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Data-analysis strategies for image-based cell profiling / Juan C Caicedo, Sam Cooper, Florian Heigwer, Scott Warchal, Peng Qiu, Csaba Molnar, Aliaksei S Vasilevich, Joseph D Barry, Harmanjit Singh Bansal, Oren Kraus, Mathias Wawer, Lassi Paavolainen, Markus D Herrmann, Mohammad Rohban, Jane Hung, Holger Hennig, John Concannon, Ian Smith, Paul A Clemons, Shantanu Singh, Paul Rees, Peter Horvath, Roger G Linington, Anne E Carpenter

Nature Methods, Volume: 14, Issue: 9, Pages: 849 - 863

Swansea University Author: Paul Rees

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DOI (Published version): 10.1038/nmeth.4397

Abstract

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involv...

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Published in: Nature Methods
ISSN: 1548-7091 1548-7105
Published: 2017
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa35840
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Abstract: Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.
Item Description: 2018: This work is the result of a collaboration between 19 laboratories worldwide at the forefront of cell image high-content analysis to develop a best practice protocol together with the open-access tools required to reliably measure cell phenotypes, signaling, cycle information etc. This is fast becoming the protocol which will be used as a standard that researchers will have to comply to in terms of quality control, data reporting and transparency to publish this type of analysis. The work is culmination of many years work with the Broad Institute of MIT and Harvard funded by several BBSRC, EPSRC and NSF grants.
Keywords: Image processing, Machine learning
College: College of Engineering
Issue: 9
Start Page: 849
End Page: 863