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779 results for “CTCF”
Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging
<p><strong>Overview</strong></p> <p>This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> <p><strong>Cell lines and conditions</strong></p> <p>In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions:</p> <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul> <p> </p> <p><strong>Data and data processing</strong></p> <p>Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the <em>Fbn2</em> gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb).</p> <p>The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations.</p> <p>Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”.</p> <p>File names are formatted follows.</p> <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> <p>For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.</p> <p> </p> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are:</p> <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>
Cohesin and CTCF control the dynamics of chromosome folding
<p>The dataset contains all the tracking data from Mach et al 2022 paper.</p> <p>Each file has the following header:</p> <p>x,y,z: spot coordinate. For dual color imaging, the distance along x,y and z across channels</p> <p>track: track id</p> <p>frame: time frame</p> <p>cell: cell id</p>
Figure 3 in De novo mutations in the genome organizer CTCF cause intellectual disability
Figure 3. - Principal component analysis (PCA) of morphometric data. The ellipse highlights the group formed by the albino and normally pigmented specimens of the same size class. The albino is represented by the white square. Squares (N3 = 20-30 cm size-class); inverse triangles (N4 = 30-40 cm), circles (N5 = 40-50 cm), lozenges (N6 = 50-60 cm), and triangles (N7 = 60-70 cm).
Figure 2 in De novo mutations in the genome organizer CTCF cause intellectual disability
Figure 2. - Regressions of log of disc width vs. log of weight (A) and log of total length vs. log of weight (B) using the albino specimen and data from 28 female individuals of G. micrura. The albino specimen is represented by the white dot.
Raw and processed SPT data for "Guided nuclear exploration increases CTCF target search efficiency"
<p><strong>Raw and processed SPT data for “Guided nuclear exploration increases CTCF target search efficiency”</strong></p> <p>Anders S. Hansen<sup>1,2,3,4,*</sup>, Assaf Amitai<sup>5,*</sup>, Claudia Cattoglio<sup>1,2,3,4</sup>, Robert Tjian<sup>1,2,3,4</sup>, Xavier Darzacq<sup>1,2,3</sup></p> <p> </p> <p>1: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, USA;</p> <p>2: Li Ka Shing Center for Biomedical and Health Sciences</p> <p>3: CIRM Center of Excellence, University of California, Berkeley, Berkeley, USA;</p> <p>4: Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, USA;</p> <p>5: Department of Chemical Engineering, MIT, Cambridge 02139, Massachusetts, USA</p> <p>*: ASH and AA contributed equally.</p> <p> </p> <p><strong>Overview</strong></p> <p>This repository contains all the raw and processed spaSPT data associated with “Guided nuclear exploration increases CTCF target search efficiency”. In total, this dataset contains data for 1,669 single-cell movies and trajectories with 85,612,029 unique displacements. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines used in this study.</li> <li>How the data was collected.</li> <li>How to read the raw single-cell SPT spaSPT data.</li> <li>How to read the processed, quality-controlled and HMM-classified data</li> </ul> <p>The document does not contain information about how the data was analyzed. For details on how the data was analyzed and for raw code to reproduce our figures, please go to <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p> </p> <p><strong>Cell lines and transient transfection constructs</strong></p> <p>In total, 20 different cell lines were studied here. The mouse embryonic stem cells (mESCs) are in the JM8.N4 background (Pettitt et al., 2009) and the human cells were U2OS osteosarcoma cells. Cell lines expressing Halo-tagged proteins were either homozygous knock-in cell lines, wild-type cell lines transiently transfected with a plasmid over-expressing the protein of interest (Lipofectamine 3000), or wild-type cell lines stably over-expressing a transgene.</p> <p>The wild-type and C59 mESC cell lines were pathogen tested using the IMPACT II test (performed by IDEXX BioResearch) and where negative for all tested pathogens (full details provided in (Hansen et al., 2017)). The wild-type and C32 U2OS cell lines were tested for mycoplasma contamination and found to be clean and further authenticated by Short Tandem Repeat Profiling (STR profiling; 100% match with U2OS; full details provided in (Hansen et al., 2017)).</p> <p>Further details on each cell line or transfection construct are provided in the table in the ReadMe PDF (the MAT-file name is the name of the MAT-file containing the raw data).</p> <p><strong>Data collection using stroboscopic photo-activation single-particle tracking (spaSPT)</strong></p> <p>For full details on how the experiments where performed, please see the Materials and Methods section of the manuscript. Here we will briefly describe the protocol.</p> <p>To systematically investigate how proteins explore the nucleus we need a very large dataset (i.e. hundreds of thousands of trajectories) of very high quality SPT data (i.e. with minimal bias and without many tracking errors). The two major sources of bias in experimental SPT data are 1) motion-blurring and 2) tracking errors (Hansen et al., 2018). First, motion-blurring biases against detecting fast-moving molecules since while the camera is exposed, a fast-moving molecule will spread its photons over many pixels and no longer resemble a diffraction limited spot, whereas a bound or slow-moving molecule will emit all its photons as a single diffraction limited spot. Since most localization algorithms readily detect diffraction limited spots, but not motion-blurs this introduces a clear bias. spaSPT overcomes this bias by strobing the excitation laser (Elf et al., 2007): using just a single 1 ms excitation pulse, we simultaneously achieve high signal-to-background (~10-fold), while minimizing motion-blur as previously demonstrated (for a more complete discussion, please see (Hansen et al., 2018)). Second, tracking errors are largely caused by high particle densities: for example, when trajectories overlap, tracking errors occur which clearly prevents accurately analysis of protein diffusion and which could lead to artefactual conclusions. By using the bright photo-activatable Janelia Fluor dyes PA-JF<sub>549</sub> and PA-JF<sub>646</sub> (Grimm et al., 2016), we can overcome this challenge (this is also known as sptPALM (Manley et al., 2008)). During the camera integration time between frame (~447 microseconds), we pulse the 405 nm photo-activation laser at an intensity such that the mean localization density is around 1 molecule per nucleus per frame. At this density, tracking errors are greatly reduced, yet we are able to collect tens of thousands of frames at this density per cell and thus obtain large amounts of data despite imaging at a very low density. For a more complete discussion of spaSPT please see (Hansen et al., 2017, 2018).</p> <p>To generate a large dataset at multiple time-scales, we collected spaSPT data (generally around 8 cells per replicate and four biological replicates; occasionally a cell would be removed from subsequent analysis if the localization density was too high and thus prone to tracking errors) at 3 camera exposure times: 4 ms (add ~447 microseconds), 7 ms (add ~447 microseconds) and 13 ms (add ~447 microseconds); this roughly corresponds to frame-rates of ~223 Hz, 133 Hz, and 74 Hz. To generate data at longer lag times (this was only done after HMM-classification; see also main paper), this data was subsampled according to the table in the ReadMe PDF.</p> <p><strong>Overview of raw, unprocessed single-cell spaSPT data</strong></p> <p>This repository contains all the raw, unprocessed spaSPT data for each single cell in the directory “UnprocessedSingleCellData”. In total, this directory contains 1669 MAT-files corresponding to the 1669 single-movies recorded here. The files are systematically named for their parent cell line, replicate number and cell number. For example, “mESC_C59_Halo-mCTCF_133Hz_Rep4_Cell8.mat” contains SPT data for mouse embryonic stem cell line clone 59 and Halo-CTCF was imaged at ~133 Hz; the file is for cell number 8 in biological replicate number 4. All the other single-cell experiments are similarly named. Each MAT-file contain all the trajectories recording from a single-cell movie and stored as a “structure array” object, “trackedPar”. trackedPar contains three variables for each trajectory:</p> <ul> <li>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of frames where the molecule was located. The first column is the x-coordinate and the second column is the y-coordinate and the units are micrometers.</li> <li>trackedPar.Frame: “Frame” is a column vector where each element is an integer describing the frame number wherein the particle was localized (each trajectory contains at most 1 gap between frames).</li> <li>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint (in units of seconds) where the molecule was localized.</li> </ul> <p>Note that this Matlab format is directly readable by Spot-On: <a href="https://spoton.berkeley.edu/">https://spoton.berkeley.edu/</a></p> <p> </p> <p><strong>Overview of HMM-classified and processed SPT data</strong></p> <p>The raw spaSPT data was processed (to remove any rare tracking errors) and all the data merged. The data was the classified into either a “bound” or “free” state using a 2-state Hidden-Markov Model (HHM; vbSPT (Persson et al., 2013)) and then temporally subsampled to generate data at longer frame rates. Full details on how the data was processed are given in the Materials and Methods section as well as on GitLab. Raw code to reproduce all the figures from unprocessed single-cell spaSPT data is also available at GitLab: <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p>All the HMM-classified data for each cell line at each frame rate is stored in a single MAT-file. The MAT-file contains two key cell arrays: “CellTracks” and “CellTrackViterbiClass”. The two cell arrays contain the XY-coordinates and classification, respectively. More, specifically, suppose trajectory <em>k</em> is made up of <em>n</em> localizations. Then:</p> <p>CellTracks{k}: a matrix of <em>n</em> rows and 2 columns containing the x,y coordinates in units of micrometers.</p> <p>CellTrackViterbiClass{k}: a column vector of length <em>n</em>-1 where each entry is an integer: either “1” or “2”. Thus, the length of this vector is 1 less than the number of rows in the CellTracks matrix. This is because only the displacements and not the localizations are classified. For example, the CellTrackViterbiClass{k} reads [1;1;1;2;2] it means that there were 6 localizations and that the first 3 displacements (1à2, 2à3, 3à4) were classified as “bound” and the last 2 displacements (4à5, 5à6) were classified as “free”. </p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Elf, J., Li, G.-W., and Xie, X.S. (2007). Probing transcription factor dynamics at the single-molecule level in a living cell. Science <em>316</em>, 1191–1194.</p> <p>Grimm, J.B., English, B.P., Choi, H., Muthusamy, A.K., Mehl, B.P., Dong, P., Brown, T.A., Lippincott-Schwartz, J., Liu, Z., Lionnet, T., et al. (2016). Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 66779.</p> <p>Hansen, A.S., Pustova, I., Cattoglio, C., Tjian, R., and Darzacq, X. (2017). CTCF and cohesin regulate chromatin loop stability with distinct dynamics. Elife <em>6</em>.</p> <p>Hansen, A.S., Woringer, M., Grimm, J.B., Lavis, L.D., Tjian, R., and Darzacq, X. (2018). Robust model-based analysis of single-particle tracking experiments with Spot-On. Elife <em>7</em>, e33125.</p> <p>Manley, S., Gillette, J.M., Patterson, G.H., Shroff, H., Hess, H.F., Betzig, E., and Lippincott-Schwartz, J. (2008). High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods <em>5</em>, 155–157.</p> <p>Persson, F., Lindén, M., Unoson, C., and Elf, J. (2013). Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat. Methods <em>10</em>, 265–269.</p> <p>Pettitt, S.J., Liang, Q., Rairdan, X.Y., Moran, J.L., Prosser, H.M., Beier, D.R., Lloyd, K.C., Bradley, A., and Skarnes, W.C. (2009). Agouti C57BL/6N embryonic stem cells for mouse genetic resources. Nat. Methods <em>6</em>, 493–495.</p> <p>Teves, S.S., An, L., Hansen, A.S., Xie, L., Darzacq, X., and Tjian, R. (2016). A dynamic mode of mitotic bookmarking by transcription factors. Elife <em>5</em>.</p> <p> </p> <p> </p> <p> </p>
CTCF loop analysis and prediction scripts and datasets
<p>Additional files accompanying the study of experimental and predicted CTCF loops in human cell lines by Oti et al.</p> <p>ctcfloopscripts_v1.0.zip: Scripts used in the analysis of CTCF-mediated loops in human cell lines; both experimental ChIA-PET loops as well as predicted loops based on ChIP-seq data. Zipped file.</p> <p>ctcf_predictedloops_ENCODE_chipseq_datasets.tar.gz: Predicted CTCF loops for 100 ENCODE ChIP-seq datasets. Gzipped tar file.</p> <p>fimo_ctcfmotifs_MA0139_hg19_2.5e-4.bed.gz: genome-wide CTCF motifs in human genome (hg19) detected by FIMO tool.</p>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Enhancer-promoter interactions and transcription are maintained upon acute loss of CTCF, cohesin, WAPL, and YY1
<p>1. spaSPT data for endogenously tagging HaloTag-YY1 in mESCs (clones YN11 and YN31).</p> <p>2. spaSPT data for HaloTag-YY1 in CTCF- (clone CD1) or RAD21- (clone RD35) depleted mESCs.</p> <p>3. sptSPT data for stably expressing HaloTag-YY1 in clone PBYN2 and YY1-HaloTag in clone PBYC3.</p> <p> </p> <div> </div>
Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations
<p>Supplementary dataset for manuscript "Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations". </p>
CTCF BINDING PATTERNS DEFINE TADS AND BOUNDARY ELEMENTS IN HUMAN AND MOUSE GENOMES
<p><em><strong>Msci thesis from Industrial Placement at Politecnico di Milano</strong></em></p> <p> </p> <p><strong>Abstract</strong></p> <p>In eukaryotes, the genome is highly packaged inside the nucleus. This hierarchical structure requires complex compartmentation and organization. Topologically associating domains (TADs) serve as architectural units in the 3D genome, functioning to regulate and constrain gene interaction. TAD boundaries are enriched in the CCCTC-binding factor (CTCF). Due to the asymmetric nature of the CTCF motif, it exhibits differing orientations based on the strand it is localized. Motif orientation is crucial for loop formation, which is facilitated in a convergent orientation. While the relationship between TADs and CTCF motif patterns has been described in humans, the model remained to be corroborated in other mammals. </p> <p>We conducted a comparative analysis by applying the research conducted in humans to the mouse genome. By integrating ChIP-seq data, we examined the CTCF sites based on ChIP-seq peak consolidation, signal value, and motif p-value. This analysis aimed to comprehend the binding profile of CTCF and elucidate its complexity in terms of binding strength and cross-tissue conservation. Mouse genome CTCF motifs were categorized into cluster patterns based on their relative orientation. Following this classification, the distribution of each pattern was studied using ChIP-seq-related descriptors. We then built a TAD boundary collection based on the insulation score. Boundary consolidation and length, insulation score, and CTCF number were used to reconcile CTCF to TAD boundary function. The cluster patterns were then mapped to TAD and TAD boundaries to elucidate their structure based on the spatial arrangement of CTCF.</p> <p>The distribution of ChIP-seq-validated CTCF sites within TAD boundaries revealed a well-defined structure, characterized by an abundance of the divergent pattern. Boundaries with the highest insulation scores exhibited both a high consolidation and CTCF enrichment. This set of boundaries was used to study TAD structure. An increment of the convergent pattern was observed inside TADs. The individual CTCF sites on the interior of the TAD were further organized into left and right sections, corresponding to an opposing directional arrangement to TAD boundaries. Significantly, the ChIP-seq data contained many unannotated peaks and non-signal motifs. These were associated with a high motif p-value, low ChIP-seq signal, low consolidation, and a less structured arrangement of CTCF clustering. Moreover, non-signal motifs were underrepresented within TAD boundaries and displayed a disorderly distribution.</p> <p>The human model of CTCF-dependent TAD structure was confirmed in mice. The model suggested the inherent alternation between convergent and divergent patterns, with these CTCF sites conferring characteristic properties upon TADs and their boundaries. These traits encompass the change of preferential genomic interaction and boundary insulation capability. Thus, the orientation-based clustering of CTCF sites was further validated as a good method to define TADs in a precise organized manner.</p>
CTCF couples long-range loop extrusion and diffusion to mediate a diverse Igk repertoire
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Increasingly efficient chromatin binding of cohesin and CTCF supports chromatin architecture formation during zebrafish embryogenesis
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DOHH2 hg38 CTCF ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>hg38 DOHH2 CTCF ChIP-seq Dataset filtered for Unique Multiread Mappability at a threshold of .75 using Umap</p>
CTCF protein-protein interactions
<p>A collection of all the interactions between CTCF and other proteins. Data from the literature, as well as the STRING database and the Integrated Interactions Database, were analyzed and merged.</p>
live cell super-resolution data_dual_color_cell_line_CTCF_E-2_deletion
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Practice dataset, CTCF mouse ChIP-seq, chr9
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Alteration of CTCF associated chromatin neighborhood inhibits TAL1-driven oncogenic transcription program and leukemogenesis
GEO Series GSE135320. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Exploration of CTCF post-translation modifications uncovers Serine-224 phosphorylation by PLK1 at pericentric regions during the G2/M transition
GEO Series GSE119697. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Spec-seq sequencing data for CTCF [2021 series]
GEO Series GSE188164. synthetic construct. 6 samples. Type: Other.
CTCF and Cohesin (Rad21) ChIP-seq in female mouse liver
GEO Series GSE130908. Mus musculus. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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