Multiplexing With MicroRNAs: Mismatch Probes and Temperature Compensation for Increased Specificity and Reduced Variability in Hybridization Assays

microRNA;assay;multiplex;hybridization;DNA;nucleic acids;cytometry

Rafferty, Conor

University:Havard University

Department:Biotechnology

关键词: microRNA;    assay;    multiplex;    hybridization;    DNA;    nucleic acids;    cytometry;   

Others  :  https://dash.harvard.edu/bitstream/handle/1/42004149/RAFFERTY-DOCUMENT-2019.pdf?sequence=1&isAllowed=y

美国|英语

来源: Digital Access to Scholarship at Harvard

【 摘 要 】

MicroRNAs constitute a very interesting class of circulating biomarkers.They are present in many body fluids, and appear to avoid degradation by enzymes or the immune system.Organs shed microRNAs into the blood stream, offering the exciting possibility of detecting solid tumors or other hidden pathologies without invasive tissue biopsy.Multiplex microRNA assays offer superior specificity and differential diagnosis relative to quantitation of single analytes. A variety of multiplex assay technologies exists, including RNA sequencing, quantitative polymerase chain reaction, and hybridization, with different tradeoffs of cost, coverage, throughput and sample volume.The focus of this work is on improving reproducibility and specificity of a hybridization-based multiplex microRNA assay. The assay binds microRNA targets in solution to complementary DNA probes conjugated in a hydrogel particle matrix. The hydrogel environment differs from a classic surface-bound or bead-bound hybridization assay and has advantages for binding nucleic assay from complex biological samples, for instance tissue lysates and serum.Non-specific binding combined with variable well temperature across a plate is shown here to be a major source of assay variability.We describe the development of novel probes to reduce non-specific binding is described, and the development of a methodology to correct for temperature. An over 10-fold reduction in non-specific binding is achieved, and well-to-well variability is reduced from ~30% in raw data to single-digit percentages in most cases.