Project description:Ribo-Seq (ribosome profiling) enables global assessment of translation efficiency, yields and fidelity. However, low-abundance transcripts receive little coverage and fall outside of the Ribo-Seq detection limit. We developed a new twist of the Ribo-Seq approach, termed transcript specific Ribo-Seq (tsRibo-Seq) to specifically detect lowly abundant messenger RNAs (mRNAs). This approach yields unprecedented depth in the coverage of individual low-abundance transcripts allowing identification of slow and fast translating regions with nucleotide resolution.
Project description:High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.
Project description:Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Our integrative approach, combining single-cell RNA sequencing with proteomics and regulator enrichment analysis, reveals 32 putative noise regulators. The approach utilizes global translation inhibition (i.e., potential protein regulators), and single-cell RNA sequencing (scRNA-seq) to quantify the changes in noise of all transcripts (i.e., potential mRNA targets). This dataset corresponds to the aforementioned scRNA-seq experiment upon translation inhibition with cycloheximide.
Project description:The ribosome is central to cellular stress responses because it serves as a sensor to activate signaling pathways that determine cell fate. While the activation of these signaling pathways by translation elongation inhibitors has been well characterized, the impact of these inhibitors on mRNA dynamics remains unclear. Here we use TimeLapse sequencing to investigate how translational stress impacts mRNA dynamics in human cells. Our results reveal that a distinct group of transcripts is stabilized in response to the translation elongation inhibitor emetine. These stabilized mRNAs are short-lived at steady state and many of them encode C2H2 zinc finger proteins. The codon compositions of these stabilized transcripts are suboptimal compared to short-lived mRNAs that are not stabilized. Finally, we show that stabilization of these transcripts is independent of the signaling pathways activated by ribosome collisions, as well as of canonical ribosome quality control factors. Our data describe a group of transcripts whose degradation is particularly sensitive to the inhibition of translation elongation.
Project description:Gene-expression noise can influence cell-fate choices across pathology and physiology. However, a crucial question persists: do regulatory proteins or pathways exist that control noise independently of mean expression levels? Our integrative approach, combining single-cell RNA sequencing with proteomics and regulator enrichment analysis, reveals 32 putative noise regulators. The approach utilizes global translation inhibition (i.e., potential protein regulators), and single-cell RNA sequencing (scRNA-seq) to quantify the changes in noise of all transcripts (i.e., potential mRNA targets). This dataset corresponds to a control bulk RNAseq experiment experiment upon translation inhibition with cycloheximide to validate the changes in mean observed with single-cell.
Project description:Quantitative analysis of the sequence determinants of transcription and translation regulation is of special relevance for systems and synthetic biology applications. Here, we developed a novel generic approach for the fast and efficient analysis of these determinants in vivo. ELM-seq (expression level monitoring by DNA methylation) uses Dam coupled to high-throughput sequencing) as a reporter that can be detected by DNA-seq. We used the genome-reduced bacterium Mycoplasma pneumoniae to show that it is a quantitative reporter. We showed that the methylase activity correlates with protein expression, does not affect cell viability, and has a large dynamic range (~10,000-fold). We applied ELM-seq to randomized libraries of promoters or 5’ untranslated regions. We found that transcription is greatly influenced by the bases around the +1 of the transcript and the Pribnow box, and we also identified several epistatic interactions (including the +1 and the “extended Pribnow”). Regarding translation initiation, we confirmed that the Shine-Dalgarno motif is not relevant, but instead, that RNA secondary structure is the main governing factor. With this in hand, we developed a predictor to help tailor gene expression in M. pneumoniae. The simple ELM-seq methodology will allow identifying and optimizing key sequence determinants for promoter strength and translation. The ELM-seq methodology allows both researchers and companies to identify and optimize in an easy and comprehensive manner, key sequence determinants for promoter strength and translation.