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Clustering phenotype populations by genome-wide RNAi and multiparametric imaging.

Fuchs F, Pau G, Kranz D, Sklyar O, Budjan C, Steinbrink S, Horn T, Pedal A, Huber W, Boutros M - Mol. Syst. Biol. (2010)

Bottom Line: With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible.Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity.Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

View Article: PubMed Central - PubMed

Affiliation: German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.

ABSTRACT
Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

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Genome-wide two-dimensional map of the perturbation phenotypes. (A) Each of the 1820 nodes represents a perturbation and is characterized by a phenoprint. Nodes are linked by a grey edge when their phenotypic distance is below 0.16. Groups of nodes with small distances form clusters and are coloured according to their most prominent cell subpopulations. The phenotypic cluster BL is characterized by a decrease in the number of cells and increase of metaphase cells. The cluster SM shows an abundance of large cells with protrusions and bright nuclei. The map provides an overview over the variety of phenoprints and shows groups of phenotypically similar perturbations. An interactive version is provided on the companion website (see http://www.cellmorph.org). (B) Representative images of cell populations from different regions of the map show the similarity of phenotypes of neighbouring genes, and the gradual variation of phenotypes on paths through the map. (C) Heat maps of phenoprints of selected genes from the clusters BL and SM. The colour code represents the strength of the increase or decrease in phenotypic properties. (D) Functional categories of the 280 confirmed phenotypes; 44% (122 of 280) were functionally uncharacterized. Source data is available for this figure at www.nature.com/msb.
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f3: Genome-wide two-dimensional map of the perturbation phenotypes. (A) Each of the 1820 nodes represents a perturbation and is characterized by a phenoprint. Nodes are linked by a grey edge when their phenotypic distance is below 0.16. Groups of nodes with small distances form clusters and are coloured according to their most prominent cell subpopulations. The phenotypic cluster BL is characterized by a decrease in the number of cells and increase of metaphase cells. The cluster SM shows an abundance of large cells with protrusions and bright nuclei. The map provides an overview over the variety of phenoprints and shows groups of phenotypically similar perturbations. An interactive version is provided on the companion website (see http://www.cellmorph.org). (B) Representative images of cell populations from different regions of the map show the similarity of phenotypes of neighbouring genes, and the gradual variation of phenotypes on paths through the map. (C) Heat maps of phenoprints of selected genes from the clusters BL and SM. The colour code represents the strength of the increase or decrease in phenotypic properties. (D) Functional categories of the 280 confirmed phenotypes; 44% (122 of 280) were functionally uncharacterized. Source data is available for this figure at www.nature.com/msb.

Mentions: In this study, we developed an automated approach using RNAi-mediated cell phenotypes, multiparametric imaging and computational modelling to obtain functional information on previously uncharacterized genes. To generate broad, computer-readable phenotypic signatures, we measured the effect of RNAi-mediated knockdowns on changes of cell morphology in human cells on a genome-wide scale. First, the several million cells were stained for nuclear and cytoskeletal markers and then imaged using automated microscopy. On the basis of fluorescent markers, we established an automated image analysis to classify individual cells (Figure 1A). After cell segmentation for determining nuclei and cell boundaries (Figure 1C), we computed 51 cell descriptors that quantified intensities, shape characteristics and texture (Figure 1F). Individual cells were categorized into 1 of 10 classes, which included cells showing protrusion/elongation, cells in metaphase, large cells, condensed cells, cells with lamellipodia and cellular debris (Figure 1D and E). Each siRNA knockdown was summarized by a phenotypic profile and differences between RNAi knockdowns were quantified by the similarity between phenotypic profiles. We termed the vector of scores a phenoprint (Figure 3C) and defined the phenotypic distance between a pair of perturbations as the distance between their corresponding phenoprints.


Clustering phenotype populations by genome-wide RNAi and multiparametric imaging.

Fuchs F, Pau G, Kranz D, Sklyar O, Budjan C, Steinbrink S, Horn T, Pedal A, Huber W, Boutros M - Mol. Syst. Biol. (2010)

Genome-wide two-dimensional map of the perturbation phenotypes. (A) Each of the 1820 nodes represents a perturbation and is characterized by a phenoprint. Nodes are linked by a grey edge when their phenotypic distance is below 0.16. Groups of nodes with small distances form clusters and are coloured according to their most prominent cell subpopulations. The phenotypic cluster BL is characterized by a decrease in the number of cells and increase of metaphase cells. The cluster SM shows an abundance of large cells with protrusions and bright nuclei. The map provides an overview over the variety of phenoprints and shows groups of phenotypically similar perturbations. An interactive version is provided on the companion website (see http://www.cellmorph.org). (B) Representative images of cell populations from different regions of the map show the similarity of phenotypes of neighbouring genes, and the gradual variation of phenotypes on paths through the map. (C) Heat maps of phenoprints of selected genes from the clusters BL and SM. The colour code represents the strength of the increase or decrease in phenotypic properties. (D) Functional categories of the 280 confirmed phenotypes; 44% (122 of 280) were functionally uncharacterized. Source data is available for this figure at www.nature.com/msb.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2913390&req=5

f3: Genome-wide two-dimensional map of the perturbation phenotypes. (A) Each of the 1820 nodes represents a perturbation and is characterized by a phenoprint. Nodes are linked by a grey edge when their phenotypic distance is below 0.16. Groups of nodes with small distances form clusters and are coloured according to their most prominent cell subpopulations. The phenotypic cluster BL is characterized by a decrease in the number of cells and increase of metaphase cells. The cluster SM shows an abundance of large cells with protrusions and bright nuclei. The map provides an overview over the variety of phenoprints and shows groups of phenotypically similar perturbations. An interactive version is provided on the companion website (see http://www.cellmorph.org). (B) Representative images of cell populations from different regions of the map show the similarity of phenotypes of neighbouring genes, and the gradual variation of phenotypes on paths through the map. (C) Heat maps of phenoprints of selected genes from the clusters BL and SM. The colour code represents the strength of the increase or decrease in phenotypic properties. (D) Functional categories of the 280 confirmed phenotypes; 44% (122 of 280) were functionally uncharacterized. Source data is available for this figure at www.nature.com/msb.
Mentions: In this study, we developed an automated approach using RNAi-mediated cell phenotypes, multiparametric imaging and computational modelling to obtain functional information on previously uncharacterized genes. To generate broad, computer-readable phenotypic signatures, we measured the effect of RNAi-mediated knockdowns on changes of cell morphology in human cells on a genome-wide scale. First, the several million cells were stained for nuclear and cytoskeletal markers and then imaged using automated microscopy. On the basis of fluorescent markers, we established an automated image analysis to classify individual cells (Figure 1A). After cell segmentation for determining nuclei and cell boundaries (Figure 1C), we computed 51 cell descriptors that quantified intensities, shape characteristics and texture (Figure 1F). Individual cells were categorized into 1 of 10 classes, which included cells showing protrusion/elongation, cells in metaphase, large cells, condensed cells, cells with lamellipodia and cellular debris (Figure 1D and E). Each siRNA knockdown was summarized by a phenotypic profile and differences between RNAi knockdowns were quantified by the similarity between phenotypic profiles. We termed the vector of scores a phenoprint (Figure 3C) and defined the phenotypic distance between a pair of perturbations as the distance between their corresponding phenoprints.

Bottom Line: With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible.Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity.Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

View Article: PubMed Central - PubMed

Affiliation: German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.

ABSTRACT
Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.

Show MeSH
Related in: MedlinePlus