Analysis of High Throughput Cellular Screening Data

Background: RNA Interference and other cellular screens

RNA interference is a mechanism for RNA-guided regulation of gene expression, in which double-stranded ribonucleic acid inhibits the expression of genes with complementary nucleotide sequences. Conserved in most eukaryotic organisms, the RNAi pathway is thought to have evolved as a form of innate immunity against viruses and also plays a major role in regulating development and genome maintenance.

Genome-wide RNAi screens can be used to decipher the connection between genotype and phenotype, by knocking down individual genes and directly observing the resulting cellular phenotype. Different experimental formats can be used to carry out high-throughput RNAi screens. In well-based arrayed screening, each well of a multi-well plate contains a different gene-targeting reagent. The main advantage of this format is that quantitative assays are possible on a fairly large population of cells.

In the past couple of years, as an alternative, several groups have started to adapt RNAi screens for cell microarrays. This is a glass slide onto which siRNA and transfection reagenrs are spotted. The array is thereafter covered with cells, which take up the reagents. This format is much more time and cost efficient, but statistical analysis of the resulting data is more involved due to lower cell numbers and lack of physical barriers between neighboring spots.

We develop statistical tools for the analysis of RNAi screens the different formats. High-Throughput microscopy images are taken from the experiments and preprocessed by image recognition algorithms. The resulting quantitative data is then fed into our statistics pipeline.

RNAither: Statistical Data Analysis Pipeline

We have developed RNAither, a Bioconductor package for the statistical analysis of high-throughput RNAi and other cellular screens. The package RNAither analyzes high-throughput screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. The package generates html reports, which can be opened in any webbrowser, for easy visualization of data and analysis results. RNAither is available for download from the Bioconductor website.

Image: Overview over RNA data generation and analysis.

Single-Cell Analysis

Recent biological and technological advances in the field of high-content high-throughput imaging enable the detailed quantification of perturbation effects on a single-cell basis using multiparametric imaging. This in turn allows to quantify many different features, such as the cell size, the cell shape and the cell's local density, which describe a single cell and its population context. We have developed methods which take this single-cell information into account for the data normalization and for the statistical analysis. Applying the methods on RNAi data studying Hepatits C and Dengue virus, we achieved improved sensitivity and specificity in comparison to already existing methods, where single-cell information is disregarded.


In collaboration projects with specialized experimental groups, we apply our methods to genome-wide RNAi screens on diverse platforms, and participate in the analysis of both, primary and secondary screens in well-based as well as in cell-microarray based platforms. A main focus in these projects is on the elucidation of virus-host interactions using RNAi, for viruses such as HIV (Aids), Vesicular Stomatitis Virus (VSV), Dengue Virus, Hepatitis C Virus (HCV) and others.

Project-Related Publications

  1. B. Knapp, I. Rebhan, A. Kumar, P. Matula, N. A. Kiani, M. Binder, H. Erfle, K. Rohr, R. Eils, R. Bartenschlager, L. Kaderali (2011). Normalizing for Individual Cell Population Context in the Analysis of high-content Cellular Screens. BMC Bioinformatics 12:485.
  2. J. Gentzsch, B. Hinkelmann, L. Kaderali, H. Irschik, R. Jansen, F. Sasse, R. Frank, T. Pietschmann (2011). Hepatitis C virus complete life cycle screen for identification of small molecules with pro- or antiviral activity. Antiviral Research 89(2):136-48.
  3. S. Reiss, I. Rebhan, P. Backes, I. Romero-Brey, H. Erfle, P. Matula, L. Kaderali, M. Pönisch, H. Blankenburg, M.-S. Hiet, T. Longerich, S. Diehl, F. Ramirez, T. Balla, K. Rohr, A. Kaul, S. Bühler, R. Pepperkok, T. Lengauer, M. Albrecht, R. Eils, P. Schirmacher, V. Lohmann, R. Bartenschlager (2011) Recruitment and activation of a lipid kinase by NS5A of the hepatitis C virus is essential for integrity of the membranous replication compartment. Cell Host and Microbe 9(1):32-45.
  4. A, Suratanee, I. Rebhan, P. Matula, A. Kumar, L. Kaderali, K. Rohr, R. Bartenschlager, R. Eils, R. König (2010). Detecting host factors involved in virus infection by observing the clustering of infected cells in siRNA screening images. Bioinformatics 26:i653-i658, doi:10.1093/bioinformatics/btq398.
  5. A. D. Ebert, M. Laussmann, S. Wegehingel, L. Kaderali, H. Erfle, J. Reichert, J. Lechner, H.-D. Beer, R. Pepperkok, W. Nickel (2010). Tec-Kinase-mediated Phosphorylation of Fibroblast Growth Factor 2 is Essential for Unconventional Secretion. Traffic, 11(6):813-26.
  6. K. Börner, J. Hermle, C. Sommer, N. P. Brown, B. Knapp, B. Glass, J. Kunkel, G. Torralba, J. Reymann, N. Beil, J. Beneke, R. Pepperkok, R. Schneider, T. Ludwig, M. Hausmann, F. Hamprecht, H. Erfle, L. Kaderali, H.-G. Kräusslich, M. J. Lehmann (2010) From experimental setup to bioinformatics: An RNAi screening platform to identify host factors and potential cellular networks involved in HIV-1 replication, Biotechnology Journal, 5(1), 39-49.
  7. N. Rieber, B. Knapp, R. Eils, L. Kaderali (2009). RNAither, an automated pipeline for the statistical analysis of high-throughput RNAi screens. Bioinformatics, 25, 678-679, doi:10.1093/bioinformatics/btp014.
  8. N. Rieber, B. Knapp, A. Kumar, P. Matula, H. Erfle, R. Pepperkok, K. Rohr, R. Eils, R. Bartenschlager, L. Kaderali (2008). A statistics pipeline for the analysis of RNAi knockout data. Statistical Computing 2008, June 1-4, Schloss Reisensburg (Günzburg)
  9. M. J. Lehmann, J. Hermle, K. Börner, L. Kaderali, H. Erfle, R. Pepperkok, H.-G. Kräusslich (2008). High-throughput siRNA screening to identify human host factors involved in pathogenesis of HIV-1. 18th Annual Meeting of the Gesellschaft für Virologie, Heidelberg, March 5-8, 2008.
  10. I. Wörz, H. Erfle, P. Matula, L. Kaderali, N. Beil, K. Rohr, R. Eils, A. Kaul, S. Bühler, R. Pepperkok, R. Bartenschlager (2008). Establishment of a high through-put screening assay to identify host cell factors involved in Hepatitis C Virus entry and replication. 18th Annual Meeting of the Gesellschaft für Virologie, Heidelberg, March 5-8, 2008.
  11. A. Merz, J. Krijnse-Locker, N. Rieber, L. Kaderali, R. Pepperkok, R. Bartenschlager (2008). Investigation of host cell factors and pathways involved in hepatitis C virus assembly and release. 18th Annual Meeting of the Gesellschaft für Virologie, Heidelberg, March 5-8, 2008.