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Assessing fibrinogen extravasation into Alzheimer's disease brain using high-content screening of brain tissue
Pritika J Narayan1, Sue-Ling Kim2, Claire Lill2
1Gravida, National Centre for Growth and Development, Faculty of Medical and Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Centre for Brain Research, Faculty of Medical and Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Biomedical Imaging Research Unit, Faculty of Medical and Health Sciences, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
Background:
Tissue microarrays are commonly used to evaluate disease pathology however methods to automate and quantify pathological changes are limited.
New Method:
This article demonstrates the utility of the VSlide scanner (MetaSystems) for automated image acquisition from immunolabelled tissue microarray slides, and subsequent automated image analysis with MetaXpress (Molecular Devices) software to obtain objective, efficient and reproducible data from immunolabelled tissue microarray sections.
Results:
Significant increases in fibrinogen immunolabelling were observed in 29 Alzheimer's disease cases compared to 28 control cases analysed from a single tissue microarray slide. Western blot analysis also demonstrated significant increases in fibrinogen immunolabelling in 6 Alzheimer's cases compared to 6 control cases. The observed changes were also validated with gold standard blinded manual H-scoring.
Comparison With Existing Method:
VSlide Metafer software offers a 'tissue microarray acquisition' plugin for easy mapping of tissue cores with their original position on the tissue microarray map. High resolution VSlide images are compatible with MetaXpress image analysis software. This article details the coupling of these two technologies to accurately and reproducibly analyse immunolabelled tissue microarrays within minutes, compared to the gold standard method of manual counting using H-scores which is significantly slower and prone to inter-observer variation.
Conclusions:
Here, we couple brain tissue microarray technology with high-content screening and automated image analysis as a powerful way to address bottle necks in data generation and improve throughput, as well as sensitivity to study biological/pathological changes in brain disease.
