Kang, A. S. W. | bioRxiv Science

bioRxiv2020

Ready-to-use nanopore platform for the detection of any DNA/RNA oligo at attomole range using an Osmium tagged complementary probe.

Nanopores can serve as single molecule sensors. We exploited the MinION, a portable nanopore device from Oxford Nanopore Technologies (ONT), and repurposed it to detect any DNA/RNA oligo (target) in a complex mixture by conducting voltage-driven ion-channel measurements. The detection and quantitation of the target is enabled by the use of a unique complementary probe. Using a validated labeling technology, probes are tagged with a bulky Osmium tag (Osmium tetroxide 2,2-bipyridine), in a way that preserves strong hybridization between probe and target. Intact oligos traverse the MinIONs nanopore relatively quickly compared to the devices acquisition rate, and exhibit count of events comparable to the baseline. Counts are reported by a publicly available software, OsBp_detect. Due to the presence of the bulky Osmium tag, probes traverse more slowly, produce multiple counts over the baseline, and are even detected at single digit attomole (amole) range. In the presence of the target the probe is "silenced". Silencing is attributed to a 1:1 double stranded (ds) complex that doesnt fit and cant traverse this nanopore. This ready-to-use platform can be tailored as a diagnostic test to meet the requirements for point-of-care cell-free tumor DNA (ctDNA) and microRNA (miRNA) detection and quantitation in body fluids.

biophysics↗

bioRxiv2020

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Single-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.

bioinformatics↗