ABSTRACT
Tissue microarray (TMA) and cell microarray (CMA) are two powerful techniques that allow for the immunophenotypical characterization of hundreds of samples simultaneously. In particular, the CMA approach is particularly useful for immunophenotyping new stem cell lines (e.g., cardiac, neural, mesenchymal) using conventional markers, as well as for testing the specificity and the efficacy of newly developed antibodies. We propose the use of a tissue arrayer not only to perform protein expression profiling by immunohistochemistry but also to carry out molecular genetics studies. In fact, starting with several tissues or cell lines, it is possible to obtain the complete signature of each sample, describing the protein, mRNA and microRNA expression, and DNA mutations, or eventually to analyze the epigenetic processes that control protein regulation. Here we show the results obtained using the Galileo CK4500 TMA platform.
Subject(s)
Gene Expression Profiling/methods , Nucleic Acids/genetics , Tissue Array Analysis/methods , Animals , Cell Line , Cell Line, Tumor , Gene Expression , Humans , Immunohistochemistry/methods , Immunophenotyping/methods , Intracellular Signaling Peptides and Proteins , Mice , Nucleic Acids/isolation & purification , Proteins/analysis , Proteins/geneticsABSTRACT
CONTEXT: In 2013 the high throughput technology known as Tissue Micro Array (TMA) will be fifteen years old. Its elements (design, construction and analysis) are intuitive and the core histopathology technique is unsophisticated, which may be a reason why has eluded a rigorous scientific scrutiny. The source of errors, particularly in specimen identification and how to control for it is unreported. Formal validation of the accuracy of segmenting (also known as de-arraying) hundreds of samples, pairing with the sample data is lacking. AIMS: We wanted to address these issues in order to bring the technique to recognized standards of quality in TMA use for research, diagnostics and industrial purposes. RESULTS: We systematically addressed the sources of error and used barcode-driven data input throughout the whole process including matching the design with a TMA virtual image and segmenting that image back to individual cases, together with the associated data. In addition we demonstrate on mathematical grounds that a TMA design, when superimposed onto the corresponding whole slide image, validates on each and every sample the correspondence between the image and patient's data. CONCLUSIONS: High throughput use of the TMA technology is a safe and efficient method for research, diagnosis and industrial use if all sources of errors are identified and addressed.
