title: Highly sensitive amplicon-based transcript quantification by semiconductor sequencing.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.forename: Jitao David
authors.initials: JD
authors.lastname: Zhang
authors.forename: Tobias
authors.initials: T
authors.lastname: Schindler
authors.forename: Erich
authors.initials: E
authors.lastname: Küng
authors.forename: Martin
authors.initials: M
authors.lastname: Ebeling
authors.affiliation: Roche Pharmaceutical Research and Early Development, Department of Pharmaceutical Sciences/ Translational Technologies and Bioinformatics, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland. ulrich.certa@roche.com.
authors.forename: Ulrich
authors.initials: U
authors.lastname: Certa
mesh_terms: D020451:Contig Mapping; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics* / Q000378:metabolism; D060888:Real-Time Polymerase Chain Reaction; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA; D059467:Transcriptome
chemical_list: D012333:RNA, Messenger