Journal: Cell Death & Disease

Article Title: The identification of BCL-XL and MCL-1 as key anti-apoptotic proteins in medulloblastoma that mediate distinct roles in chemotherapy resistance

doi: 10.1038/s41419-023-06231-y

Figure Lengend Snippet: A Flow cytometry was used to assess the number of AnnexinV+PI- + AnnexinV+PI+ cells following treatment with the indicated concentrations of cisplatin. Data are expressed as mean of N = 3 independent experiments, ±SEM. B PCA was carried out on functional group data representing the expression profiles of individual medulloblastoma cell lines. Cell lines are positioned in the 3D space defined by the first three PCs. Colour coding indicates cell line responsiveness to cisplatin, where black indicates high cisplatin sensitivity, and red indicates reduced sensitivity. C Following PCA, linear discriminant analysis (LDA) was carried out to segment the PC space into areas corresponding to cisplatin sensitivity. The 2D plot shows cell line distribution in the 2nd and 3rd PCs. D Estimated protein expression profiles for medulloblastoma tumours were generated and samples were positioned into the previously generated PC space. Samples are colour-coded by their LDA-predicted sensitivity to cisplatin-induced apoptosis. 199/266 samples were predicted to be highly sensitive (black) and 67/266 were predicted to have reduced sensitivity (red). E The same data as ( C ) overlaid on a biplot showing the contributions of each functional group to the variance accumulated in the 2nd and 3rd PCs. Scores and coefficients (and therefore axes) are scaled according to the MATLAB biplot function.

Article Snippet: Scores and coefficients (and therefore axes) are scaled according to the MATLAB biplot function.

Techniques: Flow Cytometry, Functional Assay, Expressing, Generated

Journal: NAR Genomics and Bioinformatics

Article Title: In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

doi: 10.1093/nargab/lqaa112

Figure Lengend Snippet: Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.

Article Snippet: To compute the principal components of the data we used the MATLAB function implementation of Principal Component Analysis (PCA) and visualized the results (variable loadings and principal components) using a biplot.

Techniques: In Silico, Genome Wide, DNA Amplification, Activity Assay, Amplification

Journal: eLife

Article Title: DASC, a sensitive classifier for measuring discrete early stages in clathrin-mediated endocytosis

doi: 10.7554/eLife.53686

Figure Lengend Snippet: ( A ) Δ r (percentage difference) and p-values in CCP rate by comparing 11 EAP KD conditions plus siMock (Fig. 4G) to bootstrapped siControl*. Both quantities are obtained 300 times through bootstrapping. Colored boxes with black dotted edges, correspond to the p-values in the vertical axis; boxes outlined by magenta edges, correspond to Δ r values in the horizontal axis. Red lines represent means ( p - and Δ r ¯ ) the black regions represent 95% confidence intervals and 1 standard deviations as colored blocks of the 300 bootstrap results. Legend shows the color for each condition. Significance level is indicated by the dashed horizontal lines. ( B ) Summary of phenotypes of the 11 conditions evaluated by the Δ r ¯ in CS initiation rate (CS init.), CCP%, CCP rate, and CCP median lifetime (τ CCP ), and Δ r in transferrin receptor uptake: internalized and efficiency (TfRint and TfReff) relative to control. EAP KD sorted from low Δ r ¯ CCP rate to high. ( C ) Principle component analysis (PCA). Projection of 6 variable values from 11 conditions in ( B ) into principle component space. First and second component (Component 1 and 2) account for 65.90% and 21.16% of total variance, respectively. Projection of original variable axes presented as red vectors with blue arrows.

Article Snippet: First, the original observations were re-centered, rescaled and projected into a new 2-dimensional PCA space, spanned by Component 1 and Component 2, which are linear combinations of the original 6 dimensions ( , implemented in Matlab’s function biplot ).

Techniques: Control