Predicting cell health phenotypes using image-based morphology profiling-Reference-Cited by-同舟云学术

Predicting cell health phenotypes using image-based morphology profiling

Published:2021-04-19 Issue:9 Volume:32 Page:995-1005
ISSN:1059-1524
Container-title:Molecular Biology of the Cell
language:en
Short-container-title:MBoC

Author:

Way Gregory P.¹,Kost-Alimova Maria²,Shibue Tsukasa²,Harrington William F.²,Gill Stanley²³,Piccioni Federica⁴,Becker Tim¹,Shafqat-Abbasi Hamdah¹,Hahn William C.²³,Carpenter Anne E.¹,Vazquez Francisca²,Singh Shantanu¹

Affiliation:

1. Imaging Platform, Cambridge, MA 02142

2. Cancer Program, Cambridge, MA 02142

3. Dana-Farber Cancer Institute, Department of Medical Oncology, Harvard Medical School, Boston, MA 02215

4. Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142

Abstract

We created assays to quantify various readouts related to cell health, and then devised a machine learning method to predict those readouts using images from the cheap and fast Cell Painting assay. Because such images are publicly available, cell health annotations can now be readily added to thousands of drugs and genetic perturbations.

Publisher

American Society for Cell Biology (ASCB)

Subject

Cell Biology,Molecular Biology

Reference39 articles.

1. Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting

2. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

3. Data-analysis strategies for image-based cell profiling

4. Applications in image-based profiling of perturbations

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