Gene Expression Profiling of Picogram Amounts of Mouse Universal Reference RNA Amplified Using the Miltenyi Biotec μMACS SuperAmp Technology

利用 Miltenyi Biotec μMACS SuperAmp 技术扩增皮克级小鼠通用参考 RNA,并进行基因表达谱分析。

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Abstract

High throughput RNA sequencing (RNA-Seq) has become the method of choice for the characterization and quantification of transcriptomes. However, it remains challenging to integrate RNA amplification technologies into the RNA-Seq protocol to ultimately provide for a comprehensive and bias-free transcriptome analysis from picogram quantities of input RNA. We tested the performance of the Miltenyi Biotec μMACS Super Amp Kit for expansion of low-quantity RNA samples. Starting with the mRNA isolation from 0.12 to 10 ng of Universal Mouse Reference RNA (UMRR), cDNA was synthesized and amplified by global PCR. Illumina TruSeq RNA libraries were constructed from fragmented cDNA and compared to unamplified reference libraries generated from 10 ng, 100 ng and 1 μg of UMRR. The sequence data was evaluated in terms of (i) quality, (ii) exon/gene coverage, and (iii) gene expression levels of amplified versus unamplified samples. Initial analysis showed good correlation (>80%) of gene expression values among amplified and unamplified sample groups, but a decreased correlation (74-77%) between the two groups. Gene coverage analysis indicated that many of the same genes were covered and expressed at some level in all samples. However, only one third of the exons were covered in the amplified compared to the unamplified samples. The sample amplified from an input amount of RNA equivalent to approximately 12 cells (0.12 ng) was a definite outlier, with significantly lower unique reads aligned and the lowest correlation to the other samples; significant levels of E. coli sequences were detected, likely stemming from the recombinant enzymes in the kit. Current data analysis focuses on any potential bias introduced by the μMACS Super Amp amplification procedure. The goal is to identify the properties of the genes and exons that can be profiled using this approach when only limited numbers of cells or small quantities of RNA can be obtained.

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