Journal: Nature genetics

Article Title: BRD2 Compartmentalizes the Accessible Genome

doi: 10.1038/s41588-022-01044-9

Figure Lengend Snippet: (a) (Upper panel) A schematic of acute depletion of Cohesin in mouse ESCs by the auxin-induced protein depletion system. The mini auxin-inducible degron (mAID)-HaloTag was bi-allelically knocked into the C-terminus of Rad21 gene (Cohesin subunit) by CRISPR/Cas9 in mESCs stably expressing the plant derived E3 ligase adaptor protein osTir1. (Lower panel) Adding plant derived hormone analogue (auxin) triggers rapid degradation of target protein RAD21 as revealed by single cell fluorescence imaging (HaloTag ligand JF 549 ). OCT4 immunostaining was used as a control. Scale bar, 5 μm. (b) Single-cell illustration of 3D ATAC-PALM localizations upon RAD21 depletion. A wild type control cell is also shown in the upper left corner. The color bar indicates localization density calculated by using a canopy radius of 250 nm. The experiments have been independently repeated for four times. Also see the 3D rotatory presentation in . Scale bar, 2 μm. (c) RAD21 depletion promotes global increase of accessible chromatin clustering measured by pair auto-correlation function, which describes the similarity among observations. The top panel shows a simplified two-dimensional scheme for distribution of localizations in uniform (black dots, left) or wild type (green dots, middle) or RAD21 depleted (green dots, right) conditions. g(r) represents the pair autocorrelation function of distance r calculated from a given origin point inside the space. The error bar represents standard error (SE) of the mean and two-sided Mann-Whitney U test was applied for comparing data points at g(0) . (d) The violin plot of normalized radius (upper panel) or localization density (lower panel) of top 100 ranked ACDs among 4 individual cells for Control and RAD21 depleted conditions. The localization density was determined by the total number of ATAC-PALM localizations within a 250nm(radius) spherical region. The black bar indicates the median value for each data set. (e) 3D iso -surface reconstruction of ACDs (green) identified by using the DBSCAN algorithm for Control (left panel) and RAD21 depletion (right panel) conditions. The iso -surface in grey outlines the nuclear envelope. The lower panels show 4× magnification of local regions under each condition. Scale bar, 1 μm. (f ) Enhanced clustering of transcription factor (TF) SOX2 stable binding sites upon RAD21 depletion. The pair auto-correlation function g(r) for stable SOX2 binding sites was calculated before and after RAD21 depletion. SOX2 binding events with dwell times longer than 4s were considered as stable binding events (see ). The error bars represent standard error (SE) of the mean. The two-sided Mann-Whitney U test was used for statistical testing. The inset illustrates the 3D TF binding between specific (green) and non-specific (blue) binding sites.

Article Snippet: The density-based clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) was adopted to map and visualize individual local ACDs (core DBSCAN MATLAB code from http://yarpiz.com/255/ypml110-dbscan-clustering ) as we previously described .

Techniques: CRISPR, Stable Transfection, Expressing, Derivative Assay, Fluorescence, Imaging, Immunostaining, Control, MANN-WHITNEY, Binding Assay

Journal: Nature

Article Title: Visualizing DNA folding and RNA in embryos at single-cell resolution

doi: 10.1038/s41586-019-1035-4

Figure Lengend Snippet: a, t-SNE 2D projection of the RNA expression data, clustered with DBSCAN (see ). Annotations identified by manual inspection are indicated by matching colours and numbers (labelled on the right). This number/colour legend is used for all panels. b, The relative embryonic spatial positions of cells in each group shown for three embryos. c, Distance maps from ORCA for each assigned group. d, Normalized distance maps (observed minus expected distance) for each group. The expected distance accounts for the polymer nature of DNA, whereby sequences closer in linear position along the genome are expected to be closer together in 3D space. The expected distance was calculated by fitting a power law to the distribution of 3D separation distances between barcodes as a function of their linear separation for all data in all cells.

Article Snippet: The resulting clusters were separated using density-based clustering (DBSCAN) (Matlab implementation, Yarpiz 2015).

Techniques: RNA Expression, Polymer

Journal: Nature

Article Title: A large-scale binding and functional map of human RNA-binding proteins

doi: 10.1038/s41586-020-2077-3

Figure Lengend Snippet: a , Model of ENCODE eCLIP experiments. Inputs were taken by sampling 2% of one of the two biosamples before IP. b , Example IP–western image for DCP1B IP success in K562 cells during initial IP tests performed without enzymatic steps (left) and IP failure in K562 cells during eCLIP experiments (right). This experiment was performed once. c , Pie charts indicate the number of eCLIP experiments that fell into the following categories: failure to successfully immunoprecipitate during eCLIP (IP failure), failure to yield amplifiable library in fewer than 20 PCR cycles (experiment abandoned), experiments that yielded immunoprecipitated library and were sequenced but failed quality assessment (QC failed), successful experiments that did not meet ENCODE standards but contained reproducible signal and have been released on the GEO, and successful experiments that met ENCODE standards and are available at the ENCODE Data Coordination Center (released). d , Schematic of eCLIP data quality standards. See and Supplementary Fig. for additional details. e , Confusion matrix of final classification scheme versus manual quality assessment. f , The number of CLIP per-identified clusters ( x -axis) versus the number of significantly enriched peaks ( y -axis) (fold enrichment ≥ 8 and P ≤ 0.001 from two-sided Fisher’s exact Test (or Yates’s χ 2 test where appropriate) with no hypothesis testing correction (Methods)) identified for each of 446 eCLIP experimental replicates. g , The number of significantly enriched peaks (fold enrichment ≥ 8 and P ≤ 0.001 from two-sided Fisher’s exact rest (or Yates’ χ 2 test where appropriate) with no hypothesis testing correction (Methods)) identified in each of replicate 1 and replicate 2 versus the number of reproducible peaks identified from IDR analysis for 223 eCLIP experiments. Pearson correlation and significance were determined in MATLAB. h , The number of significant and reproducible peaks identified in K562 cells ( x -axis) versus HepG2 cells ( y -axis) as in g , for all 73 RBPs with eCLIP in both cell types. Pearson correlation and significance were determined in MATLAB.

Article Snippet: To identify clusters, clustering was performed in using the DBSCAN (v1.0) MATLAB package, with options epsilon = 3 and MinPts = 2.

Techniques: Sampling, Western Blot, Immunoprecipitation

Journal: Nature

Article Title: A large-scale binding and functional map of human RNA-binding proteins

doi: 10.1038/s41586-020-2077-3

Figure Lengend Snippet: a , Stacked bars indicate significant eCLIP peaks (fold enrichment ≥8, P ≤ 0.001, and biologically reproducible by IDR) for 223 eCLIP experiments. Number of peaks is shown on a logarithmic scale; bar heights are pseudo-coloured according to the linear fraction of peaks overlapping the indicated regions of pre-RNA, mRNA, and non-coding RNAs. Data sets were hierarchically clustered to identify six clusters based on similar region profiles (Extended Data Fig. ). b , Seventeen clusters and one outlier of RBPs based on t -distributed stochastic neighbour-embedding ( t -SNE) clustering (performed in MATLAB with algorithm = exact, distance = correlation, and perplexity = 10) of unique genomic and multicopy element signal for 223 eCLIP experiments. c , For RBPs in clusters in b , heatmap indicates the average relative information for each listed RNA region or element. d , Each point indicates the fold enrichment in eCLIP of RBFOX2 in K562 cells (RBFOX2 K562 ) for a reproducible RBFOX2 eCLIP peak in HepG2 cells (RBFOX2 HepG2 ), with underlaid black histogram, separated by the difference in expression of the bound gene between K562 and HepG2 cells. Red lines indicate mean; two-sided Kolmogorov–Smirnov test. e , For each RBP profiled in both K562 and HepG2 cells ( n = 73), points indicate the fraction of peaks in the first cell type associated with a given gene class that are (blue) at least fourfold enriched, or (red) not enriched (fold enrichment ≤1) in the second cell type. Boxes indicate quartiles, green lines show mean.

Article Snippet: To identify clusters, clustering was performed in using the DBSCAN (v1.0) MATLAB package, with options epsilon = 3 and MinPts = 2.

Techniques: Expressing

Journal: Nature

Article Title: A large-scale binding and functional map of human RNA-binding proteins

doi: 10.1038/s41586-020-2077-3

Figure Lengend Snippet: a , Similar to Extended Data Fig. , knockdown-altered skipped exons were identified for each RNA-seq experiment. However, for this analysis, normalized eCLIP read density at skipped exons that were excluded (left) or included (right) upon RBP knockdown versus nSEs was calculated separately for all RBPs within the same RBP class (identified in Fig. ). The heatmap then indicates the difference between the normalized eCLIP signal for the shRNA-targeted RBP and the mean of the normalized eCLIP signal for all other RBPs within that class. Shown are all 92 pairings of RBPs with eCLIP and KD–RNA-seq data and at least 100 included or excluded altered events, with hatching indicating data sets with fewer than 100 significantly altered events. b , Heatmap indicates normalized eCLIP signal at 492 HNRNPC knockdown-induced exons in HepG2 cells relative to nSEs for HNRNPC (top) and all other RBPs within the same binding class and cell type (bottom). c , As in b , for 138 RBFOX2 knockdown-excluded exons in HepG2 cells (as shown in Fig. , but including all labels). d , Points indicate average change in Δ Ψ in two replicates of RBFOX2 knockdown ( x -axis) and QKI knockdown ( y -axis) in HepG2 cells. Shown are 93 exons that were significantly altered ( P < 0.05, FDR < 0.1, and |ΔΨ| > 0.05) from rMATS analysis of either RBFOX2 or QKI, and had at least 30 inclusion or exclusion reads in both replicates and average |Δ Ψ | > 0.05 for both RBFOX2 and QKI knockdown. Significance was determined from correlation in MATLAB. e , For each of 138 RBFOX2 knockdown-excluded skipped exons in HepG2 cells, points indicate normalized RBFOX2 eCLIP enrichment at the +60 nt position of the downstream intron ( x -axis) versus normalized QKI eCLIP enrichment at the +150 nt position of the downstream intron ( y -axis). f , As in b , for 160 TIA1 knockdown-included exons in HepG2 cells. Right, black indicates mean of 15 non-TIA1 data sets in the same binding class, with the 10th–90th percentiles indicated in grey. g , Western blot for (left) TIAL1 and (right) TIA1 of IP performed with IgG, TIA1 (RN014P, MBLI), and TIAL1 (RN059PW, MBNL) primary antibodies. This experiment was performed once. h , As in d , for TIA1 and TIAL1 at 107 TIA1 knockdown-included exons in HepG2 cells.

Article Snippet: To identify clusters, clustering was performed in using the DBSCAN (v1.0) MATLAB package, with options epsilon = 3 and MinPts = 2.

Techniques: Knockdown, RNA Sequencing, shRNA, Binding Assay, Western Blot