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Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.

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TLDR
A cell morphology enrichment analysis is proposed to assess the association between transcriptomic alterations and changes in cell morphology and is demonstrated to be utility by applying it to cell morphological changes in a human bone osteosarcoma cell line.
Abstract
Cell morphological phenotypes, including shape, size, intensity, and texture of cellular compartments have been shown to change in response to perturbation with small molecule compounds Image-based cell profiling or cell morphological profiling has been used to associate changes of cell morphological features with alterations in cellular function and to infer molecular mechanisms of action Recently, the Library of Integrated Network-based Cellular Signatures (LINCS) Project has measured gene expression and performed image-based cell profiling on cell lines treated with 9515 unique compounds These data provide an opportunity to study the interdependence between transcription and cell morphology Previous methods to investigate cell phenotypes have focused on targeting candidate genes as components of known pathways, RNAi morphological profiling, and cataloging morphological defects; however, these methods do not provide an explicit model to link transcriptomic changes with corresponding alterations in morphology To address this, we propose a cell morphology enrichment analysis to assess the association between transcriptomic alterations and changes in cell morphology Additionally, for a new transcriptomic query, our approach can be used to predict associated changes in cellular morphology We demonstrate the utility of our method by applying it to cell morphological changes in a human bone osteosarcoma cell line

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Journal ArticleDOI

A deep learning model to predict RNA-Seq expression of tumours from whole slide images.

TL;DR: It is shown that whole slide histology slides—part of routine care—can be used to predict RNA-sequencing data and thus reduce the need for additional analyses, and is illustrated in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.
Journal ArticleDOI

Image-based profiling for drug discovery: due for a machine-learning upgrade?

TL;DR: How the application of machine learning is renewing interest in image-based profiling for all aspects of the drug discovery process, from understanding disease mechanisms to predicting a drug’s activity or mechanism of action is discussed.
Journal ArticleDOI

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction.

TL;DR: The cell viability–signature relationship was used to predict viability from transcriptomics signatures, and compounds that induce cell death in tumor cell lines were identified and validated.
Journal ArticleDOI

Phenotypic drug discovery: recent successes, lessons learned and new directions

TL;DR: Recent successes with modern phenotypic drug discovery are discussed, as well as ongoing challenges and approaches to address them, and how innovation in this area may fuel the next generation of successful projects are explored.
Posted ContentDOI

Morphology and gene expression profiling provide complementary information for mapping cell state

TL;DR: In this article, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, they perturb A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses.
References
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Journal ArticleDOI

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

TL;DR: The first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler is described, which can address a variety of biological questions quantitatively.
Journal ArticleDOI

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

TL;DR: The first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules is created, and it is demonstrated that this “Connectivity Map” resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs.
Journal ArticleDOI

Qgraph: Network visualizations of relationships in psychometric data

TL;DR: The qgraph package for R is presented, which provides an interface to visualize data through network modeling techniques, and is introduced by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
Journal ArticleDOI

Cell mechanics and the cytoskeleton

TL;DR: An important insight emerging from this work is that long-lived cytoskeletal structures may act as epigenetic determinants of cell shape, function and fate.
Journal ArticleDOI

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

TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
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