Inform2

Manufactured by Akoya Biosciences

The InForm2.4.8 is a multi-channel tissue analysis platform developed by Akoya Biosciences. It is designed for the simultaneous detection and quantification of multiple protein biomarkers in tissue samples. The core function of the InForm2.4.8 is to enable advanced spatial analysis of tissue samples.

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17 protocols using inform2

Automated Quantitative Pathology Imaging

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Digital images were acquired with a Vectra 3.0 Automated Quantitative Pathology Imaging System (Akoya Biosciences). A ×10 objective lens was used for whole-slide scans to identify the regions of interest (ROIs). A ×20 objective lens was used for the multispectral images (MSIs). Seven to 15 ROIs were selected from each tissue sample for mIHC analysis. ROIs that contained at least 30% tumor cells were selected. Tissue segmentation was performed according to cytokeratin and DAPI staining. Cell segmentation and phenotyping of individual cells were performed according to individual markers and the presence of DAPI using Inform 2.4 software (Akoya Biosciences). For tumor versus stroma analysis, the stamp size for the ROIs was 670 × 502 μm². For the nearest-neighbor analysis, the stamp size for the ROIs was 1340 × 1004 μm². Consecutive tissue slides for each case were stained using the conventional H&E method to ensure the presence of tumor cells and to evaluate the fixation quality. Tissue slides were digitally scanned with the Leica SCN400F platform at ×20 and magnified at ×200 to ×400 for immune infiltration evaluation.

Duhen R., Fesneau O., Samson K.A., Frye A.K., Beymer M., Rajamanickam V., Ross D., Tran E., Bernard B., Weinberg A.D, & Duhen T. (2022). PD-1 and ICOS coexpression identifies tumor-reactive CD4+ T cells in human solid tumors. The Journal of Clinical Investigation, 132(12), e156821.

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Quantitative Spatial Immunophenotyping of Tumor Samples

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Digital mIHC images were acquired with the Vectra Polaris Automated Quantitative Pathology Imaging System (Akoya Biosciences). Whole tissue slides were scanned at 20Â magnification and visualized with Phenochart software (Akoya Biosciences) to identify the regions of interest (ROI). Between 13 and 20 ROIs were selected from each tissue sample for mIHC analysis with InForm 2.4 software (Akoya Biosciences). Cell phenotypes were enumerated in the stroma and tumor compartment for the images. Each tissue slide was analyzed in individual InForm projects.
Consecutive tissue slides for each case were stained by conventional hematoxylin and eosin method to ensure the presence of tumor and evaluate fixation quality. Tissue slides were digitally scanned with the Leica SCN400F platform at 20Â and magnified at 200Â to 400Â for immune infiltration evaluation.

Rajamanickam V., Ballesteros-Merino C., Samson K., Ross D., Bernard B., Fox B.A., Tran E., Newell P, & Duhen T. (2021). Robust Antitumor Immunity in a Patient with Metastatic Colorectal Cancer Treated with Cytotoxic Regimens. Cancer immunology research, 9(6).

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Multispectral Imaging of FFPE Samples

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Formalin-Fixed Paraffin-Embedded (FFPE) specimens from one patient both at diagnosis and recurrence were analyzed by multispectral imaging. FFPE slides were stained with anti-CD8 (clone C8/144B, Thermo Fisher Scientific, Waltham, MA, USA), anti-CD68 (clone PG-M1, Dako Agilent, Santa Clara, CA, USA) and anti-glial fibrillary acidic protein (GFAP, clone EP672Y, abcam, Cambridge, UK) antibodies for T cell, macrophage and surrounding tissue detection, respectively. DAPI (Akoya Biosciences, Marlborough, MA, USA) was used as a nuclear counterstain. The autofluorescence background signal was subtracted using an unstained control slide processed in parallel. The Mantra multispectral imaging platform (Akoya Biosciences) was employed for image acquisition at 20× magnification and data analysis was carried out with InForm 2.4.1 software (Akoya Biosciences). The immune cell infiltrate was quantified as cell density/megapixel.

Magri S., Musca B., Bonaudo C., Tushe A., Russo M.G., Masetto E., Zagonel V., Lombardi G., Della Puppa A, & Mandruzzato S. (2021). Sustained Accumulation of Blood-Derived Macrophages in the Immune Microenvironment of Patients with Recurrent Glioblastoma after Therapy. Cancers, 13(24), 6178.

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Multiplexed Immunohistochemistry for Tissue Analysis

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A multiplexed immunohistochemistry (mIHC) analysis was performed on formalin‐fixed paraffin‐embedded (FFPE) tissue slides using the Mantra multispectral imaging platform (Akoya Biosciences, Marlborough, Massachusetts). FFPE slides were stained with anti‐CD8 (clone C8/144B, Thermo Fisher Scientific), anti‐CD68 (clone PG‐M1, Dako Agilent, Santa Clara, California), anti‐HO‐1 (clone HO‐1‐1, Thermo Fisher Scientific) and anti‐glial fibrillary acidic protein (GFAP, clone EP672Y, Abcam, Cambridge, UK) antibodies. DAPI (Akoya Biosciences) was used as a nuclear counterstain. The autofluorescence background signal was subtracted by using an unstained control slide processed in parallel. The Mantra multispectral imaging platform (Akoya Biosciences) was employed for image acquisition at ×20 magnification and data analysis was carried out with InForm 2.4.1 software (Akoya Biosciences).

Magri S., Musca B., Pinton L., Orecchini E., Belladonna M.L., Orabona C., Bonaudo C., Volpin F., Ciccarino P., Baro V., Della Puppa A, & Mandruzzato S. (2022). The immunosuppression pathway of tumor‐associated macrophages is controlled by heme oxygenase‐1 in glioblastoma patients. International Journal of Cancer, 151(12), 2265-2277.

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Automated Pathology Imaging and Quantification

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Images were acquired using the Vectra^® 3 automated quantitative pathology imaging system. The images were then processed for quantification of immunohistochemical staining using Inform^® 2.6 software from Akoya Biosciences^®. The percentage positivity in the figures represents the percentage of cells in the tissue section of the tumor area that were positive for staining.

Marsh T.L., Saxman P., Cole J, & Tiedje J. (2000). Terminal Restriction Fragment Length Polymorphism Analysis Program, a Web-Based Research Tool for Microbial Community Analysis. Applied and Environmental Microbiology, 66(8), 3616-3620.

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Automated Quantitative Pathology Imaging

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Images were acquired using the Vectra ® 3 automated quantitative pathology imaging system. The images were then processed for quantification of immunohistochemical staining using Inform ® 2.6 software from Akoya Biosciences ® . The percentage positivity in the figures represents the percentage of cells in the tissue section of the tumor area that were positive for staining.

Liu X.D., Zhang Y.T., McGrail D.J., Zhang X., Lam T., Hoang A., Hasanov E., Manyam G., Peterson C.B., Zhu H., Kumar S.V., Akbani R., Pilie P.G., Tannir N.M., Peng G, & Jonasch E. (2023). SETD2 Loss and ATR Inhibition Synergize to Promote cGAS Signaling and Immunotherapy Response in Renal Cell Carcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research, 29(19).

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Automated Single-Cell Phenotyping of Tumor Subtypes

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Supervised machine learning algorithms were applied for tissue and cell segmentation (inForm 2.4.1, Akoya Biosciences). Single-cell-level imaging data were exported and further processed and analyzed using R (v3.6.2). To assign phenotypes to individual malignant epithelial cells, mean expression intensity in the relevant subcellular compartment was first used to classify cells as positive or negative for each of the 5 markers. Combinatorial expression patterns for the five markers were then used to phenotypically classify cells as basal, classical, co-expressing / IC or marker negative (3 combinations of 2 basal markers, 7 combinations of 3 classical markers, 1 pan-marker negative, 21 combinations of co-expression of basal and classical markers, Figure S4A, Table S3). Tumor subtype composition was assessed by calculating the fraction of total malignant cells positive for each cell phenotype (Figure S4B, excluding pan-marker negative cells).

Raghavan S., Winter P.S., Navia A.W., Williams H.L., DenAdel A., Lowder K.E., Galvez-Reyes J., Kalekar R.L., Mulugeta N., Kapner K.S., Raghavan M.S., Borah A.A., Liu N., Väyrynen S.A., Costa A.D., Ng R.W., Wang J., Hill E.K., Ragon D.Y., Brais L.K., Jaeger A.M., Spurr L.F., Li Y.Y., Cherniack A.D., Booker M.A., Cohen E.F., Tolstorukov M.Y., Wakiro I., Rotem A., Johnson B.E., McFarland J.M., Sicinska E.T., Jacks T.E., Sullivan R.J., Shapiro G.I., Clancy T.E., Perez K., Rubinson D.A., Ng K., Cleary J.M., Crawford L., Manalis S.R., Nowak J.A., Wolpin B.M., Hahn W.C., Aguirre A.J, & Shalek A.K. (2021). Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell, 184(25), 6119-6137.e26.

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Automated Single-Cell Phenotyping of Tumor Subtypes

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Multiplexed Tumor Immune Profiling

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The NEL810001KT Opal kit (Akoya, Marloborough, MA, USA) was used for multispectral immunophenotyping and multiplexed automatic quantification of CD3, CD4, and CD8 in tumors. Unspecific binding was blocked in Dako Antibody diluent (Dako, Glostrup, Denmark) for 10 minutes at RT. After antigen retrieval using a citrate buffer (pH = 6), samples were incubated with anti-CD8 (1:400, Cell Signaling) and then with secondary antibody and Opal-690 (1:100, Akoya) fluorochrome. Then, antigen retrieval was performed using EDTA (pH = 9) followed by anti-CD4 (1:1000, Abcam), secondary antibody, and Opal-570 (1:100, Akoya). Finally, antigen retrieval with citrate buffer (pH = 6) was applied before adding the anti-CD3 (1:75, Abcam), secondary antibody, and Opal-520 (1:100, Akoya). Ready-to-use secondary antibodies and Opals are provided by the kit. We used DAPI for nuclear staining and Diamond antifade medium (Life Technologies) for mounting the slides.
Sample scanning, spectral unmixing, and quantification of signals were conducted with the Vectra Polaris Automated Quantitative Pathology Imaging System (Akoya), using the Phenochart and InForm 2.4 software (Akoya). The number of positive cells belonging to each specific phenotype was given as number of cells/square micron.

Baraibar I., Roman M., Rodríguez-Remírez M., López I., Vilalta A., Guruceaga E., Ecay M., Collantes M., Lozano T., Alignani D., Puyalto A., Oliver A., Ortiz-Espinosa S., Moreno H., Torregrosa M., Rolfo C., Caglevic C., García-Ros D., Villalba-Esparza M., De Andrea C., Vicent S., Pío R., Lasarte J.J., Calvo A., Ajona D, & Gil-Bazo I. (2020). Id1 and PD-1 Combined Blockade Impairs Tumor Growth and Survival of KRAS-mutant Lung Cancer by Stimulating PD-L1 Expression and Tumor Infiltrating CD8+ T Cells. Cancers, 12(11), 3169.

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Quantification of Tumor-Resident Memory T Cells

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The staining procedure, including target antigens, antibody clones, dilutions, and antigen retrieval conditions is reported in Supplementary Methods. Image acquisition was performed using the Vectra Polaris multispectral imaging platform (Vectra Polaris, Akoya Biosciences). Of note, CD103 was chosen to identify TRM cells in vivo due to its well-defined expression in TRM populations, and the lack of an experimentally-validated antibody against ZNF683 for IF. Areas with non-tumor or residual normal tissue were excluded from the analysis. Representative regions of interest were selected under pathologist supervision, 6 fields of view (FOV) were acquired at 20x resolution. Once the FOV were spectrally unmixed, cell identification was performed using supervised machine learning algorithms within Inform 2.4 (Akoya, Supplementary Methods). Thresholds for "positive" staining and the accuracy of phenotypic algorithms were optimized and confirmed under pathologist supervision for each case. Quantification of cell populations per mm² are reported in Data File S6.

Francia M.V., Zabala J.C., de la Cruz F, & García Lobo J.M. (1999). The IntI1 Integron Integrase Preferentially Binds Single-Stranded DNA of the attC Site. Journal of Bacteriology, 181(21), 6844-6849.

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