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Keywords:

  • in-vivo imaging;
  • fluorescence;
  • autofluorescence;
  • immunohistochemistry;
  • multiplexing

Abstract

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

Multispectral imaging (MSI) is currently in a period of transition from its role as an exotic technique to its being offered in one form or another by all the major microscopy manufacturers. This is because it provides solutions to some of the major challenges in fluorescence-based imaging, namely ameliorating the consequences of the presence of autofluorescence and the need to easily accommodate relatively high levels of signal multiplexing. MSI, which spectrally characterizes and computationally eliminates autofluorescence, enhances the signal-to-background dramatically, revealing otherwise obscured targets. While this article concentrates on examples derived from liquid-crystal tunable filter-based technology, the intent is to showcase the advantages of multispectral imaging in general. Some technologies used to generate multispectral images are compatible with only particular optical configurations, such as point-scanning laser confocal microscopy. Band-sequential approaches, such as those afforded by liquid-crystal tunable filters (LCTFs), can be conveniently coupled with a variety of imaging modalities, which, in addition to fluorescence microscopy, include brightfield (nonfluorescent) microscopy as well as small-animal, noninvasive in-vivo imaging. Brightfield microscopy is the chosen format for histopathology, which relies on immunohistochemistry to provide molecularly resolved clinical information. However, in contrast to fluorescent labels, multiple chromogens, if they spatially overlap, are much harder to separate and quantitate, unless MSI approaches are used. In-vivo imaging is a rapidly growing field with applications in basic biology, drug discovery, and clinical medicine. The sensitivity of fluorescence-based in-vivo imaging, as with fluorescence microscopy, can be limited by the presence of significant autofluorescence, a limitation which can be overcome through the utilization of MSI. © 2006 International Society for Analytical Cytology

Multispectral imaging is the acquisition of spectrally resolved information at each pixel of an imaged scene. Many different technologies can be employed to generate such information, ranging from multi-position filter-wheels, gratings and prisms, laser-scanning single point spectrographs, electronically adjustable tunable filters, Fourier-transform imaging spectrometry, and computed tomographic imaging spectroscopy (reviewed in Ref. (1)). This report will highlight the use of liquid crystal tunable filter- (LCTF-) based multispectral imaging approaches, along with application-specific analysis tools, for a variety of imaging tasks, but should also be read as a presentation of the advantages of MSI in general.

FLUORESCENCE MICROSCOPY

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

Fluorescence imaging of paraffin-embedded, formalin-fixed tissues is often confounded by interfering autofluorescence, which can reduce the ability to detect the fluorophore(s) of interest. Unfortunately, what is worse is that tissue autofluorescence can easily be mistaken for a signal of interest, leading to erroneous results. Tissue autofluorescence is present to some degree at all excitation wavelengths, although it is strongest when UV- or blue-excitation ranges are employed. Formalin-fixation greatly enhances autofluorescence, and components such as collagen, red blood cells, and neuronal constituents can be particularly bright (2–5).

The ability to accurately quantitate the fluorescence emission of a labeled target requires that the signal being measured comes only from the fluorophore of interest, and not from a mixture of autofluorescence and fluorophore. Numerous efforts have been made to chemically eliminate tissue autofluorescence (4, 6, 7), none of which have been entirely successful, sometimes because they fail to completely eliminate the effects of autofluorescence and sometimes because they create more variability in the sample than they remove.

Using multispectral imaging to separate the contributions of the various fluorophores in a sample into their own images, or channels, is an effective method of improving both contrast (or the signal-to-noise ratio) and the quantitative accuracy of the measurement. Although MSI methodologies are, by their nature, applicable to all fluorophores, the combination of MSI methods with quantum dot-based labels and the like may be particularly fruitful (8–10). Quantum dots, when properly prepared and derivatized, are relatively immune to the effects of photobleaching. Moreover, they have comparatively narrow emission bandwidths, established by the size of the quantum dots cores, and can have large Stokes' shifts. This makes the multiplexing of fluorophores much simpler, since a single excitation wavelength can be used to excite all the species present simultaneously.

IN-VIVO IMAGING

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

Over the past decades, imaging has become a critical component of medicine with striking advances in MR- and CT-based imaging methods. For the most part, however, these imaging modalities reveal anatomical rather than molecular features, and while this has proved useful, many of the molecular and cellular changes that occur at the onset of a disease are not detectable with purely anatomical imaging. Even when disease states become evident in anatomical imaging methods, the information can be hard to interpret since it is not directly related to molecular entities, such as proteins or expressed genes; in-vivo molecular imaging is a recent development that aims to provide such information. While not usually covered in cytometry journals, the topic of in-vivo imaging is relevant to this discussion of spectral methodology, not only because the same complement of imaging hardware and software tools can be applied, but also because in-vivo imaging techniques must often be validated with microscopy-based methods, which can themselves take advantage of spectral information.

For a variety of reasons (including cost and regulatory obstacles), it is likely that in the near future much of molecular imaging will remain restricted to use in whole-animal basic research and the preclinical phases of the drug development cycle, where such approaches have already proved to be of tremendous utility. The ability to perform relevant, minimally or noninvasive imaging helps reduce costs and enables longitudinal studies of multiple processes and parameters in individual animals. PET and MRI, and more recently, optical imaging have been adapted to facilitate these studies through the use of specialized contrast agents and imaging probes that either identify the existence of certain genetically modified cells or tissues, or that produce signals roughly proportional to regional molecular abundance or the rate of specific events in molecular pathways (11–18). In human subjects, optical imaging is likely to remain restricted to relatively superficial targets because of the absorbing and scattering properties of tissue in the visible and near-infrared (NIR). However, these issues are less significant in preclinical (typically mouse) animal models: their smaller sizes allow the detection of sufficient photon flux at the surface of the animal to allow detection of signals with acceptable signal-to-noise ratios.

Optical molecular imaging systems and methodologies have been developed that use both bioluminescent (19) and fluorescent (20) signals. Bioluminescent systems typically use luciferase genes coupled with luciferin substrates as reporters. The major attraction of this approach is that although absolute light levels generated by the targets may be low, photons are generated generally only where luciferase is present, leading to low background signals. In contrast, fluorescence-based imaging requires an external light source to stimulate the emission of light from the probe, and may be accompanied by bright background signals arising from the animal's intrinsic autofluorescence. However, fluorescence is a more flexible technology, since it permits the use of a far wider range of probes, labeling methods and targets, and can be used with labels that emit in the NIR, the spectral “sweet spot” for deep tissue in-vivo imaging. The number of photons emitted is orders of magnitude greater than with bioluminescence; the presence of autofluorescence is what generally limits achievable target-to-background ratios (21).

MSI fluorescence-based methodologies can be used to image practically any of the fluorophores used in biomedical research, with best results typically achieved when the emission wavelengths of the dye are between 500 and 950 nm (22). These fluorophores can be xenografted into either a tumor implanted in an animal or can be generated by transgenic animals; they can be covalently bound to antibodies, peptides or other agents that bind to targets; or they can simply be fluorescent compounds that are introduced into an animal. An example of antibody-targeted spectral imaging and analysis can be found in the report by Gao et al., examining the distribution of quantum-dot-labeled antitumor antibodies in mice (23).

BRIGHTFIELD MICROSCOPY

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

While fluorescence microscopy has long been the method of choice for true molecular imaging, it is not favored in the clinical realm populated by pathologists and their colleagues, who typically prefer brightfield techniques employing immunohistochemistry (IHC) to determine the distribution and abundance of specific molecular features in cells and tissues. Typically, a single target molecule is detected using antibodies linked to chromogenic read-out systems, and the tissue is counterstained with a general stain such as hematoxylin or other contrasting agent to provide anatomic context. Detection and quantitation of single signals has been possible for a number of years using simple color cameras and appropriate separation and analysis software (for example, see Refs. (24, 25)). Preparation of double- and even triple-stained IHC samples are now feasible for routine use thanks to the advent of automatic staining machines and appropriate labeling reagents, but to date these are only employed if the targets that are stained do not spatially overlap (co-localize), since neither our eyes, nor conventional color cameras, are capable of easily resolving mixtures of chromogens. The conventional approach to obtaining information on a number of molecular targets from a single sample is to cut serial sections and stain each one with a different antibody. While this is straightforward in principle, there are drawbacks. The microscopist needs to look at multiple sections (some of which may no longer contain the tissue of interest) and correlate distribution of the antigens either mentally or with some potentially complicated imaging scheme. Of more importance, perhaps, is the fact that information on multiple markers is not available on a cell-by-cell basis, but only on populations of cells, since individual cells rarely span more than one section. Thus, correlative studies that look for co-occurrence of molecular phenotypes within cells (the true functional units) are not possible without multiplexing. As will be shown, spectral imaging is capable of allowing single cell multiplexed imaging using spatially overlapping chromogens in brightfield.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

Spectral Imaging Hardware

All the examples shown here were acquired using CRi's (Woburn, MA, 01801) commercially available LCTF-based imaging systems. The core technology has been described elsewhere (26–28) and compared in some detail to other spectral imaging techniques (1). Some of the features of LCTFs that make them suitable choices for many situations are as follows. They are band-sequential filters that are easily coupled to focal plane array detectors employing CMOS, standard CCD, or EMCCD technologies, among others. By sequentially tuning the filter and exposing the sensor, complete images are acquired at each wavelength, band by band. Unlike some other techniques, for example, those that use prisms or gratings to disperse and collect all wavelengths simultaneously, this design allows the user to vary the exposure time as a function of wavelength, thus optimizing signal-to-noise in situations where sensitivity (emitted photons convolved by imaging receiver characteristics) varies over the spectral range. Moreover, the wavelengths acquired can be arbitrarily spaced through the spectral range of interest, allowing the user to maximize signal-to-noise by acquiring only the most informative bands (29). Other advantages include the absence of moving parts, excellent optical properties yielding near-diffraction-limited images, spectral stability to fractions of a nanometer, and high reliability.

As always, there are also some disadvantages related to band-sequential approaches in general and/or LCTFs in particular. A band-sequential approach implies that the complete image stack (or “cube”) is built up over time; thus, if significant sample- or camera-movement occurs during the acquisition, or if high temporal resolution is needed to capture certain events, like calcium signaling transients, simultaneous multiband acquisition strategies could be more appropriate. Also, photobleaching during acquisition can be a concern, since some wavelengths will be acquired after others, and therefore will be subject to illumination longer before being detected. However, as long as some signal is still detectable, unmixing can be accomplished, and the relative intensity losses can be accounted for, if absolute quantitation (something of a chimera) is desired. Overall light throughput can be a concern: LCTFs use polarization in their spectral selection process, and transmission efficiencies are typically in the 30%-range, in comparison to traditional interference filters that can transmit ∼90% of incoming light. However, the metric of success in imaging is usually not total photons captured, but achievable signal-to-noise (or signal-to-background), and except where high speed is required, the benefit of spectral information gained generally outweighs the impact of lower transmission efficiencies. For example, compare a grayscale to a color image: as much as 2/3 or so of the available light is lost in a color sensor due to the presence of red–green–blue filter masks; however, the useful information content of a color image, captured with fewer photons, can be vastly more than that of a monochrome image of the same scene.

Image Acquisition

The Nuance and Maestro multispectral imaging systems used in this work both incorporate a LCTF optically coupled to a 1.3 megapixel CCD camera (Sony ICX285 CCD chip). The images in this manuscript were acquired using the full CCD frame at either 1 × 1 binning (1,360 × 1,024 pixels) or 2 × 2 binning (680 × 512 pixels).

1. Fluorescence microscopy: In general, a Nuance™ spectral imaging system (CRi) is mounted onto a conventional fluorescence microscope equipped with a filter cube comprised of a standard excitation filter and dichroic mirror and a long-pass emission filter, 6–15 or so images were taken at fixed exposure times (typically around 100 ms per image) every 10 nm throughout the spectral range. The resulting datasets are saved as a series of 12-bit monochrome TIFFs. The fluorescence data in this article were acquired using a Zeiss Axioplan microscope, a 20× objective and a DAPI filter cube equipped with a 480-nm long-pass emission filter.

2. Brightfield microscopy: Before acquiring a spectral dataset in brightfield, an autoexposure routine was performed while imaging a blank area of a slide to determine the exposure time necessary to nearly fill (to about 90%) the CCD wells at each wavelength. A “white cube,” or reference cube, was then acquired, followed by spectral imaging of the sample, with both cubes being acquired using the same exposure times. Finally, the spectral data was simultaneously flat-fielded and converted from transmission to optical density units by taking the negative log of the ratio of the sample divided by the “white cube” using a Beer's Law conversion (30).

3. In-vivo imaging: CRi's Maestro™ instrument was used to acquire the data in a manner analogous to that described for spectral fluorescent microscopy except that no dichroic mirror was included in the light path and the excitation light was brought to the sample using the Maestro fiber-optic illumination system. Exposure times of 200–500 ms per spectral band are typical. The spectral ranges used are indicated in the figures.

Image Analysis

RGB (red–green–blue) color images were synthesized from the spectral cube by mapping the spectral data into those color channels. Either true-color (in which spectral regions are mapped faithfully into their corresponding RGB channels) or false-color displays can be generated; the latter are useful when signals in the near-infrared (NIR—by definition mostly invisible to human vision) are acquired. All the images identified as RGB images in this report are derived from the spectral datasets and not from conventional color sensors. Spectral library development, including automated tools to identify spectral features, spectral unmixing, and composite image creation were performed as described elsewhere (22, 31). Images were unmixed with no further manipulation except being scaled for display.

Unique aspects of the methods used here include “pure” spectral computation and automated spectral feature detection. “Compute Pure Spectrum” (CPS) is used to extract the authentic spectrum of a fluorophore if it is contaminated with autofluorescence in the sample (the typical case). In essence, it subtracts the autofluorescence signal from the mixed signal to yield the “pure” label spectrum. Automated spectral feature extraction (real component analysis or RCA) is described in detail in a recent reference (22). Finally, as utilized in the example shown in Figure 3, it is possible to subtract a spectral background from an entire datacube with a specified intensity, when the data is in fluorescence or absorbance (but not transmittance) format. For example, this can be used to remove excess hematoxylin stain from cytoplasm but leave behind the denser signals in the nuclei—typically with little effect on the relative intensities of other specific labels.

RESULTS

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

Fluorescence Microscopy

Two examples are presented for fluorescence microscopy. The first (Fig. 1) demonstrates the ability of spectral imaging to separate a dim fluorescein signal from much brighter and spectrally similar tissue autofluorescence in a formalin-fixed, paraffin-embedded prostate specimen. The antibody targeted the basal cells present in normal prostate glands. The RGB image in panel A shows the appearance of the sample as one would see it through the eyepieces of the microscope or as color camera would capture the image. The spectral graphs (B) indicate the spectra of the autofluorescence (AF) signal (in pink); the spectrum of fluorescein plus AF (cyan), and the computed fluorescein spectrum (green). Note that the fluorescein spectrum completely overlaps with the AF spectrum; employing a simple narrow-band emission filter system to image this sample, as is often recommended for samples with a high degree of autofluorescence, would not eliminate the contrast problem caused by the AF. Using the AF and computed fluorescein spectrum as inputs to the unmixing step, images were generated to reveal the specific staining of the basal cells (C), and a composite image comprised of specific staining plus AF (in pink) is shown in D. Software tools are provided that allow for individual planes of the composite image to be turned on or off, and be adjusted in terms of brightness and contrast. Here, for display clarity, the AF signal was dimmed relative to the fluorescein signal (compare A to D). However, these display adjustments do not affect the unmixed data themselves, which can be used to generate quantitative measurements.

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Figure 1. Fluorescein-labeled basal cells in formalin-fixed prostate tissue. Panel A shows an RGB representation of the dataset, with the fluorescein signal barely visible due to abundant autofluorescence. The spectral graphs (B) indicate the spectra of the autofluorescence (AF) signal (in pink); the mixed spectrum of fluorescein plus AF (cyan) and the computed fluorescein spectrum (green). Unmixing using the AF and computed fluorescein spectra, images were generated to reveal the specific staining of the basal cells (C) and a composite image comprised of specific staining plus AF (in pink) is shown in D.

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There are several regions in the RGB image where the AF signal is considerably brighter than the fluorescein signal. MSI can enhance the detectability of weak fluorophores by isolating their contribution, even when their spectral signatures overlap completely with that of AF. In other words, before unmixing, the specific signal may constitute a small increment over a bright base; after unmixing, the signal, even if it is small, is contrasted against a near-zero background.

Figure 2 illustrates the application of spectral unmixing to another challenging specimen, in this case, brain (cerebellar region) labeled with two quantum-dot-coupled antibodies directed against glial fibrillary acid protein (GFAP, 605-nm QDot, yellow) and neurofilamin (NF, 655-nm QDot, red; data courtesy Ventana Medical Systems, Tucson, AZ). Nuclei were stained with DAPI. While DAPI normally generates a blue signal, because this dataset was acquired in the spectral range, 530–680 nm, only its green emission “tail” is detected here. In addition to these specific signals, a ubiquitous green–yellow AF was also detected and removed from the final image to improve contrast.

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Figure 2. Formalin-fixed cerebellum labeled with two quantum dots. Panel A shows an RGB image of the sample with glial fibrillary acidic protein (GFAP) immunolabeled with a 605-nm Qdot, and neurofilamin (NF) immunolabeled with a 655-nm Qdot. Nuclei were labeled with DAPI. In addition to the specific labels, tissue autofluorescence is present. The insert shows the spectra that were derived from this datacube (see Materials and Methods) and used to unmix into the four component images (B through D), whose border colors correspond to the colored spectral graphs and to the pseudocolors used to form the composite image, F. Thus, B identifies the nuclear signal, C, the 605-nm GFAP signals, D, the 655-nm NF signals, and E, tissue autofluorescence. Because it is unmixed in the “black” channel, it is invisible in the panel F, which accounts for the greater clarity in panel F vs. the original (panel A). Sample courtesy of Ventana Medical Systems.

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Panel A shows an RGB image of the fluorescence of the sample. The yellow and red striations of GFAP and neurofilamin (NF) protein, respectively, are visible, although not clearly, against a ubiquitous greenish autofluorescence background. Specific nuclear autofluorescent signals are also present and can be seen most prominently at the right of the image. The insert shows the spectra that were derived from this datacube using the automated spectral species detection tool, RCA (see Materials and Methods), and used to unmix into the four component images (B through D), whose border colors correspond to the colored spectral graphs and to the pseudocolors used to form the composite image, F. Thus, B identifies the specific nuclear autofluorescence signal, C, the 605-nm GFAP signals, D, the 655-nm NF signals, and E, generic autofluorescence. Because it is unmixed in the “black” channel, it is invisible in the panel F, which accounts for the greater clarity in panel F vs. the original (panel A).

Brightfield Immunohistochemistry

Brightfield imaging of IHC-labeled specimens with both spatially nonoverlapped and overlapped signals is illustrated in Figures 3 and 4. Figure 3 illustrates the practical advantage of being able to detect and separate commonly used brown (DAB, or 3,3′-diaminobenzidine) and red (Fast Red) chromogens, which may be difficult to unravel visually in densely packed, complex scenes, particularly when there is co-localization of the chromogens. In Figure 3, the sample is thymus tissue stained to display CD4+ and CD8+ cells. In a mature thymus these should be distinct cell populations—thus no signal-overlap is expected. As can be seen in the RGB image, an overly dark hematoxylin counterstain is also present. Panel B illustrates how some of the hematoxylin can be digitally removed by “subtracting” an amount sufficient to essentially isolate the signal to the cell nuclei (see Materials and Methods). Panel C shows the spectra for hematoxylin, DAB, and Fast Red used to unmix the subtracted data into the individual components shown in panels D, E, and F. Panel G is a composite image of the unmixed components, a detail of which is shown in H. Finally, panel I illustrates how the hematoxylin channel can be turned off, to display only the signals from the two IHC chromogens. As can be seen, the CD4+ and CD8+ cells are separate populations, since only pure green and red signals are present.

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Figure 3. Chromogenic double-labeling for CD4+ and CD8+ cells in thymus. CD4+ were stained with 3,3′-diaminobenzidine (DAB, brown) and CD8+ cells were stained with Fast Red, and counterstained with hematoxylin. RGB image is shown in panel A. Subtracting excess hematoxylin signal digitally is illustrated in Panel B (see Materials and Methods). Spectra (panel C) for hematoxylin, DAB, and Fast Red were used to unmix the subtracted data into the individual components (panels D, E, and F). Panel G is a composite image of the unmixed components, a detail of which is shown in H. Finally, panel I illustrates how the hematoxylin channel can be turned off, to display only the signals from the two IHC chromogens. Sample courtesy Dr. Chris van der Loos.

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Figure 4. Chromogenic double-labeled estrogen receptor (ER) and progesterone receptor (PR) in breast cancer. ER is labeled with DAB and PR is labeled with Fast Red in the presence of a hematoxylin nuclear counterstain. Panel A shows the original specimen and the spectra of the three color signals used for unmixing into the separate channels is shown in B. Panel C shows the hematoxylin signal which can be used to identify the location of all nuclei. Panels D, E, and F show binary masks indicating the location of the PR, ER, and co-localized signals, respectively. An alternative display method is illustrated in panels G, H, and I, which show in detail a region from the upper center of the whole image. The data are inverted so that the green and red signals combine to form yellow where they overlap. Sample courtesy of Chris Kerfoot, Mosaic Laboratories.

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Figure 4 illustrates a breast cancer specimen with three chromogens overlapping in the cell nuclear compartment. The hematoxylin counterstain is present in all nuclei, while antibody labeling of estrogen receptors (ER, in brown) and progesterone receptors (PR, in red) is present is partially overlapping subsets of tumor cells. Panel A shows the original specimen, and the spectra of the three color signals used for unmixing into the separate channels is shown in B. The hematoxylin, DAB, and Fast Red signals were unmixed without evident crosstalk. Panel C shows the hematoxylin signal which can be used to identify the location of all nuclei. Panels D, E, and F show binary masks indicating the location of the PR, ER, and co-localized signals respectively. The masks are created by setting thresholds on the individual unmixed chromogen images and then overlaying these on the original RGB image for display.

An alternative way of displaying the data is shown in panels G, H, and I, which show in detail a region from the upper center of the whole image. The data are inverted so that the signals look like and behave like fluorescence signals (thus, green and red combine to form yellow). The PR and ER signals have been colored red and green as before. The combined image (panel I) therefore shows any double-labeled nuclei as yellow. Further quantitative image analysis techniques can be used to generate data indicating more detailed information about the presence and degree of co-expression on a cell-by-cell basis.

In-Vivo Imaging

The benefits of spectral imaging are especially marked for the detection of faint fluorophore signals commingled with spectrally overlapping autofluorescence. An example is shown in Figure 5, which presents a pair of nude mice that have been injected subcutaneously with three fluorophores (FITC, TRITC, and Cy3.5) and that also exhibit skin autofluorescence and food autofluorescence. The challenge here is both to detect and separate these overlapping fluorophores from each other, which, except for FITC, are barely visible in the RGB image (panel A). Panel B shows the AF spectrum in pink, and the “purified” spectra of the FITC (green), TRITC (blue), Cy3.5 (red), and food (yellow) generated using the compute-pure-spectra (CPS) tool as described in Materials and Methods. These spectra are consistent with published spectra. Panels C through G show the unmixed components: FITC (panel C); TRITC (panel D); and Cy3.5 (panel E); food (panel F); and skin autofluorescence (panel G). All panels show signals which are well isolated from the others. Panel H is a composite image of the unmixed components, presented in their respective pseudocolors.

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Figure 5. Nude mice with two different species of autofluorescence and three subcutaneous fluorophore signals. The mice were injected subcutaneously with FITC, TRITC, and Cy3.5. The RGB image is shown in panel A. Panel B shows the AF spectrum (pink), and “purified” spectra of the other components with the AF contributions computationally removed (see Materials and Methods, FITC, green; TRITC, blue; Cy3.5, red; food, yellow). Panels C through G show the unmixed components with essentially no cross-talk despite spectral overlap. FITC, Panel C; TRITC, Panel D; Cy3.5, Panel E; Food, Panel F; and skin autofluorescence, Panel G. Panel H is a composite image of the unmixed components displayed in their respective pseudocolors.

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For variety, in Figure 6, we show a different problematic autofluorescence source, namely pericardiac fat in a pig heart imaged ex vivo. The experiment consisted of injecting multiple fluorescently labeled beads into a cardiac artery in an anesthetized pig to investigate the influence of particle size on perfusion patterns. The labels consisted of three sets of fluorescently tagged latex beads ranging in size from 0.1 to 20 μm in diameter (only two of which can be seen with the excitation settings used in this imaging example). After injection, the heart was removed for spectral imaging. The complete results will be reported elsewhere; here we demonstrate the separation of a bright signal from fat from spectrally similar green and orange latex beads, as well as from the dim autofluorescence of general tissue autofluorescence.

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Figure 6. Excised porcine heart injected in vivo with fluorescent perfusion markers. Green and red beads with different dimensions and thus different tissue distribution patterns were injected in-vivo into the anterior circumflex artery of a pig. After sacrifice, the heart was spectrally imaged. An RGB image of the heart is shown (Panel A). The large yellow–green region is the area of bead-distribution. The bright green object to the top right represent emissions from pericardiac fat, and the rest of the heart is visible due to general tissue autofluorescence. Panel B indicates the spectra corresponding to these features: general AF, pink; bead 1, green; bead 2, red; fat-AF, yellow. Panels C, D, E, and F are the spectrally unmixed images corresponding to bead 1, bead 2, fat-AF, and general AF, respectively. Little crosstalk is present. A composite image (minus the general AF signal for clarity) is shown in panel G. Sample courtesy Dr. Roger Hajjar, Massachusetts General Hospital.

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An RGB image of the heart is shown in Panel A. The large green–orange region is the area of distribution of the injected fluorescent beads. The bright green object to the top right is pericardiac fat, and the rest of the heart is visible due to general tissue autofluorescence. Panel B indicates the spectra corresponding to these features: general AF, pink; bead 1, green; bead 2, red; fat AF, yellow. Note how all these species overlap spectrally. Panels C, D, E, and F are the spectrally unmixed images corresponding to bead 1, bead 2, fat AF, and general AF, respectively. Little crosstalk is present. Ideally, the general AF should have been uniformly grey even where the beads were located. However, it is likely that the dense bead presence may have blocked the excitation light reaching the tissue and/or the emitted light returning from it. Finally, a composite image (minus the general AF signal for clarity) is shown in panel G.

To achieve these results, it proved necessary to measure the spectra of the beads (plus heart AF) in situ and then to compute the “pure” spectra of the individual fluorescent beads as described earlier. Using the spectra of the beads measured in suspension (rather than in the heart) did not provide optimal unmixing results, even though the measured and computed spectra were fairly similar (data not shown). This reinforces the advisability of using spectra as measured during imaging particular samples, rather than relying on previously determined spectra collected under different conditions of excitation, absorbance, and scattering. The source of the autofluorescence signal arising in the fat has not been determined—literature is scarce on the subject. It is possible that fat-soluble dietary components with appropriate fluorescence properties have accumulated in fatty tissues in this pig. For example, maize has been shown to express several fluorescent species emitting in the green and yellow range (32), consistent with the fat-signals seen here.

DISCUSSION

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

There are some specific aspects of MSI in fluorescence mode that merit further discussion. In this mode, spectral unmixing benefits measurements of label fluorescence in two ways. First, there is the advantage of improved quantitative accuracy by means of the appropriate partitioning of the optical signal into its various sources (target(s) and autofluorescence(s)). The presence of noise in the measurements will affect the overall accuracy of spectral unmixing by adding uncertainty to the mathematical matrix-inversion procedure (33). However, exposures are taken at multiple wavelengths, thereby effectively increasing the number of photons detected, and reducing the effective shot-noise. The degree to which this is a benefit is related to how distinct (orthogonal) the spectral vectors are that reflect the various fluorescent sources in a sample.

With respect to choice of imaging sensor, it is important to realize that when either autofluorescence or the desired signals are relatively bright, one may be free to use mid-range ($5,000–$15,000) rather than high-end sensors, since for shot-noise-limited situations, in which the chief noise-source scales with the brightness of the captured light, unmixing will improve signal-to-noise much more than will moving to a low-noise camera. In low-light cases, the approach typically used for increasing signal-to-noise is to employ more sensitive, highly cooled, low-read-noise (and therefore expensive) CCD cameras. However, for contrast-limited situations as opposed to read-noise limited situations, increased sensor sensitivity merely results in capturing autofluorescence more rapidly. Camera noise typical of mid-range scientific cameras in such cases is usually immaterial since shot-noise will be much larger than camera noise for all reasonable exposure times (i.e., those short enough to make thermal noise insignificant) (34).

CONCLUSION

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

We have shown that a spectral imaging approach based on band-sequential acquisition is suitable for many different imaging modalities, including microscopy in both brightfield and fluorescence, and whole-animal in-vivo imaging. In brightfield, at least three spatially and spectrally overlapping chromogens can be quantitatively resolved, thereby enabling multiparameter molecular imaging with labeling techniques generally favored by pathologists and others who, for various practical and/or historical reasons, are not inclined to use fluorescence-based imaging approaches. In fluorescence microscopy, we show the removal of significant autofluorescence present in typical clinical tissue specimens as well as demonstrate the utility of MSI with multiplexed quantum-dot labeled specimens. In addition to resolving multiple species, autofluorescence can be either suppressed or exploited as an endogenous signal. Finally, fluorescence-imaging of small animals or excised organs benefits from multispectral data acquisition and analysis.

In all cases, the technology used for these examples was straightforward and robust. The microscope systems can easily be moved from microscope to microscope, and the current software for both the microscope and in-vivo imaging systems is very similar, thus flattening the learning curve for users who may want to take advantage of both imaging platforms.

Acknowledgements

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED

The authors acknowledge their research partners and customers who have generously allowed them to share their images, including Ventana Medical Systems (for Fig. 2); Dr. Chris van der Loos, Academical Medical Center, Amsterdam; (for Fig. 3); Chris Kerfoot, Mosaic Laboratories (for Fig. 4); Umar Mahmood and Jenny Tam, Massachusetts General Hospital (for Fig. 5); and Roger Hajjar, Massachusetts General Hospital (for Fig. 6). They also thank the editor and the anonymous reviewers for their careful reading and insightful comments.

LITERATURE CITED

  1. Top of page
  2. Abstract
  3. FLUORESCENCE MICROSCOPY
  4. IN-VIVO IMAGING
  5. BRIGHTFIELD MICROSCOPY
  6. MATERIALS AND METHODS
  7. RESULTS
  8. DISCUSSION
  9. CONCLUSION
  10. Acknowledgements
  11. LITERATURE CITED