Quantitative assessment of human whole blood RNA as a potential biomarker for infectious disease
Literature Information
Paul Dickinson, Thorsten Forster, Mizanur Khondoker, Marie Craigon, Alan Ross, Petter Storm, Stewart Burgess, Paul Lacaze, Benjamin J. Stenson, Peter Ghazal
Infection remains a significant cause of morbidity and mortality especially in newborn infants. Analytical methods for diagnosing infection are severely limited in terms of sensitivity and specificity and require relatively large samples. It is proposed that stringent regulation of the human transcriptome affords a new molecular diagnostic approach based on measuring a highly specific systemic inflammatory response to infection, detectable at the RNA level. This proposition raises a number of as yet poorly characterised technical and biological variation issues that urgently need to be addressed. Here we report a quantitative assessment of methodological approaches for processing and extraction of RNA from small samples of infant whole blood and applying analysis of variation from biochip measurements. On the basis of testing and selection from a battery of assays we show that sufficient high quality RNA for analysis using multiplex array technology can be obtained from small neonatal samples. These findings formed the basis of implementing a set of robust clinical and experimental standard operating procedures for whole blood RNA samples from 58 infants. Modelling and analysis of variation between samples revealed significant sources of variation from the point of sample collection to processing and signal generation. These experiments further permitted power calculations to be run indicating the tractability and requirements of using changes in RNAexpression profiles to detect different states between patient groups. Overall the results of our investigation provide an essential first step toward facilitating an alternative way for diagnosing infection from very small neonatal blood samples, providing methods and requirements for future chip-based studies.
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