Abstract
Immunohistochemistry (IHC) is a standard histological technique that has been used for the evaluation of tissue sections for decades. However, as IHC has its limitations, new techniques involving fluorescence are being utilized more and more frequently. Fluorescent immunohistochemistry (fIHC) is a valuable research technique, as it can be used to determine interactions of multiple protein biomarkers on the same tissue section at the same time (multiplexing), maximizing the amount of data that can be extracted from each sample. fIHC is also being adopted for clinical assays, e.g. detection of anti-nuclear antibodies in Systemic Lupus Erythematosus (SLE), due to the benefits that this method has over chromogenic methods. Use of these assays is considered the “gold standard” of assessment in an increasing number of clinical conditions, which is driving further use of fIHC in discovery and translational research applications. However, as these techniques require the scientist to manually assess each slide using a fluorescent microscope, this can be laborious and time consuming. Automated image analysis systems are being used with increasing frequency by researchers, to enable accurate quantification of fIHC slides, while minimizing user interaction.
In this paper we describe a validation study to verify the correlation between results generated by digital pathology image analysis (Aperio Cellular IF Algorithm) and manual scoring, for both biological tissue samples and manually generated pseudo-images. Pseudo-images were drawn mimicking scenarios that exist in biological tissue, and 20 fluorescently labeled formalin fixed paraffin embedded (FFPE) tissue samples were selected. All tissue slides were digitized, and both pseudo-images and samples were manually scored using the Aperio ImageScope viewer. Manual assessment was then compared to results generated by the Aperio Cellular IF Algorithm, with a contingency table prepared and kappa coefficient calculated. A kappa coefficient value of >0.61 was found to exist between manual and automated assessment of both pseudoimages and biological tissue.
Introduction
Depending on both the site from which biological tissue is obtained and the invasiveness of the procedure, the amount of tissue available for analysis is typically limited. It is imperative that the researcher get as much information from that tissue as possible. Science has historically depended on chromogenic techniques to provide relevant information both diagnostically and for research purposes. Hematoxylin and Eosin (H&E) staining is employed to visualize the cellular architecture within a tissue section, while IHC stains such as HER2/neu are utilized to provide more in-depth information on the degree of positivity of biomarkers within the tissue (Nadji et al. 2005). However, IHC has its limitations. The technique relies on chromogens, which have a limited capacity for multiplexing. Fluorescent techniques such as fIHC are far more effective for multiplexing (Hollman-Hewgley et al. 2014; Stack et al. 2014), allowing scientists to extract a more extensive data set, while limiting the amount of tissue required.
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