Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes

By Beate Neumann, Thomas Walter, Jean-Karim Hériché, Jutta Bulkescher, H. Erfle, C. Conrad, Phill Rogers, I. Poser, Michael Held, U. Liebel and 17 more. Nature, 2010

864 citations recorded by the source corpus.

Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the ∼21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/040b4f257b8b048ffb0e61e5ee4a8d16ce2d32da

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Molecular Biology

Citations in the corpus

The BioGRID interaction database: 2015 update (2014, background): “At the phenotypic level, genome-wide reagent collections for systematic perturbation of gene function have led to compendia of functional profiles for many different phenotypic characteristics (16–19).”
A Census of Human Soluble Protein Complexes (2012, neutral): “and genetic associations in human populations (see Extended Experimental Procedures), RNAi phenotypes in cell culture (Neumann et al., 2010), mutational and RNAi phenotypes in other species (via orthology), and shared transcriptional regulatory motifs (Xie et al.”
High-Content Screening for Quantitative Cell Biology. (2016, methodology): “With a few exceptions (e.g., [10,11]), producing and analyzing single frame and single time point images is the modus operandi of most reported studies.”
53BP1 nuclear bodies form around DNA lesions generated by mitotic transmission of chromosomes under replication stress (2011, methodology): “A genome-wide screening platform combining automated image acquisition of cells growing on siRNA arrays and a readout using accumulation of 53BP1 in nuclear foci was described previousl”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Numerous other groups have also taken advantage of SVM classifiers to classify morphological phenotypes in human cells, including mutant morphology (Schmitz et al., 2010), cell cycle arrest phenotypes (Neumann et al., 2010), and subcellular localization classes (Conrad et al., 2004).”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, methodology): “Supervised machine learning has been an important backbone for analysis pipelines in many high-content screening projects (Kittler et al., 2007; Fuchs et al., 2010; Neumann et al., 2010; Schmitz et al., 2010; Mercer et al., 2012).”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, methodology): “For example, a previous genome-wide siRNA screen induced at least 16 distinct morphological changes in the nuclei of cells during cell division (50).”
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. (2016, methodology): “Right: ChIP-seq enrichment of individually bound and co-bound targets in each group. this, we targeted in K562 cells 13 genes (33 guides) (Table S1) that were previously identified by a mitotic arrest phenotype in a genome-wide imaging screen in HeLa cells (Figure S6D) (from Neumann et al., 2010).”
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2016, neutral): “In both cases, CNNs here have out-performed recent state-of-the-art systems (e.g., accuracies of 80–90% have been typical) [2, 18].”
Applications in image-based profiling of perturbations. (2016, methodology): “For example, the MitoCheck project used time-lapse microscopy to track and profile individual cells, successfully documenting and classifying hundreds of genetic regulators associated with mitosis [78].”
Deep learning massively accelerates super-resolution localization microscopy (2018, background): “ANNA-PALM may also be beneficial for high-throughput imaging screens, for example, of drug treatments or gene knockouts 39–41 .”
The Protein Composition of Mitotic Chromosomes Determined Using Multiclassifier Combinatorial Proteomics (2010, neutral): “In contrast, cluster analysis, which has been used both in this study and in other recent work (Theis et al., 2009; Neumann et al., 2010), can only integrate data for proteins that have a value for every classiﬁer.”