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Biology subjects

Atay, O.

Publications and source records attributed to Atay, O..

2 recordsLinked to original sources

A Highly Scalable and Rapidly Deployable RNA Extraction-Free COVID-19 Assay by Quantitative Sanger Sequencing

There is currently an urgent unmet need to increase coronavirus disease 2019 (COVID-19) testing capability to effectively respond to the COVID-19 pandemic. However, the current shortage in RNA extraction reagents as well as limitations in qPCR protocols have resulted in bottlenecks in testing capacity. Herein, we describe a novel molecular diagnostic for COVID-19 based on Sanger sequencing. This assay uses the addition of a frame-shifted spike-in, a modified PCR master mix, and custom Sanger sequencing data analysis to detect and quantify SARS-CoV-2 RNA at a limit of detection comparable to existing qPCR-based assays, at 10-20 genome copy equivalents. Crucially, our assay was able to detect SARS-CoV-2 RNA from viral particles suspended in transport media that was directly added to the PCR master mix, suggesting that RNA extraction can be skipped entirely without any degradation of test performance. Since Sanger sequencing instruments are widespread in clinical laboratories and commonly have built-in liquid handling automation to support up to 3840 samples per instrument per day, the widespread adoption of qSanger COVID-19 diagnostics can unlock more than 1,000,000 tests per day in the US.

microbiology↗

A novel high-throughput molecular counting method with single base-pair resolution enables accurate single-gene NIPT

Next-generation DNA sequencing is currently limited by an inability to count the number of input DNA molecules. Molecular counting is particularly needed when accurate quantification is required for diagnostic purposes, such as in single-gene non-invasive prenatal testing (sgNIPT) and liquid biopsy. We developed Quantitative Counting Template (QCT) molecular counting for reconstructing the number of input DNA molecules using sequencing data. We then used QCT molecular counting to develop sgNIPT of sickle cell disease, cystic fibrosis, spinal muscular atrophy, alpha-thalassemia, and beta-thalassemia. Incorporating molecular count information into a statistical model of disease likelihood led to analytical sensitivity and specificity of >98% and >99%, respectively. Validation of sgNIPT was further performed with maternal blood samples collected during pregnancy, and sgNIPT was 100% concordant with newborn follow-up.

genetics↗