Immunohistochemistry is a powerful lab technique for evaluating protein localization and expression within tissues. Current semi-automated methods for quantitation introduce subjectivity and often create irreproducible results. Herein, we describe methods for multiplexed immunohistochemistry and objective quantitation of protein expression and co-localization using multispectral imaging.
Immunohistochemistry is a commonly used clinical and research lab detection technique for investigating protein expression and localization within tissues. Many semi-quantitative systems have been developed for scoring expression using immunohistochemistry, but inherent subjectivity limits reproducibility and accuracy of results. Furthermore, the investigation of spatially overlapping biomarkers such as nuclear transcription factors is difficult with current immunohistochemistry techniques. We have developed and optimized a system for simultaneous investigation of multiple proteins using high throughput methods of multiplexed immunohistochemistry and multispectral imaging. Multiplexed immunohistochemistry is performed by sequential application of primary antibodies with secondary antibodies conjugated to horseradish peroxidase or alkaline phosphatase. Different chromogens are used to detect each protein of interest. Stained slides are loaded into an automated slide scanner and a protocol is created for automated image acquisition. A spectral library is created by staining a set of slides with a single chromogen on each. A subset of representative stained images are imported into multispectral imaging software and an algorithm for distinguishing tissue type is created by defining tissue compartments on images. Subcellular compartments are segmented by using hematoxylin counterstain and adjusting the intrinsic algorithm. Thresholding is applied to determine positivity and protein co-localization. The final algorithm is then applied to the entire set of tissues. Resulting data allows the user to evaluate protein expression based on tissue type (ex. epithelia vs. stroma) and subcellular compartment (nucleus vs. cytoplasm vs. plasma membrane). Co-localization analysis allows for investigation of double-positive, double-negative, and single-positive cell types. Combining multispectral imaging with multiplexed immunohistochemistry and automated image acquisition is an objective, high-throughput method for investigation of biomarkers within tissues.
Immunohistochemistry (IHC) is a standard lab technique for detection of protein within tissue, and IHC is still widely used in both research and diagnostic pathology. The evaluation of IHC staining is often semi-quantitative, introducing potential bias into interpretation of results. Many semi-quantitative approaches have been developed which incorporate both staining intensity and staining extent into final diagnosis 1-4. Other systems include scoring intensity and subcellular location in order to better localize expression 5. Incorporation of average scores from multiple viewers is often utilized in order to minimize the effects of single viewer bias 6. Despite these efforts, subjectivity in analysis still remains, particularly when evaluating the extent of staining 7. Protocol standardization and minimizing subjectivity from human input is paramount to creating accurate, reproducible IHC results.
There are other options besides IHC for determining protein expression within tissues. Within the research setting, immunohistochemistry has traditionally been viewed as a means to examine protein localization 8, while other techniques such as immunoblotting are viewed as gold standard for investigating protein expression. Determining tissue or cell compartment-specific expression is difficult without incorporating advanced techniques such as cell fractionation or laser capture microdissection 9,10. The use of fluorescent antibodies on tissue slides offers a reasonable compromise, but background autofluorescence due to NADPH, lipofuscins, reticular fibers, collagen, and elastin can make accurate quantitation difficult 11.
Automated computational pathology platforms are a promising direction for more objective quantitation of pathology staining 12-15. Combining multispectral imaging with tissue microarrays facilitates high-throughput analysis of protein expression in large sample sizes. With these techniques, analysis of protein co-localization, staining heterogeneity, and tissue and subcellular localization is possible while substantially reducing both inherent biases and time necessary for analysis, while returning data in a continuous rather than categorical format 16. Therefore, the purpose of this study was to demonstrate the utility of and methodology for performing multiplexed immunohistochemistry with analysis, using multispectral imaging software.
This protocol is written for manual, multiplex immunohistochemical staining of a single tissue section slide with four optimized monoclonal antibodies. As a representative experiment, nuclear anti-rabbit estrogen receptor alpha (ERα) and androgen receptor (AR) are multiplexed with membrane-bound anti-mouse CD147 and membrane-bound anti-mouse E-cadherin. Any antibody of choice may be substituted for the antibodies listed herein, but each combination of antibodies requires separate optimization. Pre-treatment for all the antibodies must be identical. The AR and CD147 antibodies should be optimized individually and then as a cocktail. Each antibody is detected using a biotin-free polymer system and one of 4 unique chromogens.
NOTE: The protocol herein describes staining and analysis of a tissue microarray (TMA), described previously 12,17,18. The 4 µm thick TMA section was obtained from a paraffin block using a standard microtome.
NOTE: A spectral library for the 4 chromogens and counterstain should be created for image quantitation. In order to do this, the optimized protocol for each individual antibody should be run with one antibody per slide, minus the final counterstain. A fifth slide should be stained with hematoxylin to generate the 5 images needed to create the spectral library.
1. Multiplex Immunohistochemistry
2. Automated Image Acquisition and Analysis
3. Tissue Segmentation
4. Cell Segmentation
5. Phenotyping of Cells
NOTE: Accurate cell segmentation is required in order to obtain accurate cell phenotyping, and the phenotyping feature is trainable.
6. Scoring IHC and Co-Localization
7. Applying the Algorithm and Batch Analysis
8. Analysis of Exported Data
In Figure 1, training is performed on prostate tissues to segment images into epithelial and stromal portions, along with the non-tissue compartment. By using the epithelial membrane marker E-cadherin, cell segmentation was performed to separate the nucleus, cytoplasm, and membrane portions, shown in Figure 2.
In one experiment, we used multiplexed IHC to investigate the expression and localization of AR, ERα, E-cadherin, and CD147, as shown in Figure 3. Using these techniques, we are able to identify cells positive for nuclear expression of both ERα and AR (Figure 3B–3C) despite overlapping colorimetric signals, as shown with green arrows in Figure 3A. We found marked differences in the proportion of double positive stromal cells within different states of prostate disease (Figure 3D). We quantified cell membrane-specific expression of CD147 by using E-cadherin as a marker protein (red arrows in Figure 3A), and we were the first group to investigate membrane specific CD147 expression in prostate tissues. We found a significant decrease in CD147 expression in association with prostate cancer progression (Figure 3E) and found an important association with post-surgical prognosis 19.
There are instances where poor experiment design can lead to inaccurate tissue segmentation. In Figure 4, the algorithm applied to a set of tissues did not accurately segment epithelial and stromal compartments (Figure 4C). In this experiment, α-smooth muscle actin (α-SMA) was used to mark the stromal compartment. Because α-SMA and smooth muscle is decreased or lost in some tumors (Figure 4A), the algorithm created through tissue segmentation (Figure 4B) was unable to accurately differentiate between epithelial and stromal compartments from morphometric data alone. Care should be taken when choosing protein markers for tissue or cell compartments.
Figure 1: Tissue Segmentation Using Multispectral Imaging Software. A prostate tissue microarray was stained using multiplexed immunohistochemistry for androgen receptor (AR), estrogen receptor-alpha (ERα), E-cadherin, and CD147. A set of training images were imported into multispectral imaging software representing tissue types and disease states of the entire set of images (A). Tissue categories were created including stroma (green), epithelia (red), and non-tissue (blue), and categories were defined by manually drawing on top of training images (B). After drawing on training images, an algorithm for tissue segmentation was created and applied to the training set of images (C). Please click here to view a larger version of this figure.
Figure 2: Cell Segmentation into Nuclear, Cytoplasmic, and Membrane Compartments. The nuclear compartment was defined for cell segmentation by setting a minimum threshold for hematoxylin counterstaining (A). The cytoplasm was defined in relation to the nucleus by using pre-set algorithms within the software (B). E-cadherin was used as a membrane marker and a minimum mean OD threshold was applied to define the membrane compartment (C). This technique allows simultaneous quantitation of protein expression in all subcellular compartments (D). Please click here to view a larger version of this figure.
Figure 3: Analysis of Co-localization and Membrane-specific Protein Expression. Multiplexed IHC was used to investigate the expression and localization of androgen receptor (AR), estrogen receptor-alpha (ERα), E-cadherin, and CD147 (A). DAB (brown chromogen) was used to mark AR (B) and a red chromogen used to mark ERα (C). We were able to identify spatially overlapping biomarkers (green arrows in A) and quantify (D) the proportion of cells with nuclear co-localization of ERα and AR within prostate stroma. By using E-cadherin (black chromogen) to define the plasma membrane (red arrows in A), we quantified membrane-specific expression of CD147 within prostate tissues (E) 19. One-way analysis of variance (ANOVA) was used for statistical analysis, error bars reflect standard error of the mean, and asterisks represent p<0.05. Please click here to view a larger version of this figure.
Figure 4: Inadequate Tissue Segmentation Resulting from Experimental Design. Benign and malignant prostate tissues were stained for α-smooth muscle actin (α-SMA; green chromogen), androgen receptor (red chromogen), and androgen receptor variant 7 (DAB chromogen). α-SMA was used as a marker of stroma and is decreased in some tumors, including prostate (A). When training was performed (B) and the tissue segmentation algorithm was applied, the stromal and epithelial compartments were inadequately segmented due to the absence of α-SMA staining (C). Please click here to view a larger version of this figure.
The use of traditional immunohistochemistry for evaluating protein expression is limited by subjective, semi-quantitative methods of analysis 22,23. Advance platforms have been created for high-throughput analysis of biomarker expression and localization. Detailed segmentation of both tissue and subcellular compartments allows users to study biomarker expression, localization, and co-localization with other markers of interest. In previous studies, we have demonstrated the utility of IHC and multispectral imaging, particularly when used to study proteins localized to the same cellular compartment 18,20,21. When combined with tissue microarrays 24, these techniques allow for faster and more objective quantitation of protein expression than allowed by manual analysis by a pathologist.
One issue with the use of immunohistochemistry for protein quantitation is irreproducibility of results due to the subjective nature of analysis and inherent differences in technique and reagents. A significant advantage of using platforms like multispectral imaging for measuring protein expression is reproducibility of results. Data from computerized pathology platforms correlate highly with manual analysis by a pathologist while returning data in a continuous format and substantially reducing work time, particularly when working with large sample sizes 12,16,25. Studies have shown high overall concordance in results between different multispectral platforms 14. Furthermore, we have previously demonstrated that staining results are highly reproducible even when there is variability in the number of proteins investigated 12. The use of automated pathology platforms for both staining and analysis will not eliminate all discrepancies in results, such as those stemming from antibodies created towards different epitopes, but these platforms do substantially reduce bias and irreproducibility commonly associated with immunohistochemistry.
There are particular steps within the protocol that are essential for returning accurate, reproducible results. Appropriate experimental design through selection of proteins to be included as tissue or subcellular markers is important for accurate segmentation. When performing positivity analysis, the selection of a lower threshold for positive staining has a large effect on the final results. While choosing a threshold for "on/off" proteins such as nuclear transcription factors is rather straightforward, finding a threshold for more heterogeneously expressed proteins is difficult. This should ideally be performed in collaboration with a board-certified genitourinary pathologist or as an averaged score across multiple observers to find the ideal threshold for analysis.
It is important to recognize some limitations of current versions of this technology. When defining the cytoplasm compartment, three approaches can be used: (1) staining with a cytoplasm-specific marker, (2) staining with a membrane-specific marker and using the nucleus-to-membrane marker distance as cytoplasm, and (3) using a drawing approach to manually define the boundaries of the cytoplasm in relation to the nucleus. From our experience, using a membrane-specific marker is the most accurate technique. The manual drawing approach is normally accurate if nuclei are centrally located or if the biomarker of interest is uniformly distributed throughout the cytoplasm. Accurately defining the cytoplasm of stromal cells like fibroblasts and smooth muscle cells remains difficult and should be taken into consideration when designing an experiment.
Another limitation of the technology is the dependence upon nuclei for cell segmentation. If plane of section excludes the nucleus of a particular cell, this cell will not be included in analysis. If there is no visible cytoplasm between adjacent nuclei or clumps of nuclei, these are often recognized as one large nuclear lump, rather than distinct nuclei. Generally speaking, hematoxylin counterstain is normally adequate for acceptable nuclear segmentation, and manipulation of software settings such as maximum threshold for nucleus size can fix most issues with nuclear segmentation.
A final limitation of automated pathology platforms is unreliable segmentation of tissues resulting from poor experimental design. The importance of choosing appropriate tissue and cell biomarkers for answering the question of interest cannot be overstated. As we demonstrated in our representative results, an epithelial or stromal marker that significantly changes expression between disease states can pose problems when creating an algorithm for tissue segmentation. It is worth noting that there are alternative options when difficult analyses like this arise. For example, individual images can be analyzed by manually drawing all tissue compartments, rather than by applying an algorithm from a training set of images. This provides the advantage of segmentation that is perfectly in line with what the user desires, but it also introduces subjectivity and can significantly increase the time required to finish an analysis. Appropriate experimental design is the easiest way to expedite analysis and maximize the accuracy of tissue and cell segmentation.
This technology and protocol have many future applications. Numerous biomarkers for particular disease states have been identified but not validated. High throughput objective analysis with multispectral platforms facilitates validation of these biomarkers. Further, the evaluation of expression and co-localization of multiple proteins can provide insights into poorly-understood signaling pathways. Undoubtedly, reducing the inherent subjectivity associated with analysis of immunohistochemical staining is valuable for understanding the expression and localization of a wide range of protein markers.
The authors have nothing to disclose.
The authors thank the University of Wisconsin Translational Research Initiatives in Pathology laboratory, in part supported by the UW Department of Pathology and Laboratory Medicine and UWCCC grant P30 CA014520, for use of its facilities and services.
Xylene | Fisher Chemical | X3F1GAL | NFPA rating:Health – 2, Fire – 3 , Reactivity-0 |
Ethyl Alcohol-200 proof | Fisher Scientific | 4355223 | NFPA rating:Health – 0, Fire – 3 , Reactivity-0 |
Tris Base | Fisher Scientific | BP152-500 | NFPA rating:Health – 2, Fire – 0 , Reactivity-0 |
Tris Hydroxymethyl aminomethane HCl | Fisher Scientific | BP153-1 | NFPA rating:Health – 2, Fire – 0 , Reactivity-0 |
Tween 20 | Chem-Impex | 1512 | NFPA rating:Health – 0, Fire –1 , Reactivity-0 |
Phosphate-buffered saline | Fisher Scientific | BP2944-100 | NFPA rating:Health – 1, Fire –0 , Reactivity-0 |
Peroxidazed | Biocare Medical | PX968 | Avoid contact with skin and eyes. May cause skin irritation and eye damage. |
Diva Decloaker | Biocare Medical | DV2004 | This product has been classified as non‐hazardous based on the physical and/or chemical nature and/or concentration of ingredients. |
Estrogen Receptor alpha | Thermo Fisher Scientific-Labvision | RM9101 | Not classified as hazardous |
Androgen Receptor | SCBT | sc-816 | Not classified as hazardous |
CD147 | Biodesign | P87535M | Not classified as hazardous |
E-cadherin | Dako | M3612 | Not classified as hazardous |
Renoir Red Andibody Diluent | Biocare Medical | PD904 | It is specially designed to work with Tris-based antibodies |
DeCloaking Chamber | Biocare Medical | Model DC2002 | Take normal precautions for using a pressure cooker |
Barrier pen-Immuno Edge | Vector Labs | H-4000 | |
Denaturing Kit-Elution step | Biocare Medical | DNS001H | Not classified as hazardous |
Mach 2 Goat anti-Rabbit HRP Polymer | Biocare Medical | RHRP520 | Not classified as hazardous |
Mach 2 Goat anti-Rabbit AP Polymer | Biocare Medical | RALP525 | Not classified as hazardous |
Mach 2 Goat anti-Mouse HRP Polymer | Biocare Medical | M3M530 | Not classified as hazardous |
Betazoid DAB Chromogen Kit | Biocare Medical | BDB2004 | 1. DAB is known to be a suspected carcinogen. 2. Do not expose DAB components to strong light or direct sunlight. 3. Wear appropriate personal protective equipment and clothing. 4. DAB may cause sensitization of skin. Avoid contact with skin and eyes. 5. Observe all federal, state and local environmental regarding disposal |
Warp Red Chromogen Kit | Biocare Medical | WR806 | Corrosive. Acid that may cause skin irritation or eye damage. |
Vina Green Chromogen Kit | Biocare Medical | BRR807 | Harmful if swallowed |
Bajoran Purple Chromogen Kit | Biocare Medical | BJP807 | Flammable liquid. Keep away from heat, flames and sparks. Harmful by ingestion or absorption. Avoid contact with skin or eyes, and avoid inhalation. |
Cat Hematoxylin | Biocare Medical | CATHE | Purple solution with a mild acetic acid (vinegar) scent. May be irritating to skin or eyes. Avoid contact with skin and eyes. Avoid ingestion. |
XYL Mounting Media | Richard Allen | 8312-4 | NFPA rating:Health – 2, Fire – 3 , Reactivity-0 |
1.5 Coverslips | Fisher Brand | 22266858 | Sharp edges |
Incubation (Humidity)Chamber | obsolete | obsolete | Multiple vendors available |
Convection Oven | Stabil- Therm | C-4008-Q | |
Background Punisher Blocking Reagent | Biocare Medical | BP974 | This product is not classified as hazardous. |
inForm software | PerkinElmer | CLS135781 | Primary multispectral imaging software used in manuscript |
Nuance software | PerkinElmer | NUANCEEX | Software used for making spectral libraries within manuscript |
Vectra microscope and slide scanner | PerkinElmer | VECTRA | Automated slide scanner and microscope for obtaining IM3 image cubes |