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

Podsumowanie

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.

Streszczenie

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.

Wprowadzenie

In the past few years, immunotherapy has emerged as a very promising treatment for many types of cancers, including RCC. Particularly, immunotherapy based on the inhibition of inhibitory checkpoints like PD-1 and CTLA-4 has been reported to be clinically effective^1,2,3,4,5,6. Monoclonal antibodies against CTLA-4, PD-1, or Program Death Ligand 1 (PD-L1) are already approved in several cancers and lead to long lasting clinical responses in more than 20% of patients⁷. Nevertheless, not all patients are responders, the cost of the treatment is high, and these treatments are toxic, leading to potential serious autoimmune-like side effects. Therefore, the current challenge is to identify predictive markers to those new immunotherapies. The rate of mutations in the tumor, the expression of PD-L1, or the levels of intratumoral CD8⁺ T cell infiltration have been reported to correlate with clinical response. However, this association is still too weak to recommend the use of these clinical biomarkers in clinical practice except for the companion test for PD-L1 before the administration of Pembrolizumab in non-small cell lung cancer (NSCLC) patients^8,9,1011,12. It has been demonstrated that the co-expression of many inhibitory receptors like PD-1, Tim-3, Lag-3, and CTLA-4, induces a cell exhaustion phenotype and resistance to therapy^13,14,15. Since peripheral blood is not representative of the tumor microenvironment, it is of high interest to analyze the phenotypic features of the cells in situ. PD-1 and Tim-3 co-expressing T cells are known to be functionally impaired cells in several contexts^13,16,17. In this study, the prognostic impact of the co-expression of the two inhibitory receptors PD-1 and Tim-3 on CD8⁺ T cells was assessed.

Up until now, studying the co-expression of multiple markers on tumor infiltrating lymphocytes (TILs) has mainly been performed by flow cytometry analysis, making it necessary to work on fresh tumors and therefore precluding retrospective analyses. With conventional in situ staining, only one staining at a time can be performed, and the characterization of the cell type that co-expresses the markers is not possible. For example, PD-L1 is expressed by many cell types of the tumoral microenvironment, making it difficult to define by conventional immunohistochemistry analysis which cells expressing PD-L1 are the more relevant for correlative studies. In this work, we developed an innovative in situ multiparametric immunofluorescence method with computer-counting to correlate the co-expression of PD-1 and Tim-3 by tumor infiltrating CD8⁺ T-cells with clinical outcomes in RCC. This technique has several advantages, including the possibility to analyze at a single cell level and multiple markers at the same time using a multispectral camera that can capture restricted intervals >10 nm through liquid crystal filters¹⁸. Moreover, the procedure is automatic which enables an inter-operator reproducibility and a shortened analysis compared to manual techniques¹⁹. In the cancer field, several studies reported convincing multiple stainings of immune molecules like PD-1, PD-L1, and CD8 in Merkel-cell carcinoma, lung cancer, and head and neck cancer^20,21,22,23. The automated cell count is possible with training by the user (phenotyping step). The fluorescence is measured in the different cell compartments (nuclei, cytoplasm, and membrane).

Here, different subsets of tumor-infiltrating CD8⁺ T cells expressing PD-1 and/or Tim-3 in a large cohort of RCC were counted and the results were correlated with clinical gravity scores and survival parameters. It was also possible to analyze membrane fluorescence intensity at the cellular resolution with Mean Fluorescence Intensity (MFI) data like in cytometry. As far as we know, this represents the first study reporting prognostic results using this multispectral imaging based count technique.

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Protokół

This study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee (CPP Ile de France nr. 2012-05-04). Informed consent was obtained from the participant included in the cohorts.

1. Tissue Material

1. Collect RCC tissue samples on the day of surgery. Handle the surgical specimen at room temperature (pathology department).
2. Collect a tumor sample of approximately 0.5 cm x 0.5 cm x 0.5 cm size in a dry tube. Snap freeze in liquid nitrogen and store at -80 °C.
3. Coat the samples in optimal cutting temperature compound. Section the samples with a cryostat at -20 °C into 4 - 6 µm thick sections. Let the samples air dry on slides for 12 h and directly store them at -80 °C to avoid desiccation.
4. Check the quality of the sample by viewing a Hematoxylin and Eosin stained section.

2. In Situ Immunofluorescence Staining of TILs

1. Procedural guidelines
   1. Perform all experimental steps at room temperature.
   2. For all steps, perform the dilutions with Tris Buffer Saline (TBS; see Table of Materials).
   3. Perform the wash in TBS for all the steps except after the primary antibody incubation. For the latter, use Tris Buffer Saline Tween20 (TBST, see Table of Materials). For buffer composition and reconstitution see Supplemental Table 1.
   4. Use a humidity chamber for antibody incubations and a staining jar for washing.
   5. Do not let the section dry out during the procedure.
2. Pretreatment
   1. Thaw the slide containing the tissue sample and carefully dry the slide around the specimen with a paper towel. Delimitate the reaction area containing the tissue with a hydrophobic barrier pen (see Table of Materials). Dry for 2 min.
   2. Fix the samples in 100% acetone for 5 min, dry for 2 min, and wash with TBS for 10 min.
3. Saturation and blockade
   1. Pretreat the slides for 10 min with 3 drops of avidin 0.1%, tap and/or flick the slide to distribute the avidin and remove air bubbles, and then treat for 10 min with 3 drops of biotin 0.01% (see Table of Materials), tap, and/or flick. Wash with TBS.
   2. Perform an Fc receptors blockade. Apply 100 µL of a 5% volume/volume of normal serum diluted in TBS. Use serum from the same host species as the labeled secondary or tertiary antibody (see Table of Materials); here, donkey serum was used. Incubate for 30 min.
   3. During the incubation, briefly spin the anti-CD8, PD-1, and Tim-3 antibodies (Table of Materials). Prepare the mix of primary antibodies in TBS as described in Supplemental Table 2.
4. Immuno-staining for CD8, PD-1, and Tim-3
   1. Prepare the primary antibody mixture. Refer to Table of Materials and Supplemental Table 2 for the antibodies used (anti-CD8, PD-1, Tim-3, and their corresponding secondary antibodies) and their concentrations.
   2. Tap and/or flick the remaining donkey serum (added in step 2.3.2). Incubate the slides with 100 µL of the non-labeled primary antibodies mix for 1 h in a humidified chamber.
   3. During the incubation, briefly spin the Cyanine 5 anti-rabbit, AF488-anti-goat, and biotinylated anti-mouse antibodies, and prepare the mix of secondary antibodies as described in Supplemental Table 2.
   4. Wash the slides in TBST for 5 min. Dry the slides.
   5. Incubate the slides with 100 µL of the secondary antibodies for 30 min in a humidified chamber.
   6. Prepare the mixture of tertiary antibody containing Cy3-streptavidin as described in Supplemental Table 2.
   7. Wash the slides in TBS for 10 min. Dry the slides.
   8. Incubate the slides with 100 µL of the tertiary antibody mixture for 30 min.
   9. Wash the slides in TBS for 10 min. Dry the slides.
5. Cell mounting
   1. Mount the slides in a 4',6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI)-containing mounting medium (1.5 µg/mL DAPI) with a coverslip compatible for fluorescence microscopy (see Table of Materials).
      NOTE: As a negative control for each experiment, one slide with isotype-matched antibodies was used at the same concentration as corresponding antibodies. For this staining, we used mouse IgG1, rabbit IgG, and goat IgG negative control antibodies (see Table of Materials). As a positive control, we used human hyperplasic tonsil which is known to be positive for the tested markers, for each experiment. A typical staining is described in the results (Figure 1). Mono-staining of CD8, PD-1, and Tim-3 should be performed individually (one staining per slide) with the same pre-treatment and without DAPI. A slide in the same experimental conditions without any antibody and only DAPI mounting medium should also be performed. A slide should be imaged using identical experimental conditions without any antibody and without DAPI.

3. Fluorescence Analysis and Automated Cell Count

1. Image acquisition with the automated microscope
   1. Create a protocol.
      1. Turn on the automated microscope software and the fluorescence illuminator.
      2. In the menu bar, click on "File," "create protocol," select "DAPI," "FITC," and "TRITC."
      3. Click "Next," "Tissue section," and click "Next," "Protocol name." Choose a name, for example, "CD8-PD-1-Tim-3," click "Next," and "save."
      4. Manually place a slide on the stage.
      5. In the menu bar, click "setup," "settings." Click "set home Z" in the new window.
      6. Adjust exposure time with a positive control slide i.e. a CD8, PD-1 and Tim-3 triple-stained with DAPI sample.
      7. In the control bar click “set exposure.”
      8. Adjust the exposure time for each filter until a sufficient but not saturating signal is obtained.
      9. For monochrome imaging, perform the following:
      10. Select acquisition and under 'autofocus' choose DAPI filter.
      11. In the "scan area limit", move the objective upper to the upper left corner of the slide. Click "Mark". Do the same for the lower right corner.
          NOTE: This step delimitates the area of low power imaging (LP imaging) at 4X; be aware to place the mark well so as to surround all the samples of the series.
      12. For high power (HP) imaging, perform the following:
      13. Under 'Autofocus', choose "DAPI".
      14. Under 'Acquisition' band, choose "DAPI", "FITC", "Cy3", and "Cy5". Click "OK". On the menu bar click "File", "Save protocol".
   2. Scan the slides.
      1. Load the protocol by clicking "File", "load protocol" from the menu bar. Click "Start".
      2. Enter 'Lab ID' (folder location for image storage). Enter Slide ID to identify the slide. Click "Next".
      3. Click "Monochrome Imaging" (to scan the whole slide under bright field light and acquire sequential images using a 4x objective).
         NOTE: This produces a grayscale overview of the slide.
      4. Click "Find Specimen". Select the area of tissue to be scanned at low resolution (4x): hold the 'Ctrl' key and click on a field using the cursor to select or deselect fields (Figure 2A).
      5. Click "LP Imaging" (4x imaging) for fluorescent Red Blue Green image acquisition of each field.
      6. For HP field selection, hold down 'Ctrl' key and click on a field using the cursor to select or deselect fields that correspond to the area of tissues that will be scanned at high resolution (20x). Select 5 fields (Figure 2B).
      7. Click "HP Imaging" (20x imaging) for a multispectral image acquisition of each field (Figure 2C).
      8. Click "Data Storage" to store images in the folder that was selected at the beginning in LabID.
         NOTE: The process is summarized and illustrated in Figure 2. For each step (tissue detection, field selection) an algorithm can be incremented and trained by the user for a whole-automated scan process.
2. Fluorescence image analysis and automated cell counting with coupled analysis software
   1. Build the spectral library.
      1. Open the image analysis software.
      2. On the left panel, open "Build libraries". Under 'load image', click on "Browse" and select one mono-stained image (e.g., CD8/Cyanine 5).
      3. Select the fluorophore, (e.g., Cyanine 5). Click "extract". Click "Save" to store in library. Click "Save".
      4. Repeat the same process for PD-1, Tim-3, and DAPI mono-stained slides in order to integrate the spectrum of each fluorophore of interest.
         NOTE: Building the spectral library integrates the spectrum for each fluorophore used for the specific tissue (here, kidney), but "synthetic" libraries that already exist can be used. As the autofluorescence spectrum is strictly relative to the tissue, it is important to perform one auto-fluorescence and spectral library per project.
   2. Create a new project.
      1. From the menu bar, click "File", "New Project". In the 'Find feature' tab, select "cell segmentation". In the 'Phenotyping' tab, select "phenotyping". Click "Create".
      2. Integrate the representative images in the project.
         1. In the 'File' tab, click on "open image". Choose 10 to 30 representative images of the whole series. Add the non-stained slide to remove autofluorescence. On the right panel: select library source. Select fluorophore and choose those corresponding to the spectral library previously built.
      3. To remove tissue autofluorescence, click the "AF button" and then select the area of autofluorescence on the blank slide.
      4. Image treatment and composite image generation.
         1. Click "Prepare all" to integrate the fluorescence library and the autofluorescence spectra to generate the composite image (Figure 3). Click on the 'eye' icon to open the 'view editor' panel and select the data displayed: select the markers CD8, PD-1, Tim-3, and DAPI. Remove the autofluorescence. Follow the steps presented by the software.
      5. Segment the cells.
         1. To segment the cells, in 'Compartment', select "Nuclei" and "Membrane". In the "Nuclei" tab, set DAPI as the nuclear counterstain.
         2. In the 'Maximum and Minimum Size (px)' tab enter "40" and "176" as minimum and maximum sizes, respectively. For 'Minimum' signal, set "0.13". In "Split" tab, set "2.60". In "Nuclei" tab, set "0.81". Select "use membrane signal to aid segmentation".
         3. Click "Segment cells" at the bottom part of the screen. Check the segmentation of nuclei and membranes of cells and retry with different parameters if it does not match the DAPI and membrane fluorescence of the cells.
            NOTE: The software recognizes the cells with the nuclear DAPI staining and based on the cell size. Adjust the cell parameters (size, split, pixels) according to the project.
      6. Phenotype the cells.
         1. In the 'Phenotype' tab, click "add". Create cell phenotype categories: CD8 (blue dot)/CD8-PD-1 (red dot)/CD8-PD-1-Tim-3 (green dot)/other (black dot) (Figure 4).
         2. Select more than 5 examples of cells of each category. Click "Train classifier".
            NOTE: This generates a statistical classification algorithm by the software.
         3. Click "Phenotype all" to obtain the phenotype of each cell.
            NOTE: The software gives the phenotype of one cell with a confidence interval (CI) of accuracy.
         4. Improve the algorithm by training the software until the difference between eye and automated count is concordant (error < 5%). Choose a CI that is acceptable for the cells of interest.
            NOTE: We chose to make the training on the basis of 55% CI as acceptable.
      7. Save the algorithm and project.
      8. Under 'File' select "project". Name it and click "Save".
   3. Perform batch analysis of the series.
      1. Select "Batch analysis". Select the project. Add the images to analyze. Select a folder of storage for the data. Click "Run". Check the quality of the composite image. Verify the phenotyping for all the images of the series.
         NOTE: The coupled image analysis software integrates all the cell compartments (membrane/cytoplasm/nuclei) signals. The phenotyping step is crucial albeit the training of the algorithm can be a time-consuming step. All the data of each image are in one .txt file. All the data of one cell (particularly its phenotype given with its CI) are in one line. For each slide, the image acquisition and subsequent counts were performed on 5 fields.
3. Integration of the raw data and generation of the automated cell count
   1. Compute the data with a statistical program.
      1. Compute all the data from the 5 images corresponding to the 5 fields analyzed for 1 patient. Use a statistical platform, and have an experienced statistician, data manager, or data scientist perform the analysis. Build a script that automatically counts the cells depending on their phenotype, selecting the CI > 55%.
      2. Put all the .txt files of the series in the same folder.
   2. Extract the data.
      1. Put all the .txt files in one folder. Run the automatic script built in step 3.3.1. Open the output .csv file generated by the R script. Save as .xl format. The output gathers the number of cells for each phenotype (i.e., CD8 alone, CD8-PD-1, CD8-PD-1-Tim-3) for each patient.
      2. Calculate the average number of cells for each patient per field: divide the number of cells per the number of fields (i.e., 5) to normalize the number of cells for each patient per field.
      3. Calculate the percentage of PD-1⁺ cells among total CD8⁺ T cells, and PD-1⁺ Tim-3⁺ cells among total CD8⁺ T cells. See Figure 5 for summary of this step.
         NOTE: R language (or other programming software of choice) is required to correctly create and execute the commands, and so an experienced user or statistician/data scientist is essential. The R program can compute the data of the same patient, but the name of the 5 .txt files of one patient should have an identical beginning, corresponding to a unique patient identifier.
4. Statistical analysis
   1. Use statistical software for the statistical analysis of the results.
   2. Perform appropriate statistical analyses with the help of a statistician.
      1. Use the non-parametric Wilcoxon's signed rank tests for comparison of PD-1 MFI between two cell phenotypes (Figure 6). Correlate the covariate and histological characteristics with a Pearson's chi-squared test.
      2. Use a Kaplan-Meier method to estimate the progression free survival, and Cox regression models to estimate the covariate effects on time-to-event outcomes, such as overall survival and disease-free survival.
      3. Consider p-values lower than 0.05 as significant.

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Wyniki

Using the general protocol described above, we aimed to quantify intratumoral CD8⁺ T cells co-expressing the inhibitory receptors PD-1 and Tim-3 in frozen tissues from patients with RCC, and to correlate the results with clinical outcomes²⁵.

Optimization of the CD8/PD-1/Tim-3 Staining:

Different species of antibodies ...

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Dyskusje

Modifications and Troubleshooting:

The tissue quality is an important parameter; it can be easily checked by hematoxylin and eosin coloration.

One advantage of the technique is the possibility of multiplexed stainings, but to avoid fluorophore spillover, it recommended to choose emission wavelengths with delta of 10 nm minimum. Here, because we had 4 stainings including DAPI, we chose to spread out the wavelengths (DAPI: 460 nm, AF488: 519 nm, Cyani...

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Materiały

Name	Company	Catalog Number	Comments
Vectra 3 Automated Quantitative Pathology Imaging	Perkin Elmer	CLS142338
inForm cell analysis 2.1.	Perkin Elmer	CLS135781
R software	https://www.r-project.org
Dakopen delimiting pen	Dako	S2002
Tris Buffer Salin TBS Tablets	Takara, Bio Inc.	TAKT9141Z	pH7.6 100 tablet
Tris Buffer Salin Tween 20 TBS(+Tween20)	Takara, Bio Inc.	TAKT9142Z	pH 7.6 100 tablets
Biotin blocking system	Dako	X0590	Avidin 0.1% and Biotin 0.01%
normal donkey serum	Jackson Immunoresearch	017-000-001	5% vol./vol. concentration
Fluoroshield with DAPI	Sigma-aldrich	F6057	1.5 µg/mL concentration
Knittel glass coverslip	Knittel Gläser,	100039	24x60 mm 100 cover slips
Rabbit anti-CD8 Clone P17-V	novus	NBP1-79055	use at 4µg/mL
Mouse anti-PD-1 Clone NAT	abcam	ab52587	use at 2 µg/mL
Goat anti-Tim-3	R&D	AF2365	use at 3 µg/mL
Rabbit anti-PD-L1 Clone SP142	Roche	7309457001	use at 1 µg/mL
Mouse AF647 labeled pan- Keratin Clone C11	Cell Signalling	4528	use at 0.5 µg/mL
Goat anti-human gal9	R&D	AF2045	use at 0.3 µg/mL
Cyan 5 conjugated donkey anti-rabbit	Jackson Immunoresearch	711-175-152	use at 5 µg/mL
Biotinylated F(ab’2) donkey anti-mouse IgG	Jackson Immunoresearch	715-066-150	use at 3 µg/mL
Alexa Fluor488 conjugated donkey anti-goat IgG	abcam	ab150133	use at 5 µg/mL
Cy3 labeled streptavidin	Amersham	PA43001	use at 3 µg/mL
negative control mouse IgG1	Dako	X0931	use at 2 µg/mL
IgG from goat serum	Sigma-aldrich	I5256	use at 3 µg/mL
IgG from rabbit serum	Sigma-aldrich	I5006	use at 4µg/mL