Assessing fibrinogen extravasation into Alzheimer's disease brain using high-content screening of brain tissue microarrays

• Fibrinogen extravasation is significantly increased in Alzheimer's disease brain.
• High content analysis of brain tissue microarrays is demonstrated.
• Novel coupling of VSlide imaging and MetaXpress image analysis is used.
• HCS findings corroborated with Western blot results and manual H-scores.

BackgroundTissue microarrays are commonly used to evaluate disease pathology however methods to automate and quantify pathological changes are limited.New methodThis 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.ResultsSignificant 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 methodVSlide 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.ConclusionsHere, 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.