Similar Datasets
Project description:Differential gene expression across cell types underlies development and cell physiology in multicellular organisms. Caenorhabditis elegans is a powerful, extensively used model to address these biological questions. A remaining bottleneck relates to the difficulty to obtain comprehensive tissue-specific gene transcription data, since available methods are still challenging to execute and/or require large worm populations. Here, we introduce the RNA Polymerase DamID (RAPID) approach, in which the Dam methyltransferase is fused to a ubiquitous RNA polymerase subunit to create transcriptional footprints via methyl marks on the DNA of transcribed genes. To validate the method, we determined the polymerase footprints in whole animals, in sorted embryonic blastomeres and in different tissues from intact young adults by driving tissue-specific Dam fusion expression. We obtained meaningful transcriptional footprints in line with RNA-sequencing (RNA-seq) studies in whole animals or specific tissues. To challenge the sensitivity of RAPID and demonstrate its utility to determine novel tissue-specific transcriptional profiles, we determined the transcriptional footprints of the pair of XXX neuroendocrine cells, representing 0.2% of the somatic cell content of the animals. We identified 3901 candidate genes with putatively active transcription in XXX cells, including the few previously known markers for these cells. Using transcriptional reporters for a subset of new hits, we confirmed that the majority of them were expressed in XXX cells and identified novel XXX-specific markers. Taken together, our work establishes RAPID as a valid method for the determination of RNA polymerase footprints in specific tissues of C. elegans without the need for cell sorting or RNA tagging.
| S-EPMC7768263 | biostudies-literature
Project description:Gene expression is a major determinant of cell fate and physiology, yet it is notoriously difficult to characterize for the widely used model system C. elegans. By using a new method based on in vivo covalent modification of DNA by RNA polymerases, we determine genome-wide transcription patterns in single tissues of embryos or young adult animals. We show that the method is able to identify actively transcribed genes in tissues representing down to 0.2% of the somatic cells in adult animals. Additionally, this method can be fully performed in a single laboratory by using third generation sequencing methods (ONT).
2020-10-28 | GSE157415 | GEO
Project description:Gene expression is a major determinant of cell fate and physiology, yet it is notoriously difficult to characterize for the widely used model system C. elegans. By using a new method based on in vivo covalent modification of DNA by RNA polymerases, we determine genome-wide transcription patterns in single tissues of embryos or young adult animals. We show that the method is able to identify actively transcribed genes in tissues representing down to 0.2% of the somatic cells in adult animals. Additionally, this method can be fully performed in a single laboratory by using third generation sequencing methods (ONT).
2020-10-28 | GSE157416 | GEO
Project description:Tissue-specific transcription footprinting using RNA PolII DamID (RAPID) in C. elegans
| PRJNA661258 | ENA
Project description:Tissue-specific transcription footprinting using RNA PolII DamID (RAPID) in C. elegans [Blastomeres]
| PRJNA661263 | ENA
Project description:Tissue-specific transcription footprinting using RNA PolII DamID (RAPID) in C. elegans [Young adult]
| PRJNA661264 | ENA
Project description:DNA adenine methylation identification (DamID) is a powerful method to determine DNA binding profiles of proteins at a genomic scale. The method leverages the fusion between a protein of interest and the Dam methyltransferase of E. coli, which methylates proximal DNA in vivo. Here, we present an optimized procedure, which was developed for tissue-specific analyses in Caenorhabditis elegans and successfully used to footprint genes actively transcribed by RNA polymerases and to map transcription factor binding in gene regulatory regions. The present protocol details C. elegans-specific steps involved in the preparation of transgenic lines and genomic DNA samples, as well as broadly applicable steps for the DamID procedure, including the isolation of methylated DNA fragments, the preparation of multiplexed libraries, Nanopore sequencing, and data analysis. Two distinctive features of the approach are (i) the use of an efficient recombination-based strategy to selectively analyze rare cell types and (ii) the use of Nanopore sequencing, which streamlines the process. The method allows researchers to go from genomic DNA samples to sequencing results in less than a week, while being sensitive enough to report reliable DNA footprints in cell types as rare as 2 cells per animal.
| S-EPMC8411031 | biostudies-literature
Project description:The epidermis of Caenorhabditis elegans is an essential tissue for survival because it contributes to the formation of the cuticle barrier as well as facilitating developmental progression and animal growth. Most of the epidermis consists of the hyp7 hypodermal syncytium, the nuclei of which are largely generated by the seam cells, which exhibit stem cell-like behaviour during development. How seam cell progenitors differ transcriptionally from the differentiated hypodermis is poorly understood. Here, we introduce Targeted DamID (TaDa) in C. elegans as a method for identifying genes expressed within a tissue of interest without cell isolation. We show that TaDa signal enrichment profiles can be used to identify genes transcribed in the epidermis and use this method to resolve differences in gene expression between the seam cells and the hypodermis. Finally, we predict and functionally validate new transcription and chromatin factors acting in seam cell development. These findings provide insights into cell type-specific gene expression profiles likely associated with epidermal cell fate patterning.
| S-EPMC7613258 | biostudies-literature
Project description:The formation of long-term memories requires changes in the transcriptional program and de novo protein synthesis. One of the critical regulators for long-term memory (LTM) formation and maintenance is the transcription factor CREB. Genetic studies have dissected the requirement of CREB activity within memory circuits, however less is known about the genetic mechanisms acting downstream of CREB and how they may contribute defining LTM phases. To better understand the downstream mechanisms, we here used a targeted DamID approach (TaDa). We generated a CREB-Dam fusion protein using the fruit fly Drosophila melanogaster as model. Expressing CREB-Dam in the mushroom bodies (MBs), a brain center implicated in olfactory memory formation, we identified genes that are differentially expressed between paired and unpaired appetitive training paradigm. Of those genes we selected candidates for an RNAi screen in which we identified genes causing increased or decreased LTM.
| S-EPMC10289670 | biostudies-literature
Project description:Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors (TFs) can bind. Thus, identification of TF binding sites (TFBSs) is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches used for TFBS prediction, such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq), are widely used but have their drawbacks, including high false-positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns; however, these also have limitations. We have developed a footprinting method to predict TF footprints in active chromatin elements (TRACE) to improve the prediction of TFBS footprints. TRACE incorporates DNase-seq data and PWMs within a multivariate hidden Markov model (HMM) to detect footprint-like regions with matching motifs. TRACE is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement for pregenerated candidate binding sites or ChIP-seq training data. Compared with published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.
| S-EPMC7397869 | biostudies-literature