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SuperArray Improves Microarray Analysis of Archived FFPE Clinical Samples
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SuperArray Bioscience has announced the availability of an improved method for isolating RNA from Formalin-Fixed Paraffin-Embedded (FFPE) samples of sufficient quality and quantity for microarray-based gene expression analysis.
They report that the method increases percent positive calls on high-density microarrays over other available kits and methods by reversing more of the cross-links introduced by the archiving process.
Their ArrayGrade™ FFPE RNA Isolation Kit and Service now bring these profiling capabilities to even more areas of biological research including cancer and pathology.
Archived FFPE tissue samples are readily available material from human disease studies for which pathology and clinical outcomes are known and well documented.
Analyzing gene expression patterns or "profiles" in these archived tissues facilitates retrospective studies by correlating gene expression patterns with the relevant disease states.
Such profiles may serve as additional biomarkers for disease classification, staging and other diagnostic or prognostic purposes.
However, the generation of FFPE blocks for archival storage involves formalin-based cross-linking.
This process makes RNA a poor substrate for the enzymes required for the detection of gene expression profiles by molecular biology techniques such as microarrays.
"SuperArray has developed an advanced method for isolating RNA from FFPE blocks that reverses more cross-links than other existing methods," said Dr. Xiao Zeng, SuperArray's Contract Services Coordinator.
"The resulting RNA then improves the sensitivity of microarrays providing researchers with more positive calls, and allowing them to discover more candidate genes for biomarker discovery, screening, and validation," continued Zeng.
"We have years of experience in gene expression profiling with microarrays as well as isolating RNA of sufficient quantity and quality for these experiments from a variety of biological source materials," added Zeng.
"We can now help researchers unlock the gene expression profiles within their archived FFPE samples and correlate those profiles with their clinical data," Zeng concluded.
