Richard M. Levenson

Multiplexed ion beam imaging of human breast tumors

Immunohistochemistry (IHC) is a tool for visualizing protein expression that is employed as part of the diagnostic workup for the majority of solid tissue malignancies. Existing IHC methods use antibodies tagged with fluorophores or enzyme reporters that generate colored pigments. Because these reporters exhibit spectral and spatial overlap when used simultaneously, multiplexed IHC is not routinely used in clinical settings. We have developed a method that uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters. Multiplexed ion beam imaging (MIBI) is capable of analyzing up to 100 targets simultaneously over a five-log dynamic range. Here, we used MIBI to analyze formalin-fixed, paraffin-embedded human breast tumor tissue sections stained with ten labels simultaneously. The resulting data suggest that MIBI can provide new insights into disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

Multispectral imaging in biology and medicine: Slices of life †

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.

In vivo optical imaging and dynamic contrast methods for biomedical research

This paper provides an overview of optical imaging methods commonly applied to basic research applications. Optical imaging is well suited for non-clinical use, since it can exploit an enormous range of endogenous and exogenous forms of contrast that provide information about the structure and function of tissues ranging from single cells to entire organisms. An additional benefit of optical imaging that is often under-exploited is its ability to acquire data at high speeds; a feature that enables it to not only observe static distributions of contrast, but to probe and characterize dynamic events related to physiology, disease progression and acute interventions in real time. The benefits and limitations of in vivo optical imaging for biomedical research applications are described, followed by a perspective on future applications of optical imaging for basic research centred on a recently introduced real-time imaging technique called dynamic contrast-enhanced small animal molecular imaging (DyCE).

Spectral imaging perspective on cytomics.

Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is proposed as a tool that can simplify and enrich the extraction of morphological and molecular information. Simple‐to‐use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution; these can be exploited by sophisticated analysis tools.

Visualization of Microscopy-Based Spectral Imaging Data from Multi-Label Tissue Sections

Combining images taken with light of specific wavelengths can dramatically enhance light‐microscopic images. This technology is enabled by the availability of programmable filters that can be set to transmit light only of particular wavelengths. Spectral imaging technologies have become an important part of microscopy, and are particularly useful for analyzing samples that have been labeled with multiple (two or more) molecular markers. The most commonly used methodology for separating the markers from each other is linear unmixing, which results in a quantitative image of the location and amount of each marker present in the sample. The very complexity of these multilabel samples requires a high degree of sophistication in methods to visualize the results of unmixing. This article describes a wide range of useful visualization tools designed to better enable discrimination of different features in multilabeled tissue or cell samples. These commercially available tools can be attached to the standard laboratory light microscope to significantly enhance the power of light microscopy. Curr. Protoc. Mol. Biol. 84:14.19.1‐14.19.15.

Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging

The role of immunohistochemistry (IHC) in the management of cancer has expanded to provide improved diagnostic classification, as well as guidance on disease prognosis, therapy, and relapse. These new tasks require evaluation of an increasing number of protein targets; however, conventional multiplexing, usually achieved using serial tissue sections stained for a single analyte per slide, can exhaust small biopsy specimens, complicate slide-to-slide protein expression correlation, and leave insufficient material for additional molecular assays. A new approach, mass spectrometry immunohistochemistry (MSIHC), compatible with high levels of target multiplexing and suitable for use on formalin-fixed, paraffin-embedded samples can circumvent many of these issues. The strategy employs antibodies that are labeled with elemental mass tags, such as isotopically pure lanthanides not typically found in biological specimens, rather than with typical fluorophores or chromogens. The metal-labeled antibodies are then detected in tissue using lasers or ion beams to liberate the tags for subsequent mass spectrometry detection. Within a given multiplexed IHC panel, the metal labels are selected so that their respective masses do not overlap. More than 30 antibodies have been imaged simultaneously, and up to 100 antibodies could potentially be detected at once if the full available mass spectrum is deployed. MSIHC has a number of advantages over conventional IHC techniques. Background due to autofluorescence is absent and the dynamic range is 105, exceeding immunofluorescence and chromogenic IHC by 100-fold and 1000-fold, respectively. Detection of labeled primary antibodies improves assay linearity over both chromogenic and fluorescent IHC. Multiplexed mass-tagged antibodies incubated simultaneously with tissue do not appear to cross-interfere, and because the mass tags do not degrade, samples are stable indefinitely. The imaging resolution of multiplexed ion-beam imaging can be better than light microscopy. With appropriate instrumentation, MSIHC has the potential to transform research and clinical pathology practice.

Detection of malignancy in cytology specimens using spectral-spatial analysis

Despite low sensitivity (around 60%), cytomorphologic examination of urine specimens represents the standard procedure in the diagnosis and follow-up of bladder cancer. Although color is information-rich, morphologic diagnoses are rendered almost exclusively on the basis of spatial information. We hypothesized that quantitative assessment of color (more precisely, of spectral properties) using liquid crystal-based spectral fractionation, combined with genetic algorithm-based spatial analysis, can improve the accuracy of traditional cytologic examination. Images of various cytological specimens were collected every 10 nm from 400 to 700 nm to create an image stack. The resulting data sets were analyzed using the Los Alamos-developed GENetic Imagery Exploitation (GENIE) package, a hybrid genetic algorithm that segments (classifies) images using automatically ‘learned’ spatio-spectral features. In an evolutionary fashion, GENIE generates a series of algorithms or ‘chromosomes’, keeping the one with best fitness with respect to a user-defined training set. First, we tested the system to determine if it could recognize malignant cells using artificial cytology specimens constructed to completely avoid the requirement for human interpretation. GENIE was able to differentiate malignant from benign cells and to estimate their relative proportions in controlled mixtures. We then tested the system on routine cytology specimens. When targeted to detect malignant urothelial cells in cytology specimens, GENIE showed a combined sensitivity and specificity of 85 and 95%, in samples drawn from two separate institutions over a span of 4 years. When trained on cases initially diagnosed as ‘atypical’ but with unequivocal follow-up by biopsy, surgical specimen or cytology, GENIE showed efficiency superior to the cytopathologist with respect to predicting the follow-up result in a cohort of 85 cases. We believe that, in future, this type of methodology could be used as an ancillary test in cytopathology, in a manner analogous to immunostaining, in those situations when a definitive diagnosis cannot be rendered based solely on the morphology.

Pigeons (Columba livia) as Trainable Observers of Pathology and Radiology Breast Cancer Images

Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.

Spectral imaging for brightfield microscopy

Molecular medicine now requires molecular pathology. While fluorescence has traditionally been used for high-resolution multiplexed molecular imaging, clinical practitioners prefer non-fluorescent multicolor methods. However, in brightfield, typically, only one color is used at a time, which precludes assessment of co-expression on a cell-by-cell basis. Similar constraints apply to brightfield in-situ hybridization techniques. Double- and triple-staining procedures are rarely performed in non-research settings not only because the wet chemistry can be difficult, but also because it can be challenging or impossible to determine visually where and to what extent different chromogens may physically overlap. Spectral imaging can be useful in this context. Two methods of acquiring spectral images are described, along with their application to multicolor immunohistochemistry and transmission in-situ hybridization (TRISH): 1) liquid crystal tunable filters; and 2) a novel, spectrally agile light source. This source emits white light of any desired color temperature, or single 10-nm wavelength bands in the range 420 to 700 nm, or any combination of wavelengths with individual intensity control. Both methods are allied with a grayscale camera and appropriate algorithms to analyze multicolor samples of clinical significance. Spectrally unmixed images clearly separate signals linked to different chromogens, even with spectral and spatial overlap. Intriguing challenges in matching mathematical algorithms to these specific problems remain: how many bands are enough? What are the optimal unmixing procedures? What automated tools can be applied to speed and simplify the procedures?

Technical considerations in longitudinal multispectral small animal molecular imaging

In a previous study, we investigated physical methods to reduce whole-body, diet-related autofluorescence interference in several mouse strains through changes in animal diet. Measurements of mice with an in vivo multispectral imaging system over a 21-day period allowed for the quantification of concentration changes in multiple in vivo fluorophores. To be an effective instrument, a multispectral imaging system requires a priori spectral knowledge, the form and importance of which is not necessarily intuitive, particularly when noninvasive in vivo longitudinal imaging studies are performed. Using an optimized spectral library from a previous autofluorescence-reduction study as a model, we investigated two additional spectral definition techniques to illustrate the results of poor spectral definition in a longitudinal fluorescence imaging study. Here we systematically evaluate these results and show how poor spectral definition can lead to physiologically irrelevant results. This study concludes that the proper selection of robust spectra corresponding to each specific fluorescent molecular label of interest is of integral importance to enable effective use of multispectral imaging techniques in longitudinal fluorescence studies.