npj Genomic MedicineGenomicsPeer-reviewed journal article
TAC-seq: targeted DNA and RNA sequencing for precise biomarker molecule counting
Hindrek TederiD, Mariann KoeliD, Priit PaluojaiD, Tatjana JatsenkoiD, Kadri RekkeriD, Triin LaiskiD, Viktorija Kukuškina, Agne Velthut‐MeikasiD, Olga FjodorovaiD, Maire PetersiD, Juha KereiD, Andres SalumetsiD, Priit PaltaiD, Kaarel KrjutškoviD
Abstract
Targeted next-generation sequencing based biomarker detection methods have become essential for biomedical diagnostics. In addition to their sensitivity and high-throughput capacity, absolute molecule counting based on unique molecular identifier (UMI) has high potential to increase biomarker detection accuracy even further through the reduction of systematic technical biases. Here, we present TAC-seq, a simple and cost-effective targeted allele counting by sequencing method that uses UMIs to estimate the original molecule counts of different biomarker types like mRNAs, microRNAs and cell-free DNA. We applied TAC-seq in three different applications and compared the results with standard sequencing technologies. RNA samples extracted from human endometrial biopsies were analyzed using previously described 57 mRNA-based receptivity biomarkers and 49 selected microRNAs at different expression levels. Cell-free DNA aneuploidy testing was based on cell line (47,XX,+21) genomic DNA. TAC-seq mRNA biomarker profiling showed identical clustering results to full transcriptome RNA sequencing, and microRNA detection demonstrated significant reduction in amplification bias, allowing to determine minor expression changes between different samples that remained undetermined by standard sequencing. The mimicking experiment for cell-free DNA fetal aneuploidy analysis showed that TAC-seq can be applied to count highly fragmented DNA, detecting significant (p=4.8x10-11) excess of molecules in case of trisomy 21. Based on three proof-of-principle applications we show that TAC-seq is a highly accurate and universal method for targeted nucleic acid biomarker profiling.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 35
- FWCI
- 1.16
- Field percentile
- top 23%
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