multiplexed immunohistochemistry imaging and quantitation a review with an assessment of tyramide signal amplification multispectral imaging and multiplex analysis

Methods xxx (2014) xxx–xxx Contents lists available at ScienceDirect Methods journal homepage: www.elsevier.com/locate/ymeth Multiplexed immunohistochemistry, imaging, and quantitation: A review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis Edward C Stack, Chichung Wang, Kristin A Roman, Clifford C Hoyt ⇑ PerkinElmer, Inc., Waltham, MA 02451, USA a r t i c l e i n f o Article history: Received 23 April 2014 Revised 12 August 2014 Accepted 29 August 2014 Available online xxxx Keywords: Multiplexed Multispectral TSA Quantitative Pathology Biomarkers Cancer a b s t r a c t Tissue sections offer the opportunity to understand a patient’s condition, to make better prognostic evaluations and to select optimum treatments, as evidenced by the place pathology holds today in clinical practice Yet, there is a wealth of information locked up in a tissue section that is only partially accessed, due mainly to the limitations of tools and methods Often tissues are assessed primarily based on visual analysis of one or two proteins, or 2–3 DNA or RNA molecules Even while analysis is still based on visual perception, image analysis is starting to address the variability of human perception This is in contrast to measuring characteristics that are substantially out of reach of human perception, such as parameters revealed through co-expression, spatial relationships, heterogeneity, and low abundance molecules What is not routinely accessed is the information revealed through simultaneous detection of multiple markers, the spatial relationships among cells and tissue in disease, and the heterogeneity now understood to be critical to developing effective therapeutic strategies Our purpose here is to review and assess methods for multiplexed, quantitative, image analysis based approaches, using new multicolor immunohistochemistry methods, automated multispectral slide imaging, and advanced trainable pattern recognition software A key aspect of our approach is presenting imagery in a workflow that engages the pathologist to utilize the strengths of human perception and judgment, while significantly expanding the range of metrics collectable from tissue sections and also provide a level of consistency and precision needed to support the complexities of personalized medicine Ó 2014 The Authors Published by Elsevier Inc This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Introduction In the current push to drive a tailored approach to clinical care using clues hidden in tissue samples, there is significant effort underway to understand genomic alterations in order to match small molecule drugs to specific disease types Yet genetics has not yielded the significant successes hoped for [1,2] Perhaps the untapped contextual information, remaining in the tissue and captured through multiplexed labeling of proteins with subsequent image analysis, can help reveal additional needed information There have been various strategies employed in an attempt to characterize tissues from a histological perspective Current pathology practice utilizes chromogenic immunohistochemistry (IHC) [3] However, far more powerful multiplexed IHC (mIHC) ⇑ Corresponding author at: PerkinElmer, 68 Elm Street, Hopkinton, MA 01748, USA E-mail address: clifford.hoyt@perkinelmer.com (C.C Hoyt) approaches are now available, offering greater insights into disease heterogeneity and the characterization of systems biology mechanisms driving disease, as well as helping to conserve limited tissues An added benefit of mIHC is improved accuracy through application of image analysis, with the use of landmark markers specifically to indicate tissue architecture Landmark markers can also accelerate scan times This workflow also can increase a pathologist’s productivity by automatically measuring parameters hard to achieve reliably by eye, while needing the pathologist as an integral part of the workflow to review analysis results Though mIHC offers greater insight into molecular cascades and preserves tissue context, in current practice multiplexed stained samples can be difficult to interpret Since mIHC often employs fluorescence, where multiple targets can blend together complicating resolution, this has the potential to muddle visual assessment With formalin-fixed, paraffin-embedded (FFPE) tissues, there is also the potential for tissue autofluorescence, further complicating visual interpretation http://dx.doi.org/10.1016/j.ymeth.2014.08.016 1046-2023/Ó 2014 The Authors Published by Elsevier Inc This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx Current imaging metrics can effectively address multiplexing complications, through multispectral unmixing strategies Within oncology, this has broad potential for designing combinatorial therapeutic approaches by revealing co-expression, pathway configurations, and spatial relationships among cell types With improved accuracy of data, automation, and faster scanning, multispectral mIHC has the capacity to induce a significant paradigm shift in tissue analysis In this review, we will describe the varied methodologies that support multiplexing, with a particular focus on mIHC in the pathology workflow In particular, we will assess a practical application of mIHC using Tyramide signal amplification (TSA) in conjunction with multispectral image analysis, which offers improved mIHC using similar species antibodies, while also providing quantitatively reproducible multiplexed data, batch to batch Review 2.1 Multiplexed staining methods 2.1.1 Brightfield multiplexing In order to perform mIHC on FFPE tissues in brightfield microscopy, chromogenic deposition of various chromogens/enzyme pairs is used While this is useful when distinguishing different cell types, it is more challenging to assess when trying to co-localize targets within cells [4] Some of the specific chromogens available for brightfield mIHC include: 3,30 -diaminiobenzidine (DAB) and nickel enhanced DAB (DAB-Ni), which produce an insoluble brown or black precipitate, respectively; 3-amino-9-ethylcarbazole (AEC), which produces a red precipitate that is susceptible to organic solvents; Vector VIP which produces an insoluble purple precipitate; and nitro blue tetrazolium/5-bromo-4-chloro-3-indolyl-phosphate (NBT/BCIP), which produces a deep blue precipitate (a more complete listing is available in Table 1) These chromogens can be visualized with either horseradish peroxidase (HRP; DAB, DAB + Ni, AEC and VIP) or alkaline phosphatase (AP; NBT/BCIP) In addition, several counterstaining dyes can enhance brightfield multiplexing, such as methyl green or hematoxylin, which stain nuclei green or blue, respectively While brightfield multiplexing is possible, there are several factors to consider A primary concern remains the need to set up primary antibodies so that there is no cross reactivity, through separate conjugations with HRP or AP But this limits multiplexing capacity, which can be partially overcome with sequential staining strategies [5–8] This results in a labor intensive protocol where multiplexing is limited due to tissue degradation after successive serial IHC assays [9] There is also the potential for chromogenic overlap, and the significant risk of obscuring one color by another In particular, there is also overlap of chromogenic spectra, which limits the degree of chromogenic multiplexing in brightfield [4] Hence while chromogenic IHC is a valuable tool and widely utilized in many pathology labs, the ability to practically multiplex beyond targets is limited 2.1.2 Fluorescent multiplexing Fluorescence mIHC takes advantage of light emission with different spectral peaks against a dark background The basic principle behind fluorescent IHC relies on the ability of individual fluorophores to be excited by one wavelength and emit at a longer specific wavelength (a phenomena known as a Stokes shift) For IHC utilizing fluorophores as reporters, there are two basic ways this can be accomplished: via direct or indirect labeling In direct IHC fluorescence, a fluorophore is directly conjugated to a primary antibody In indirect IHC fluorescence, the fluorophore is conjugated to a secondary antibody, which is specific for the primary antibody Indirect IHC fluorescence can also take advantage of amplification strategies, through either multiple secondary antibodies binding to a single primary, or via robust amplification approaches, such as the avidin–biotin complex (ABC) There are multiple fluorophores available for IHC applications (such as Alexa or Cy dyes, see Table for Cy dyes we have successfully multiplexed), and more recently, fluorescent quantum dot nanocrystals, which have narrower emission peaks when compared to standard fluorophores In order to utilize fluorophores, there are certain microscopic requirements enabling proper visualization These include a very bright light source, as well as paired excitation/emission filter sets specific to the fluorophores employed For example, if using fluorescein, a filter set needs to provide an excitation wavelength (k) of 494 nm, and an emission filter needs to pass an emission k of 517 nm Similarly, a 525 nm quantum dot nanocrystal, where emission k is a function of nanocrystal size, requires an excitation filter that provides a k of $400 nm, and an emission filter for a k of 525 nm Choice of filter sets is an important consideration, as it represents a physical limitation in multiplexing capacity Often, the number of wavelength band passes that can be fit into the visible wavelength range will limit the number of fluorophores that can be utilized without crosstalk to marker fluorophores, along with DAPI Generally, number of filter cube sets each fluorescent microscope can accommodate is or Another consideration when planning any fluorescent mIHC assay is the potential for, and likelihood of, co-localization of different fluorophores In the case where co-localization occurs, complications can occur in analysis if the co-localization causes colors to mix, as red and green might, to provide a degree of yellow In this instance, the relative contribution of red and green is extremely difficult to determine using standard image analysis, and as such must be planned to achieve successful multiplexing Indeed, fluorescent mIHC has been successfully demonstrated in FFPE tissue in differing multiplex levels Mason et al [10] demonstrated 2-plex fluorescent IHC in FFPE tonsil interrogating CD79 and PCNA, while recently, Bogusz et al [11] interrogated active BCR signaling in diffuse large B-cell lymphoma with various 2-plex combinations of CD20 coupled with either pLYN, pSYK, or pBTK In contrast, 4-plex fluorescent IHC using quantum dots to interrogate the tumor microenvironment in gastric cancer has also been demonstrated [12] In all instances, indirect labeling with primary-specific conjugated secondary antibodies was performed This raises an important issue involving antibody species and the ability to Table Typical chromogens in multiplexed IHC assays Chromogen Catalytic agent Deposition color DAB DAB + Ni AEC VIP NBT/BCIP Vulcan Fast Red Vector Black Nova Red TMB HRP HRP HRP HRP AP AP AP HRP HRP Brown Black Red Purple Deep blue Red Black Deep red Blue Table Sample fluorescent dyes for multiplexed IHC assays Fluorescent dye Excitation k (nm) Emission k (nm) Coumarin Fluorescein TMR Cyanine Cyanine 3.5 Cyanine Cyanine 680 402 494 550 550 581 648 669 443 517 570 570 596 667 688 Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx successfully multiplex, considering the constraining limits this can impose on multiplexing capacity In general, indirect fluorescent mIHC can only accommodate one species of primary (e.g rabbit, mouse, or goat) for each target of interest, each accommodated by a specific secondary antibody conjugated with a specific fluorophore This represents a significant impediment should a protocol attempt to employ all rabbit primary antibodies There are a few strategies to overcome such limitations, but their ability to so without complication is limited For example, fluorophores can be directly conjugated to primary antibodies Several studies have demonstrated protocols for conjugating quantum dot fluorophores to primary antibodies [13,14] These protocols are time consuming and labor intensive, making their practicality for many labs difficult Circumventing this approach, alternative strategies have taken advantage of serial staining techniques, such as multi-epitope-ligand-cartography (MELC), a fluorescent multiplex in situ strategy with the capacity to simultaneously visualize protein expression Berndt and colleagues [15] examined the inflammatory status of Barrett’s esophagus and esophageal adenocarcinoma in a sequential, highly multiplexed fluorescent MELC assay To accomplish this, the MELC assay keeps the field of view constant, and all multiplexing takes place there As a consequence, the MELC method excludes all other areas of the tissue sample outside the constant field of view There is also a significant cost associated with the robotic integration with a standard inverted microscope that must be considered Taken together, it is apparent that such issues demonstrate hurdles to using standard methods for direct or indirect fluorescence IHC to achieve any power of multiplexing There are, however, relatively novel detection strategies that can circumvent some of the complicating factors outlined above Tyramide signal amplification (TSA) relies on amplification of signal, which then covalently binds to the epitope in a highly specific manner [16,17] This is ideal for fluorescent multiplexing of similar species antibodies, as individual primary/secondary antibody complexes can be removed for serial IHC, yet the covalent signal for each remains for interrogation This method has been successfully employed to achieve 3-plex fluorescent IHC in intratubular germ cell neoplasias [18] using similar species primary antibodies In an analogous study, successful 3-plex fluorescent IHC utilized TSA in order to multiplex rabbit primary antibodies against vasopressin, corticotropin releasing factor, and thyroid hormone [19] Importantly, the TSA immunostaining protocol has been optimized to demonstrate excellent signal to noise through reduced background [17] While TSA is quantitative, it is important to note that it is driven by enzymatic amplification, bearing similarly with chromogenic deposition However, unlike chromogenic deposition, TSA is uniquely suited for serial staining, due to covalent Tyramide binding, as noted above Importantly, this method is similar in cost to standard chromogenic detection methods, allows for the use of multiple similar species primary antibodies, and supports fluorescent mIHC assessment across an entire tissue section 2.2 Imaging approaches and systems for multiplexed detection 2.2.1 Multiplexed imaging approaches One main reason mIHC is a sought after assay is due to the potential it holds for informing on biology through capturing multidimensional data related to tissue architecture, spatial distribution of multiple cell phenotypes, and co-expression of signaling and cell cycle markers But this requires imaging of multiple biomarkers, making imaging a central component in the mIHC workflow Several strategies have been proposed to accomplish imaging of mIHC, each leveraging different multiplexing and imaging modalities In ‘serial sectioning’ multiplexing, or feature analysis on consecutive tissue sections (FACTS; [20]), multiplexing (brightfield or fluorescent) IHC is performed on individual serial sections, which are then individually imaged Once imaged, feature extraction is performed in order to align sections for subsequent expression analysis While this approach can stain sequential sections, the true multiplexing in such an arrangement is significantly limited due to the inability to capture information regarding cellular co-expression and signaling cascade linkages within individual cells Perhaps more prescient is the potential to miss significant tissue features due to the contribution of three-dimensional shifts which can occur through sequential tissue sections Another multiplexing approach recently described involves extraction of proteins from a single tissue section using a series of layered membranes (L-IHC; [21]) that extract proteins by size, on which IHC can then be performed to interrogate protein expression The signal from each of the membranes is then imaged in a microarray reader, allowing for protein expression analysis [22,23] These images can then be overlaid on the original tissue section image in order to provide expression information While this approach utilizes only one tissue section, thereby taking the first step toward a comprehensive mIHC strategy, the IHC is still sequential, and as indicated such approaches are limited by the inability to capture any information regarding co-expression or signaling cascade linkages within individual cells And in instances where L-IHC may appear to offer some degree of co-expression analysis, the tight interplay of multiple tissue types (e.g epithelial, fibroblast, inflammatory/immune cells) requires proteins to be captured in situ, within specific contextual confines, in order to truly capture co-expression profiles, thus limiting the multiplexing capacity of this approach In summary, multiplexing and imaging as described in the examples above does not adequately capture the true power of mIHC, where multiple markers are interrogated in a single section, with the tissue context completely conserved This can be achieved through brightfield IHC using multiple chromogens, or through fluorescent IHC using different fluorophores Importantly the subsequent image analysis is conducted to evaluate each of the various reporters Yet as outlined above, there is a limited multiplexing capacity when using chromogens, leaving fluorescent IHC uniquely positioned to offer significant improvements in multiplex detection 2.2.2 Modes of multiplexing Fluorescent IHC can be accomplished as described above In this configuration, each fluorophore is separately imaged, and then images are merged together In this way fluorescent detection can provide co-localization confirmation [24], but quantitation of each signal is somewhat limited, especially in instances where autofluorescence is present A potential improvement in fluorescent multiplexed IHC is possible through the use confocal laser scanning microscopy (CLSM; [25]) More specifically, analysis of tissue involving true quantitation of fluorescent mIHC could benefit from the application of spectral CLSM [26,27] This technology utilizes multiple lasers for detection, but importantly, possesses specific optics that allow for spectral detection By using spectral, or more properly, multispectral detection, the spectra of each individual marker is ‘unmixed’ allowing for isolation of the spectra of interest from each specific marker, supporting quantitative fluorescent IHC Using spectral CLSM, Pauly et al [28] successfully analyzed autofluorescent macrophages in fresh lung tissue from smokers More recently, 5-plex fluorescent IHC interrogated CD20, IgD, MIB-1, CD3, and CD68 expression in tonsil via sequential IHC of multiple species primary antisera, detected with species-specific quantum dots and assessed with spectral CLSM [29] In contrast to spectral CLSM, multispectral analysis can also be accomplished without the need for multiple laser sources, using a standard fluorescent microscope equipped with a multispectral Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx camera In this configuration, the spectral information from a multiplexed panel of targets is captured through the multispectral camera In order for the spectral information to be reliably unmixed and quantitated, correct examples of each fluorophore emission spectra, as well as a representative autofluorescence spectrum from an unstained sample, in the context to be used, must be registered in a multispectral library (Fig 1) This spectral library forms the cornerstone of target quantitation, as the intensity of each fluorescent target is extracted from the multispectral data using linear unmixing [30] Beyond microscopy-based multispectral imaging, recent advances have been made through mass spectrometry-based laser microscopy (MIBI) approaches, recently referred to as ‘next-gen immunohistochemistry’ [31] In this approach, lanthanide metal- conjugated primary antibodies are used for interrogation in a manner consistent with standard IHC From there, samples on slides are subjected to mass spectrometry In one instance [32] mass cytometry was performed, wherein a high-resolution laser ablation system was employed to image 32 proteins at a cellular resolution of lm In a somewhat different mass spectrometry approach [33], a rasterizing oxygen duoplasmatron ion beam was employed to sweep breast tissue samples, liberating ions from lanthanide adducts This system was used to analyze human FFPE breast cancer samples via a 10-plex assay Both studies produce multicolor composite images and quantitative capacity in a dynamic range perhaps double that of quantitative immunofluorescence While the methodologies are complex, the potential for mass-spectroscopy is developing greater practical demonstration Fig Spectral library from an mIHC assay Individual examples of ER (fluorescein), PR (A594), Her2 (Cy5), ki67 (Cy3), CK (coumarin), and DAPI, as well as autofluorescence from an unstained section, were spectrally analyzed to generate a spectral library in support of multispectral unmixing in a multiplexed assay For each epicube in the system, the spectra observed are captured, so that across all epicubes, the complete spectral properties of each independent signal can be effectively utilized for spectral unmixing Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx 2.2.3 Staining strategies for multiplexing For each of the multiplexing modes above, various and specific staining strategies can be employed to influence not only the individual degree of multiplexing capacity, but also the degree of complex tissue analysis For example, it has been recently demonstrated that iterative staining on the same defined region of interest can produce significant multiplexing beyond 60 targets [34] In this analysis, fluorescent IHC was performed on a tissue sample, and the slide was then imaged After imaging, the slide was removed, and the fluorescent tag was inactivated through incubation in an alkaline H2O2 buffer Another round of fluorescent IHC was then performed for the detection of another target, and again, the slide was returned by the stage to the previous location where the first image was captured and another image was acquired, after which the fluorescent reporter was inactivated and the process repeated For all images, DAPI nuclear counterstain guided a Fourier-based alignment In this scheme, the spectra by which each of the fluorescent intensities were measured results from the broad spectral profiles captured from each of the emission cubes of the system, subtracted from any autofluorescence contributed by an unstained sample This is the main reason this system functions like a standard fluorescent system, but with higher potential multiplexing capacity However, given the complexity of this approach, and its limited field of view on a tissue section, there will probably be many challenges to translating it into a practical and economical pathology workflow Capturing a fully representative sampling of heterogeneity across whole sections is very important, as different regions of a tumor can contain very significant differences of phenotype [35] A limited area of analysis, as described above, can be contrasted with a more stringent sampling paradigm where multiplexing can be performed across an entire tissue section containing multiple regions of interest (e.g the entire visible tumor) For instance, it would be relevant when interrogating a tumor that possesses any degree of multiplexed staining to sample the entire tumor area and capture the complete extent of tissue and cellular heterogeneity, as was recently demonstrated in prostate cancer, with a multiplex fluorescent quantum dot panel consisting of RANKL, phosphoNF-jB-p65, VEGF, phospho-c-Met, and Neuropillin-1 [36] In this study, the authors employed an automated morphology tool (InForm, PerkinElmer, Waltham, MA) to accurately identify all tumor regions at 4Â magnification Once the entire section was ‘segmented’ the entire tumor area was imaged at 10 nm k from 420 nm to 720 nm using the Vectra spectral imaging system (PerkinElmer, Waltham, MA) Each of the images captured was then spectrally unmixed in order to effectively quantitate each of the biomarkers, as well as autofluorescence, based on their specific spectral properties In order to capture all elements of tumor heterogeneity, the authors sampled from five individual fields and found that activated c-Met signaling was more pronounced in castrate-resistant prostate cancer compared with hormone-responsive prostate cancer While the degree to which multiplexing is achieved differs from the example of Gerdes et al [34] above, there are several advantages to a multispectral, multiplexed approach Perhaps foremost the perfect pixel-registration of component label planes and high-resolution imagery enabled by having all labels in place and imaged simultaneously, and the accurate removal of autofluorescence signal from label signals Also, by enabling whole slide sampling, this approach fits more seamlessly within the standard pathology workflow, capturing an important element of pathology-based mIHC imaging 2.3 Software for analyzing multiplexed images 2.3.1 Quantitation of chromogenic and fluorescent signals Thus far, the focus has been on the ways in which mIHC can be performed, and how images are captured However, the ultimate goal is reliable and specific data to support research, and ultimately to advance clinical care This requires image analysis tools that can reliably identify and quantitate all targets of interest In brightfield mIHC, signals are chromogenic and provide color and contrast through absorbing different regions of the visible spectrum Unmixing requires conversion of transmitted light signals to optical density and then linear decomposition based on characteristic absorption spectra In an examination of rabbit liver tissues, a 3-plex IHC panel consisting of cytokeratin, CD31, and Ki67 [37] was successfully interrogated using InForm for multispectral image (MSI) analysis For each sample, the chromogens used to visualize cytokeratin, CD31, and Ki67 were Vector Red, DAB, and Vector Blue, respectively For analysis, cells were segmented, and Ki67 positivity was determined using a threshold of average optical density of the unmixed ki67 chromogen Interestingly, the determined Ki67 positive cells nearly mimicked that number of positive Ki67 cells quantified by two independent pathologists, supporting the successful unmixing of the Ki67 signal It is worth noting the choice of chromogen for Ki67 – Vector Blue One of the practical challenges of using chromogenic mIHC remains the ability to accurately quantitate each chromogen, when optical densities are at levels typically used for visual assessment The useful range of optical densities to support reliable unmixing leads to staining ‘intensity’ substantially lighter than done typically [38] There is significant debate regarding the usefulness of DAB as a quantifiable chromogen within any multiplex arrangement [4,38,39] Indeed, as above, DAB was used to generate a landmark of CD31 expressing sinusoid cells to help localize Ki67 positive cells within both hepatocyte tissue and non-parenchymal tissues of the sinusoidal space In contrast, fluorescent signals are more amenable to quantitation given their linear and additive nature and relatively welldefined emission spectra Indeed, fluorescein intensity falls proportionally with a reduction in the antigen on which it was reporting [40] With such linear responsiveness, quantitation of fluorescent intensities in a fluorescent mIHC context has the potential to advance tissue interrogation when coupled with both multispectral instrumentation and the image analysis tools to support proper unmixing of captured spectra An example of the effectiveness of fluorescent mIHC can be seen in the relative expression patterns of CK18, CD34, and cleaved Caspase 3, assessed in FFPE human tonsil [41] Each marker was interrogated using specific fluorescent quantum dots, and the multispectral images captured were subsequently analyzed using InForm By quantitating the fluorescent intensity for each marker on a per-pixel basis, the analysis revealed a co-localization pattern where 12% of the total CD34 was co-localized with CK18, while only 1% of the total CK18 was co-localized with CD34 This analysis is made possible as a result of fluorescent intensity quantitation, and demonstrates the significant potential of multispectral analysis in supporting multiplexed IHC studies In addition to InForm, other image analysis software packages can support mIHC analyses For example, Definiens image analysis software (Definiens, Carlsbad, CA) has been used in conjunction with a multispectral imaging system (Nuance, PerkinElmer) to analyze CK18, AMACR, and AR expression in prostate cancer [42] In this study, the imaging software was used to segment the tissue samples based on CK18 staining, and subsequent intensity analyses allowed determination of AR+ and/or AMACR+ epithelial cells It was subsequently demonstrated that AR expression in AMACR negative epithelial cells is highly correlated with prostate-specific antigen (PSA) recurrence Yet another imaging software package, Precision (Leica, Butlers Grove, IL) can also report on mIHC, by providing tissue segmentation and intensity measurement capabilities But unlike InForm or Definiens, Precision image analysis software, which is often paired with Aperio Scanscope (Leica), does Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx not possess multispectral analysis capacity, thus limiting its quantitative potential 2.3.2 Image source: monochrome, RGB, and multispectral unmixing In order to effectively analyze captured images, the software employed must be able to extract fluorescent or chromogenic intensity information from the image This is, of course, completely depended on the type of image captured For example, in monochromatic imaging, utilizing a monochromatic camera, there is only one color represented, in varying intensities Thus, for the software, there is only one color that can be extracted and analyzed for each filter cube available This significantly limits any fluorescent mIHC assay, as there is no effective way for the imaging software to separate the various reporter fluorophores that share similar excitation properties, but differ in emission spectra Additionally, there is no effective way to isolate the varying contribution of autofluorescence [43] present in FFPE tissues In contrast to monochromatic images, a standard three channel RGB camera extends the color detection capacity, providing a greater complexity of color for the imaging software In this context, images captured by an RGB camera are an amalgamation of the three available channels (red, green, and blue) This can be beneficial to a mIHC assay if the fluorescent signals fall discretely within each of the individual detection channels, and by maximizing the filter cube sets employed However, this represents certain imaging limitations that are difficult to overcome As noted above, nearly all FFPE tissue sections retain a varying degree of tissue autofluorescence, and in fluorescent IHC, this can be spread heterogeneously over significant portions of the fluorescent spectra (see Fig 1) So with three channels over which autofluorescence could distribute, it is difficult to separate and distinguish this signal from the signal of interest with any imaging software suite that could extrapolate from the RGB data Use of filter cubes can improve multiplexing capacity, but the limited channels available in an RGB context are potentially subject to spectral overlap, which the imaging software cannot unmix effectively Unmixing by the imaging software is central to supporting the promise of mIHC, and perhaps the most efficient way in which to analyze any mIHC assay is by MSI analysis Unlike an RGB camera, a multispectral camera can be configured to capture discrete intervals (P10 nm) across the entire visible spectrum from 420 nm to 720 nm, through the utilization of a liquid crystal tunable filter [7] This ability to capture discrete spectral intervals allows for tailoring of the spectra to accommodate partitioning of the specific signals expected, i.e the emission spectra of the fluorophores making up an mIHC assay (see Fig 1), including the autofluorescent spectra [43] Consequently, the data from the multispectral camera can be accessed by the imaging software (e.g InForm or Definiens), and as a result of the discrete spectral intervals, unmixing of the combined spectra into the individual spectra associated with the fluorophore components of the mIHC panel is possible 2.3.3 Guides to tissue analyses: visual, semi-automated, and automated In devising a strategy to analyze most clinical tissue samples, a priori decisions are needed about target contexts, such as epithelial or stromal components of tumor Once decided, the next question leads to how such tissue segmentation can be accomplished Perhaps most basically, this can be accomplished through visual assessment, requiring both the time and efforts of an experienced pathologist While this is very common in pathology practice, it has specific limitations in the context of fluorescent mIHC One of the basic impediments results from the lack of familiarity pathologists have with fluorescence imagery, which does not render tissue architecture nearly as well as H&E or chromogenic IHC In the case of chromogenic mIHC, overlapping colors become essentially dark brown, making it very difficult to visually interpret stain intensity where stains are co-localized More generally, the human eye is poorly suited to assessing stain intensity, having evolved to assess patterns and very weak signals rather than intensity These issues significantly limit visual assessment of mIHC assays [44,45] Alternatively, in semi-automated analysis, the imaging software presents the images captured from each tissue sample processed for mIHC to a trained pathologist to indicate, typically through annotations drawn on the image with the mouse, areas to be analyzed In this context, the pathologist individually traces the areas to be analyzed using the images of all processed tissue samples While still somewhat labor intensive, this allows for the appropriate regions to be segmented for image analysis The imaging software then restricts the multispectral unmixing to the segmented regions of interest, thus getting the benefit of objective, consistent quantitation of signal intensities provided by image analysis This method, while still requiring significant efforts of a pathologist, does allow for multispectral unmixing of the fluorophores of interest within the regions of interest specified for analysis, overcoming the limitations of the human eye in the context of mIHC But perhaps the most efficient method for tissue segmentation is a fully automated analysis [5] In this instance, the imaging software is trained to recognize particular regions of interest (e.g tumor) at a lower power (4Â) ‘survey’ of the whole slide, based on spectral elements associated with tissue morphology (e.g., autofluorescence spectra, DAPI spectra, etc.) Training is performed by having a trained individual manually select regions of tissue of interest and of no interest, in a series of images that represent the full range of tissue contexts the software will need to correctly segment, based on the needs of the mIHC assay (Fig 2) With the software trained to identify tissue of interest, the slide imaging system can then take high magnification imagery of areas of interest for detailed quantitation and characterization The same machine learning software can then identify cells and cellular sub-compartments in the higher power magnification to automate data collection within the specific region(s) of interest For all multispectral images, the software analyzes fluorescent intensity (fluorescent units – FU) on a per-pixel basis, and then averages these intensities to create compartment intensities (e.g nuclear, cytoplasmic, or membranous) for each cell within the segmented region Once the higher magnification images have been segmented, a trained pathologist reviews the segmentation maps to ensure fidelity with the intended tissue segmentation strategy Then, by examining compartment intensities, the pathologist can set threshold values to support downstream analyses The typical time spent by a pathologist reviewing results is significantly less than the time required to manually annotate and score images for analysis Once segmentation is deemed satisfactory, the imaging software data collected from regions of interest are aggregated as detailed above This strategy is a very efficient way in which to perform MSI analysis of mIHC, and importantly, while this method still requires pathology oversight, it can decrease the pathologist workload, resulting in a beneficial multispectral multiplexing strategy 2.3.4 Tumor and landmark markers for automation While automated tissue segmentation can be of great value for analysis of mIHC, there are certain considerations that must be made to aid in effective tissue segmentation In general, the ability to separate tumor form non-tumor tissue is a straightforward exercise for a trained pathologist using a standard H&E But there are many times when additional IHC must be performed to aid in the diagnosis [46] In the same way, certain IHC stains can be performed which aid in tissue segmentation by establishing biological landmarks indicating specific cellular attributes There are certain IHC stains that can be performed to indicate cells of an epithelial origin, such as cytokeratin This stain effectively Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx Fig Automated tissue segmentation Fluorescent multiplexed IHC (Panel A, see Section 5) of breast tissue stained with ER (green), PR (purple), Her2 (red) and Ki-67 (yellow) is used to train InForm image analysis for tissue segmentation (Panel B) Once segmentation is verified (Panel C), a spectral composite of the multiplexed IHC array is created (Panel D), based on the multispectral unmixed spectra for each fluorescent probe Bar in A – 100 lm highlights all epithelial cells within any tissue sample, and importantly, this stain can help to accurately guide tissue segmentation of tumors with epithelial origin, such a prostate, lung, and breast Such landmark stains can work in conjunction with a nuclear counterstain, such as DAPI, so as to help guide subcellular staining patterns and pinpoint each individual cell Thus, when these landmark stains are coupled with fluorescent mIHC, they significantly aid in tissue segmentation and subsequent image analysis, but it must be remembered these stains also increase the multiplexing by adding two additional fluorophores (for both DAPI and cytokeratin) to the mIHC protocol Nevertheless, these stains vastly facilitate automated image analysis 2.3.5 IHC controls guiding assay repeatability It is important when considering assay reproducibility that strategies are in place to ensure successful multiplexing is achieved for every assay performed [47] One of the principle reproducibility strategies often employed is the use of known, previously characterized positive and negative samples (positive and negative controls) that can be consistently assessed in successive multiplexed assays in order to confirm assay reproducibility [46] Additionally, this can also be accomplished with known internal controls that are concomitantly positive An example of such a strategy is demonstrated by cytokeratin, which stains both normal and carcinogenic epithelial cells Regardless of how such control of the IHC process is achieved, it is a crucial element that should not be overlooked By ensuring assay reproducibility, a mIHC assay will be much more easily employed, and potentially integrated into a clinical workflow 2.4 Integrating multiplex staining and multispectral imaging in the pathology workflow 2.4.1 Supporting, facilitating, and extending the critical role of the pathologist As indicated in the examples of tissue segmenting above, the pathologist plays an integral role in the mIHC assay, while imaging systems with the capacity to inform on mIHC assays offer significant support For example, multiplexing offers deeper insight into tissue and cellular processes, aiding diagnostic potential In addition, the ability to multiplex also conserves limited pathology tissue resources [48] And perhaps most salient, mIHC offers the potential to extend the role of the pathologist and fill a gap in current practice between genomics on one side, and classic histopathology on the other [3] 2.4.2 Simulated brightfield views of fluorescence samples Another important advance afforded by the imaging software, that can support the pathology workflow is the ability to take fluorescent IHC images, which in and of themselves often lack sufficient context to be useful to the pathologists eye, and create simulated brightfield images Since all images retain multispectral information, elements of the spectra can be used to simulate various brightfield views, such as H&E, or hematoxylin with DAB chromogen (Fig 3) With this view, data extrapolation is wholly supported by MSI analysis, yet classic pathology views can be provided, enhancing understanding of complex multiplexed staining patterns, and improving the presentation of mIHC assays 2.4.3 System training, automation, and results review In order to effectively integrate mIHC and MSI into the workflow, it’s important that all workflow elements cooperate to achieve the desired results For this to occur, and subsequently enhance the pathology workflow, efficient and effective tissue segmentation training is required (see Fig 2) By introducing sufficient examples of the tissue types which would be encountered by the imaging software, tissue segmentation can be leveraged in the imaging software to effectively identify specific patterns in the tissues, and segment areas based on training inputs, allowing automatic segmentation of all remaining samples Once all samples are segmented, the pathologist is able to review the images to ensure proper tissue segmentation, and the MSI analysis can then generate intensity measurement for all samples This data is then available to support many quantitative aspects, such as specific Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx Fig Simulated IHC images Fluorescent multiplexed IHC, examining the expression of ER (green), PR (purple), Her2 (red) and Ki-67 (yellow) in breast cancer (Panel A, see Section 5), was spectrally unmixed using InForm Subsequent simulated H&E (Panel B) and simulated hematoxylin and DAB indicating Her2 expression (Panel C) images were generated to create classic pathology views Bar in C = 50 lm cellular compositions, target co-localization, and positive staining percentages This spectrally unmixed data is contextually rich, allowing any number of analytical questions to be adequately addressed The expression of estrogen (ER), progesterone (PR) and human epidermal growth factor (Her2) receptors are important biomarkers used to classify breast cancer subtypes [49], and the IHC classification of each of these markers in individual samples correlates well with gene expression The ability to assess these markers within the context of the tumor is becoming increasingly more necessary, as the importance of tumor heterogeneity is more widely recognized [50] Given the prognostic importance of these biomarkers their assessment is critical in an era of improved detection where shifts in biopsy are emerging as new standards of care [51], demonstrating overall improvements in breast cancer care Yet this also suggests that tissue sample availability for biomarker analysis may be increasingly limited [48] This led us to assess a novel mIHC approach that could provide accurate assessment of ER, PR, Her2 and Ki67 expression To this end, we evaluated ER, PR, Her2, and Ki67 in human breast cancer using a novel mIHC protocol called Opal, which allows for the detection of similar species antibodies in a single section Expression profiles of each marker were evaluated alone in a singleplex assay, or comingled within a multiplexed assay, in order to verify the utility of this novel mIHC approach multiplexed biomarkers following this protocol (data not shown), demonstrating to a degree the practical scalability of this method Additionally, most of the assays our group has performed have focused on phenotypic cell markers While we have not examined biomarkers whose expression may be more variable, it is likely that even they can be interrogated with this method For any expressed biomarker, thresholds for staining can be assigned (as positive or negative) or in instances where threshold values are not assigned, the data can be interrogated as a continuous variable, which would support a potentially greater understanding of a biomarkers specific expression pattern In practice, TSA supports the Opal method very well when combined with multispectral image analysis Through covalent binding, the TSA conjugated fluorophores remain bound to the targeted epitope, allowing for sequential IHC to achieve the higher multiplexing levels of Opal Evaluation of staining with each TSA fluorophore revealed a very favorable signal-to-noise ratio, with no aberrant background staining, as well as very specific compartmental staining (e.g nuclear or membranous) consistent with accepted biological roles It is important to note that such staining requires diligent optimization Antigen retrieval, as performed using microwave technology, requires optimization to ensure both proper antigen retrieval and endogenous HRP quenching, all while ensuring complete antibody striping and tissue viability In addition, properly balanced HRP concentrations are required to prevent TSA dimer formation, typically achieved through titration of primary antibodies, though also can be modified through titration of the secondary antibody 3.1 Opal mIHC method 3.2 Opal sensitivity and interassay reproducibility The Opal method utilizes individual TSA conjugated fluorophores to detect multiple targets within a mIHC assay This requires initial assessment of each target in a singleplex IHC assay to generate spectral libraries required for mIHC image analysis (see Fig 1) Once each target has been optimized as a singleplex, the Opal multiplexed assay is performed utilizing an iterative staining method (Fig 4) Using this method offers the advantage of multiplexing similar species primary antibodies without species crossreactivity, due to the antibody stripping protocol (microwave treatment), which removes both the primary and the HRP-conjugated secondary When performed manually to achieve a 5-plex assay (ER, PR, Her2, Ki67 and CK), the Opal method can be performed over two days While this may require more time than individually stained samples, when the batching sizes increase (e.g $20 samples), the Opal method takes perhaps less time than performing stains on 20 slides (100 total assays) Another consideration worth noting is the ability to increase the numbers of targets within a multiplexed assay Our group has successfully To assess Opal sensitivity, the expression of each target was assessed within singleplex and multiplex contexts in order to evaluate potential alterations in a multiplexed arrangement For each target, the mean intensity was calculated for ER, PR, Her2, and Ki67 staining, and correlational analyses were performed to compare the levels of target expression alone versus its expression in a multiplexed assay In this instance, mean intensity is a surrogate for positivity calls For ER, analysis of mean intensity alone and multiplexed revealed a significant correlation (Fig 5, R2 = 0.8320, Pearson r = 0.9122, p < 0.0001) Similarly, comparison of the mean intensity of PR staining (Fig 6, R2 = 0.6211, Pearson r = 0.7881, p < 0.0001), Her2 staining (Fig 7, R2 = 0.9884, Pearson r = 0.9942, p < 0.0001), and Ki67 staining (Fig 8, R2 = 0.7720, Pearson r = 0.8786, p < 0.0001), alone and multiplexed, also revealed significant correlations For all markers, staining profiles indicated that each marker performed similarly, whether alone or in combination within a multiplexed panel, highlighting the potential benefits of mIHC analyses Results and discussion Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx Fig Opal mIHC method The Opal mIHC assay protocol is very similar to a standard IHC assay, with the notable exception being the iterative nature of the protocol enabled by a post-visualization microwave step in a citrate buffer (similar to that used for antigen retrieval and endogenous HRP quenching) This allows for a simplified multiplexing strategy, where primary antibodies can be chosen based on performance, and not on species Fig Analysis of ER expression in multiplex and singleplex contexts To assess expression of ER within a multiplexed context (ER, PR, Her2, and Ki67 combined spectral composite, panel A), ER was isolated for fluorescent intensity (FU) measurement (spectral composite of ER from multiplex, panel B) ER intensity was also assessed in a sister serial section as a singleplex (panel C) Analyses of average ER fluorescent intensities for all cases (N = 31) between single and multiplexed contexts demonstrated a highly significant correlation (R2 = 0.8320, Pearson r = 0.9122, p < 0.0001, panel D) Bar in C = 100 lm Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 10 E.C Stack et al / Methods xxx (2014) xxx–xxx Fig Analysis of PR expression in multiplex and singleplex contexts To assess expression of PR within a multiplexed context (ER, PR, Her2, and Ki67 combined spectral composite, panel A), PR was isolated for fluorescent intensity (FU) measurement (spectral composite of PR from multiplex, panel B) PR intensity was also assessed in a sister serial section as a singleplex (panel C) Analyses of average PR fluorescent intensities for all cases (N = 33) between single and multiplexed contexts demonstrated a significant correlation (R2 = 0.6211, Pearson r = 0.7881, p < 0.0001, panel D) Bar in C = 100 lm Fig Analysis of Her2 expression in multiplex and singleplex contexts To assess Her2 expression within a multiplexed context (ER, PR, Her2, and Ki67 combined spectral composite, panel A), Her2 was isolated for fluorescent intensity (FU) measurement (spectral composite of Her2 from multiplex, panel B) Her2 fluorescent intensity was also assessed in a sister serial section as a singleplex (panel C) Analyses of average Her2 intensities for all cases (N = 34) between single and multiplexed contexts demonstrated a highly significant correlation (R2 = 0.9884, Pearson r = 0.9942, p < 0.0001, panel D) Bar in C = 100 lm To assess interassay variability in the Opal multiplexed staining method, two serial stained breast cancer TMA sections were evaluated, with the mean intensity calculated for ER, PR, Her2, and Ki67 staining Again, mean intensity serves as a surrogate for positivity calls Correlational analyses were performed to compare the levels of target expression between each multiplexed assay, and for all targets, the correlations were highly significant (Fig 9) For ER, analysis of mean intensity between each multiplexed section revealed a significant correlation (Fig 9A, R2 = 0.9451, Pearson r = 0.9722, p < 0.0001), while analysis of Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx 11 Fig Analysis of Ki67 expression in multiplex and singleplex contexts To assess Ki67 expression within a multiplexed context (ER, PR, Her2, and Ki67 combined spectral composite, panel A), Ki67 was isolated for fluorescent intensity (FU) measurement (spectral composite of Ki67 from multiplex, panel B) Ki67 intensity was also assessed in a sister serial section as a singleplex (panel C) Analyses of average Ki67 fluorescent intensities for all cases (N = 30) between single and multiplexed contexts demonstrated a highly significant correlation (R2 = 0.7720, Pearson r = 0.8786, p < 0.0001, panel D) Bar in C = 100 lm Fig Analysis of ER, PR, Her2 and Ki67 expression between multiplexed assays in series To assess the levels of target expression between each breast cancer TMA multiplexed assay, correlational analyses were performed For ER, analysis of mean fluorescent intensity (FU) between TMA1 and TMA2 stained in series revealed a highly significant correlation (R2 = 0.9451, Pearson r = 0.9722, p < 0.0001, panel A) Assessment of PR intensity between each multiplexed section also revealed a highly significant correlation (R2 = 0.9149, Pearson r = 0.9565, p < 0.0001, panel B) Analysis of Her2 expression between each multiplexed assay resulted in a highly significant correlation (R2 = 0.9852, Pearson r = 0.9926, p < 0.0001, panel C), while Ki67 also exhibited a highly significant correlation (R2 = 0.9639, Pearson r = 0.9679, p < 0.0001, panel D) mean intensity for PR (Fig 9B, R2 = 0.9149, Pearson p < 0.0001), Her2 (Fig 9C, R2 = 0.9852, Pearson p < 0.0001), and Ki67 (Fig 9D, R2 = 0.9369, Pearson p < 0.0001), between each multiplexed section also r = 0.9565, r = 0.9926, r = 0.9679, revealed a significant correlation Taken together, these data indicate the highly reproducible nature of the Opal method, and the quantitative capacity which is retained within the multiplexed approach Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 12 E.C Stack et al / Methods xxx (2014) xxx–xxx Conclusions The ability to interrogate multiple immunohistochemical markers within a single tissue section is often a complicated assay, which can be performed using either chromogenic or fluorescent detection methods Once achieved, there remains the question of which imaging modality is most appropriate, and then finally, what image analysis strategy will derive the most useful information from which to draw biological conclusions The aim of this review was to highlight the multiple multiplexing methods, with the hope of offering a broad overview of the current staining methods and imaging modalities Importantly, in this context, this review also aimed to assess a practical application of mIHC using TSA, which offered the potential for a simplified mIHC assay In the evaluation of the Opal method, the specific issue addressed focused on the value of mIHC in providing useful expression information from multiple markers, all informing within the exact same cellular and tissue-specific context ER, PR, Her2, and Ki67 were analyzed in a multiplexed assay, allowing for highly contextualized expression data that informs on the stratification of breast cancer based on these markers [52], demonstrating the potential of this approach and supporting the utility of the mIHC assay for potential clinical use The expression of each marker in the context of mIHC was highly correlated with its expression when singly assessed, within serials sister sections This finding demonstrates that the Opal method and mIHC when coupled with multispectral image analysis to deliver highly quantified data, allows for more discrete pathological assessment Importantly, the data also reveal the highly reproducible capacity of the Opal method, signifying the stability and suitability for the Opal mIHC method As well, the Opal mIHC method also offers a significant improvement in pathology workflow, in part by reducing the need for multiple tissue sections to support several individual stains Thus with similar expression patterns for ER, PR, Her2, and Ki67 observed when stained alone, or combined in a multiplexed assay, along with significant reproducibility, the Opal method can support the ability to successfully multiplex within the context of fluorescent multispectral IHC Given the highly reproducible nature of the multiplexing method detailed above, it positions this technology well for performance in a clinical setting where Clinical Laboratory Improvement Amendments (CLIA) or Lab Developed Tests (LDT) requirements are necessary Due to the stringent nature of any clinical test performed for diagnostic purposes, rigorous controls would be required for the mIHC assay In terms of the mIHC, use of positive and negative controls, as outlined above, helps to satisfy some clinical requirements, as does subsequent evaluation, including documentation of the proper location for immunoreactivity, background and edge effect, and inappropriate staining (e.g nuclear stain in the cytoplasm) Since these are all pathology-based, the proper controls for interpretation can easily be met Similarly, reliable image analysis can be controlled through standardization of the imaging protocols and threshold call, along with rigorous daily documented inspections of the hardware and daily establishment of verified control slides to guide intensity measurements of the system With mIHC and multispectral analysis integrated into the pathologists workflow, this methodology can offer unparalleled pathology support, enabling deeper insight into tissue and cellular processes, and ultimately aiding diagnostic potential in order to improve clinical care Materials and methods at the Baylor College of Medicine lm sections of the TMA, with patient samples represented in triplicate were mounted on SuperFrost Plus slides (Fisher) 5.2 Singleplex IHC For each singleplex assay, the target was stained alone To this, slides were deparaffinized in xylene and rehydrated in graded ethanols Antigen retrieval was performed in citrate buffer (citrate pH 6.0) using microwave heating (MWT) The slides were incubated in antibody diluent for 10 Primary antibodies for ER (Abcam SP1, 1:2000), PR (Cell Signaling D8Q2J, 1:2000), Her2 (Cell Signaling 29D8, 1:1000), Ki67 (Dako Mib-1, 1:3000) or cytokeratin (CK; Dako AE1/AE3, 1:2000) were then incubated for h in a humidified chamber at room temperature Detection of each primary antibody was carried out using the appropriate rabbit or mouse SuperPicture Polymer Detection HRP kit (Life Technologies, CA) Visualization of each target was accomplished using fluorescently labeled TSA (ER, fluorescein TSA Plus, 1:50; Her2, Cy5 TSA Plus, 1:50; and Ki67, Cy3 TSA Plus, 1:50; PerkinElmer, MA: PR, Alexa 594 TSA, 1:50; Life Technologies, Grand Island, NY); and CK (coumarin TSA; PerkinElmer, MA) Nuclei were visualized with DAPI (1:2000, Invitrogen, CA), and the slides were again incubated in citrate buffer (pH 6.0) using MWT All slides were then coverslipped using Vectashield hardset mounting media (Vector Labs, CA) 5.3 Multiplexed IHC For multiplexed staining using the Opal protocol, the slides were deparaffinized in xylene and rehydrated in ethanol Antigen retrieval was performed in citrate buffer (pH 6.0) using MWT Primary rabbit antibodies for ER (1:2000) were incubated for h in a humidified chamber at room temperature, followed by detection using the rabbit SuperPicture Polymer Detection HRP kit Visualization of ER was accomplished using fluorescein TSA Plus (1:50), after which the slide was placed in citrate buffer (pH 6.0) and heated using MWT In a serial fashion, the slide was then incubated with primary rabbit antibodies for Her2 (1:1000) for h in a humidified chamber at room temperature, followed by detection using the rabbit SuperPicture Polymer Detection HRP kit Her2 was visualized using Cy5 TSA Plus (1:50).The slide was again placed in citrate buffer (pH 6.0) and subject to MWT, and then incubated with primary rabbit antibodies for PR (1:2000) for h in a humidified chamber at room temperature, followed by detection using the rabbit SuperPicture Polymer Detection HRP kit PR was then visualized using Alexa 594 TSA (1:50), and the slide was placed in citrate buffer (pH 6.0) for MWT The slide was then incubated with primary mouse antibodies for Ki67 (1:3000) for h in a humidified chamber at room temperature, followed by detection using the mouse SuperPicture Polymer Detection HRP kit followed by visualization using Cy3 TSA Plus (1:50) The slide was again placed in citrate buffer (pH 6.0) and heated using MWT The slide was then incubated with the last antibody, CK (1:1000), for h in a humidified chamber at room temperature, followed by detection using the mouse SuperPicture Polymer Detection HRP kit CK was visualized using Coumarin TSA Plus (1:50) The slide was again placed in citrate buffer (pH 6.0) and heated using MWT Nuclei were subsequently visualized with DAPI (1:2000), and the section was coverslipped using Vectashield Hardset mounting media Using this Opal method, five primary antibodies were sequentially applied to a single TMA slide 5.1 Tissue samples 5.4 Multispectral analysis A collection of 39 formalin fixed paraffin embedded breast cancer samples included in a TMA were used These samples were obtained with consent from the Anatomic Pathology Department Each of the individually stained sections (ER-fluorescein, PRA594, Her2-Cy5, Ki67-Cy3, CK-Coumarin, and DAPI) were utilized Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 E.C Stack et al / Methods xxx (2014) xxx–xxx in order to establish the spectral library of fluorophores required for multispectral analysis (see Fig 1) The TMA slides were scanned using the Vectra slide scanner (PerkinElmer) under fluorescent conditions For each core, the Vectra captured the fluorescent spectra at 20 nm wavelength intervals from 420 nm to 720 nm, with identical exposure times, and combined these captures to create a single stack image, which retained the unique spectral signature of all mIHC markers To analyze the spectra for all fluorophores included, InForm image analysis software (PerkinElmer) was used Briefly, InForm employs a pattern recognition learning algorithms that supports a tissue classifier tool, which was trained to segment tissue regions in breast neoplasms between tumor epithelium and surrounding stroma compartments, utilizing both the cytokeratin and DAPI spectral components (see Fig 2) InForm then further segmented tumor tissue to reveal individual nuclei and associated cytoplasm and membrane areas within the confines of the defined region, via DAPI and CK spectra respectively InForm then used each specific fluorescent spectra in order to generate pixel intensities (FU) for every pixel within every sub-cellular compartment within the appropriately segmented region of interest (fluorescein for ER, Cy3 for Ki67, Alexa 594 for PR – nucleus; and Cy5 for Her2 – membrane) InForm then generated an intensity report for all markers within either the nuclear or paired membranous compartments of each tumor cell analyzed, where a mean intensity was registered based on all pixel FUs for every cell and its corresponding subcellular compartments For each marker, the mean fluorescent intensity (FU) per case was then determined, as a base point from which positivity calls could be established when a positivity threshold for each mark was determined Acknowledgement The authors wish to thank Dr Alejadro Contreas, Baylor College of Medicine, Department of Pathology and Immunology, for the generous provision of breast cancer tissue samples employed in this study References [1] R.B Corcoran, H Ebi, A.B Turke, E.M Coffee, M Nishino, A.P Cogdill, R.D Brown, P Della Pelle, D Dias-Santagata, K.E Hung, K.T Flaherty, A Piris, J.A Wargo, J Settleman, M Mino-Kenudson, J.A Engelman, Cancer Discov (2012) 227–235 [2] J.C Lee, S.H Jang, K.Y Lee, Y.C Kim, Cancer Res Treat 45 (2013) 79–85 [3] J.A Ramos-Vara, M.A Miller, Vet Pathol 51 (2014) 42–87 [4] C.M van der Loos, J Histotechnol 33 (2010) 31–40 [5] W Huang, K Hennrick, S Drew, Hum Pathol 44 (2013) 29–38 [6] J.L Lanciego, P.H Goede, M.P Witter, F.G Wouterlood, J Neurosci Methods 74 (1997) 1–7 [7] R.M Levenson, J.R Mansfield, Cytometry A 69 (2006) 748–758 [8] A.F Setiadi, N.C Ray, H.E Kohrt, A Kapelner, V Carcamo-Cavazos, E.B Levic, S Yadegarynia, C.M van der Loos, E.J Schwartz, S Holmes, P.P Lee, PLoS ONE (2010) e12420 [9] G Glass, J.A Papin, J.W Mandell, J Histochem Cytochem 57 (2009) 899–905 [10] D.Y Mason, K Micklem, M Jones, J Pathol 191 (2000) 452–461 [11] A.M Bogusz, R.H Baxter, T Currie, P Sinha, A.R Sohani, J.L Kutok, S.J Rodig, Clin Cancer Res 18 (2012) 6122–6135 [12] C.W Peng, X.L Liu, C Chen, X Liu, X.Q Yang, D.W Pang, X.B Zhu, Y Li, Biomaterials 32 (2011) 2907–2917 [13] Y Xing, Q Chaudry, C Shen, K.Y Kong, H.E Zhau, L.W Chung, J.A Petros, R.M O’Regan, M.V Yezhelyev, J.W Simons, M.D Wang, S Nie, Nat Protoc (2007) 1152–1165 [14] P Zrazhevskiy, X Gao, Nat Commun (2013) 1619 [15] U Berndt, L Philipsen, S Bartsch, Y Hu, C Rocken, W Bertram, M Hammerle, T Rosch, A Sturm, Mol Cancer (2010) 177 [16] M.N Bobrow, T.D Harris, K.J Shaughnessy, G.J Litt, J Immunol Methods 125 (1989) 279–285 13 [17] M.N Bobrow, K.J Shaughnessy, G.J Litt, J Immunol Methods 137 (1991) 103– 112 [18] R.T Mitchell, M.E Camacho-Moll, J Macdonald, R.A Anderson, C.J Kelnar, M O’Donnell, R.M Sharpe, L.B Smith, K.M Grigor, W.H Wallace, H Stoop, K.P Wolffenbuttel, R Donat, P.T Saunders, L.H Looijenga, Mod Pathol (2014) [19] Z.E Toth, E Mezey, J Histochem Cytochem 55 (2007) 545–554 [20] Johnson, T., Voelker, F., Potts, S., and Young, G D (2010) In Digital Pathology, San Diego, CA [21] S.S Park, J.W Gillespie, B James, M Lebowitz, J Clin Oncol 27 (2012) [22] J.Y Chung, T Braunschweig, G Baibakov, M Galperin, A Ramesh, M Skacel, G Gannot, V Knezevic, S.M Hewitt, Proteomics (2006) 767–774 [23] J.Y Chung, S.M Hong, B.Y Choi, H Cho, E Yu, S.M Hewitt, Clin Cancer Res 15 (2009) 660–667 24 Grimm, M., Renz, C., Munz, A., Hoefert, S., Krimmel, M., and Reinert, S (2013) Odontology / the Society of the Nippon Dental University [25] M.P Schonermark, O Bock, A Buchner, R Steinmeier, U Benbow, T Lenarz, Nat Med (1997) 1167–1171 [26] M.E Dickinson, G Bearman, S Tille, R Lansford, S.E Fraser, Biotechniques 31 (2001) 1274–1276 1272, 1278 [27] T Haraguchi, T Shimi, T Koujin, N Hashiguchi, Y Hiraoka, Genes Cells (2002) 881–887 [28] J.L Pauly, E.M Allison, E.L Hurley, C.E Nwogu, P.K Wallace, G.M Paszkiewicz, Microsc Res Tech 67 (2005) 79–89 [29] T.J Fountaine, S.M Wincovitch, D.H Geho, S.H Garfield, S Pittaluga, Mod Pathol 19 (2006) 1181–1191 [30] J.R Mansfield, Vet Pathol 51 (2014) 185–210 [31] D.L Rimm, Nat Methods 11 (2014) 381–383 [32] C Giesen, H.A Wang, D Schapiro, N Zivanovic, A Jacobs, B Hattendorf, P.J Schuffler, D Grolimund, J.M Buhmann, S Brandt, Z Varga, P.J Wild, D Gunther, B Bodenmiller, Nat Methods 11 (2014) 417–422 [33] M Angelo, S.C Bendall, R Finck, M.B Hale, C Hitzman, A.D Borowsky, R.M Levenson, J.B Lowe, S.D Liu, S Zhao, Y Natkunam, G.P Nolan, Nat Med 20 (2014) 436–442 [34] M.J Gerdes, C.J Sevinsky, A Sood, S Adak, M.O Bello, A Bordwell, A Can, A Corwin, S Dinn, R.J Filkins, D Hollman, V Kamath, S Kaanumalle, K Kenny, M Larsen, M Lazare, Q Li, C Lowes, C.C McCulloch, E McDonough, M.C Montalto, Z Pang, J Rittscher, A Santamaria-Pang, B.D Sarachan, M.L Seel, A Seppo, K Shaikh, Y Sui, J Zhang, F Ginty, Proc Natl Acad Sci U.S.A 110 (2013) 11982–11987 [35] M.C Haffner, T Mosbruger, D.M Esopi, H Fedor, C.M Heaphy, D.A Walker, N Adejola, M Gurel, J Hicks, A.K Meeker, M.K Halushka, J.W Simons, W.B Isaacs, A.M De Marzo, W.G Nelson, S Yegnasubramanian, J Clin Investig 123 (2013) 4918–4922 [36] P Hu, G.C Chu, G Zhu, H Yang, D Luthringer, G Prins, F Habib, Y Wang, R Wang, L.W Chung, H.E Zhau, PLoS ONE (2011) e28670 [37] C.M van der Loos, O.J de Boer, C Mackaaij, L.T Hoekstra, T.M van Gulik, J Verheij, J Histochem Cytochem 61 (2013) 11–18 [38] D.L Rimm, Nat Biotechnol 24 (2006) 914–916 [39] E Barrow, D.G Evans, R McMahon, J Hill, R Byers, J Clin Pathol 64 (2011) 208–214 [40] D Killander, A Levin, M Inoue, E Klein, Immunology 19 (1970) 151–156 [41] E Sweeney, T.H Ward, N Gray, C Womack, G Jayson, A Hughes, C Dive, R Byers, Biochem Biophys Res Commun 374 (2008) 181–186 42 Teverovskiy, M., Vengrenyul, Y., Tabesh, A., Sapir, M., Fogarasi, S., Pang, H Y., Khan, F M., Hamann, S., Capodeici, P., Clayton, M., Kim, R., Fernandez, G., MesaTejada, R., and Donovan, M J (2008) Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging In Proc 5th IEEE Int Symp Biomed Imag.: From Nano to Macro pp 300 - 303 [43] J.R Mansfield, K.W Gossage, C.C Hoyt, R.M Levenson, J Biomed Opt 10 (2005) 41207 [44] R.L Camp, M Dolled-Filhart, B.L King, D.L Rimm, Cancer Res 63 (2003) 1445– 1448 [45] D.L Rimm, J.M Giltnane, C Moeder, M Harigopal, G.G Chung, R.L Camp, B Burtness, J Clin Oncol 25 (2007) 2487–2488 [46] Canadian Association of Pathologists-Association canadienne des Pathologistes National Standards Committee, E.E Torlakovic, R Riddell, D Banerjee, H El-Zimaity, D Pilavdzic, P Dawe, A Magliocco, P Barnes, R Berendt, D Cook, B Gilks, G Williams, B Perez-Ordonez, B Wehrli, P.E Swanson, C.N Otis, S Nielsen, M Vyberg, J Butany, Am J Clin Pathol 133 (2010) 354–365 [47] L.D True, Histochem Cell Biol 130 (2008) 473–480 [48] A.W Welsh, C.B Moeder, S Kumar, P Gershkovich, E.T Alarid, M Harigopal, B.G Haffty, D.L Rimm, J Clin Oncol 29 (2011) 2978–2984 [49] A.A Onitilo, J.M Engel, R.T Greenlee, B.N Mukesh, Clin Med Res (2009) 4– 13 [50] A.S Leong, Z Zhuang, Pathobiology 78 (2011) 99–114 [51] J Bonnema, C.J van de Velde, Ann Oncol 13 (2002) 1531–1537 [52] L Braun, F Mietzsch, P Seibold, A Schneeweiss, P Schirmacher, J ChangClaude, H Peter Sinn, S Aulmann, Mod Pathol 26 (2013) 1161–1171 Please cite this article in press as: E.C Stack et al., Methods (2014), http://dx.doi.org/10.1016/j.ymeth.2014.08.016 ... component label planes and high-resolution imagery enabled by having all labels in place and imaged simultaneously, and the accurate removal of autofluorescence signal from label signals Also, by enabling... is reliable and specific data to support research, and ultimately to advance clinical care This requires image analysis tools that can reliably identify and quantitate all targets of interest... other image analysis software packages can support mIHC analyses For example, Definiens image analysis software (Definiens, Carlsbad, CA) has been used in conjunction with a multispectral imaging