We thank the study participant. We thank Louise Rousseau, coordinator of the HBP biobank for recovery of the tissue samples and all associated clinical information. We acknowledge the molecular pathology and cell imaging core facilities at the CRCHUM and Michael Persch from Visiopharm for excellent technical assistance. Funding: This study was supported by grants from the Canadian Liver Foundation, Fonds de recherche du Qu&#233;bec&#8211;Sant&#233; (FRQS) AIDS and Infectious Disease Network (R&#233;seau SIDA-MI), and the Canadian Network on Hepatitis C (CanHepC). CanHepC is funded by a joint initiative from the Canadian Institutes of Health Research (CIHR) (NHC-142832) and the Public Health Agency of Canada. M.F.M. received fellowships from the Universit&#233; de Montr&#233;al, Bourse Gabriel Marquis, and the FRQS. T.F. received doctoral fellowships from CIHR and CanHepC. S.T. holds the Roger-Des-Groseillers Chair in hepatobiliary and pancreatic oncological surgery, Universit&#233; de Montr&#233;al.
Author contributions: M.F.M. designed, performed experiments, and analyzed data. T.F. designed experiments. A.C-B. provided technical guidance. G.S. performed all the pathological assessment of the study subject and provided input on all the pathological aspects. L.M. performed H&#38;E staining, optimized, and performed image acquisition. M.N.A. performed the PSR stain and provided valuable technical input. N.B. contributed to the image analysis. S.T. is the principal investigator for the HBP biobank and is responsible for overseeing the overall operation of the biobank. He also provided invaluable input on all aspects of the project and its clinical implications. M.F.M, T.F., and N.H.S. conceptualized and designed the study. N.H.S. supervised the work and obtained funding. M.F.M., T.F., A.C-B, and N.H.S. wrote the manuscript. All authors reviewed and approved the manuscript.

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The authors declare no conflicts of interest.

Simple, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to map the immune landscape in cancer and other immunological disorders. Here, we describe a strategy that integrates widely available labeling and digital analysis techniques to expand the multiplexing capability and multidimensional assessment of imaging assays12,13,17,19. The staining of three serial sections for different markers, and the reuse of sections through stripping and reprobing techniques, enabled us to visualize 11 parameters in addition to H&#38;E and PSR stains. Six images from these sections were aligned in an automated fashion using the tissue alignment module. The alignment was precise at the individual cell level for images originating from the same section and highly concordant for images originating from neighboring sections. Virtual multiplexing enabled us to determine how markers visualized in one section relate spatially to markers visualized in another contiguous section. While some of the stainings labelled COIs, others labelled TCs, allowing us to quantify COIs in the different TCs. The use of software tools for the automated quantification of COIs greatly simplified and accelerated the processing of images. Moreover, digital analysis was applied to whole tissue sections instead of selected fields of view, resulting in an unbiased representation of the TME. Furthermore, because the tissue coordinates of COIs were registered, it was possible to generate tissue heatmaps.
There are several areas in this protocol where troubleshooting may be needed. First, poor antigen retrieval can affect the quality of mIF, therefore the type of antigen retrieval buffer and duration should be optimized for the specific assay/biomarker conditions used. Second, the type of blocking solution used should be adapted to the tissues/antigen/species of primary and secondary antibodies. In our hands, the addition of 10% total serum from the species where the tissue comes from blocked Fc receptors, and thus reduced nonspecific antibody binding. Addition of 10% of serum from the species the secondary antibodies were raised in would minimize direct nonspecific attachment of secondary antibodies to the tissue section. Third, validation of the specificity of the primary and secondary antibodies using the proper positive and negative controls is essential. Fourth, increased autofluorescence in some channels and diffusion of DAPI upon primary antibody stripping are also common. To address the enhanced autofluorescence, we used primary/secondary antibody pairs where the specific signal had intensity values at least 5x that of the background. Finally, some high affinity antibodies cannot be eluted with regular stripping procedures. In this case, we recommend using such antibodies in the last round of labeling. The user may have to try different staining sequences to find the optimal configuration for the antibodies of interest. Efficiency of stripping should be confirmed before proceeding to a second or third round of labeling.
The main limitation and challenge of this strategy is finding the right combinations of primary and secondary fluorescent antibodies for the markers of interest. Finding primary antibodies raised in different species or with different isotypes that could be used simultaneously is limited by what is commercially available. Most whole slide scanners are equipped with lamps and filters that allow imaging a maximum of five channels, and secondary antibodies in the right species and right fluorophore are not always available. We partially overcame these limitations using serial stainings and sequential labeling. Several antibody combinations may need to be tested to arrive at the best combination for the markers of interest. Another limitation is the quality of the DAPI staining, because stripping and reprobing may not always allow performing nuclei segmentation.
The tissue align module requires minimal training and no programming skills from users. The software theoretically allows alignment of an unlimited number of images. However, precise alignment depends on relatedness of sections, where closer sections that are more histologically concordant are more accurately aligned. We used the Author module of VIS for generating the APPs. Basic knowledge of image analysis is needed for creating APPs, but this is equally the case when using any other image analysis software. The unique advantages of VIS as compared to other image analysis software include automated alignment of images from sections prepared using different methods (e.g., IF, histochemistry, IHC). This allows colocalization studies of multiple markers of interest using virtual multiplexing. Furthermore, the flexible and user-friendly design of APPs allows user-specific customization. Automated quantification and mapping, and the possibility of processing whole tissue sections, saves time and reduces bias compared to manual counting by visual inspection.
This strategy is a very useful research tool for tissue immunology in the context of cancer and autoimmunity but remains unvalidated for clinical use. With additional standardization and validation, it can be used in the future for multiple applications (e.g., to map the immune landscape in cancer to predict and monitor the response to immunotherapeutic agents). It can also be adapted to different inflammatory conditions (e.g., inflammatory bowel disease) to combine pathological evaluation with prognostic biomarkers.
The main critical steps in this protocol are the efficiency/specificity of the labeling and the robustness of the designed APPs for the intended use or biomarker. Hence, regular validation by visual inspection, especially upon designing a new APP, is essential. The efficient use of multiple rounds of stripping and reprobing or different types of stains on the same section are critical components and can be tissue or section specific. Verifying the efficiency of such processes before proceeding with large batch analysis is critical.
In summary, we provide a strategy that maximizes the quantitative and spatial information that can be obtained from valuable clinical tissue samples. The resources, equipment, and knowledge required to implement this methodology are widely accessible. We propose this methodology as a useful guide for planning assays aiming at identifying, quantifying, and mapping immune cell populations in the TME.

Cancer development is the result of a multistep process involving reciprocal interactions between malignant cells and the TME. Other than tumor cells, the TME is composed of nonmalignant cells, stromal cells, immune cell populations, and extracellular matrix (ECM)1. The spatial organization of the different cellular and structural components of the tumor tissue and the dynamic exchange between the cancer and neighboring non-cancer cells ultimately modulate tumor progression and response to therapy2,3,4. It has been shown that the immune response in cancer is spatiotemporally regulated5,6. Different immune cell populations infiltrating the neoplastic lesion and the adjacent tissue exhibit distinctive spatial distribution patterns and varied activation and differentiation states associated with different functions (e.g., pro- versus antitumor). These different immune populations and their parameters coevolve overtime with the tumor and the stromal compartments.
The emergence of technologies allowing single cell multiomics profiling has exponentially increased our understanding of the numerous cellular and molecular networks regulating carcinogenesis and tumor progression. However, most single cell-based high-throughput analytical tools require tissue disruption and single cell isolation, resulting in loss of information about the spatial organization of cells and cell-cell interactions7. Because the location and arrangement of specific immune cells in the TME have diagnostic and prognostic value, technologies allowing spatial resolution are an essential complement of single cell-based immune profiling techniques.
Traditionally, imaging techniques like immunohistochemistry (IHC) and multiplex immunofluorescence (mIF) have been restricted to a small number of biomarkers that can be visualized simultaneously. This limitation has hampered the study of the spatiotemporal dynamics of tumor-infiltrating immune cells, which are typically defined by several phenotypic markers. Recent advances in imaging and analytical tools have expanded the possibilities of multiplexing. New antibody-based labeling technologies like histo-cytometry and imaging mass cytometry have been used to spatially separate up to 12 and 32 biomarkers, respectively8,9. Mass spectrometry imaging, a technique not requiring labeling, has the potential to image thousands of biomarkers simultaneously in a single tissue section10,11. Although these techniques have already shown great potential for dissecting the tissue immune landscape in cancer, they use highly sophisticated and expensive equipment and software and are not readily accessible to the majority of researchers.
Alternatively, the multiplexing capability of traditional IHC and mIF has been expanded through the use of serial imaging, sequential rounds of labeling, and spectral imaging7,12,13,14,15,16. These techniques generate multiple images from the same or from serial tissue sections that can be consolidated into virtual multiparameter slides using image analysis software. As a result, the number of markers that can be visualized and analyzed simultaneously increases.
Here, we propose a strategy for the rational design of tissue multiplex assays using commercially available reagents, affordable microscopy equipment, and user-friendly software (Figure 1). This methodology integrates serial imaging, sequential multiplex labeling, whole tissue imaging, and tissue alignment to generate virtual multiparameter slides that can be used for automated quantification and mapping of immune cells in tissue sections. Using this strategy, we created one virtual slide comprising 11 biomarkers plus two frequently used histological stains: hematoxylin and eosin (H&#38;E) and picrosirius red (PSR). Multiple immune cell populations were identified, located, and quantified in different tissue compartments and their spatial distribution resolved using tissue heatmaps. This strategy maximizes the information that can be gained from limited clinical specimens and is applicable to formalin-fixed paraffin-embedded (FFPE) archived tissue samples, including whole tissue, core needle biopsies, and tissue microarrays. We propose this methodology as a useful guide for designing custom assays for identification, quantification, and mapping of immune cell populations in the TME.

<table><tbody><tr><td>Antigen Retrieval Solution: Sodium Citrate Buffer (10 mM Sodium Citrate, 0.05% v/v Tween 20, pH 6.0)</td><td></td><td></td><td></td></tr><tr><td>Blocking Solution: 1 % BSA, 10 % filtered human serum, 10 % filtered donkey serum, 0.1 % Tween 20, and 0.3% Triton in PBS</td><td></td><td></td><td></td></tr><tr><td>Bovine serum albumin (BSA)</td><td>Multicell</td><td>800-095-EG</td><td></td></tr><tr><td>Coplin jars (EASYDIP SLIDE STAINING SYSTEM)</td><td>Newcomersupply</td><td>5300KIT</td><td></td></tr><tr><td>Cover slides</td><td>Fisherbrand</td><td>12-545E 22*50</td><td></td></tr><tr><td>Direct Red 80</td><td>Sigma Aldrich</td><td>365548</td><td></td></tr><tr><td>Donkey Serum</td><td>Sigma Aldrich</td><td>D9663</td><td></td></tr><tr><td>Ethanol 100%</td><td></td><td></td><td></td></tr><tr><td>Electric pressure cooker</td><td>Salton</td><td></td><td></td></tr><tr><td>Eosin</td><td>Leica Biosystems</td><td>3801600</td><td>CAUTION, eye irritation</td></tr><tr><td>Fast Green FCF</td><td>Sigma Aldrich</td><td>F7252</td><td>CAUTION, harmful by inhalation, ingestion and skin absortion</td></tr><tr><td>FFPE section (4&amp;mu;m) slides</td><td></td><td></td><td></td></tr><tr><td>Glycine 0,1 M in PBS</td><td></td><td></td><td></td></tr><tr><td>Hematoxylin Stain Solution, Gil 1. Formulation, Regular Strength</td><td>Ricca Chemical Company</td><td>3535-32</td><td></td></tr><tr><td>Holder (EasyDip Staining Jar Holder)</td><td>Newcomersupply</td><td>5300RK</td><td></td></tr><tr><td>Human Serum</td><td>Gemini</td><td>22210</td><td></td></tr><tr><td>Humidity chamber</td><td>Millipore Sigma</td><td>Z670138-1EA</td><td></td></tr><tr><td>Pap pen</td><td>abcam</td><td>ab2601</td><td></td></tr><tr><td>PBS</td><td></td><td></td><td></td></tr><tr><td>PBS-Tween 20 (0.1% v/v)</td><td></td><td></td><td></td></tr><tr><td>Permount Mounting Media</td><td>Fisher Chemical</td><td>SP15-500</td><td></td></tr><tr><td>Picric Acid 1.3 %</td><td>Sigma Aldrich</td><td>P6744</td><td>CAUTION, skin and eye irritation</td></tr><tr><td>Picro-Sirius Red/Fast Green solution: Fast Green 0.1 % w/v + Sirius Red 0.2 % w/v in 1,3 % picric acid solution</td><td></td><td></td><td></td></tr><tr><td>Primary Antibody Anti-&amp;alpha;SMA</td><td>Mouse IgG2a 1A4</td><td>Sigma A2547</td><td>Dilution 1/100</td></tr><tr><td>Primary Antibody Anti-CD34</td><td>Mouse IgG1 HPCA1/763</td><td>Novus Biologicals NBP2-44568</td><td>Dilution 1/250</td></tr><tr><td>Primary Antibody Anti-Cytokeratin 8/18</td><td>Rabbit EP17/EP30</td><td>Agilent IR09461-2</td><td>Ready to use</td></tr><tr><td>Primary Antibody Anti-CD68</td><td>Mouse KP1</td><td>Abcam ab955</td><td>Dilution 1/200</td></tr><tr><td>Primary Antibody Anti-Desmin</td><td>Rabbit Polyclonal</td><td>Invitrogen PA5-16705</td><td>Dilution 1/200</td></tr><tr><td>Primary Antibody Anti-CD4</td><td>Mouse N1UG0</td><td>Affymetrix 14-2444</td><td>Dilution 1/250</td></tr><tr><td>Primary Antibody Anti-FoxP3</td><td>Rabbit 1054C</td><td>R &amp;amp; D MAB8214</td><td>Dilution 1/100</td></tr><tr><td>Primary Antibody Anti-MPO</td><td>Goat Polyclonal</td><td>R &amp;amp; D Systems AF3667</td><td>Dilution 1/250</td></tr><tr><td>Primary Antibody Anti-CD3</td><td>Rabbit SP7</td><td>Abcam ab16669</td><td>Dilution 1/200</td></tr><tr><td>Primary Antibody Anti-CD8</td><td>Mouse C8/144B</td><td>Invitrogen 14-0085-80</td><td>Dilution 1/200</td></tr><tr><td>Secondary Antibody Donkey anti-mouse A488</td><td>Polyclonal</td><td>Invitrogen A-21202</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Donkey anti-Rabbit A488</td><td>Polyclonal</td><td>Invitrogen A-21206</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Donkey anti-goat A568</td><td>Polyclonal</td><td>Invitrogen A-11057</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Donkey anti-rabbit A647</td><td>Polyclonal</td><td>Invitrogen A-31573</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Rat anti-mouse IgG1 A647</td><td>RMG1-1</td><td>Biolegend 406618</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Goat anti-mouse IgG2a CF568</td><td>Polyclonal</td><td>Sigma Aldrich SAB4600315</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Donkey anti-mouse DyLight 755</td><td>Polyclonal</td><td>Invitrogen SA5-10171</td><td>Dilution 1/500</td></tr><tr><td>Secondary Antibody Donkey anti-rabbit DyLight 755</td><td>Polyclonal</td><td>Invitrogen SA5-10043</td><td>Dilution 1/500</td></tr><tr><td>SDS</td><td>BioShop</td><td>SDS001,500</td><td>CAUTION, oral skin and eye toxicity</td></tr><tr><td>Shandon multi-program robotic slide stainer</td><td>LabX</td><td>11384903</td><td></td></tr><tr><td>Shandon Xylene Substitute,</td><td>Thermo Fisher Scientific</td><td>CA89413-336</td><td>CAUTION, Flammable, skin and eye irritation, Harmful when inhaled</td></tr><tr><td>Shaking water bath</td><td></td><td></td><td></td></tr><tr><td>SlowFade Gold antifade reagent with DAPI</td><td>Invitrogen</td><td>S36938</td><td></td></tr><tr><td>Sodium Citrate Dihydrate</td><td>Millipore Sigma</td><td>1545801</td><td>CAUTION, eye irritation</td></tr><tr><td>Stripping Buffer: mix 20 ml 10% w/v SDS with 12.5 ml 0.5 M Tris-HCl (pH 6.8), and 67.5 ml ultra-pure water. Under a fume hood, add 800 uL of 2-mercapto ethanol (114,4 mM final concentration)</td><td></td><td></td><td></td></tr><tr><td>Triton X-100</td><td>Sigma Aldrich</td><td>T8787-50ML</td><td></td></tr><tr><td>Tris-HCl</td><td>BioShop</td><td>77-86-1</td><td></td></tr><tr><td>Tween 20</td><td>Fisher Scientific</td><td>BP337-500</td><td></td></tr><tr><td>VIS Software</td><td>Visiopharm</td><td></td><td></td></tr><tr><td>Whole slide scanner Olympus BX61VS</td><td>Olympus</td><td></td><td>Microscope: Olympus Slide Scanner BX61VS, 5 slides scanner, motorized stage, autofocus. Camera: Lightsource: Xcite-120. Filters: BrightLine&amp;reg; Sedat filter set (# LED-DA/FI/TR/Cy5-4X4M-B-000, Semrock)</td></tr><tr><td>Xylene</td><td>Sigma Aldrich</td><td>214736-4L</td><td>CAUTION, Flammable, skin and eye irritation, Harmful when inhaled</td></tr><tr><td>Xylene : Ethanol solution (1:1 v/v)</td><td></td><td></td><td></td></tr><tr><td>2-mercaptoethanol</td><td>Sigma</td><td>M6250</td><td>CAUTION, harmful by ingestion, inhalation, fatal if sking absortion. Eye irritation. Use fume hood</td></tr></tbody></table>

visualization, quantification, and mapping of immune cell populations in the tumor microenvironment

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

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Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density ...

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Research

JoVE Journal

Immunology and Infection

16.9K Views.  Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM). De ruimtelijke organisatie van de immuuncellen in de tumormicro-omgeving heeft klinische waarde. Deze strategie kan gemakkelijk worden gebruikt voor het visualiseren, kwantificeren en in kaart brengen van immuuncellen in tumorweefsel. Immunotherapie heeft een revolutie teweeggebracht in de behandeling van kanker.Echter, niet alle patiënten reageren goed. Deze strategie kan dienen om de weefsel-immuun handtekeningen die een goede anti-tumor reactie kan voorspellen definiëren. Voordat ...

Video: Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it is now well established that the immune infiltrate in human tumors can correlate with prognosis and response to therapy. The analysis of the immune infiltrate in the tumor microenvironment has become a major challenge for the classification of patients and the response to treatment.
The co-expression of inhibitory receptors such as Program Cell Death Protein 1 (PD1; also known as CD279), Cytotoxic T Lymphocyte Associated Protein 4 (CTLA-4), T-Cell Immunoglobulin and Mucin Containing Protein-3 (Tim-3; also known as CD366), and Lymphocyte Activation Gene 3 (Lag-3; also known as CD223), is a hallmark of T cell exhaustion. We developed a multiparametric in situ immunofluorescence staining to identify and quantify at the cellular level the co-expression of these inhibitory receptors. On a retrospective series of frozen tissue of renal cell carcinomas (RCC), using a fluorescence multispectral imaging technology coupled with an image analysis software, it was found that co-expression of PD-1 and Tim-3 on tumor infiltrating CD8+ T cells is correlated with a poor prognosis in RCC. To our knowledge, this represents the first study demonstrating that this automated multiplex in situ technology may have some clinical relevance.

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

Studying tumor microenvironment may identify prognostic or predictive biomarkers of clinical response to immunotherapy. Presented here, is an innovative method based on in situ fluorescence multispectral imaging to analyze and count automatically various subpopulations of CD8+ T cells. This reproducible and reliable technique is suitable for large cohort analyses.

Immune cells are important components of the tumor microenvironment and influence tumor growth and evolution at all stages of carcinogenesis. Notably, it ...

Cancer Research

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for immunotherapies. Recent clinical advances in immunotherapy highlight the need for reproducible methods to accurately and thoroughly characterize the tumor and its associated immune infiltrate. Tumor enzymatic digestion and flow cytometric analysis allow broad characterization of numerous immune cell subsets and phenotypes; however, depth of analysis is often limited by fluorophore restrictions on panel design and the need to acquire large tumor samples to observe rare immune populations of interest. Thus, we have developed an effective and high throughput method for separating and enriching the tumor immune infiltrate from the non-immune tumor components. The described tumor digestion and centrifugal density-based separation technique allows separate characterization of tumor and tumor immune infiltrate fractions and preserves cellular viability, and thus, provides a broad characterization of the tumor immunologic state. This method was used to characterize the extensive spatial immune heterogeneity in solid tumors, which further demonstrates the need for consistent whole tumor immunologic profiling techniques. Overall, this method provides an effective and adaptable technique for the immunologic characterization of subcutaneous solid murine tumors; as such, this tool can be used to better characterize the tumoral immunologic features and in the preclinical evaluation of novel immunotherapeutic strategies.

Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune microenvironment.

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for ...

Expanding the Comprehension of the Tumor Microenvironment using Mass Spectrometry Imaging of Formalin-Fixed and Paraffin-Embedded Tissue Samples

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the tumor immune landscape. Although standard immunohistochemistry is suitable for studying tissue architecture, it is limited to the analysis of a small number of markers. Conversely, techniques such as flow cytometry can evaluate multiple markers simultaneously, although information about tissue morphology is lost. In recent years, multiplexed strategies that integrate phenotypic and spatial analysis have emerged as comprehensive approaches to the characterization of the tumor immune landscape. Herein, we discuss an innovative technology combining metal-labeled antibodies and secondary ion mass spectrometry focusing on the technical steps in assay development and optimization, tissue preparation, and image acquisition and processing. Before staining, a metal-labeled antibody panel must be developed and optimized. This hi-plex image system supports up to 40 metal-tagged antibodies in a single tissue section. Of note, the risk of signal interference increases with the number of markers included in the panel. After panel design, particular attention should be given to the metal isotope assignment to the antibody to minimize this interference. Preliminary panel testing is performed using a small subset of antibodies and subsequent testing of the entire panel in control tissues. Formalin-fixed, paraffin-embedded tissue sections are obtained and mounted on gold-coated slides and further stained. The staining takes 2 days and closely resembles standard immunohistochemical staining. Once samples are stained, they are placed in the image acquisition instrument. Fields of view are selected, and images are acquired, uploaded, and stored. The final stage is image preparation for the filtering and removal of interference using the system&#39;s image processing software. A disadvantage of this platform is the lack of analytical software. However, the images generated are supported by different computational pathology software.

In the era of cancer immunotherapy, interest in elucidating tumor microenvironment dynamics has increased strikingly. This protocol details a mass spectrometry imaging technique with respect to its staining and imaging steps, which allow for highly multiplexed spatial analysis.

Advances in immune-based therapies have revolutionized cancer treatment and research. This has triggered growing demand for the characterization of the ...

Multiplex Immunofluorescence and Spatial Image Analysis of the Tumor Microenvironment

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers.
We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types.
This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment  

In this article, a protocol for manual tyramide signal amplification (TSA) multiplex immunofluorescence (mIF) combined with image analysis and spatial analysis is described. This protocol can be used with formalin-fixed paraffin-embedded (FFPE) sections for the staining of two to six antigens per slide depending on the slide scanner available in the laboratory.

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending ...

Mapping the Tumor Immune Landscape: An In-Depth Multiplex IHC Analysis Protocol

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...

Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model

Specialized immune cells that infiltrate the tumor microenvironment regulate the growth and survival of neoplasia.&#160; Malignant cells must elude or subvert anti-tumor immune responses in order to survive and flourish. Tumors take advantage of a number of different mechanisms of immune &#8220;escape,&#8221; including the recruitment of tolerogenic DC, immunosuppressive regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSC) that inhibit cytotoxic anti-tumor responses. Conversely, anti-tumor effector immune cells can slow the growth and expansion of malignancies: immunostimulatory dendritic cells, natural killer cells which harbor innate anti-tumor immunity, and cytotoxic T cells all can participate in tumor suppression. The balance between pro- and anti-tumor leukocytes ultimately determines the behavior and fate of transformed cells; a multitude of human clinical studies have borne this out. Thus, detailed analysis of leukocyte subsets within the tumor microenvironment has become increasingly important. Here, we describe a method for analyzing infiltrating leukocyte subsets present in the tumor microenvironment in a mouse tumor model. Mouse B16 melanoma tumor cells were inoculated subcutaneously in C57BL/6 mice. At a specified time, tumors and surrounding skin were resected en bloc and processed into single cell suspensions, which were then stained for multi-color flow cytometry. Using a variety of leukocyte subset markers, we were able to compare the relative percentages of infiltrating leukocyte subsets between control and chemerin-expressing tumors. Investigators may use such a tool to study the immune presence in the tumor microenvironment and when combined with traditional caliper size measurements of tumor growth, will potentially allow them to elucidate the impact of changes in immune composition on tumor growth. Such a technique can be applied to any tumor model in which the tumor and its microenvironment can be resected and processed.

This protocol describes a method for the detailed evaluation of leukocyte subsets within the tumor microenvironment in a mouse tumor model. Chemerin-expressing B16 melanoma cells were implanted subcutaneously into syngeneic mice. Cells from the tumor microenvironment were then stained and analyzed by flow cytometry, allowing for detailed leukocyte subset analyses.

Specialized immune cells that infiltrate the tumor microenvironment regulate the growth and survival of neoplasia.&#160; Malignant cells must elude or ...

Medicine

Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

The spatial heterogeneity of the tumor microenvironment (TME) is a critical determinant of therapeutic response, particularly for immune-oncology agents, where success depends on the distribution of specific immune cell subpopulations. Over the past decade, multiple sophisticated technologies have been introduced to achieve detailed resolution of the TME using two-dimensional sections from either formalin-fixed, paraffin-embedded (FFPE) or fixed-frozen tissues. While these thin sections are easier to procure and analyze, they lack the three-dimensional architecture needed to reliably and comprehensively characterize a tumor. To address this limitation, a tissue mounting and imaging technique was developed to enable the three-dimensional analysis of tumor lesions in their native in vivo state. This protocol outlines the procurement of human tumor tissue, the mounting of samples on custom-printed platforms, and staining procedures for post-fixation samples. The multiplexed immunofluorescence technique, IBEX (Iterative Bleaching Extends Multiplexity), was adapted to characterize the three-dimensional TME with up to 15 markers for tumor, immune, and stromal cells using commercially available antibodies. Imaging depths of up to 100 &#181;m were achieved using an inverted white-light laser confocal microscope with a custom-printed imaging adaptor and commercial glass-bottom dishes to ensure optimal tissue orientation. This protocol highlights the potential of the IBEX method to expand multiplexed immunofluorescence studies, providing a more comprehensive understanding of TME composition.

This article describes a protocol for multiplex immunofluorescence optimized to characterize the three-dimensional architecture of peritoneal metastases.

The spatial heterogeneity of the tumor microenvironment (TME) is a critical determinant of therapeutic response, particularly for immune-oncology agents, ...

Predictive Immune Modeling of Solid Tumors

Immunotherapies show promise in the treatment of oncology patients, but complex heterogeneity of the tumor microenvironment makes predicting treatment response challenging. The ability to resolve the relative populations of immune cells present in and around the tumor tissue has been shown to be clinically-relevant to understanding response, but is limited by traditional techniques such as flow cytometry and immunohistochemistry (IHC), due the large amount of tissue required, lack of accurate cell type markers, and many technical and logistical hurdles. One assay (e.g., the ImmunoPrism Immune Profiling Assay) overcomes these challenges by accommodating both small amounts of RNA and highly degraded RNA, common features of RNA extracted from clinically archived solid tumor tissue. The assay is accessed via a reagent kit and cloud-based informatics that provides an end-to-end quantitative, high-throughput immuno-profiling solution for Illumina sequencing platforms. Researchers start with as few as two sections of formalin-fixed paraffin-embedded (FFPE) tissue or 20-40 ng of total RNA (depending on sample quality), and the protocol generates an immune profile report quantifying eight immune cell types and ten immune escape genes, capturing a complete view of the tumor microenvironment. No additional bioinformatic analysis is required to make use of the resulting data. With the appropriate sample cohorts, the protocol may also be used to identify statistically significant biomarkers within a patient population of interest.

The use of an RNA-based approach to determine quantitative immune profiles of solid tumor tissues and leverage clinical cohorts for immune-oncology biomarker discovery is described through a molecular and informatics protocols.

Immunotherapies show promise in the treatment of oncology patients, but complex heterogeneity of the tumor microenvironment makes predicting treatment ...

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise invisible processes. Advanced cell assays closely mimicking in vivo scenery are establishing themselves as novel ways to visualize and measure the bidirectional tumor-host interplay influencing the progression of cancer. Here we describe two versatile protocols to recreate highly controllable 2D and 3D co-cultures in microdevices, mimicking the complexity of the tumor microenvironment (TME), under natural and therapy-induced immunosurveillance. In section 1, an experimental setting is provided to monitor crosstalk between adherent tumor cells and floating immune populations, by bright field time-lapse microscopy. As an applicative scenario, we analyze the effects of anti-cancer treatments, such as the so-called immunogenic cancer cell death inducers on the recruitment and activation of immune cells. In section 2, 3D tumor-immune microenvironments are assembled in a competitive layout. Differential immune infiltration is monitored by fluorescence snapshots up to 72 h, to evaluate combination therapeutic strategies. In both settings, image processing steps are illustrated to extract a plethora of immune cell parameters (e.g., immune cell migration and interaction, response to therapeutic agents). These simple and powerful methods can be further tailored to simulate the complexity of the TME encompassing the heterogeneity and plasticity of cancer, stromal and immune cells subtypes, as well as their reciprocal interactions as drivers of cancer evolution. The compliance of these rapidly evolving technologies with live-cell high-content imaging can lead to the generation of large informative datasets, bringing forth new challenges. Indeed, the triangle &#39;&#39;co-cultures/microscopy/advanced data analysis&#34; sets the path towards a precise problem parametrization that may assist tailor-made therapeutic protocols. We expect that future integration of cancer-immune on-a-chip with artificial intelligence for high-throughput processing will synergize a large step forward in leveraging the capabilities as predictive and preclinical tools for precision and personalized oncology.

In the age of immunotherapy and single-cell genomic profiling, cancer biology requires novel in vitro and computational tools for investigating the tumor-immune interface in a proper spatiotemporal context. We describe protocols to exploit tumor-immune microfluidic co-cultures in 2D and 3D settings, compatible with dynamic, multiparametric monitoring of cellular functions.

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise ...

Real-Time Analysis of Resident T-cell Migration in Different Tumor Regions Using Confocal Microscopy

Source: Peranzoni, Elisa, et al. <a href="https://app.jove.com/v/55709">Ex Vivo Imaging of Resident CD8 T Lymphocytes in Human Lung Tumor Slices Using Confocal Microscopy.</a> J. Vis. Exp. (2017).
This video describes the imaging of resident tumor-infiltrating CD8 T cells labeled with fluorescently coupled antibodies within human lung tumor slices. This technique permits real-time analyses of CD8 T cell migration using confocal microscopy.

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

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