The advent of facile genome engineering using the bacterial RNA-guided CRISPR-Cas9 system in animals and plants is transforming biology. We review the history of CRISPR (clustered regularly interspaced palindromic repeat) biology from its initial discovery through the elucidation of the CRISPR-Cas9 enzyme mechanism, which has set the stage for remarkable developments using this technology to modify, regulate, or mark genomic loci in a wide variety of cells and organisms from all three domains of life. These results highlight a new era in which genomic manipulation is no longer a bottleneck to experiments, paving the way toward fundamental discoveries in biology, with applications in all branches of biotechnology, as well as strategies for human therapeutics.

The new frontier of genome engineering with CRISPR-Cas9

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr
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/pdf/enrichr-interactive-and-collaborative-html5-gene-list-1dn7g68rn1.pdf

Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool

Macrophage Diversity Enhances Tumor Progression and Metastasis

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

/pdf/spatial-reconstruction-of-single-cell-gene-expression-data-5583gr0ht4.pdf

Spatial reconstruction of single-cell gene expression data

Stimulated cells and cancer cells have widespread shortening of mRNA 3'-untranslated regions (3'UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in introns and last exons. U1 snRNP (U1), vertebrates' most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread premature transcription termination and mRNA shortening. Here we show that low U1 AMO doses increase cancer cells' migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3'UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.

/pdf/u1-snrnp-regulates-cancer-cell-migration-and-invasion-in-4wdiaemkkl.pdf

U1 snRNP regulates cancer cell migration and invasion in vitro

Abstract RNA molecules can form secondary and tertiary structures that can regulate their localization and function. Using enzymatic or chemical probing together with high-throughput sequencing, secondary structure can be mapped across the entire transcriptome. However, a limiting factor is that only population averages can be obtained since each read is an independent measurement. Although long-read sequencing has recently been used to determine RNA structure, these methods still used aggregate signals across the strands to detect structure. Averaging across the population also means that only limited information about structural heterogeneity across molecules or dependencies within each molecule can be obtained. Here, we present Single-Molecule Structure sequencing (SMS-seq) that combines structural probing with native RNA sequencing to provide non-amplified, structural profiles of individual molecules with novel analysis methods. Our new approach using mutual information enabled single molecule structural interrogation. Each RNA is probed at numerous bases enabling the discovery of dependencies and heterogeneity of structural features. We also show that SMS-seq can capture tertiary interactions, dynamics of riboswitch ligand binding, and mRNA structural features.

/pdf/long-read-single-molecule-rna-structure-sequencing-using-3qmo1nj8.pdf

Long-read single-molecule RNA structure sequencing using nanopore

Advances in genomics and molecular biology have revealed an abundance of small open reading frames (sORFs) across all types of transcripts. While these sORFs are often assumed to be non-functional, many have been implicated in physiological functions and a significant number of sORFs have been described in human diseases. Thus, sORFs may represent a hidden repository of functional elements that could serve as therapeutic targets. Unlike protein-coding genes, it is not necessarily the encoded peptide of an sORF that enacts its function, sometimes simply the act of translating an sORF might have a regulatory role. Indeed, the most studied sORFs are located in the 5′UTRs of coding transcripts and can have a regulatory impact on the translation of the downstream protein-coding sequence. However, sORFs have also been abundantly identified in non-coding RNAs including lncRNAs, circular RNAs and ribosomal RNAs suggesting that sORFs may be diverse in function. Of the many different experimental methods used to discover sORFs, the most commonly used are ribosome profiling and mass spectrometry. These can confirm interactions between transcripts and ribosomes and the production of a peptide, respectively. Extensions to ribosome profiling, which also capture scanning ribosomes, have further made it possible to see how sORFs impact the translation initiation of mRNAs. While high-throughput techniques have made the identification of sORFs less difficult, defining their function, if any, is typically more challenging. Together, the abundance and potential function of many of these sORFs argues for the necessity of including sORFs in gene annotations and systematically characterizing these to understand their potential functional roles. In this review, we will focus on the high-throughput methods used in the detection and characterization of sORFs and discuss techniques for validation and functional characterization.

/pdf/small-open-reading-frames-how-to-find-them-and-determine-qvic1kuw.pdf

Small Open Reading Frames, How to Find Them and Determine Their Function

The poly(A) tail is a homopolymeric stretch of adenosine at the 3'-end of mature RNA transcripts and its length plays an important role in nuclear export, stability, and translational regulation of mRNA. Existing techniques for genome-wide estimation of poly(A) tail length are based on short-read sequencing. These methods are limited because they sequence a synthetic DNA copy of mRNA instead of the native transcripts. Furthermore, they can identify only a short segment of the transcript proximal to the poly(A) tail which makes it difficult to assign the measured poly(A) length uniquely to a single transcript isoform. With the introduction of native RNA sequencing by Oxford Nanopore Technologies, it is now possible to sequence full-length native RNA. A single long read contains both the transcript and the associated poly(A) tail, thereby making transcriptome-wide isoform-specific poly(A) tail length assessment feasible. We developed tailfindr-an R-based package for estimating poly(A) tail length from Oxford Nanopore sequencing data. In this chapter, we describe in detail the pipeline for transcript isoform-specific poly(A) tail profiling based on native RNA Nanopore sequencing-from library preparation to downstream data analysis with tailfindr.

Transcript Isoform-Specific Estimation of Poly(A) Tail Length by Nanopore Sequencing of Native RNA.

The rate of translation can vary depending on the mRNA template. During the elongation phase the ribosome can transiently pause or permanently stall. A pause can provide the nascent protein with the time to fold or be transported, while stalling can serve as quality control and trigger degradation of aberrant mRNA and peptide. Ribosome profiling has allowed for the genome-wide detection of such pauses and stalls, but due to library-specific biases, these predictions are often unreliable. Here, we take advantage of the deep conservation of protein synthesis machinery, hypothesizing that similar conservation could exist for functionally important locations of ribosome slowdown, here collectively called stall sites. We analyze multiple ribosome profiling datasets from phylogenetically diverse eukaryotes: yeast, fruit fly, zebrafish, mouse and human to identify conserved stall sites. We find thousands of stall sites across multiple species, with the enrichment of proline, glycine and negatively charged amino acids around conserved stalling. Many of the sites are found in RNA processing genes, suggesting that stalling might have a conserved role in RNA metabolism. In summary, our results provide a rich resource for the study of conserved stalling and indicate possible roles of stalling in gene regulation.

/pdf/deep-conservation-of-ribosome-stall-sites-across-rna-4houkglfvs.pdf

Deep conservation of ribosome stall sites across RNA processing genes.


• Background With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. • Results Here, we introduce ORFik, a user-friendly R/Bioconductor toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5’UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames. As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5’ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. • Availability http://bioconductor.org/packages/ORFik

https://biorxiv.org/content/10.1101/2021.01.16.426936v1.full.pdf

Eivind Valen

Papers

Long-read single-molecule RNA structure sequencing using nanopore

Small Open Reading Frames, How to Find Them and Determine Their Function

Transcript Isoform-Specific Estimation of Poly(A) Tail Length by Nanopore Sequencing of Native RNA.

Deep conservation of ribosome stall sites across RNA processing genes.

ORFik: a comprehensive R toolkit for the analysis of translation