Aptamer-based multiplexed proteomic technology for biomarker discovery

适体 生物标志物发现 计算生物学 蛋白质组 蛋白质组学 生物标志物 寡核苷酸 核酸 DNA微阵列 DNA 生物 生物信息学 化学 分子生物学 生物化学 基因 基因表达
作者
Larry Gold,Deborah Ayers,Jennifer Bertino,C. Bock,Ashley Bock,Edward N. Brody,Jeff Carter,Virginia Cunningham,Andrew B Dalby,Bruce Eaton,Tim Fitzwater,Dylan Flather,Ashley Forbes,Trudi Foreman,Cate Fowler,Bharat Gawande,Meredith Goss,Magda Gunn,Shashi Kumar Gupta,Dennis Halladay
出处
期刊:Nature Precedings [Springer Nature]
被引量:373
标识
DOI:10.1038/npre.2010.4538.1
摘要

Abstract Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.
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