Spectral Imaging and Pathology: Seeing More

overview [generalist | histology | molecular diagnostics]

Spectral Imaging and Pathology: Seeing More Richard M. Levenson, MD CRI, Inc, Woburn, MA DOI: 10.1309/KRNFWQQEUPLQL76L

왘 The development of molecular

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medicine has led to new demands on pathologists to render prognostic and therapy-shaping analyses beyond those obtainable through purely visual examination of standard specimens. One technique that delivers new power to the pathologist is spectral imaging coupled with microscopy. While the results are encouraging, it is clear that the field is still young, and more experience is required to determine how this technology will be most useful in the clinical arena.

Anatomic pathology is a visual discipline. Microscopic scenes of hematoxylin and eosin-stained tissues yield both low-power spatial clues as well as insights on the structural and functional state of individual cells, thousands of which may be visible at a time. Add to this the fact that assays such as immunohistochemistry and in situ hybridizations can provide molecular information within the architectural contexts, and it is surprising that digital and quantitative imaging continues to play no more than a small role in clinical practice. This is still true despite remarkable improvements in the capabilities, cost, and availability of cameras (sensors) and computers. A number of factors are responsible for this. The human eye-brain combination is skilled at perceiving and interpreting visual scenes and can rapidly evaluate a sample at low magnification and, with facility, switch to high power for validation or for further detail. To do the same electronically may require the collection and analysis of literally thousands of high-resolution digital images from an entire sample. Secondly,

pathology is difficult, and each imagebased interpretative system must specialize in the nuances of a particular organ and disease. Thirdly, current imaging hardware-software configurations often require trained operator assistance, although gradually the demands on the user are lessening. Finally, while subjective (“1+, 2+, 3+”) grading or scoring schemes are widely used, there is little discernible enthusiasm among practicing pathologists for objective, quantitative image-based descriptors, at least for metrics such as nuclear grade or dysplasia. Reasons for this include the fact that the case for many quantitative schemes have, until recently, not been compelling; that multi-institutional image analysis protocols are difficult to implement; and that recognized standards for image analysis have not been established. On the other hand, molecularly based techniques are being adopted into pathology at an ever-increasing rate. Many of these rely on suspensions or extracts of cells or tissues, but others, such as immunohistochemistry and fluorescence in situ hybridization, are performed on tissue sections or isolated cells and the results evaluated through a microscope. In contrast to conventional histopathology, the simplified readout (absence or presence of a certain color) and the (semi-) quantitative nature of the techniques (percent of cells positive, degree of positivity, number of signals per cell) are more readily approached through digital image analysis. Spectral imaging is a relatively new technique that acquires an image along with an optical spectrum captured at every pixel. Its performance improvement over visual examination and conventional grayscale or 3-color imaging has led to the development of

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novel, qualitative, as well as quantitative tools for interpreting conventionally stained pathology samples, overcoming some, although not all, of the obstacles outlined above. In addition, spectral imaging facilitates the use and extension of molecular pathology approaches; in particular, permitting the detection and analysis of multiple probes simultaneously. Spectral imaging can be applied to fluorescencebased assays, but it offers particular promise for increasing the utility of standard brightfield microscopy familiar to all pathologists. Multiplexing in Biomedicine The genomics revolution and the arrival of truly molecular medicine have led to demands on pathologists to provide more information on individual patients’ tumors. An accurate diagnosis, perhaps accompanied by broad statistical predictions of how the patient might fare must now be supplemented with predictive and prescriptive information specific to the individual patient. While a great deal of effort is being applied to DNA-chip-based RNA expression arrays for molecular profiling of tumors,1,2 and more recently, proteomic approaches, it is not clear whether or how they will be deployed, for a variety of reasons, including issues of reproducibility, cost, throughput, and actual incremental utility in patient management. At present, traditional anatomic assessment of tumors, coupled with clinical staging, seems to perform at about the level of expression array systems, at least in terms of prognostic ability. What may emerge is a practice in which molecular (DNA, RNA, protein) characteristics, particularly those that qualify or disqualify patients for particular treatments, are evaluated by

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pathologists in relatively intact histological specimens. This approach avoids the problem of extracting and, if necessary, amplifying analytes prior to detection, a process that eliminates anatomic correlates, and may mix dissimilar histological elements into a single soup, the so-called “Waring Blender” problem. While in situ imaging is already the standard of care in breast cancer, in which estrogen-receptor and Her2/neu levels are evaluated using immunohistochemistry or fluorescence in-situ hybridization (FISH), demands for these assays will continue to increase, forming the economic rationale for such companies as ChromaVision, which markets a quantitative immunohistochemistry imaging system. It has been estimated that more than 500 new drugs are currently under development that will require the patient to be molecularly qualified prior to treatment (D. Rimm, personal communication). A capability of performing robust multiplexed image-based molecular assays will be valuable. Moreover, it is clear that for many situations, the averaged, populationwide molecular phenotype is far less informative than knowing patterns of co-expression at the single cell-level. This is well known in hematopathology and is becoming clear in breast cancer. The development of novel fluorescent reagents and spectral “barcoding” labeling strategies has opened up new opportunities for multiplexed imaging, with the potential to detect 10s to 1,000s of different labels simultaneously.3-6 Finally, there are other issues that affect the performance of even unmultiplexed imaging. An example is that of autofluorescence, the unwanted emission of light by unstained tissue samples that can obscure the detection of specific fluorescent labels. Some conventionally fixed and fluorescently labeled histological specimens may be unreadable when imaged using conventional techniques.7 Problems With Current Approaches Visual examination is the backbone of pathology. However, this

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subjective method is ill-suited for quantitative and reproducible assessments of such continuous but important variables such as nuclear grade, etc. Even the quantitative technique of mitotic-figure enumeration is still a manual procedure. On the other hand, imaging is increasingly being used to quantify immunostains, and for this use either a monochrome camera with 2 or 3 filters in a filter wheel, or a color RGB (redgreen-blue) camera is typically employed.8-10 However, despite major effort, it has proven difficult to impossible to use standard RGB imagery to quantitatively unmix immunohistochemistry chromogens (such as DAB or Fast Red) from themselves or from the typical hematoxylin counterstain, except in favorable conditions. It is essentially impossible to unmix more than 3 signals using just the 3 (broad) RGB channels. Fluorescence microscopy typically uses so-called filter cubes (sets of excitation and emission filters with a matched dichroic mirror) and often a grayscale rather than color camera. More elaborate setups that substitute filter wheels in the excitation and emission positions are also used, providing increased flexibility, at the cost of increased expense and complexity.11 Two- or 3-color based fluorescence detections are routinely achievable, but doing more is difficult. Cross-talk corrections to handle spillover of the fluorescent signals from 1 wavelength channel to another are often necessary, and adoption of new fluorescent reagents may require the purchase of additional filters. New multispectral confocal microscopes have been developed,12 but these are of a cost and complexity that would generally preclude their use in routine clinical settings. In any case, pathologists generally dislike having to resort to fluorescent methods, since it often means moving to another (expensive, shared) instrument, disrupting work-flow, turning down the lights, watching images photobleach under prolonged observation, storing labile specimens refrigerated and in the dark, etc. If multiplexing could easily be accomplished using brightfield methods,


overall happiness (at least among pathologists) should increase. Spectral Imaging Hardware A variety of technologies are now available for use in combination with microscopy, including liquid crystal tunable filters (LCTFs) (CRI),13 acousto-optical tunable filters (AOTFs),14 Fourier-transform interferometry (Applied Spectral Imaging),15 line-scanning prism or gratings-based devices (Lightform, Spectral Imaging Ltd), and more. Since the focus of this article is what can be done with spectral imaging rather than how the imaging technology works, only 2 exemplars will be discussed in detail, namely, systems based on liquid crystal tunable filters and a novel tunable illumination source. Liquid Crystal Tunable Filters The newly released Nuance Multispectral Imaging System from CRI utilizes CRI’s VariSpec liquid crystal tunable filters [I1]. These filters behave like a filter-wheel with hundreds of filters, but have no moving parts. They transmit saturated colors within narrow spectral bands whose peak positions can be electronically and randomly changed (or “tuned”) to any other wavelength with about 1-nm precision

[I1] The CRI Nuance multispectral imaging system. The hardware components (liquidcrystal-tunable-filter, coupling optics and a megapixel grayscale CCD) are enclosed in the housing that attaches to any microscope equipped with a standard (1x) C-mount coupler. The filter can be rapidly tuned to transmit any narrow spectral region within the visible range (400 to 720 nm) with about 1-nm precision. The monitor displays an example of the spectral analysis software included with the system.

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and within 50 ms. These filters come in a variety of spectral ranges (eg, VIS: 420-720 nm; or NIR: 850-1800 nm) and their bandwidths (spectral resolution) range typically from 7 to 20 nm. The Nuance system encloses a VIS or NIR filter, coupling optics, and a cooled scientific-grade monochrome CCD into a device that mounts directly onto any microscope equipped with a standard C-mount adapter. In other words, it simply replaces standard Cmount-equipped digital cameras. Software coordinates the tasks of tuning the filter and taking images at each tuned wavelength. The Nuance systems are well suited to enhance fluorescence-based analyses, having proved useful for multicolor immunofluorescence or FISH assays, as well as for the identification and elimination of interfering autofluorescence. They are also capable of doing brightfield-based spectral imaging for assisted diagnostics or assisting with analysis of chromogen-based assays. Image quality, achievable using LCTF-based systems, is excellent.

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SpectraLamp Agile Spectral Light Source A novel approach for brightfield microscopy utilizes a CRI-developed spectrally agile light source [F1]. This illumination system, which replaces the standard lamp at the rear of most microscopes, can deliver any combination or intensity of wavelengths from 420 to 700 nm, including white light.16 It can function as a straightforward spectral illuminator (putting out 1 wavelength at a time), but its unique capability for creating custom mixtures of light holds out the promise of obtaining full information from all relevant spectral bands in a handful of exposures with good signal-to-noise performance. As with the Nuance, switching wavelengths is coordinated with camera acquisition, but unlike the latter, it is for brightfieldmode only; for typical pathology applications this should be fine. Acquiring a Spectral Image Whether one uses a Nuance LCTF or the SpectraLamp, the technique for acquiring a spectral data stack is more

[F1] A beta version of the CRI SpectraLamp tunable illumination source. It mounts on many models of microscopes in place of the standard halogen brightfield source and can generate white light (for routine viewing) as well as 29, 10-nm-wide bands of spectral illumination from 420 to 700 nm (blue to red). Spectral images are captured rapidly with a grayscale CCD as the illuminator tunes through multiple wavelengths. Analysis of the resulting datasets is performed with the same software tools provided with the Nuance system.

[F2] Screen-shot of Nuance spectral analysis software. A spectral stack (450 to 700 nm in 10-nm steps) taken of an H&E-stained colon specimen is shown. The image is an RGB “distillation” of the spectral data and behind every pixel is an associated optical spectrum visible as the user moves a cursor. The spectral library controls the left of the main window allow the user to select spectra to be used for further analytical processes such as classification or unmixing. The red and yellow lines indicate spectra taken from an RBC and from an epithelial nucleus. The white line is the “live” spectrum associated with the cross-hair, in this case located over epithelial cytoplasm. Note that it is different from the 2 previously stored spectra.

or less identical. The system presents a live image of what is being imaged by the camera. Different camera exposure times are required for each wavelength and an autoexposure procedure determines optimal exposures for each image. Because microscope illumination is never perfectly uniform (typically


brighter in the center and darker in the corners), a “white stack” can be acquired from a clear area of the slide and used to “flat-field” the image stack (wavelength by wavelength), to mathematically correct for such illumination unevenness. Then an image stack can be acquired. This is done by instructing the system to start at 1 wavelength and take an image, then step 10 or 20 nm spectrally, take another image, and so on, until the last wavelength is reached. The spectral stack can then be saved, or further processed by flat-fielding, or by converting it from transmission units to absorbance units—a maneuver necessary for quantitative analysis of brightfield images. The whole procedure can be automated, and with fast cameras and reasonable illuminations, 1,300 x 1,000 pixel data stacks with 10 to 15 wavelengths may be acquired in 2 to 5 seconds. Analyzing a Spectral Image The spectral image stack is then “distilled” for presentation into an RGB image with an optical spectrum “behind” every pixel. One can move a mouse over the image and instantaneously see the resulting spectra presented in a separate window [F2]. The spectra can be captured from selected pixels and used in various analytical procedures. They can also be saved and re-used, especially in a clinical environment in which sample preparation procedures are standardized. The whole procedure of spectral acquisition and analysis can be automated, so that all is required is to select the area to be imaged and to press a button. Even that step may be eliminated as these techniques are merged with whole-slide scanning instrumentation. What can be done with a spectral image? Typically, the 2 tasks that present themselves are classification and unmixing. Classification is equivalent to segmentation, the task of assigning either pixels or objects (blobs) into different classes, and is an exclusive operation. In other words, a pixel or object can only be 1 thing or another. One may wish to separate nuclei from cytoplasm, or malignant nuclei from

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benign, or to classify white blood cells into different groups. Conventional hematoxylin-and-eosin- or Papanicolaou-stained pathology sections can have sufficient spectral content to allow the classification of cells of different lineage or to separate normal from neoplastic cells. Analysis of such specimens may succeed using spectral “signatures” and simple segmentation algorithms based on spectral similarity measures, but may also require more advanced analysis techniques including sophisticated, machine-learning systems. Unmixing is the response to situations in which there may be more than 1 measured analyte per structure. For example, multiple cell surface markers or nuclear antigens will almost certainly overlap spatially. If one wants to know how much progesterone receptor AND estrogen receptor is present in a single cell, the 2 signals have to be unmixed and quantified separately. One may also want to remove the contribution of a counterstain from the intensity of the chromogen used to detect a molecular species, or to eliminate the obscuring presence of autofluorescence, which can make assessment of a fluorescent signal in fixed tissues problematic at best. The mathematics behind unmixing can be fairly simple17 and well known to spectroscopists who have been doing this in non-imaging mode for decades, but more sophisticated tools can also be applied.18,19 Applications of Spectral Imaging in Pathology Fluorescence Applications: Immunofluorescence (IF) and In-Situ hybridization (ISH). These molecular techniques are intended to detect diagnostically or prognostically significant molecules via probes coupled (directly or indirectly) to fluorophores. Immunofluorescence is used to detect antigens (usually proteins) whereas ISH uses nucleic acids or nucleic acid analogues (eg, PNAs) to detect DNA or RNA species. It is often desirable to detect and analyze several markers simultaneously. Spectral techniques are well suited to accomplishing such multiplexing.

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The use of conventional fluorescent dyes poses problems because the wavelength gap between the excitation maximum and the emission maximum (the Stokes shift) may be as little as 10 to 15 nm. Various excitation regions are necessary to excite a large number of dyes, either in sequence or simultaneously, in which case, a double- or triple-dichroic filter cube set is often used. Such an arrangement chops up the emission spectra into relatively narrow bands and it can be difficult to distinguish different fluors by their spectral behavior within these small emission regions. Quantum dots—nanometer-sized crystals semiconductors—are promising new fluorescent reagents20 that can be excited in a single wavelength region, eg, 350 to 490 nm, and, depending on the size of the crystal, emit anywhere in the visible to the near-IR. An additional benefit over conventional dyes is that they are very resistant to photobleaching. When coated with

derivatizable layers, these clusters can be made water-soluble and chemically reactive for binding to antibodies and nucleic acids. Multiple colors are already available commercially, and it is possible to create families of quantum dot dyes that have peak-emissions that vary by as little as 5 nm. Thus, many (10s) of probes can be excited with a single excitation band and, more importantly, spectrally resolved. Spectral unmixing is particularly helpful with the problem of separating desired fluorescent signals from autofluorescence, (ie, the unwanted fluorescent emissions of a sample that occur even in the absence of any exogenous fluorescent labels). I2 shows how a fluorescent protein can be visualized in transfected tissue culture cells, and demonstrates how this procedure has the potential benefit of increasing detectability of faint signals by removing background without diminishing the intensity of the desired signals. A similar unmixing demonstration, this time

247 [I2] Spectral unmixing of fluorescent signals from autofluorescence-I. Tissue culture cells were transfected with a hybrid cytoplasmic protein linked to a CFP (cyan fluorescent protein) moiety, and a spectral stack taken from 500 to 600 nm (in 10–nm steps) using a 40x lens. Spectral information was then used to unmix the CFP signal from the autofluorescence. A. RGB image distilled from the spectral stack—this closely resembles what was visible through the microscope eyepieces. B. Autofluorescence signal only. Note the improved contrast compared to A—this was accomplished by also unmixing the scattered light contributing to background haze. C. CFP signal only. Note that it is mostly confined to the cytoplasmic compartment and that some of the brightly autofluorescent cells are negative for CFP. D. Combined image of B and C, pseudo-colored for clarity.


[I3] Spectral unmixing of fluorescent signals from autofluorescence-II. Tissue section was immunostained using an antibody directed against a vascular antigen and coupled to quantum-dot fluorescing with an emission peak at 640 nm. A spectral stack was taken from 550 to 700 nm (in 10-nm steps) using a 20x lens and spectral information used to unmix tissue autofluorescence from the quantum-dot signal. A. RGB-imaged distilled from spectral stack—this approximates what was visible through the microscope. B. Unmixed autofluorescence signal. C. Unmixed quantum-dot signal. D. Combined image of B and C (pseudo-colored).

248 [I4] Spectral multiplexing in FISH. A clinical specimen of breast cancer (formalin-fixed, paraffinembedded) probed for her2/neu (red) and chromosome 17 (green) with nuclei counterstained with DAPI. Imaged using Nuance LCTF system in conjunction with a Vysis triple-dichroic filter set, with images taken from 450 to 700 nm in 20-nm steps using a 40x lens. Left panel: RGB image distilled from spectral stack. This closely resembled what was visible by eye. Note the orange autofluorescence arising mostly from connective tissue components around the cells. Right panel: results of spectral unmixing into DAPI, chromosome 17, Her2/neu and autofluorescence channels. The latter is not included in the final composite image, which has greatly improved clarity over the original.


looking at quantum dots as a fluorescence label for immunohistochemistry, is shown in I3. Fluorescence in situ hybridization shares with IF the same problems of multiplexing and autofluorescence. Particularly difficult are assays that look at interphase cells in fixed histology specimens; such samples as brain or prostate tissue can have high autofluorescence levels that may make IF or FISH unrewarding. An example illustrating the benefit of spectral imaging on a clinical specimen of breast cancer is shown in I4. Here the separate nuclear (DAPI), Her2/neu, and chromosome-17 fluorescent signals are separated into distinct channels, and contrast is improved by near-complete elimination of tissue autofluorescence. Brightfield Applications: Immunohistochemistry (IHC). Despite the availability of numerous chromogens, typically only a single color is used per slide; if more than 1 antigen is to be analyzed, serial sections are made and a different antibody is applied to each. This procedure generates more slides and possibly more preparation steps than if the reagents could be multiplexed on a single slide. If coexpression of antigens in a single cell is an important question, as it might be in some lymphoma or breast cancer investigations,21 and if flow cytometry is not available or suitable, then double- or triple-staining single slides with different chromogen-coupled antibodies may be helpful. Multi-staining procedures are infrequently performed not only because the technique is still somewhat tricky, but also because it is difficult, by eye, to tell where and to what extent different stains spatially overlap (due to co-expression of 2 or more of the antigens in the same cellular compartment). In fluorescence, multiplexed signals are more easily resolved, even if they overlap spatially, because the emitted light is linearly additive, whereas absorbing chromophores reduce transmitted light in a non-linear fashion. If the brightfield image is converted mathematically from transmittance units to absorbance (optical density) units, the signals become linearly additive, but

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this procedure still does not solve the following 2 problems: 1) intense staining can result in virtual opacity; and 2) the difficulty of visually resolving more than one chromogen if they spatially overlap. Thus immunofluorescence, despite its inconveniences, is the current method of choice for multiplexed molecular analysis. However, spectral imaging can make feasible the use of brightfield techniques in examination of double- and triple-stained specimens; this may be one of its most important applications.22 A common stain combination is DAB (brown) with a counterstain of hematoxylin (blue). While it may be easy to detect the brown stain visually, if the brown and blue stains overlap, as they will if the antigen is a nuclear one, it is difficult to get a quantitative estimate of the density of the brown stain in isolation. An example of unmixing DAB from hematoxylin is given in I5, which presents the case of a germinal center stained for a nuclear proliferation marker, ki67. The original image is spectrally unmixed into just the hematoxylin and just the DAB stains, the latter grayscale image is suitable for quantitative analysis. Further development of software tools will allow automatic measurement of stain intensity on a regional or per-cell basis. A multicolor immunohistochemically stained lymph node is shown in which brown, red, and blue (DAB, Fast Red, and hematoxylin) are spectrally unmixed and presented as separate signals [I6]. Brightfield Applications: Spectral Imaging of Conventionally Stained Pathology Samples. For reasons of historical precedent, simplicity, cost, and utility, standard pathology specimens are now almost universally stained with hematoxylin (blue, with affinity for nuclear structures) and eosin (red, with affinity for much of everything else). Other stains used for cytology specimens include Giemsa and the Papanicolaou stain (used, of course, for the Pap smear). Each of these stain-combinations exhibits spectral complexity that can be of use in the analysis of clinical material. Currently, pathologists evaluate stained

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[I5] Brightfield immunohistochemistry-I. Germinal center of lymph node stained with antibodies to proliferation marker, ki67, and counterstained with hematoxylin. The sample was spectrally imaged (using a SpectraLamp tunable illumination source) from 440 to 680 nm in 20-nm steps using a 20x lens and the DAB (brown) stain spectrally unmixed from the blue hematoxylin. Note that the hematoxylin stain should be present in every nucleus. A. RGB image distilled from spectral stack. B. Unmixed DAB signal. Note that there is no cross-talk with the blue hematoxylin signal. C. Hematoxylin signal. Note that the intensity of the nuclear staining is more or less uniform, regardless of the presence or absence of the DAB chromogen. D. Pseudo-colored composite image of B and C.

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[I6] Brightfield immunohistochemistry-II. Lymph node stained for kappa and lambda chain with DAB and Fast Red respectively, and counterstained with hematoxylin. A. RGB image. B. Unmixed DAB signal. C. Unmixed Fast Red signal. D. Unmixed hematoxylin signal. E. Pseudo-colored composite image. The individual signals are incorporated as separate “layers” in the image and can be turned on or off independently using such software tools as Photoshop. This is very useful for evaluating the presence of double- or triple-staining of individual cells or structures.


[I7] Use of spectral imaging to segment nuclei in H&E-stained specimens. Prostate cancer was spectrally imaged from 450 to 680 nm in 10-nm steps using a 20x lens. The data was converted to absorbance (optical density) units and separated into eosin- and hematoxylinsignals. A. RGB image distilled from spectral stack. B. Eosin signal (which is indeed present in nuclei as well). C. Hematoxylin signal revealing nuclei isolated from connective tissue components and suitable for subsequent morphometric analysis, if desired. D. Composite image. This technique can also be used to change color values for different stained species to increase contrast or “readability.”

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tissue sections looking for disorders of cellular size, shape, and organization through a process of pattern recognition and structural evaluation of overall tissue architecture. Spatial clues usually are paramount, and specific color information is rarely absolutely necessary. In fact, although no pathologist would be content with viewing actual specimens in black and white, until recently, most pathology texts were illustrated almost exclusively with grayscale photomicrographs. Cancer and other derangements are manifested by alterations in macromolecular make-up of affected cells—these changes can cause alterations in the strength, pattern, and spectral properties of histological stains.23 The color and appearance of stains may vary depending on what they bind to and whether other stains form part of the coordination complex. The phenomenon, known as metachromasia, is the most visible of these alterations, being exhibited, for example, when stains that

normally stain bluish appear purple. While metachromasia is detectable by eye, more subtle changes in the absorbance spectrum of a stain (or stain-combination such as H&E) can only be appreciated by more sensitive color measurement techniques. Another potential application of spectral techniques to H&E analysis may be their use in image segmentation as a precursor to tissue morphometry, the quantitative analysis of cellular and tissue architecture. It is still difficult, for example, to reproducibly separate nuclei from cytoplasmic regions because of variations in staining intensities. Spectral tools, particularly unmixing the hematoxylin signal from the eosin signal can robustly capture isolated nuclear features, as shown in I7. Spectral-Spatial Analysis The development of morphometry techniques usually involves laborious hand-tuning of algorithms intended to


distinguish 1 class of lesions from another, and, typically, color-based information is rarely used. It may be desirable to combine spatial metrics with useful spectral features, but how best to accomplish this is not obvious. Fortunately, machine-learning tools developed for certain remote-sensing tasks24 may translate well to solving problems in automated histopathology. A preliminary result is shown in I8, which demonstrates the application of automatically generated spatial/spectral algorithms to the task of distinguishing normal from malignant breast epithelial cells in a case of cancerization (infiltration of architecturally normal breast lobules by breast cancer cells). Early results like this suggest that spectral/spatial analysis will be a fruitful avenue to explore, and may lead to systems that can assist pathologists in the evaluation of difficult specimens, either by discriminating otherwise confusable diagnostic entities, or by providing morphological measurements that, in comparison to subjective evaluations, can more precisely estimate biological properties of a lesion. Conclusions Spectral imaging is a promising technique that offers pathologists new tools for both qualitative and quantitative tasks. As the speed of the hardware increases, as the sophistication of the software tools continues to improve, and as costs fall to affordable levels, spectral imaging is likely to play an increasingly important role, not just in research centers, but also on the desks of individual practitioners. Acknowledgements: Thanks to colleagues, collaborators and providers of interesting samples. Helpful individuals include: Jeff Beckstead, Interscope Technologies; Thomas Grogan, University of Arizona; Klaus Hahn, Scripps Research Institute, Allen Gown, Phenopath; Stuart Schnitt, Beth Israel Deaconess Medical Center, David Rimm, Yale University Medical Center. This work was supported in part by SBIR funding from the National Cancer Institute (#1 R44 CA88684).

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10. Bacus SS, Goldschmidt R, Chin D, et al. Biological grading of breast cancer using antibodies to proliferating cells and other markers. Am J Pathol. 1989;135:783-792. 11. Eils R, Uhrig S, Saracoglu K, et al. An optimized, fully automated system for fast and accurate identification of chromosomal rearrangements by multiplex-FISH (M-FISH). Cytogenet Cell Genet. 1998;82:160-171. 12. Dickinson ME, Bearman G, Tilie S, et al. Multi-spectral imaging and linear unmixing add a whole new dimension to laser scanning fluorescence microscopy. Biotechniques. 2001;31:1272, 1274-1276, 1278. 13. Hoyt C. Liquid crystal tunable filters clear the way for imaging multiprobe fluorescence. Biophotonics International. 1996;July/August:49-51. 14. Wachman ES, Niu W, Farkas, DL. AOTF microscope for imaging with increased speed and spectral versatility. Biophysical Journal. 1997;73:1215-1222. 15. Garini Y, Katzir N, Cabib D, et al. (1996) Spectral Bio-Imaging. In Wang, X.F. and Herman, B. (eds.), Fluorescence Imaging Spectroscopy and Microscopy. John Wiley & Sons, Inc., Vol. 137.

[I8] Spectral/spatial analysis. An H&E-stained breast lobule that had been partially infiltrated by cancer cells (“cancerization”) was spectrally imaged from 440 to 700 nm in 10-nm steps using a 20x lens. Some of the normal and malignant cells in panel A were used as the training set for a genetic algorithm-based machine-learning technique, and the resulting algorithm was applied to the whole sample. A. Original H&E specimen—RGB image derived from the spectral stack. B. An adjacent serial section was stained for cancer-related cytokeratin to show the approximate position of the malignant cells in the original specimen. C. Output of the spectral/spatial classification algorithm. Blue indicates the pixels adjudged by the algorithm to belong to the “malignant” class. Note the reasonable correlation between these results and the location of cancer nests in the adjacent immunostained section.

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5. Speicher M, Gwyn Ballard S, Ward D. Karyotyping human chromosomes by combinatorial multi-fluor FISH. Nat Genet. 1996;12:368-375. 6. Szuhai K, Bezrookove V, Wiegant J, et al. Simultaneous molecular karyotyping and mapping of viral DNA integration sites by 25color COBRA-FISH. Genes Chromosomes Cancer. 2000;28:92-97. 7. Neumann M, Gabel D. Simple method for reduction of autofluorescence in fluorescence microscopy. J Histochem Cytochem. 2002;50:437-439. 8. Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001;23:291-299. 9. Lehr HA, Teeling P. Complete chromogen separation and analysis in double immunohistochemical stains using Photoshopbased image analysis. J Histochem Cytochem. 1999;47:119-126.

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16. Miller PM, Levenson, RM. Beyond image cubes: an agile lamp for practical, near-100% photon-efficient spectral imaging. Proc. SPIE. 2001;4259:1-7. 17. Farkas DL, Du C, Fisher GW, et al. Noninvasive image acquisition and advanced processing in optical bioimaging. Comput Med Imaging Graph. 1998;22:89-102. 18. Haaland DM, Easterling RG, Vopicka DA. Multivariate least-squares methods applied to the quantitative spectral analysis of multicomponent samples. Appl. Spectroscopy. 1985;39:73-84. 19. Martinez MJ, Aragon AD, Rodriguez AL, et al. Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays. Nucleic Acids Res. 2003;31:e18. 20. Han M, Gao X, Su JZ, et al. Quantum-dottagged microbeads for multiplexed optical coding of biomolecules. Nat Biotechnol. 2001;19:631-635. 21. Shackney SE, Pollice AA, Smith CA, et al. Intracellular coexpression of epidermal growth factor receptor, Her- 2/neu, and p21ras in human breast cancers: evidence for the existence of distinctive patterns of genetic evolution that are common to tumors from different patients. Clin Cancer Res. 1998;4:913-928. 22. Macville MV, Van Der Laak JA, Speel EJ, et al. Spectral imaging of multi-color chromogenic dyes in pathological specimens. Anal Cell Pathol. 2001;22:133-142. 23. Frable WJ. Needle aspiration biopsy of pulmonary tumors. Sem Resp Med. 1982;4:161169. 24. Lei W. An adaptive genetic algorithm and its application to image segmentation. Multispectral Image Processing and Pattern Recognition. 2002.

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