NEW YORK (GenomeWeb) – Targeted whole-transcriptome sequencing can be a sensitive and cost-effective approach for large-scale gene expression analysis and mRNA marker screening, according to a new study by researchers at the Medical College of Wisconsin.
In their study, published online in BMC Genomics last month, the researchers compared Thermo Fisher Scientific's Ion AmpliSeq Transcriptome Human Gene Expression Kit, a targeted RNA sequencing method, with RNA-seq on Illumina and Ion Torrent platforms, and found that both approaches performed well for gene expression analysis.
The AmpliSeq method, they concluded, "clearly stands as a highly robust approach for large-scale, genome-wide differential gene expression analysis" and is "a very sensitive and competent approach for very large-scale mRNA marker screening in cellular models."
According to the authors, led by Ulrich Broeckel in the department of pediatrics at the Medical College of Wisconsin, RNA-seq has several advantages over microarrays and RT-qPCR for gene expression analysis, such as its wide coverage of transcripts, high sensitivity, ability to detect allele-specific differential expression, and ability to identify novel transcripts.
"However, in certain situations, RNA-seq may not be a practical choice," they wrote, for example when only small amounts of RNA are available; when the main goal of a study is to assess gene expression changes, for which single-base resolution is not needed; for large studies where RNA-seq would create data storage and data analysis challenges; or for diagnostic applications that require short turnaround times. "In these and other situations, a targeted, quantitative RNA-sequencing method with high accuracy and reproducibility can offer a better approach," they said.
For their study, they focused on the AmpliSeq Transcriptome Human Gene Expression Kit, which targets and amplifies more than 20,000 human RNAs in parallel, generating short amplicons of about 150 bases for each, and which requires just 10 nanograms of total RNA. This strategy, they wrote, leads to shorter turnaround times and needs fewer sequence reads than RNA-seq, which sequences entire transcripts.
To assess the performance of the AmpliSeq kit for analyzing gene expression differences, they initially applied it to two commonly used commercially available reference RNA samples — the Agilent Universal Human Reference RNA, and the Ambion FirstChoice Human Brain Reference Total RNA — which have both been used in the past to assess other platforms.
They sequenced the AmpliSeq libraries on the Ion Torrent Proton platform and compared the results to RNA-seq data generated previously by other groups for the same samples, both on the Illumina HiSeq and on the Ion Proton. Overall, AmpliSeq identified between 65 percent and 72 percent of the genes found by RNA-seq. All approaches, they found, provided similar differential gene expression results, which correlated well with RT-qPCR, and AmpliSeq showed better performance for more abundant transcripts. Also, because AmpliSeq only considers reads matching defined target regions, it could avoid issues resulting from non-specific mapping, they noted.
Overall, AmpliSeq is "a very versatile and cost-effective approach for large-scale gene expression analysis with high accuracy," they wrote, making it "a highly attractive method for very large-scale studies including replication analysis or other studies that require a very large number of samples to increase statistical power."
To better mimic typical gene expression studies, the researchers also tested the AmpliSeq method on four samples from two patient-derived induced pluripotent stem cell-derived cardiomyocyte lines, which differ both genetically and phenotypically, and compared the results to Ion Proton RNA-seq data they generated. Both platforms correlated well and were able to identify global expression patterns that are based on known differences between the cell lines.
"Our study strongly suggests that AmpliSeq is a highly reliable tool for gene expression quantification as demonstrated by the analysis of both reference libraries and real life samples such as iPSC disease models," the authors wrote.
Broeckel told GenomeWeb that his team currently uses the AmpliSeq approach as a standard method for expression analysis when it develops new protocols or conducts experiments that do not require the additional content regular RNA-seq could provide right away. For example, he said, the researchers use AmpliSeq on a large number of samples first and then follow up with RNA-seq on the most interesting ones. They also plan to use AmpliSeq as a readout for screening projects, he added.