Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency [scATAC]

ABSTRACT: Ectopic expression of OCT4, SOX2, KLF4 and MYC (OSKM) transforms differentiated cells into induced pluripotent stem cells. To refine our mechanistic understanding of reprogramming, especially of the early stages, we profiled chromatin accessibility and gene expression at single-cell resolution across a finely sampled time course of human fibroblast reprogramming. Using neural networks that map DNA sequence to ATAC-seq profiles at base-resolution, we annotated cell-state-specific predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of Tn5-bias corrected TF footprints, linked peaks to putative target genes, and elucidated rewiring of TF-to-gene cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution, connect TF stoichiometry and motif syntax to diversification of cell fate trajectories, and provide new perspectives on the dynamics and role of transient regulatory elements in somatic silencing.

ORGANISM(S): Homo sapiens

PROVIDER: GSE242421 | GEO | 2023/09/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency [scRNA]

Project description:Ectopic expression of OCT4, SOX2, KLF4 and MYC (OSKM) transforms differentiated cells into induced pluripotent stem cells. To refine our mechanistic understanding of reprogramming, especially of the early stages, we profiled chromatin accessibility and gene expression at single-cell resolution across a finely sampled time course of human fibroblast reprogramming. Using neural networks that map DNA sequence to ATAC-seq profiles at base-resolution, we annotated cell-state-specific predictive transcription factor (TF) motif syntax in regulatory elements, inferred affinity- and concentration-dependent dynamics of Tn5-bias corrected TF footprints, linked peaks to putative target genes, and elucidated rewiring of TF-to-gene cis-regulatory networks. Our models reveal that early in reprogramming, OSK, at supraphysiological concentrations, rapidly open transient regulatory elements by occupying non-canonical low-affinity binding sites. As OSK concentration falls, the accessibility of these transient elements decays as a function of motif affinity. We find that these OSK-dependent transient elements sequester the somatic TF AP-1. This redistribution is strongly associated with the silencing of fibroblast-specific genes within individual nuclei. Together, our integrated single-cell resource and models reveal insights into the cis-regulatory code of reprogramming at unprecedented resolution, connect TF stoichiometry and motif syntax to diversification of cell fate trajectories, and provide new perspectives on the dynamics and role of transient regulatory elements in somatic silencing.

2023-09-06 | GSE242423 | GEO

Transcription factor stoichiometry, motif affinity and syntax regulate single-cell chromatin dynamics during fibroblast reprogramming to pluripotency [multiome]

2023-09-06 | GSE242419 | GEO

ChIP-nexus data for Oct4, Sox2, Nanog and Klf4 in mouse embryonic stem cells

Project description:The goal of this study was discover the transcription binding synthax for the key differentiation TFs in mouse embryonic stem cells. Genes are regulated through enhancer sequences, in which transcription factor binding motifs and their specific arrangements (syntax) form a cis-regulatory code. To understand the relationship between motif syntax and transcription factor binding, we train a deep learning model that uses DNA sequence to predict base-resolution binding profiles of four pluripotency transcription factors Oct4, Sox2, Nanog, and Klf4. We interpret the model to accurately map hundreds of thousands of motifs in the genome, learn novel motif representations and identify rules by which motifs and syntax influence transcription factor binding. We find that instances of strict motif spacing are largely due to retrotransposons, but that soft motif syntax influences motif interactions at protein and nucleosome range. Most strikingly, Nanog binding is driven by motifs with a strong preference for ~10.5 bp spacings corresponding to helical periodicity. Interpreting deep learning models applied to high-resolution binding data is a powerful and versatile approach to uncover the motifs and syntax of cis-regulatory sequences.

2020-11-21 | GSE137193 | GEO

Enhancers display sequence flexibility constrained by transcription factor motif syntax [Drosophila motif pasting STARR-seq]

Project description:The information about when and where each gene is to be expressed is mainly encoded in the DNA sequence of enhancers, sequence elements that comprise binding sites (motifs) for different transcription factors (TFs). Most of the research on enhancer sequences has been focused on TF motif presence, while the enhancer syntax, i.e. the flexibility of important motif positions and how the sequence context modulates the activity of TF motifs, remain poorly understood. Here, we explore the rules of enhancer syntax by a two-pronged approach in Drosophila melanogaster S2 cells: we (1) replace important motifs by an exhaustive set of all possible 65,536 eight-nucleotide-long random sequences and (2) paste eight important TF motif types into 763 motif positions within 496 enhancers. These complementary strategies reveal that enhancers display constrained sequence flexibility and the context-specific modulation of motif function. Important motifs can be functionally replaced by hundreds of sequences constituting several distinct motif types, but only a fraction of all possible sequences and motif types restore enhancer activity. Moreover, TF motifs contribute with different intrinsic strengths that are strongly modulated by the enhancer sequence context (the flanking sequence, presence and diversity of other motif types, and distance between motifs), such that not all motif types can work in all positions. Constrained sequence flexibility and the context-specific modulation of motif function are also hallmarks of human enhancers and TF motifs, as we demonstrate experimentally. Overall, these two general principles of enhancer sequences are important to understand and predict enhancer function during development, evolution and in disease.

2022-12-15 | GSE211654 | GEO

Enhancers display sequence flexibility constrained by transcription factor motif syntax [Human motif pasting STARR-seq]

2022-12-15 | GSE211657 | GEO

Enhancers display sequence flexibility constrained by transcription factor motif syntax [Drosophila random variant STARR-seq]

2022-12-15 | GSE211655 | GEO

Cooperative binding of Oct4, Sox2, and Klf4 with stage-specific transcription factors orchestrates reprogramming [RNA-seq]

Project description:Oct4, Sox2, Klf4, and cMyc (OSKM) reprogram somatic cells to pluripotency. To gain a mechanistic understanding of their function, we mapped OSKM-binding, stage-specific transcription-factors (TFs), and chromatin-states in discrete reprogramming stages and performed loss- and gain-of-function experiments. We found that early in reprogramming OSK extensively bind somatic-enhancers and initiate their decommissioning by recruiting Hdac1. Concurrently, OSK engage other sites, including specific pluripotency-enhancers, and induce the relocation of somatic TFs to these sites and away from somatic-enhancers, extending somatic-enhancer decommissioning genome-wide. Pluripotency-enhancer selection early in reprogramming occurs predominantly at sites with high OSK-motif densities and requires collaborative binding by OSK. Most pluripotency-enhancers are selected later and occupied by OS and stage-specific-TFs like Esrrb. Overexpression of stage-specific-TFs influences reprogramming efficiency by changing OSK-occupancy, somatic-enhancer decommissioning, and pluripotency-enhancer selection. We propose that collaborative interactions among OSK and with stage-specific-TFs direct both somatic-enhancer decommissioning and pluripotency-enhancer selection, which drives the enhancer reorganization underlying reprogramming

2017-01-19 | GSE90894 | GEO

Cooperative binding of Oct4, Sox2, and Klf4 with stage-specific transcription factors orchestrates reprogramming [ChIP-seq]

2017-01-19 | GSE90893 | GEO

Cooperative binding of Oct4, Sox2, and Klf4 with stage-specific transcription factors orchestrates reprogramming [ATAC-seq]

2017-01-19 | GSE90892 | GEO

De novo inference of thermodynamic binding energies using deep learning models of in vivo transcription factor binding

Project description:We introduce Affinity Distillation (AD), a method for extracting thermodynamic affinities de-novo from in-vivo immunoprecipitation experiments using deep learning. We show that neural networks modeling base-resolution in-vivo binding profiles of yeast and mammalian TFs can accurately predict energetic impacts of varying underlying DNA sequence on TF binding. Systematic comparisons between Affinity Distillation predictions and other predictive algorithms consistently show that Affinity Distillation more accurately predicts affinities across a wide range of TF structural classes and DNA sequences. Affinity Distillation relies on in-silico marginalization against many sequence backgrounds, resulting in a higher dynamic range and more accurate predictions than motif discovery algorithms. Moreover, we show that Affinity Distillation can learn differential paralog-specific affinities, thereby making it possible to more accurately reconstruct regulatory networks in cells.

2022-06-29 | GSE207001 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data