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Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond., NAR Genomics and Bioinformatics, 3 (4), ISSN:2631-9268

Reading amino acids by nanopore Nanopore technology enables sensing of minute chemical changes at the single-molecule level by detecting differences in an ion current as molecules are drawn through a membrane-embedded pore. The sensitivity is sufficient to discriminate between nucleotide bases in nanopore sequencing, and other applications of this technology are promising. Brinkerhoff et al . developed a nanopore-based, single-molecule approach in which a protein was sequentially scanned in single-amino-acid steps through the narrow construction of a nanopore, and ion currents were monitored to resolve differences in the amino acid sequence along the peptide backbone (see the Perspective by Bošković and Keyser). The peptide reader was capable of reliably detecting single-amino-acid substitutions within individual peptides. An individual protein could be re-read many times, yielding very high read accuracy in variant identification. These proof-of-concept nanopore experiments constitute a promising basis for the development of a single-molecule protein sequencer. —DJ

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 MinION’s nanopore relatively quickly compared to the device’s 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 doesn’t fit and can’t 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.

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.