6533b82ffe1ef96bd1295e4f
RESEARCH PRODUCT
Single-cell analysis of population context advances RNAi screening at multiple levels
Berend SnijderRaphael SacherPauli RämöPrisca LiberaliKarin MenchNina WolfrumLaura BurleighCameron C ScottMonique H VerheijeJason MercerStefan MoeseThomas HegerKristina TheusnerAndreas JurgeitDavid LamparterGiuseppe BalistreriMario SchelhaasCornelis A M De HaanVarpu MarjomäkiTimo HyypiäPeter J M RottierBeate SodeikMark MarshJean GruenbergAli AmaraUrs GreberAri HeleniusLucas PelkmansLs PathologieLs VirologiePb SibStrategic Infection Biologysubject
toImage ProcessingDruggabilityGenomeImage analysis0302 clinical medicineComputer-AssistedSX00 SystemsX.ch2604 Applied MathematicsSingle-cell analysisRNA interferenceModels2400 General Immunology and MicrobiologyImage Processing Computer-AssistedViralRNA Small Interfering0303 health scienceseducation.field_of_studyApplied MathematicsSystems BiologyGenomics10124 Institute of Molecular Life SciencesCell biologycell variabilityComputational Theory and MathematicsCellular MicroenvironmentVirus DiseasesVirusesRNA ViralRNA InterferenceSingle-Cell AnalysisGeneral Agricultural and Biological SciencesInformation SystemsSystems biologyVirus infectionPopulationContext (language use)Genomics1100 General Agricultural and Biological SciencesBiologySmall InterferingModels BiologicalGeneral Biochemistry Genetics and Molecular BiologySX08 LipidX03 medical and health sciencesViral ProteinsCell-to-cell variability; Image analysis; Population context; RNAi; Virus infection1300 General Biochemistry Genetics and Molecular BiologyHumansComputer Simulationeducation030304 developmental biologyGeneral Immunology and MicrobiologyCell-to-cell variabilityReproducibility of ResultsBayes TheoremcellBiologicalPopulation contextRNAi570 Life sciences; biologyRNA030217 neurology & neurosurgeryHeLa Cellsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-04-24 | Molecular Systems Biology |