A spectral library containing the emitting spectral profile of all 6 fluorophores (5 TSAs + Dapi) was created with the Nuance Image Analysis software (PerkinElmer) using multispectral images obtained from single stained slides for each marker and associated fluorophore. Two ovarian cancer sections were subjected to identical slide processing without the use of TSA reagents, in order to determine the autofluorescence profile of ovarian cancer tissue. The phenotyping analysis was performed using inForm 2.1.0 image analysis software (PerkinElmer). The images were segmented into specific tissue categories of tumor islets, stroma and no tissue, based on the cytokeratin and DAPI expression, after manually drawing training regions on each image by a qualified pathologist (PGF). Individual cells were segmented using the counterstained-based cell segmentation algorithm. Following tissue and cell segmentation, scoring was performed by using manually specified threshold values for each marker and then was normalized per mm2 of tumor and stromal area following the formula pixels × 0.246 × 10−6 = 1mm2, from the exported data.
Nuance image analysis software
Manufactured by PerkinElmer
Sourced in United States
Nuance Image Analysis software is a comprehensive image analysis platform designed to provide advanced quantitative analysis capabilities for various imaging modalities. The software offers a suite of tools and algorithms to extract meaningful data from complex images, enabling researchers and scientists to gain deeper insights into their samples and experiments.
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Lab products found in correlation
2 protocols using nuance image analysis software
1
Multiplex Immunofluorescence Tissue Analysis
2
Multispectral Imaging and Cell Phenotyping
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To separate each multispectral image cube into its individual components (spectral unmixing) and enable the colour-based identification of T-cell subtypes and α-SMA-positive cells, the Nuance Image Analysis software (Perkin Elmer, Waltham, MA, USA) was used to create a spectral library containing the emitting spectral peaks of all fluorophores obtained from single stained slides for each marker and associated fluorophore. All spectrally unmixed and segmented images were then analysed using inForm 2.1 image analysis software. Based on the identification of the DAPI-stained nuclear/cell morphological features and their patterns of fluorophore expression, immune and other cells were phenotyped into five classes: CD3+CD8+ T cells, CD3+Foxp3+ T cells, CD3+CD8−T cells, α-SMA+ cells, and other cells.
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