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
Introduction
Figure 1
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
ƒƒ
typical range of tissue autofluorescence. This allows for
suppression of autofluorescent background signal.
When imaging multiple longer wavelength reporters (i.e.
ƒƒ
NIR) with overlapping spectrum. This allows for cross-talk
suppression and distinct detection of the unique reporters.
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.
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.
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.
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).
Figure 4
Figure 2
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).
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).
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 5
Figure 7
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. 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.
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)
ƒƒ
Generate spectral model(s)
ƒƒ
Evaluate models in vivo
ƒƒ
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).
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.
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 (purplepink) and tissue autofluorescence (yellow). A clear discrimination of the Leishmaniaderived signal from the tissue-derived autofluorescence is given.
Figure 11
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 8
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 12
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 9
Fig. 9. Spectral unmixing of the signal coming from Leishmania expressing redfluorescence protein and autofluorescence in the rabbit foot pad infection model
Fig. 12. Top) Spectrally unmixed fluorescence images of mice bearing multiple MDAMB-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.
Unlocking the Full Potential of in vivo Fluorescent Imaging
dsRed-cell reporter (red) frog and skin autofluorescence (blue) signals separated using
multispectral imaging.
Multispectral fluorescent imaging clearly defines location of YPF (yellow) reporter
signal location.
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).
RFP-tumor (red) and tissue autofluorescence (green) signal separated using
multispectral imaging.
GFP-insulin (green) fusion construct and multispectral imaging with autofluorescence
subtraction (red) confirm adenovirus gene delivery. Courtesy Dr. A. Banja
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.
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.
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