Sequencing cell-type-specific transcriptomes with SLAM-ITseq.

Analysis of cell-type-specific transcriptomes is vital for understanding the biology of tissues and organs in the context of multicellular organisms. In this Protocol Extension, we combine a previously developed cell-type-specific metabolic RNA labeling method (thiouracil (TU) tagging) and a pipeline to detect the labeled transcripts by a novel RNA sequencing (RNA-seq) method, SLAMseq (thiol (SH)-linked alkylation for the metabolic sequencing of RNA). By injecting a uracil analog, 4-thiouracil, into transgenic mice that express cell-type-specific uracil phosphoribosyltransferase (UPRT), an enzyme required for 4-thiouracil incorporation into newly synthesized RNA, only cells expressing UPRT synthesize thiol-containing RNA. Total RNA isolated from a tissue of interest is then sequenced with SLAMseq, which introduces thymine to cytosine (T>C) conversions at the sites of the incorporated 4-thiouracil. The resulting sequencing reads are then mapped with the T>C-aware alignment software, SLAM-DUNK, which allows mapping of reads containing T>C mismatches. The number of T>C conversions per transcript is further analyzed to identify which transcripts are synthesized in the UPRT-expressing cells. Thus, our method, SLAM-ITseq (SLAMseq in tissue), enables cell-specific transcriptomics without laborious FACS-based cell sorting or biochemical isolation of the labeled transcripts used in TU tagging. In the murine tissues we assessed previously, this method identified ~5,000 genes that are expressed in a cell type of interest from the total RNA pool from the tissue. Any laboratory with access to a high-throughput sequencer and high-power computing can adapt this protocol with ease, and the entire pipeline can be completed in <5 d.This work was supported by grants from Cancer Research UK (C13474/A18583, C6946/A14492) and the Wellcome Trust (104640/Z/14/Z, 092096/Z/10/Z) to E.A.M.; and a grant from the European Research Council (ERC-StG-338252 miRLIFE) to S.L.A. The IMP is generously supported by Boehringer Ingelheim. W.M. was supported by the Nakajima Foundation and St John’s College Benefactors’ Scholarship. K.G. was supported by a Swiss National Foundation postdoc mobility fellowship

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Comparison of EJC-enhanced and EJC-independent NMD in human cells reveals two partially redundant degradation pathways

Nonsense-mediated mRNA decay (NMD) is a eukaryotic post-transcriptional gene regulation mechanism that eliminates mRNAs with the termination codon (TC) located in an unfavorable environment for efficient translation termination. The best-studied NMD-targeted mRNAs contain premature termination codons (PTCs); however, NMD regulates even many physiological mRNAs. An exon-junction complex (EJC) located downstream from a TC acts as an NMD-enhancing signal, but is not generally required for NMD. Here, we compared these “EJC-enhanced” and “EJC-independent” modes of NMD with regard to their requirement for seven known NMD factors in human cells using two well-characterized NMD reporter genes (immunoglobulin μ and β-Globin) with or without an intron downstream from the PTC. We show that both NMD modes depend on UPF1 and SMG1, but detected transcript-specific differences with respect to the requirement for UPF2 and UPF3b, consistent with previously reported UPF2- and UPF3-independent branches of NMD. In addition and contrary to expectation, a higher sensitivity of EJC-independent NMD to reduced UPF2 and UPF3b concentrations was observed. Our data further revealed a redundancy of the endo- and exonucleolytic mRNA degradation pathways in both modes of NMD. Moreover, the relative contributions of both decay pathways differed between the reporters, with PTC-containing immunoglobulin μ transcripts being preferentially subjected to SMG6-mediated endonucleolytic cleavage, whereas β-Globin transcripts were predominantly degraded by the SMG5/SMG7-dependent pathway. Overall, the surprising heterogeneity observed with only two NMD reporter pairs suggests the existence of several mechanistically distinct branches of NMD in human cells

SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting.

Cell type-specific transcriptome analysis is an essential tool for understanding biological processes in which diverse types of cells are involved. Although cell isolation methods such as fluorescence-activated cell sorting (FACS) in combination with transcriptome analysis have widely been used so far, their time-consuming and harsh procedures limit their applications. Here, we report a novel in vivo metabolic RNA sequencing method, SLAM-ITseq, which metabolically labels RNA with 4-thiouracil in a specific cell type in vivo followed by detection through an RNA-seq-based method that specifically distinguishes the thiolated uridine by base conversion. This method has successfully identified the cell type-specific transcriptome in three different tissues: endothelial cells in brain, epithelial cells in intestine and adipocytes in white adipose tissue. As this method does not require isolation of cells or RNA prior to the transcriptomic analysis, SLAM-ITseq provides an easy yet accurate snapshot of the transcriptional state in vivo

BMC Bioinformatics / Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets

Background Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches. Results Here, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking to uncouple true SNPs from nucleotide conversions to facilitate a robust and sensitive quantification of nucleotide-conversions. As a first application, we implement this strategy as SLAM-DUNK for the analysis of SLAMseq profiles, in which 4-thiouridine-labeled transcripts are detected based on T>C conversions. SLAM-DUNK provides both raw counts of nucleotide-conversion containing reads as well as a base-content and read coverage normalized approach for estimating the fractions of labeled transcripts as readout. Conclusion Beyond providing a readily accessible tool for analyzing SLAMseq and related time-resolved RNA sequencing methods (TimeLapse-seq, TUC-seq), DUNK establishes a broadly applicable strategy for quantifying nucleotide conversions.(VLID)489159

A strand-specific switch in noncoding transcription switches the function of a Polycomb/Trithorax response element

x, 174 p.; 20 cm