Shifting from single marker analysis to more complex multiplex IHC analysis methods is an important step towards improving the outcomes of immunohistochemistry studies. Yet, different multiplex immunohistochemistry (mIHC) techniques generate tons of image data for examination.
Our team has compiled some proven tips for effectively analyzing multiplex images with automated methods in the IKOSA Platform. With our practical advice, you can efficiently handle multiplex images and achieve accurate results.
Understanding the basics of multiplex IHC
Multiplexed Immunohistochemistry techniques have become increasingly crucial in the diagnosis and treatment of cancer. These techniques offer significant advantages over conventional IHC methods or mass spectrometry, since in the latter valuable information is lost due to sample preparation. We discuss different ways mIHC will make your research efforts more productive.
Mapping the tumor microenvironment with multiplex IHC methods
The tumor immune microenvironment (TIME) is a complex system of factors that interact to support the development and survival of cancer cells. It includes immune cells, antigens, cytokines, regulatory T-cells, tumor-associated lymphocytes, macrophages, and myeloid-derived suppressor cells. The interaction of these factors with tumor cells determines cancer progression as well as immune- and treatment response. Thus, the precise assessment of the TIME has become a critical factor in the evaluation of immunotherapy effectiveness.
Current cancer research utilizes many different techniques to analyze the TIME. These include flow cytometry, multiplex bead-based immunoassays, chromogenic immunohistochemistry, immunofluorescence, and spatial transcriptomics (Boisson et al. 2021). Multiplexed IHC methods have a significant advantage over flow cytometry and transcriptomic analysis since they don’t require fresh tissue samples and provide spatial information. Besides, many laboratories are already equipped with fluorescence microscopes and automatic stainers, which means no further investment in additional equipment is necessary to conduct successful IHC research (Koh et al. 2020; Taube et al. 2020).
Another important field of use of multiplexed IHC is the Cell Painting method for morphological profiling. The Cell Painting Assay is a high-content technique widely used in drug discovery where cell phenotypes are stained with different fluorescent dyes and displayed in separate multiplexed channels. It enables researchers to identify cellular components like the nucleus (DNA), endoplasmic reticulum (ER), nucleoli, cytoplasmic RNA, actin, Golgi, plasma membrane (AGP), and mitochondria (Chandrasekaran et al. 2021). One important advantage of the Cell Painting method is the simultaneous detection of different morphological structures and cell types within a single tissue section.
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IHC methods are widely used in current pathology research to examine the expression of biomarkers predictive and prognostic of cancer. While singleplex assays provide insights about a single biomarker, multiplex assays allow you to look at multiple markers at a time.
The main reason for choosing this method is the need for simultaneous evaluation of multiple tissue structures and cell types using the same tissue specimen. This helps, especially if you analyze limited tissue samples and small slide areas.
Did you know?
Using state-of-the-art multiplex imaging methods you can extract complex quantitative data on up to 50 multiple biomarkers on a single cell level (Taube et al. 2020).
Yet, gathering data with multiplex immunoassay techniques involves multiple steps, including tissue processing, tissue staining, imaging, AI-aided image analysis and statistical evaluation. We provide you with some tips to optimize each step of the mIHC workflow and gain optimal results.
While the singleplex method would require you to repeat the procedure on multiple individual tissue sections, multiplexed staining allows you to label an entire array of cell types and biomarkers on a single area (Fassler et al. 2020; Huss, 2021). This adds an extra level of depth to your analysis and allows you to examine biomarker distribution within tissue sections.
Did you know?
Key terminology: IHC staining is targeted at a specific antigen/protein marker representative of a target cell type (McKay et al., 2017).
The do’s and don’ts of multiplexed methods
In the context of immunohistological assays, antibodies are widely used to label particular molecular structures due to their diversity and ability to bind target antigens. A primary antibody typically targets a specific protein, while a secondary antibody targets structures of the primary antibody. The resulting antigen-antibody complex is then visualized by the means of detection reagents or fluorochromes at the area where the antigen and antibody interact.
Essential tips for chromogenic multiplex immunohistochemistry (cIHC)
Researchers widely use IHC methods to detect immune cells, tumor and cell proliferation marker expression, degenerative disorders, and infectious diseases.
Did you know?
Key terminology: Chromogenic IHC staining (cIHC) relies on enzyme-labeled antibodies to mark the presence of antigens in tissue sections (Tsutsumi 2021).
In singleplex assays, the chromogen diaminobenzidine (DAB) is often used to locate a marker of interest, which usually results in brown coloring. Dual, triplex, or multiplex staining associated with new chromogens makes the simultaneous staining of different target antigens possible. The latter are often not even on the same cellular or subcellular level (e.g. membrane and nucleus). Specifically, the cancer grading of tumors can be performed by employing multicolor chromogenic stains like p53 and KI-67 (Tsutsumi 2021, Boisson et al. 2021).
We have listed some best practices for multiplexed chromogenic staining, which will help you extract more information from your FFPE sections:
- Antibody selection
- Chromogen dye selection
- Consecutive staining of a single slide
- Standardized IHC protocol
- Minimizing background staining
- Reducing staining artifacts
- Handling endogenous pigments
While fluorescent multiplex techniques are less sensitive than chromogenic stains, they usually result in more accurate and distinct localization of antigens (Tsutsumi 2021). To help you choose the suitable IHC technique for your research, we provide you with an overview of the benefits and pitfalls of each method.
| Advantages | Disadvantages | |
|---|---|---|
| Fluorescent Detection | Easier Multiplexing: more colors and narrower emission spectra Co-localization: Fluorescent dyes allow better separation of co-localized targets Higher Dynamic Range More specific | Lower Sensitivity: Enzyme-conjugated antibodies can be amplified to increase sensitivity. Photobleaching: Exposure to light may diminish fluorescent signals over time. The Signal will fade over time. Autofluorescence |
| Chromogenic Detection | Greater Sensitivity: Signal amplification via indirect chromogenic detection (see below) increases signal strength Longer Lasting Signal Simplicity: Chromogenic dyes are relatively easy to use and require standard brightfield microscopy only | Co-localization: Difficult to distinguish mixed color from single color when targets co-localize. Narrower Dynamic Range Difficult Multiplexing: Fewer colors and broader emission spectra than fluorescent dyes. Less specific |
Tips and tricks
When choosing the primary antibody for your cIHC assay, consider factors such as sensitivity and specificity. Monoclonal antibodies typically target one specific antigen and protein family, while polyclonal antibodies apply to known and unknown isoforms of an antigen (Taube et al. 2020).
Key Terminology
The process of evaluating antibody specificity and sensitivity is referred to as antibody validation.
Identification biomarkers including for example CD3, CD4, CD20 and CD68 are widely used to visualize cell phenotypes in FFPE sections. Further, the colocalization of multiple markers helps identify different cell phenotypes. Our article on the principles of colocalization analysis in microscopy offers an extensive review of this method. The table below provides an overview of markers for identifying common cell types within the tumor immune microenvironment.
| Target | Chromogen markers |
|---|---|
| T-cells (general) | CD3, CD4, CD8 |
| Helper T-cells | CD4 |
| Cytotoxic T-cells | CD8 |
| Regulatory T-cells | FOXP3 |
| B-cells | CD19, CD20 |
| Monocytes | CD11b, CD14 |
| Myeloid cells | CD16 |
| Stromal cells | CD31, CD34 |
| Cancer cells | K17 |
| NK cells | CD56, CD161 |
Tips and tricks
A combination of two or more chromogens in a single cell or tissue feature results in a unique coloring allowing you to detect cases of biomarker co-expression (e.g., a yellow chromogen applied on top of a purple chromogen produces a distinct orange coloring) (Taube et. al. 2020).
Multiplexed immunohistochemical consecutive staining on a single slide involves repeating staining cycles with one chromogen at a time. After each staining cycle, the mIHC slide undergoes scanning with an imaging device, after which the chromogen is destained, and a new dye is applied. This technique allows the generation of a multiplexed image containing a specific marker for each staining cycle. (Taube et al. 2020).
Bring multiplex image analysis research to the next level.
Understanding Fluorescent Stains and Multiplexed Immunofluorescence
Fluorescent stains, alternatively referred to as fluorochromes or fluorophores, constitute chemical compounds that, upon light excitation, emit light within a specific range of the visible spectrum. They provide a higher degree of spectral separation compared to chromogenic dyes (Shakya, 2020).
The underlying process of this technique involves the absorption of light by a fluorescent molecule followed by the emission of light with a different wavelength. The emission allows the detection of a particular fluorescence signal. In other words, fluorophores are excited at a specific wavelength whereby their emission intensity can be measured. The wavelength of light corresponds to an individual channel.
Key Terminology
Fluorescent staining involves using fluorophore-labeled antibodies to detect target antigens.
| Fluorescent Dye | Target | Color (approx. peak emission wavelength) |
|---|---|---|
DAPI / 4′,6-diamidino-2-phenylindole | DNA, adenine–thymine-rich regions in DNA | Blue (457 nm) |
| Nuclear yellow /Hoechst S769121 | DNA | Green (504 nm) |
| cell-permeant SYTO 59/SYTO Red Fluorescent Nucleic Acid Stain Sampler Kit (S-11340) | DNA | Red (659 nm) |
| SYTO 9 green fluorescent nucleic acid stain | DNA | Green (503 nm) |
| FITC | any, depends on antigen (dye conjugated to primary antibodies) | Green (516 nm) |
| TRITC | any, depends on antigen (dye conjugated to primary antibodies) | Yellow (570 nm) |
| Cy5 | any, depends on antigen (dye conjugated to primary antibodies) | Red (670 nm) |
| Cy7 | any, depends on antigen (dye conjugated to primary antibodies) | (far-) red (780 nm) |
Frequently, the spectral profiles of the fluorophores closely overlap on the wavelength spectrum, leading to signal mixing. Multispectral imaging (MSI) allows the separation of multiple fluorophores even with overlapping emission spectra. It increases accuracy by minimizing the “bleed-through” of signals to neighboring channels. Each signal is displayed in a separate visualization. MSI primarily helps to accomplish complex biomarker multiplexing in fluorescence imaging of tissue samples and in vivo preclinical imaging (Fereidouni et al., 2018). Another proven approach when encountering these phenomena is to employ mathematical linear signal unmixing (Taube et al., 2020).
Considerations and potential issues using multiplexed immunofluorescence
- Overlapping spectral signatures
- Dealing with autofluorescence
- Dealing with signal bleed-throughs
- Dealing with photobleaching
- Dealing with signal fade over time
After you have collected numerous multiplexed images from your assays, it is time to analyze your data. Quantitative data gathered with the help of IHC image analysis software is ultimately used in different univariate and multivariate statistical tests to identify relevant prognostic and predictive parameters associated with specific molecular markers (Pell et al. 2019). Let’s dive into the process of mICH image analysis with the help of the latest AI-driven software.
Analyze up to 10 channels with our Sparkfinder app.
Top mIHC image analysis features to explore
Automated image analysis is currently the standard method for interpreting immunohistological staining patterns. State-of-the-art software solutions powered by deep learning technology enable the standardized and reproducible quantification of mIHC data. Advanced AI-based models help researchers detect, segment, and classify different types of cells in digital tissue slides based on biomarker expression levels.
What to look for when picking the right mIHC image analysis solution
There are a variety of software products for the analysis of IHC images available on the market. In order to choose the right tools for your research project, you have to be well-informed about the capabilities they bring in. Typically, an image analysis solution meeting most requirements of current mIHC research should provide the following features (Aeffner et al. 2019):
- Color definition
- Nuclear detection
- Biomarker detection
- Morphological segmentation
- Expression categories
- Image analysis output
Images obtained from mIHC-stained tissues require a multichannel viewing feature, which is of course supported on the IKOSA Platform. A showcase example of an application designed for mIHC image analysis is the Sparkfider app.
In the IKOSA image viewer, you can adjust the multichannel settings to conveniently fine-tune intensity dynamics across different channels for the best image view. Additionally, the software offers color definition options, allowing you to choose specific color labels associated with each stain directly on the interface. This means that when viewing multiplexed images in IKOSA, you can effortlessly enable the visibility of each channel and select the colors you prefer for viewing the image.
Another factor to consider within this context is the number of color channels an IHC image analysis solution supports. Some software platforms only support 3 or 4 (lower-plex) channels, which might limit you when performing a high-plex mIHC study. IKOSA allows you to analyze up to 10 channels. State-of-the-art image analysis solutions consider high-plex imaging methods’ rising importance.
Most of the tools available on the market support nucleus- or cell detection. In addition to merely identifying cell nuclei on your slides, advanced applications also offer valuable insights into additional quantitative parameters such as nuclear size, color threshold, roundness, and eccentricity (Aeffner et al., 2019).
Automatically measure the distance (center-to-center) to the nearest cell in pixels.
The Sparkfinder App available on the IKOSA Platform provides reliable and advanced measurements of:
- mean circularity
- mean eccentricity
- mean equivalent diameter
- mean perimeter
- nearest neighbor analysis
- object area
- coordinates of detected objects
- channel intensity
Marker detection capabilities allow you to identify the stain of your interest at the right cellular compartment. The availability of advanced mIHC intensity quantification features is another consideration when choosing the right software product. Based on your predefined intensity thresholds, you can easily assign cells to certain marker expression categories such as negative, low, medium, or high, by filtering them within the provided CSV or Excel file. (Aeffner et al. 2019).
What does advanced spatial mapping reveal about the tumor microenvironment?
Using automated segmentation techniques you can gain valuable spatial information about the intratumoral and the peritumoral TIME. Modern IHC software solutions make the spatial mapping of the tumor microenvironment possible, providing a reliable evaluation of the distance between detected objects, such as immune cells and cancer cells. Spatial parameters like the nearest neighbor distance (NNDist) allow you to examine the number of immune cells within a certain distance from the nearest tumor mask (Fassler et al. 2020).
Based on the detection of specific mIHC staining patterns, image analysis software can help you localize and quantify immune cell infiltrates within the tumor microenvironment and even molecules of interest on a subcellular level (Pell et al. 2019). Additionally, automated methods provide further information about the density of each cell type (Koh et al. 2020). These are essential factors in assessing response to immunotherapy treatment.
Further, the colocalization of multiple markers in a single tissue area is a task almost impossible for the human eye. While colocalization analysis is challenging in the case of cIHC, fluorescent IHC images are more suitable for detecting two co-expressed signals with the help of modern image analysis software.
Multiplexed methods make a huge difference in current cancer research without a doubt. So, wait no longer and give automated multiplex IHC analysis a go!
Explore the multiplex IHC analysis capabilities available in IKOSA
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Lead content writer, life science professional, and simply a passionate person about technology in healthcare
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References
Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., … & Bowman, D. (2019). Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association. Journal of pathology informatics, 10(1), 9.
Boisson, A., Noël, G., Saiselet, M., Rodrigues-Vitória, J., Thomas, N., Fontsa, M. L., … & Garaud, S. (2021). Fluorescent multiplex immunohistochemistry coupled with other state-of-the-art techniques to systematically characterize the tumor immune microenvironment. Frontiers in Molecular Biosciences, 8, 673042.
Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., & Carpenter, A. E. (2021). Image-based profiling for drug discovery: due for a machine-learning upgrade?. Nature Reviews Drug Discovery, 20(2), 145-159.
Fassler, D. J., Abousamra, S., Gupta, R., Chen, C., Zhao, M., Paredes, D., … & Saltz, J. (2020). Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagnostic pathology, 15(1), 1-11.
Fereidouni, F., Griffin, C., Todd, A., & Levenson, R. (2018). Multispectral analysis tools can increase the utility of RGB color images in histology. Journal of Optics, 20(4), 044007.
Huss, R., Schmid, C., Manesse, M., Thagaard, J., & Maerkl, B. (2021). Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies. Advances in Cell and Gene Therapy, 4(3), e113.
Koh, J., Kwak, Y., Kim, J., & Kim, W. H. (2020). High-throughput multiplex immunohistochemical imaging of the tumor and its microenvironment. Cancer Research and Treatment: Official Journal of Korean Cancer Association, 52(1), 98-108.
Mazzaschi, G., Madeddu, D., Falco, A., Bocchialini, G., Goldoni, M., Sogni, F., … & Tiseo, M. (2018). Low PD-1 Expression in Cytotoxic CD8+ Tumor-Infiltrating Lymphocytes Confers an Immune-Privileged Tissue Microenvironment in NSCLC with a Prognostic and Predictive Value. Prognostic and Predictive Role of NSCLC Immune Context. Clinical cancer research, 24(2), 407-419.
McKay, H. S., Margolick, J. B., Martínez-Maza, O., Lopez, J., Phair, J., Rappocciolo, G., … & Bream, J. H. (2017). Multiplex assay reliability and long-term intra-individual variation of serologic inflammatory biomarkers. Cytokine, 90, 185-192.
Pell, R., Oien, K., Robinson, M., Pitman, H., Rajpoot, N., Rittscher, J., … & Morden, J. (2019). The use of digital pathology and image analysis in clinical trials. The Journal of Pathology: Clinical Research, 5(2), 81-90.
Taube, J. M., Akturk, G., Angelo, M., Engle, E. L., Gnjatic, S., Greenbaum, S., … & Bifulco, C. B. (2020). The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation. Journal for immunotherapy of cancer, 8(1).
Tsutsumi, Y. (2021). Pitfalls and caveats in applying chromogenic immunostaining to histopathological diagnosis. Cells, 10(6), 1501.