Project description:Super-Low Input Carrier Cap Analysis of Gene Expression (SLIC-CAGE) to identify transcription start sites (TSS) and existing genome-wide maps of islet histone marks to characterise the contribution of transcriptional regulation of LKB1-mediated control of gene expression in mouse β-cells. SLIC-CAGE was performed as in (Cvetesic et al. - doi: 10.1101/gr.235937.118) using 100 ng of total RNA extracted as described above from islets isolated from a 18 week old female mouse on a C57BL6/J background. 12 cycles were performed for library amplification. The amplified library was purified with AMPure XP beads and visualised using a HS DNA chip (Bioanalyzer, Agilent). The library was sequenced on a HiSeq2500 instrument with paired-end, 150bp reads). SLIC-CAGE paired-end sequencing data was aligned to GENCODE assembly annotation version GRCm38.p6 using the STAR alignment tool v2.4.2a.
Project description:We developed SLIC-CAGE (Super-Low Input Carrier-CAGE) approach to capture 5'end of RNA polymerase II transcripts from as little as 5-10 ng of total RNA. The dramatic increase in sensitivity compared to existing CAGE methods is achieved by specially designed, selectively degradable carrier RNA. We tested SLIC-CAGE on Saccharomyces cerevisiae (BY4741 strain) and produced libraries from 1-100 ng of total cellular RNA. We also produced S. cerevisiae nAnT-iCAGE libraries as the current gold-standard CAGE libraries using the recommended 5 micrograms of total cellular RNA to assess the quality of SLIC-CAGE libraries produces with up to 1000-fold less material. We provide a direct comparison between SLIC-CAGE and the latest nanoCAGE protocol (libraries created using S. cerevisiae total RNA) and show that SLIC-CAGE produces unbiased libraries of higher complexity and quality than nanoCAGE. Finally, we provide SLIC-CAGE libraries on mouse embryonic stem cells (E14) using 5-100 ng of total cellular RNA as starting material.
Project description:We developed SLIC-CAGE (Super-Low Input Carrier-CAGE) approach to capture 5'end of RNA polymerase II transcripts from as little as 5-10 ng of total RNA. The dramatic increase in sensitivity compared to existing CAGE methods is achieved by specially designed, selectively degradable carrier RNA. We apply SLIC-CAGE on mouse primordial germ cells embryonic day (E) 11.5 - 2 biological replicates.
Project description:We used SLIC-CAGE to map transcriptional start sites in cortical neurons from Cornelia de Lange Syndrome (CdLS) patients and control individuals. SLIC-CAGE was performed using nuclear RNA isolated from pre-frontal cortical grey matter. Usage of nuclear RNA allows enrichment of unstable RNAs, such as RNA originating from enhancer transcription. We characterised promoter-level gene expression in cortical neurons from CdLS patients and found deregulation of hundreds of genes enriched for neuronal functions.
Project description:SLIC-CAGE (Super-Low Input Carrier-CAGE) development: comparison with Saccharomyces cerevisiae nAnTi-CAGE and nanoCAGE libraries and validation of SLIC-CAGE on Mus musculus total RNA
Project description:We used SLIC-CAGE to map transcription start sites (TSSs) of mouse primordial germ cells from embryonic days 9.5-16.5, postnatal oocytes (P6, P14 and MII), and early 2-cell and 4-cell mouse embryos. We use this TSS data to show that the mouse germline development starts with the somatic promoter code with a prominent switch to the maternal code (W-box dependent) occurring during the follicular oogenesis. We also find that the promoters of gonadal germ cells are characterised by a previously unknown divergence from the somatic transcription initiation. This divergence is distinct from the promoter code used later by the developing oocytes and reveals genome-wide promoter remodelling during early female and male germline development.
Project description:Used a DNA tag sequencing and mapping strategy called gene identification signature (GIS) analysis, in which 5' and 3' signatures of full-length cDNAs are accurately extracted into paired-end ditags (PETs) that are concatenated for efficient sequencing and mapped to genome sequences to demarcate the transcription boundaries of every gene. GIS analysis is potentially 30-fold more efficient than standard cDNA sequencing approaches for transcriptome characterization. Keywords: Paired End DiTags