A viral toolkit for recording transcription factor-DNA interactions in live mouse tissues
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE128493 | GEO | 2020/03/30
REPOSITORIES: GEO
Similar Datasets
Project description:Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions are recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high performance computing environment and to conduct downstream analyses.
2023-02-01 | GSE223926 | GEO
Project description:Calling cards technology using self-reporting transposons enables the identification of DNA-protein interactions through RNA sequencing . Here, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells by introducing a DNA barcode into the calling card itself. An additional barcode incorporated during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recover in vitro binding sites for four basic helix-loop-helix transcription factors with important roles in cell fate specification: ASCL1, MYOD1, NEUROD2, and NGN1. Further, simultaneous calling cards and transcriptional profiling during transcription factor overexpression identified both binding sites and gene expression changes for two of these factors. In sum, RNA-based identification of transcription factor binding sites and gene expression through barcoded self-reporting transposon calling cards and transcriptomes is an efficient and powerful method to infer gene regulatory networks in a population of cells.
2022-02-04 | GSE195992 | GEO
Project description:A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.
2020-02-04 | GSE144657 | GEO
Project description:In situ assays of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we use scCC to map the binding of BRD4 and three additional TFs in cultured cells. We then apply this technique to discover bromodomain-dependent cell state dynamics in K562 cells. Finally, we map BRD4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.
2020-07-23 | GSE148448 | GEO
Project description:The basic body plan and major physiological axes have been highly conserved during mammalian evolution, yet only a small fraction of the human genome sequence appears to be subject to evolutionary constraint. To quantify cis- versus trans-acting contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution. Here we show that mouse TF footprints conjointly encode a regulatory lexicon that is ~95% similar with that derived from human TF footprints. However, only ~20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Furthermore, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results indicate that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry, enabling and potentiating cis-regulatory plasticity. We performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution.
2014-11-19 | E-GEOD-51341 | biostudies-arrayexpress
Project description:We measured transcription factor binding and expression of designed syththetic promoter libraries using Calling Cards Reporter Arrays (CCRAs). In this study, we showed that CCRAs is able to make quantatitive measurements for many TFs in yeast. We then demonstrate the quantitative analysis of cooperative interactions by measuring Cbf1p binding at synthetic promoters with multiple sites. Finally, we characterize the binding and expression of a group of TFs, Tye7p, Gcr1p, and Gcr2p, that act together as a “TF collective”, an important but poorly characterized model of TF cooperativity. We demonstrate that Tye7p often binds promoters without its recognition site because it is recruited by other collective members, whereas these other members require their recognition sites, suggesting a hierarchy where these factors recruit Tye7p but not vice versa. Our experiments establish CCRA as a useful tool for quantitative investigations into TF binding and function.
2020-01-30 | GSE144437 | GEO
Project description:"Calling Cards" for DNA-binding proteins in mammalian cells
| PRJNA151051 | ENA
Project description:Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors'(TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding event at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure transcription factor binding and mRNA expression from single cells CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these unique advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. Here, we introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. The package introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF function via CC data.
2024-01-01 | GSE248420 | GEO
Project description:In this study, single-cell sequencing of the BALF of CARDS patients yielded the following results: (1) IL1B+ neutrophils were significantly increased in the BALF of CARDS patients with exacerbated disease, which was associated with poor prognosis of CARDS patients, and this type of neutrophils underwent fatty acid metabolism reprogramming in comparison with other types of neutrophils, which may be related to their high-inflammatory response. (2) IL1B+ neutrophils promoted TNF secretion from hyperinflammatory type M1 macrophages through a caspase-dependent pathway, suggesting that neutrophil-macrophage interactions play an important role in the mechanism of CARDS development.
2024-05-15 | GSE240580 | GEO
Project description:Pseudohyphal growth is a developmental pathway seen in some strains of yeast, in which cells elongate and form multicellular filaments in response to environmental stresses. Regulation of this process is complex and involves multiple signaling pathways and a large number of transcription factors (TFs). We used multiplexed transposon M-bM-^@M-^\Calling CardsM-bM-^@M-^] to simultaneously record the genome-wide binding patterns of 28 TFs in nitrogen-starved yeast. We were able to identify TF targets relevant for pseudohyphal growth, producing a detailed map of the regulatory network that governs this process. Using tools from graph theory, we identified 8 transcription factors that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA binding preferences for these key transcription factors were not known. Using Calling Card data, we predicted the in vivo DNA binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA-looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them to more rapidly execute the filamentous growth program when nitrogen-starved if they have been previously exposed to this condition. These data contain mapped insertion sites from Ty5 transposon "Calling Cards" in M-NM-#1278b yeast sequenced using an Illumina Hiseq. Other sequencing data present are RNA-seq reads.
2014-02-11 | E-GEOD-54831 | biostudies-arrayexpress