation filters can be selected for programming an acquisition in the Capture dialogue (Fig. 6). Multispectral acquisitions should be acquired as part of a Protocol for subsequent multispectral modeling and/or unmixing.
Figure 6
Fig. 6. Selecting multiple filters for multispectral imaging.
Optimization and Multispectral Modelling
Initial imaging and study setup can include preliminary steps for optimizing the setup and modeling:
�� Imaging of fluorophore (in vitro)�� Generate spectral model(s) �� Evaluate models in vivo
To begin, we recommend imaging a dilution series of the fluorophore using the intended filters determined as described above. Once an image is acquired, the spectral profile is created by fitting the Gaussian curves to the experimental curves of the fluorophores (Fig. 7).
Figure 7
Fig. 7. Generating the spectral profile of the fluorophore in Bruker MS software. Yellow points represent experimental data to be fit by the green curve. The green curve consists of one or several Gaussian curves
Applying Spectral Models
Once a spectral profile has been optimized, the model can be directly recalled and applied to subsequent datasets without modification. To apply an existing model to a new dataset select + at the Unmixed Images panel, choose the desired models and select the Unmix button, as shown at Fig. 8. The system applies a least-squares fit to solve the multispectral model and assign models to individual pixels (4). For this, the summed spectrum in each pixel is matched towards all possible sum combinations from the reference library. Additional constraints (such as non-negativity) are added to the unmixing algorithms. Therefore, the essential part of successful spectral unmixing is to obtain accurate spectra for the library fluorophores. Once the spectral contribution from each fluorophore has been determined, the acquired stack can be segregated into individual images for each fluorophore.
Every ‘unmixed’ image consists of the superimposed images from the separate ‘channels’ (Alexa 680 and Alexa 700 in the abovementioned example). Each of these images can be opened in Bruker MI software and analysed using relevant tools applied to the regions of interest (ROI), such as mean, sum and net intensities, area, and perimeter, as well as automatic ROI finding function and image math. In other words, intensities measured at the camera may be related to “true” signal intensity of an emitting object inside an animal in complicated ways. After the signals are captured at the sensor, subsequent multispectral analysis yields quantitatively accurate component-specific data(1).
Multispectral Imaging in Infection, Oncology, and Particle Tracking Studies
Studies using green or red genetic fluorescent reporters or even mul¬tiple NIR fluorophores can benefit from fluorescent spectral modeling. Utilizing multispectral imaging, the distinct spectral profile for a reporter can be identified and modeled to reduce autofluorescence. Figures 9 and 10 demonstrate imaging of a Leishmania infected rabbit foot pad model with subtraction of tissue autofluorescence using multispectral methods (M. Leevy, University Notre Dame, USA, Unpublished).
In another example, multispectral imaging was applied in an in vivo polymeric particle tracking study (5). After 4 hrs. p.i., the Cy7 labeled particles were detected in the liver region (Fig. 11) and cross-talk signal obtained within the GI region, likely produced by dietary Chlorophyll, was separated, providing clear localization of the particle signal.
In a nanoparticles/tumor study, the biodistribution of nanoparticles of different sizes within the same tumor mice was monitored(6). Nanoparticles were differentially labeled and multispectral imaging was applied to separate the signals for circulated nanoparticles (Figure 12).
Figure 9
Fig. 9. Spectral unmixing of the signal coming from Leishmania expressing red-fluorescence protein and autofluorescence in the rabbit foot pad infection model
Figure 10
Fig. 10. (A) Spectrally unmixed Leishmania fluorescence and tissue autofluorescence (blue). No discrimination of the signal vs. autofluorescense is given. (B) Spectrally unmixed Leishmania fluorescence (purple). (C) Spectrally unmixed tissue autofluoresence (yellow). (D) Spectrally unmixed Leishmania fluorescence (purple-pink) and tissue autofluorescence (yellow). A clear discrimination of the Leishmania-derived signal from the tissue-derived autofluorescence is given.
Figure 11
Fig. 11 Left: Spectral unmixing to discriminate signal coming from Cy7-labelled doughnuts (yellow) and the gut fluorescence resulting from the chlorophyll component in the mouse pellets (green). Cy7-labelled doughnuts are observed solely in the liver. Right: Histology on cryostat sections of the liver showing rhodamine B doughnuts (red, black arrows) in the liver parenchyma.
Figure 8
Fig. 8. Applying existing spectral models (example of Alexa 680 and Alexa 700). A) The data initially appears as an unmixed dataset when viewed in the Bruker Multispectral Software, B) Models are selected from a library. C) Models are applied with the Unmix button, and D) pixels are assigned to specific models and image display can be adjusted.
Figure 12
Fig. 12. Top) Spectrally unmixed fluorescence images of mice bearing multiple MDA-MB-435 tumors (yellow arrows) injected simultaneously with both 15 nm and 100 nm gold nanoparticless; particles were coated with PEG 5 kDa, and fluorescently labelled with fluorophores X670 and Alexa Fluor 750, respectively. Bottom) Spectrally unmixed fluorescence images of tumor-bearing mice (yellow arrows denote location of tumor) co-injected with 45 nm and 75 nm fluorescent-tagged gold nanoparticles.
GFP-insulin (green) fusion construct and multispectral imaging with autofluorescence subtraction (red) confirm adenovirus gene delivery. Courtesy Dr. A. Banja
dsRed-cell reporter (red) frog and skin autofluorescence (blue) signals separated using multispectral imaging.
Nanoparticle (red) multispectral fluorescent imaging in rabbit model.
Multispectral imaging of tdTomato (red) and RFP (orange) expressing E. coli¸ and imaged with Luc-E. coli (blue).
Unlocking the Full Potential of in vivo Fluorescent Imaging
Multispectral fluorescent imaging clearly defines location of YPF (yellow) reporter signal location.
RFP-tumor (red) and tissue autofluorescence (green) signal separated using multispectral imaging.
References
[1] Levenson RM, Lynch DT, Kobayashi H, Backer JM, Backer MV (2008). Multiplexing with multispectral imaging: from mice to microscopy. ILAR J 49-78.
[2] Frangioni, JV (2003). In vivo near-infrared fluorescence imaging. Curr Opin Chem Biol.;7:626-34.[3] Weissleder R, Ntziachristos V (2003). Shedding light onto live molecular targets. Nat Med. 9(1) 123-8.[4] Farkas DL, Du C, Fisher GW, Lau C, Niu W, Wachman ES, Levenson RM (1998). Non-invasive image acquisition
and advanced processing in optical bioimaging. Comput Med Imaging Graph. 22(2):89-102.[5] Alexander L, Dhaliwal K, Simpson J, Bradley M. (2008) Dunking doughnuts into cells--selective cellular
translocation and in vivo analysis of polymeric micro-doughnuts. Chem Commun (Camb). 14;(30):3507-9[6] Chou LY, Chan WC (2012). Fluorescence-tagged gold nanoparticles for rapidly characterizing the size-dependent
biodistribution in tumor models. Unpublished.
Bruker BioSpin
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Conclusion
In vivo multispectral fluorescent imaging can allow for subtraction of tissue autofluoresscence and imaging of multiple fluorophores. This provides for enhanced signal-to-noise ratios and advanced multiplexing, resulting in more powerful study design.
