Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
Project description:The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser.
Project description:The vast number of noncoding RNAs in bacteria suggests that major post-transcriptional circuits beyond those controlled by the global RNA-binding proteins Hfq and CsrA may exist. To identify additional globally acting RNPs we have developed a method (gradient profiling by sequencing; Grad-seq) to partition the full ensemble of cellular RNAs based on their biochemical behavior. Consequently, we discovered transcripts that commonly interact with the osmoregulatory protein ProQ in Salmonella enterica. We show that ProQ is a conserved abundant RNA-binding protein with a wide range of targets, including a new class of ProQ-associated small RNAs that are highly structured and function to regulate mRNAs in trans. Based on its ability to chart the functional landscape of all cellular transcripts irrespective of their length and sequence diversity, Grad-seq promises to aid the discovery of major functional RNA classes and RNA-binding proteins in many organisms.
Project description:All known silencing small (s)RNAs operate via ARGONAUTE(AGO)-family proteins within RNA-induced-silencing-complexes (RISCs). Based on AGOs conserved biochemical properties, we have developed a universal, 15-min benchtop extraction procedure allowing simultaneous purification of all classes of RISC-associated sRNAs known to date, without prior knowledge of the samples-intrinsic AGO repertoires. Optimized into a user-friendly kit, the method –coined “TraPR” for Trans-kingdom, rapid, affordable Purification of RISCs– operates irrespectively of the organism, tissue, cell type or bio-fluid of interest, including from minute amounts of input material. The method is highly suited for direct sRNA deep-sequencing, with TrAPR-generated libraries being qualitatively and quantitatively at least on-par with those obtained via gold-standard procedures involving tedious polyacrylamide gel excisions. TraPR considerably improves the quality and consistency of sRNA sample preparation including from notoriously difficult-to-handle tissues/bio-fluids such as starchy storage roots and mammalian plasma, and regardless of RNA contaminants or samples’ RNA-degradation status.
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
Project description:RNA exhibits a large diversity of conformations. Three thousand nucleotides of 23S and 5S ribosomal RNA from a structure of the large ribosomal subunit were analyzed in order to classify their conformations. Fourier averaging of the six 3D distributions of torsion angles and analyses of the resulting pseudo electron maps, followed by clustering of the preferred combinations of torsion angles were performed on this dataset. Eighteen non-A-type conformations and 14 A-RNA related conformations were discovered and their torsion angles were determined; their Cartesian coordinates are available.