Journal: Molecular Systems Biology

Article Title: Single-cell analysis of population context advances RNAi screening at multiple levels

doi: 10.1038/msb.2012.9

Figure Lengend Snippet: Cell-to-cell variability in comparative RNAi screens of virus infection. ( A ) Experimental and computational overview of the 34 small-scale and 7 druggable genome screens analysed in this study. Individual screens are denoted as a combination of the virus and cell line abbreviation (for example, DEN-1_KY for DEN-1 infection in HeLa Kyoto cells). ( B ) Bootstrapped hierarchical clustering of the average population context-determined patterns of virus infection for each screen . Infection efficiencies per quantile bin of the multidimensional population context and cellular state space were calculated, and z -score log 2 transformed. Median values along all dimensions per screen were clustered . ( C ) Heat map of virus infection-induced phenotypes ( , and ). Grey boxes indicate insufficient data points. ( D ) Heat map depicting average cell-to-cell variability infection pattern strength and reproducibility. The strength and reproducibility was calculated as the average of bootstrapped cell-to-cell variability pattern correlations observed between pooled populations of cells selected from different sets of perturbations per screen . ( E ) Table of viruses with characteristic virus properties, virus names, families, and abbreviations (see for more information ). ( F ) A schematic outline of the cell population context and cellular state space model used throughout this study. Non-mitotic and non-apoptotic cells denote interphase cells. ( G ) Example scatter plots of the data used to calculate the pattern strength and reproducibility. SV40_CNX (green dots, correlation coefficient of 0.91) shows a very strong and reproducible pattern of cell-to-cell variability, whereas MHV_TDS (red dots, correlation coefficient of 0.05) shows no measurable pattern of infection. Individual dots represent infection indices for 2 bins of equal cellular state and population context and with at least 30 cells in each bin, but sampled from different sets of perturbations.

Article Snippet: The data were clustered using a novel bootstrapped hierarchical clustering implementation in Matlab made specifically for this study (available for download from http://www.infectome.org and see ), using a correlation distance measure and average cluster method.

Techniques: Virus, Infection, Transformation Assay

Journal: Molecular Systems Biology

Article Title: Single-cell analysis of population context advances RNAi screening at multiple levels

doi: 10.1038/msb.2012.9

Figure Lengend Snippet: Single-cell modelling of causality in population context-determined cell-to-cell variability of infection in RNAi screens. ( A – C ) Examples of the causal modelling (see ) of direct (A), population context-mediated (B) and masked (C) perturbation effects, as well as their corresponding infection and population context measurements in various RNAi screens. Black arrowheads in the causal graphs denote inferred directionality of effects, whereas their absence indicates effects where both directions had the same likelihood. Red and green edges denote the sign of the pairwise single-cell bootstrapped correlations between nodes of the network (red=negative, green=positive). Orange nodes in the causal graphs represent virus infection; Light blue nodes represent siRNAs indicated by the gene name and their siRNA number (grouped by parentheses if effects for different siRNAs are identical). Grey nodes represent population context parameters (Pop. size, population size; Edge, cell islet edges; LCD, Local cell density). Population context and cellular state nodes not causally linked to infection have been omitted for clarity. The bar plots show normalized z -score values for infection (orange bars) and 3 × z -score values (to put them on the same scale as infection) for population context measurements (grey bars). Error bars denote s.d. ( n =3) around median values. ( D ) Quantification of fully indirect (blue) and fully masked (red) siRNA effects for the YFV and VACV large-scale screens. siRNA phenotypes were classified based on their direct and total measurements, as indicated in the legend.

Article Snippet: The data were clustered using a novel bootstrapped hierarchical clustering implementation in Matlab made specifically for this study (available for download from http://www.infectome.org and see ), using a correlation distance measure and average cluster method.

Techniques: Infection, Virus

Journal: Molecular Systems Biology

Article Title: Single-cell analysis of population context advances RNAi screening at multiple levels

doi: 10.1038/msb.2012.9

Figure Lengend Snippet: RNAi perturbations of population context-determined infection patterns. ( A ) Single-cell bootstrapped correlations between virus infection and location on cell islet edges are shown for the 147 siRNAs in each of the 34 small-scale screens. Bootstrapped correlations were calculated combining single-cell measurements from triplicate experiments. The grey region around zero indicates weak correlations. Dot colour and dot size depict the median direct effects of siRNAs on infection and the population size, respectively. Insets on the right show the observed patterns for SFV infection in HeLa Kyoto cells, in which TRIO (upper), or ABL1 (lower), is silenced (Blue=DAPI, green=SFV infection) (see also ). Additional RNAi phenotypes of STK40 and QSK, which have multiple siRNAs (number in parentheses) targeting the same gene that cluster together in the dot plots (clustering indicated with black bold lines), are highlighted. ( B ) Measured indices of SFV infection in HeLa Kyoto cells located or not on cell islet edges. Median and s.d. of triplicate measurements are shown for TRIO-silenced cells (red), ABL1-silenced cells (blue) and scrambled (non-targeting) siRNA-silenced cells (grey). ( C ) Protein levels of Trio (red) are higher in sparsely growing cells, whereas protein levels of ABL1 (blue) are higher in cells in densely growing cells. Solid lines and dots indicate median values of mean intensity per cell, for varying local cell densities. Light-coloured regions indicate the inter-quartile range of intensities of individual cells. ( D ) Silencing of TRIO increases CME (as measured by transferrin uptake relative to that of non-perturbed cells) more strongly in single cells at lower local cell densities (red), whereas ABL1 silencing increases CME more strongly in single cells at high local cell densities. The solid lines represent median values, and transparent region 0.5 × inter-quartile ranges of single-cell values.

Article Snippet: The data were clustered using a novel bootstrapped hierarchical clustering implementation in Matlab made specifically for this study (available for download from http://www.infectome.org and see ), using a correlation distance measure and average cluster method.

Techniques: Infection, Virus

Journal: Molecular Systems Biology

Article Title: Single-cell analysis of population context advances RNAi screening at multiple levels

doi: 10.1038/msb.2012.9

Figure Lengend Snippet: Towards a systems-level analysis of host factors regulating infection across mammalian viruses. ( A ) Bootstrapped hierarchical clustering of all 147 direct siRNA effects of the 34 small-scale RNAi screens. Per branch-point, three bootstrapped empirical P -values were calculated: Red, approximately unbiased P -value; Green: sub-tree P -value; Blue: leaf-set (i.e. clade or cluster) P -value. The tree with the highest summed edge P -value from 10 6 bootstrapped trees is shown (see ). ( B ) Direct effect of DYRK3 (orange) and FRAP1 (blue) silencing for each assay. Per screen, the median value of all three siRNAs, and median of triplicates is shown (i.e. similar to a two out of three criterion). Asterisks indicate gene-silencing phenotypes, which were independently validated (see ). Distribution below the x -axis depicts typical data density of the normal distribution. Note that for visual purposes the x -axis is inverted, with negative values (reduced infection phenotypes) on the right side. The correlation of DYRK3/FRAP1 phenotypes over all assays is −0.48 ( P <0.0038). ( C ) Gene scores for selected genes are shown for all small-scale RNAi screens of virus infection. Bar colours correspond to different genes (see legend). ( D ) Functional annotation enrichment P -values are visualized for six functional annotation categories on the clustering of the 34 small-scale RNAi screens. Enrichment was calculated using a rank-based Kolmogorov–Smirnov method (see ). Node colour indicates enrichment in down-hits (red) or up-hits (green), and node size indicates P -value of enrichment (see legend).

Article Snippet: The data were clustered using a novel bootstrapped hierarchical clustering implementation in Matlab made specifically for this study (available for download from http://www.infectome.org and see ), using a correlation distance measure and average cluster method.

Techniques: Infection, Virus, Functional Assay