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• W1997297715 abstract "Antibody-based proteomics applied to tissue microarray (TMA) technology provides a very efficient means of visualizing and locating antigen expression in large collections of normal and pathological tissue samples. To characterize antigen expression on TMAs, the use of image analysis methods avoids the effects of human subjectivity evidenced in manual microscopical analysis. Thus, these methods have the potential to significantly enhance both precision and reproducibility. Although some commercial systems include tools for the quantitative evaluation of immunohistochemistry-stained images, there exists no clear agreement on best practices to allow for correct and reproducible quantification results. Our study focuses on practical aspects regarding (i) image acquisition (ii) segmentation of staining and counterstaining areas and (iii) extraction of quantitative features. We illustrate our findings using a commercial system to quantify different immunohistochemistry markers targeting proteins with different expression patterns (cytoplasmic, nuclear or membranous) in colon cancer or brain tumor TMAs. Our investigations led us to identify several steps that we consider essential for standardizing computer-assisted immunostaining quantification experiments. In addition, we propose a data normalization process based on reference materials to be able to compare measurements between studies involving different TMAs. In conclusion, we recommend certain critical prerequisites that commercial or in-house systems should satisfy in order to permit valid immunostaining quantification." @default.
• W1997297715 created "2016-06-24" @default.
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• W1997297715 date "2009-08-10" @default.
• W1997297715 modified "2023-09-28" @default.
• W1997297715 title "Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis" @default.
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• W1997297715 doi "https://doi.org/10.1002/pmic.200800936" @default.
• W1997297715 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19670370" @default.
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