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

Nucleic Acids Res

Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.

Published: November 2018

AI Article Synopsis

  • Changes in cell morphology, such as shape and size, can result from treatment with small molecule compounds, and these changes can indicate shifts in cellular function.
  • The LINCS Project has analyzed the effects of over 9,500 compounds on cell lines, linking gene expression data with changes in cell morphology to explore their relationship.
  • A proposed cell morphology enrichment analysis aims to connect transcriptomic changes with morphological variations and predict how new transcriptomic data can affect cell appearance, with a demonstration using a human osteosarcoma cell line.

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6212779PMC
http://dx.doi.org/10.1093/nar/gky626DOI Listing

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