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8,451 results for “Chromatin”
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 1df
<p>smTIRF-FRET Data for Fig 1, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
<p>During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent. </p>
Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"
<p>Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions".</p> <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>
Sparks et al, Heterogeneity in tumor chromatin-doxorubicin binding revealed by in vivo fluorescence lifetime imaging confocal endomicroscopy: In vitro data
<p>Data is divided into three folders:</p> <ul> <li>Sparks_et_al_FIG2_Histone_vs_free_GFP <ul> <li>data for Sparks et al Figure 2</li> <li>main text section: <em>'FRET between chromatin-bound GFP and doxorubicin'</em></li> </ul> </li> <li>Sparks_et_al_FIG3_in_vitro_dose_response <ul> <li>data for Sparks et al Figure 3#</li> <li>main text section:<em> 'FLIM endomicroscope can monitor doxorubicin cellular uptake'</em></li> </ul> </li> <li>Sparks_et_al_SuppFIG2_endoscope_spectral_cross_talk <ul> <li>data for Sparks et al Supplementary Figure 2</li> <li>Supplementary information</li> </ul> </li> </ul> <p><strong>Cell lines</strong></p> <p>IGROV-1 cell lines were cultured in CO<sub>2</sub> dependent media with 10% fetal bovine serum and 1% Pen Strep at 37 ˚C. Before experiments, cells were grown to 80% confluence. For measuring doxorubicin uptake by fluorescence an IGROV-1 cell line stably expressing GFP fused to Histone-1 (H1) was made using the PiggyBac transposon system. As a control to show that effect of doxorubicin on GFP depends on whether it is fused to H1 or not, a stable whole cell expression of GFP by lentiviral transfection and selection by Geneticin was made. For bioluminescence imaging of xenograft tumors, all IGROV-1 cell lines were made to stably express firefly luciferase.</p> <p>To investigate the effect of doxorubicin on other histones, IGROV-1 cells were transiently transfected with a Histone-2B-GFP plasmid (gift from Kurt Anderson) using the Lipofectamine® 2000 reagent.</p> <p>IGROV-1 cells were obtained from Crick institute cell services and confirmed as IGROV-1 by Short Tandem Repeats (STR) profiling and no mycoplasma was detected.</p> <p><strong>In vitro experiments</strong></p> <p>IGROV-1 cells were grown to 80% confluence in 75 ml flasks before being re-plated in 12 or 24 well plates or 35 ml glass bottomed dishes and allowed to attach to the surface for 24 hours before experiments.</p> <p>To study how the fluorescence of GFP labelled H1 labelled IGROV-1 cells changes with doxorubicin treatment, fluorescence intensity and lifetime distributions were measured from cells after 3 hours of incubation with doxorubicin of varying concentrations (0, 0.18, 0.9, 1.8, 9, 18 µM) by serial dilutions of a stock solution with PBS. After 3 of hours, cells were washed in PBS then fixed for 20 minutes in 4% PFA. Cells were then imaged in PBS. Doxorubicin hydrochloride (Sigma-Aldrich, D1515-10 mg) was dissolved in PBS to a concentration of 9 mM and stored at -20˚C.</p>
Sparks et al, Heterogeneity in tumor chromatin-doxorubicin binding revealed by in vivo fluorescence lifetime imaging confocal endomicroscopy: in vivo data
<p>Data is divided into three folders:</p> <ul> <li>Sparks_et_al_FIG_6_IP_intranodule_heterogeneity <ul> <li>data for Sparks et al Figure 6</li> <li>main text section: <em>'FRET between chromatin-bound GFP and doxorubicin'</em></li> </ul> </li> <li>Sparks_et_al_FIG4_5_6_IP_IV_chemo_comparison <ul> <li>data for Sparks et al Figures 4,5 & 6</li> <li>main text section:<em> 'FLIM endomicroscope can monitor doxorubicin cellular uptake'</em></li> </ul> </li> <li>Sparks_et_al_FIG6_IP__internodule_heterogeneity <ul> <li>data for Sparks et al Figure 6</li> <li>main text section: <em>'Intra-tumor heterogeneity'</em></li> </ul> </li> </ul> <p><strong>In vivo experiments</strong></p> <p>Murine xenografts were prepared by intraperitoneal (IP) injection of IGROV-1 cancer cells. IGROV-1 cells were grown to 80% confluence before being trypsinized and re‑suspended in PBS at a concentration of cells per ml. cells were injected into ICRF nude mice. After 14 days post-injection, the presence of intraperitoneal tumors was confirmed by bioluminescence imaging. Briefly, an IVIS bioluminescence imaging system was used to image isoflurane anesthetized mice. 100 µl of D-luciferin (luciferase substrate) at 30mg ml<sup>-1</sup> was injected IP 10 minutes before recording of bioluminescence images. The presence of peritoneal tumors was confirmed if bioluminescence signals from the peritoneum were above background noise 10-30 minutes after D‑luciferin injections. Following confirmation of tumors, in vivo fluorescence imaging experiments were carried out after 21 days. To study differences in drug uptake between intravenous or intraperitoneal delivery, prior to imaging mice were subject to IP or IV doxorubicin-based chemotherapy for 1.5, 3 or 24 hours. Imaging involved terminal procedures, mice were anesthetized then peritoneal tumors were exposed by minor surgery and inspected with the CEM.</p> <p>All animal model procedures were approved by The Francis Crick Institute Biological Ethics Committee and UK Home Office authority provided by Project License 70/8380.</p> <p> </p> <p> </p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4e
<p>The single-molecule FRET dataset underlying Fig. 4e of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4f
<p>The single-molecule FRET dataset underlying Fig. 4f of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4d
<p>The single-molecule FRET dataset underlying Fig. 4d of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
Histone tail dynamics in partially disassembled nucleosomes during chromatin remodeling: Simulation dataset
<p>Dataset of molecular dynamics simulations of partially disassembled nucleosomes.</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories</p> <p>NAMD 2.12 (multi-core with CUDA) was used for the simulations.</p>
Processed data from "Chromatin information content landscapes inform transcription factor and DNA interactions"
<p><strong>Chromatin information content landscapes inform transcription factor and DNA interactions</strong></p> <p>Authors: Ricardo D’Oliveira Albanus, Yasuhiro Kyono, John Hensley, Arushi Varshney, Peter Orchard, Jacob O. Kitzman, Stephen C. J. Parker</p> <p><a href="https://doi.org/10.1101/777532">https://doi.org/10.1101/777532</a></p> <p> </p> <p>This record contains the processed data used in our manuscript. For instructions on how to use or regenerate this data, please refer to <a href="https://github.com/ParkerLab/chromatin_information">https://github.com/ParkerLab/chromatin_information</a>.</p>
Raw data for Figures in: LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions, Ferraioli et al., Nucleic Acids Res. 2024
<p>These datasets represent raw data for the preparation of Figures in:</p> <p><span>Ferraioli S, Sarigol F, Prakash C, Filipczak D, <strong>Foisner R</strong>, Naetar N. (2024) </span>LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions<span>. <em>Nucleic Acids Res.</em>2024 Sep 4:gkae752. doi: 10.1093/nar/gkae752.</span></p>
Chromatin Network Retards Nucleoli Coalescence
<p>This repository contains the simulated trajectories in our investigation of the role of the chromatin network in retarding nucleoli coalescence. </p>
Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors
<p>Cohesin plays vital roles in chromatin folding and gene expression regulation, cooperating with such factors as cohesin loaders, unloaders, and the insulation factor CTCF. Although models of regulation have been proposed (e.g., loop extrusion), how cohesin and related factors collectively or individually regulate the hierarchical chromatin structure and gene expression remains unclear. We have depleted cohesin and related factors and then conducted a comprehensive evaluation of the resulting 3D genome, transcriptome and epigenome data. We observed substantial variation in depletion effects among factors at topologically associating domain (TAD) boundaries and on interTAD interactions, which were related to epigenomic status. Gene expression changes were highly correlated with direct cohesin binding and gain of TAD boundaries than with the loss of boundaries. Moreover, cohesin was broadly enriched in active compartment A chromosomes, which were retained after CTCF depletion. Our results demonstrate context-specific roles of cohesin for gene expression and chromatin folding.</p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Mature chromatin packing domains persist after RAD21 depletion in 3D
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Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
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Data from: Depletion of lamins B1 and B2 promotes chromatin mobility and induces differential gene expression by a mesoscale-motion dependent mechanism
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ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA
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Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
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Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
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