Systematic morphological profiling of human gene and allele function via Cell Painting

Rohban, Mohammad Hossein; Singh, Shantanu; Wu, Xiaoyun; Berthet, Julia B; Bray, Mark-Anthony; Shrestha, Yashaswi; Varelas, Xaralabos; Boehm, Jesse S.; Carpenter, Anne E.

doi:10.7554/elife.24060

Cited by 148 publications

(167 citation statements)

References 74 publications

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“…Other groups are generating proteomic or high-content imaging readouts following perturbation (Litichevskiy et al, 2017); (Rohban et al, 2017) consistent with early reports of feasibility of annotating compounds based on cellular consequences (Seiler et al, 2008). …”

Section: Discussionsupporting

confidence: 54%

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Subramanian

Narayan

Corsello

et al. 2017

Cell

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2,407

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Summary We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

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Section: Discussionsupporting

confidence: 54%

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Subramanian

Narayan

Corsello

et al. 2017

Cell

Self Cite

2,407

2,332

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“…Additional approaches and screening readouts, such as cellular morphology (Rohban et al, 2017), may be able to better classify complex subunits that predominantly have morphological effects rather than fitness effects. This is particularly relevant for the utility of these datasets in the context of emerging genes-to-variants studies, and for the functional characterization of other human disease-linked genetic mutations.…”

Section: Discussionmentioning

confidence: 99%

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

et al. 2018

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Summary Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9- based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks which recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently-evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone and in combination with large-scale biophysical data to enhance our understanding of mammalian protein complexes in normal and disease states.

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“…Popular staining choices are Hoechst dyes for DNA (and thereby, cell nuclei), mitotracker for mitochondria, phalloidin for actin, concanavalin A for lectins, and wheat germ agglutinin for membranes (Bray et al 2016) . The formed structures are easily visually distinguished and can be accurately detected with automated computational methods (Rohban et al 2017;Pärnamaa and Parts 2017) .…”

Section: Introductionmentioning

confidence: 99%

Segmenting nuclei in brightfield images with neural networks

Fishman

Salumaa

Majoral

et al. 2019

Preprint

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Identifying nuclei is a standard first step to analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we demonstrate that it is possible to accurately segment nuclei directly from brightfield images using deep learning. We confirmed that three convolutional neural network architectures can be adapted for this task, with U-Net achieving the best overall performance, Mask R-CNN providing an additional benefit of instance segmentation, and DeepCell proving too slow for practical application. We found that accurate segmentation is possible using as few as 16 training images and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work liberates a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

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Systematic morphological profiling of human gene and allele function via Cell Painting

Cited by 148 publications

References 74 publications

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

Segmenting nuclei in brightfield images with neural networks

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