Tissue microarrays and digital image analysis

Proteomics in systems toxicology

Proteins are the ultimate product of gene expression. As they hinge between gene transcription and phenotype, they offer a more realistic perspective of toxicopathic effects, responses and even susceptibility to insult than targeting genes and mRNAs while dodging some inter-individual variability that hinders measuring downstream endpoints like metabolites or enzyme activity. Toxicologists have long focused on proteins as biomarkers but the advent of proteomics shifted risk assessment from narrow single-endpoint analyses to whole-proteome screening, enabling deriving protein-centric adverse outcome pathways (AOPs), which are pivotal for the derivation of Systems Biology informally named Systems Toxicology. Especially if coupled pathology, the identification of molecular initiating events (MIEs) and AOPs allow predictive modeling of toxicological pathways, which now stands as the frontier for the next generation of toxicologists. Advances in mass spectrometry, bioinformatics, protein databases and top-down proteomics create new opportunities for mechanistic and effects-oriented research in all fields, from ecotoxicology to pharmacotoxicology.

Preparation, construction and high-throughput automated analysis of human brain tissue microarrays for neurodegenerative disease drug development

A major challenge in the treatment of neurodegenerative disorders is the translation of effective therapies from the lab to the clinic. One approach to improve this process is the use of human brain tissue microarray (HBTMA) technology to aid in the discovery and validation of drug targets for brain disorders. In this protocol we describe a platform for the production of high-quality HBTMAs that can be used for drug target discovery and validation. We provide examples of the use of this platform and describe detailed protocols for HBTMA design, construction and use for both protein and mRNA detection. This platform requires less tissue and reagents than single-slide approaches, greatly increasing throughput and capacity, enabling samples to be compared in a more consistent way. It takes 4 d to construct a 60 core HBTMA. Immunohistochemistry and in situ hybridization take a further 2 d. Imaging of each HBTMA slide takes 15 min, with subsequent high-content analysis taking 30 min-2 h.

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

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.

Can Archival Tissue Reveal Answers to Modern Research Questions?: Computer-Aided Histological Assessment of Neuroblastoma Tumours Collected over 60 Years

Despite neuroblastoma being the most common extracranial solid cancer in childhood, it is still a rare disease. Consequently, the unavailability of tissue for research limits the statistical power of studies. Pathology archives are possible sources of rare tissue, which, if proven to remain consistent over time, could prove useful to research of rare disease types. We applied immunohistochemistry to investigate whether long term storage caused any changes to antigens used diagnostically for neuroblastoma. We constructed and quantitatively assessed a tissue microarray containing neuroblastoma archival material dating between 1950 and 2007. A total of 119 neuroblastoma tissue cores were included spanning 6 decades. Fourteen antibodies were screened across the tissue microarray (TMA). These included seven positive neuroblastoma diagnosis markers (NB84, Chromogranin A, NSE, Ki-67, INI1, Neurofilament Protein, Synaptophysin), two anticipated to be negative (S100A, CD99), and five research antibodies (IL-7, IL-7R, JAK1, JAK3, STAT5). The staining of these antibodies was evaluated using Aperio ImageScope software along with novel pattern recognition and quantification algorithms. This analysis demonstrated that marker signal intensity did not decrease over time and that storage for 60 years had little effect on antigenicity. The construction and assessment of this neuroblastoma TMA has demonstrated the feasibility of using archival samples for research.

Quantitative histological assessment of xenobiotic-induced liver enzyme induction and pituitary-thyroid axis stimulation in rats using whole-slide automated image analysis

Preclinical evaluation of a new compound, RO2910, identified a hypertrophic response in liver, thyroid gland, and pituitary gland (pars distalis). We aimed to develop and validate automated image analysis methods to quantify and refine the interpretation of semi-quantitative histology. Wistar-Han rats were administered RO2910 for 14 days. Liver, thyroid, and pituitary gland tissues were processed for routine histology and immunolabeled with anti-thyroid stimulating hormone (TSH) antibody (pituitary) and anti-topoisomerase II antibody (thyroid). Glass slides were scanned, image analysis methods were developed and applied to whole-slide images, and numerical results were compared with histopathology, circulating hormone levels, and liver enzyme mRNA expression for validation. Quantitative analysis of slides had strong individual correlation with semi-quantitative histological evaluation of all tissues studied. Hepatocellular hypertrophy quantification also correlated strongly with liver enzyme mRNA expression. In the pars distalis, measurement of TSH weak-staining areas correlated with both hypertrophy scores and circulating TSH levels. Whole-slide image analysis enabled automated quantification of semi-quantitative histopathology findings and a more refined interpretation of these data. The analysis also enabled a direct correlation with non-histological parameters using straightforward statistical analysis to provide a more refined dose- and sex-response relationship and integration among affected parameters. These findings demonstrate the utility of our image analysis to support preclinical safety evaluations.

Digital imaging in pathology: whole-slide imaging and beyond

Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.