Summary
Objectives:
Recent progress in automated tissue analysis (tissomics) provides reproducible phenotypical
characterization of histological specimens. We introduce informatics tools to cluster
and correlate quantitative tissue profiles with gene expression data. The great potential
of synergies between tissue analysis and bioinformatics and its perspectives in medical
research and computational diagnostics are discussed.
Methods:
Key enablers in microscopic imaging and machine vision are reviewed to perform a
high-throughput tissue analysis. Methodologies are described and results are demonstrated
that support a combined analysis of tissue with gene expression profiles whereby the
consideration of individual responses is key.
Results:
Comprehensive histomorphometric profiles, extracted using machine vision, provide
information regarding the components and heterogeneity of a tissue in a reproducible
format amenable to data mining and analysis. Tissue quantitative information can be
placed in synergetic context with bioinformatics data, such as gene expression profiles,
for a more comprehensive stratification of individual responses. From a bioinformatics
point of view tissue data are co-variants that support the identification of candidate
genes relevant in tissue injury or disease.
Conclusions:
Progress in automated analytics enables the generation of quantitative data about
tissue previously limited to visual histopathology. Such reproducible data sets can
be statistically correlated and clustered throughout the continuum of bioinformatics.
The combined approach supports a system-wide view of biology and has a potential to
accelerate developments for a personalized computational diagnosis.
Keywords
Digital tissue imaging - phenotyping - tissomics - multi-sample comparison - computational
diagnosis - biomedical informatics - systems biology
