5. NOVEL APPROACH ENABLING THE SIMULTANEOUS DETECTION OF SNV AND CNV FOR PGT-M AND PGT-A USING A SINGLE-TUBE ASSAY

Abstract

Introduction Cystic fibrosis is caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, CFTR. An individual must inherit two defective cystic fibrosis alleles, one from each parent, to have the disease. Worldwide, over 70,000 individuals are affected by this monogenic disease. Preimplantation genetic testing (PGT) aims at selecting healthy embryos by testing for chromosome abnormalities and monogenic diseases. Often a limited number of cells or genetic material from the embryo is available to perform the detection of both aneuploidies and monogenic diseases with accuracy. To overcome this challenge, we developed a novel single tube assay enabling the simultaneous detection of both copy number variations (CNV) across the whole genome and single nucleotide variants (SNV) or small insertion/deletion (indels) located in the CFTR gene target region. Material and Methods The assay was performed using a modified SMARTer® PicoPLEX® Gold Single Cell DNA-Seq kit (Takara Bio USA, R300669). More specifically, the assay included simultaneous whole genome amplification (WGA) and targeted enrichment of selected regions of the CFTR gene. All 23 mutations of CFTR recommended to be tested by the American College of Medical Genetics (ACMG) were covered. As proof of principle, we prepared libraries from either single cells or five cells using model cell lines purchased from the Coriell Institute that contain different known CFTR variants, such as delF508, or aneuploidies of different sizes (2.5MB to 25 MB). In addition, libraries were made from trophectoderm biopsies isolated in the clinic from human blastocysts containing known CFTR mutations. The resulting libraries were sequenced on a MiSEQ to achieve 1M reads per sample (2 × 75 cycles). Results The percentage of sequencing reads allocated to both applications (CNV and SNV detections) was optimized to maximize the performance of the assay. To detect copy number variations, a shallow and even coverage of the genome is sufficient (∼0.025x). However, to detect variants, SNPs or small indels, the targeted regions of interest required a robust and deeper coverage (500x). At a sequencing depth of 1M reads, the coverage of the genome was ensured by 95% of the reads and the targeted regions of CFTR were covered by ∼5% of the reads. When using the five-cell model system, the 2.5 and 25MB chromosomal losses and the characterized heterozygous variants of CFTR were detected virtually 100% of the time (n of 18). When using single cells, we consistently detected the 25MB deletion (n of 18). Also, the expected heterozygous CFTR variants were detected over 80% of the time. Conclusion The sequencing reads allocated to both applications was optimized to maximize the performance of both CNV and SNV detection. Over 20 samples were successfully processed on a single MiSeq run. From 5 sorted cells our new approach provided representative and uniform coverage of the genome, suitable for detection of copy number variations (CNV). From the same assay, mutations contained in the CFTR gene were also accurately reported. Studies with TE biopsies with known CFTR mutations are now in progress and will be reported at the meeting.