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Sci Rep. 2019 Feb 19;9(1):2262. doi: 10.1038/s41598-018-38458-7.

High-throughput, Efficient, and Unbiased Capture of Small RNAs from Low-input Samples for Sequencing.

Author information

1
The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, 94143, USA.
2
Department of Urology, University of California, San Francisco, CA, 94143, USA.
3
The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, 94143, USA. Robert.Blelloch@ucsf.edu.
4
Department of Urology, University of California, San Francisco, CA, 94143, USA. Robert.Blelloch@ucsf.edu.

Abstract

MicroRNAs hold great promise as biomarkers of disease. However, there are few efficient and robust methods for measuring microRNAs from low input samples. Here, we develop a high-throughput sequencing protocol that efficiently captures small RNAs while minimizing inherent biases associated with library production. The protocol is based on early barcoding such that all downstream manipulations can be performed on a pool of many samples thereby reducing reagent usage and workload. We show that the optimization of adapter concentrations along with the addition of nucleotide modifications and random nucleotides increases the efficiency of small RNA capture. We further show, using unique molecular identifiers, that stochastic capture of low input RNA rather than PCR amplification influences the biased quantitation of intermediately and lowly expressed microRNAs. Our improved method allows the processing of tens to hundreds of samples simultaneously while retaining high efficiency quantitation of microRNAs in low input samples from tissues or bodily fluids.

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