Single‐cell analysis of population context advances RNAi screening at multiple levels

Molecular Systems Biology

Berend Snijder⁵  Raphael Sacher³  Pauli Rämö³  Prisca Liberali⁵  Karin Mench⁵  Nina Wolfrum⁵  Laura Burleigh⁶  Cameron C Scott¹  Monique H Verheije⁹  Jason Mercer⁸  Stefan Moese⁸  Thomas Heger⁸  Kristina Theusner¹⁰  Andreas Jurgeit⁵  David Lamparter³  Giuseppe Balistreri⁸  Mario Schelhaas⁸  Cornelis A M De Haan⁹  Varpu Marjomäki⁷  Timo Hyypiä²  Peter J M Rottier⁹  Beate Sodeik¹⁰  Mark Marsh^4,5  Jean Gruenberg¹  Ali Amara⁶  Urs Greber⁵  Ari Helenius⁸ 

[1]Biochemistry Department, University of Geneva, Geneva, Switzerland

[2]Department of Virology, University of Turku, Turku, Finland

[3]Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

[4]Cell Biology Unit, MRC-Laboratory for Molecular Cell Biology, University College London, London, UK

[5]Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

[6]Unité de pathogénie virale moléculaire INSERM 819, Institut Pasteur, Paris, France

[7]Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland

[8]Institute of Biochemistry, ETH Zürich, Zürich, Switzerland

[9]Virology Division, Department of Infectious Diseases and Immunology, Utrecht University, Utrecht, The Netherlands

[10]Institute of Virology, Hannover Medical School, Hannover, Germany

关键词: cell‐to‐cell variability;    image analysis;    population context;    RNAi;    virus infection;   

DOI  :  10.1038/msb.2012.9

来源: Wiley

【 摘 要 】

Abstract

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment.

Synopsis

A large set of high-content RNAi screens investigating mammalian virus infection and multiple cellular activities is analysed to reveal the impact of population context on phenotypic variability and to identify indirect RNAi effects.

• Cell population context determines phenotypes in RNAi screens of multiple cellular activities (including virus infection, cell size regulation, endocytosis, and lipid homeostasis), which can be accounted for by a combination of novel image analysis and multivariate statistical methods.
• Accounting for cell population context-mediated effects strongly changes the reproducibility and consistency of RNAi screens across cell lines as well as of siRNAs targeting the same gene.
• Such analyses can identify the perturbed regulation of population context dependent cell-to-cell variability, a novel perturbation phenotype.
• Overall, these methods advance the use of large-scale RNAi screening for a systems-level understanding of cellular processes.

【 授权许可】

CC BY-NC-SA   
Copyright © 2012 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.