Zhang Jitao David, Schindler Tobias, Küng Erich, Ebeling Martin, Certa Ulrich
Roche Pharmaceutical Research and Early Development, Department of Pharmaceutical Sciences/ Translational Technologies and Bioinformatics, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland.
BMC Genomics. 2014 Jul 5;15(1):565. doi: 10.1186/1471-2164-15-565.
In clinical and basic research custom panels for transcript profiling are gaining importance because only project specific informative genes are interrogated. This approach reduces costs and complexity of data analysis and allows multiplexing of samples. Polymerase-chain-reaction (PCR) based TaqMan assays have high sensitivity but suffer from a limited dynamic range and sample throughput. Hence, there is a gap for a technology able to measure expression of large gene sets in multiple samples.
We have adapted a commercially available mRNA quantification assay (AmpliSeq-RNA) that measures mRNA abundance based on the frequency of PCR amplicons determined by high-throughput semiconductor sequencing. This approach allows for parallel, accurate quantification of about 1000 transcripts in multiple samples covering a dynamic range of five orders of magnitude. Using samples derived from a well-characterized stem cell differentiation model, we obtained a good correlation (r = 0.78) of transcript levels measured by AmpliSeq-RNA and DNA-microarrays. A significant portion of low abundant transcripts escapes detection by microarrays due to limited sensitivity. Standard quantitative RNA sequencing of the same samples confirms expression of low abundant genes with an overall correlation coefficient of r = 0.87. Based on digital AmpliSeq-RNA imaging we show switches of signaling cascades at four time points during differentiation of stem cells into cardiomyocytes.
The AmpliSeq-RNA technology adapted to high-throughput semiconductor sequencing allows robust transcript quantification based on amplicon frequency. Multiplexing of at least 900 parallel PCR reactions is feasible because sequencing-based quantification eliminates artefacts coming from off-target amplification. Using this approach, RNA quantification and detection of genetic variations can be performed in the same experiment.
在临床和基础研究中,用于转录谱分析的定制面板正变得越来越重要,因为只对特定项目的信息基因进行检测。这种方法降低了数据分析的成本和复杂性,并允许对样本进行多重分析。基于聚合酶链反应(PCR)的TaqMan分析具有高灵敏度,但动态范围和样本通量有限。因此,需要一种能够在多个样本中测量大量基因集表达的技术。
我们采用了一种市售的mRNA定量分析方法(AmpliSeq-RNA),该方法基于高通量半导体测序确定的PCR扩增子频率来测量mRNA丰度。这种方法允许在多个样本中对约1000个转录本进行平行、准确的定量,覆盖五个数量级的动态范围。使用来自一个特征明确的干细胞分化模型的样本,我们通过AmpliSeq-RNA和DNA微阵列测量的转录本水平获得了良好的相关性(r = 0.78)。由于灵敏度有限,很大一部分低丰度转录本无法通过微阵列检测到。对相同样本进行的标准定量RNA测序证实了低丰度基因的表达,总体相关系数为r = 0.87。基于数字AmpliSeq-RNA成像,我们展示了干细胞分化为心肌细胞过程中四个时间点信号级联的切换。
适用于高通量半导体测序的AmpliSeq-RNA技术允许基于扩增子频率进行可靠的转录本定量。至少900个平行PCR反应的多重分析是可行的,因为基于测序的定量消除了来自非靶向扩增的假象。使用这种方法,可以在同一实验中进行RNA定量和遗传变异检测。