title: A method for quantitative analysis of standard and high-throughput qPCR expression data based on input sample quantity.
abstract: Over the past decade rapid advances have occurred in the understanding of RNA expression and its regulation. Quantitative polymerase chain reactions (qPCR) have become the gold standard for quantifying gene expression. Microfluidic next generation, high throughput qPCR now permits the detection of transcript copy number in thousands of reactions simultaneously, dramatically increasing the sensitivity over standard qPCR. Here we present a gene expression analysis method applicable to both standard polymerase chain reactions (qPCR) and high throughput qPCR. This technique is adjusted to the input sample quantity (e.g., the number of cells) and is independent of control gene expression. It is efficiency-corrected and with the use of a universal reference sample (commercial complementary DNA (cDNA)) permits the normalization of results between different batches and between different instruments--regardless of potential differences in transcript amplification efficiency. Modifications of the input quantity method include (1) the achievement of absolute quantification and (2) a non-efficiency corrected analysis. When compared to other commonly used algorithms the input quantity method proved to be valid. This method is of particular value for clinical studies of whole blood and circulating leukocytes where cell counts are readily available.
authors.affiliation: Neurology, SUNY Downstate Medical Center, Brooklyn, New York, United States of America; Neurology, UJCM, Krakow, Poland.
authors.forename: Mateusz G
authors.initials: MG
authors.lastname: Adamski
authors.affiliation: Department of Physics, Harvard University, Cambridge, Massachusetts, United States of America; Institute for Quantum Computing, Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Patryk
authors.initials: P
authors.lastname: Gumann
authors.affiliation: Neurology, SUNY Downstate Medical Center, Brooklyn, New York, United States of America.
authors.forename: Alison E
authors.initials: AE
authors.lastname: Baird
mesh_terms: D016022:Case-Control Studies; D002452:Cell Count; D018076:DNA, Complementary / Q000235:genetics; D005786:Gene Expression Regulation; D057166:High-Throughput Screening Assays; D006801:Humans; D012015:Reference Standards; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods* / Q000592:standards*; D013223:Statistics as Topic; D020521:Stroke / Q000235:genetics
chemical_list: D018076:DNA, Complementary
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