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Microscopy-Based High-Content Screening (High-Content Imaging): Home

Literature, tools and other resources for microscopy-based high-content screening (HCS), also referred to as high-content analysis (HCA) or high-content imaging (HCI). Covered areas include microscopy theory, screening procedures, image analysis, phenotyp
 
 
Welcome to the topic guide on microscopy-based high-content screening!

 

This guide provides literature references and links to other resources for those interested in the different aspects of microscopy-based high-content screening (HCS), which is also referred to as high-content imaging (HCI) or high-content analysis (HCA).

Literature

General literature for HCS

Bray M, Carpenter A. Advanced Assay Development Guidelines for Image-Based High Content Screening and Analysis, 2012 https://www.ncbi.nlm.nih.gov/books/NBK126174/

 

Assay Guidance Manual. Sittampalam GS, Coussens NP, Brimacombe K, Grossman A, Arkin M, Auld D et al (eds). Bethesda (MD), 2004 https://www.ncbi.nlm.nih.gov/books/NBK53196/

 

Boutros M, Heigwer F, Laufer C. Microscopy-Based High-Content Screening. Cell 2015; 163: 1314-1325. doi: 10.1016/j.cell.2015.11.007. https://www.ncbi.nlm.nih.gov/pubmed/26638068

 

Echeverri CJ, Perrimon N. High-throughput RNAi screening in cultured cells: a user's guide. Nat Rev Genet 2006; 7: 373-384. doi: 10.1038/nrg1836. http://www.ncbi.nlm.nih.gov/pubmed/16607398

 

Trask OJ. Guidelines for Microplate Selection in High Content Imaging. Methods Mol Biol 2018; 1683: 75-88. doi: 10.1007/978-1-4939-7357-6_6. https://www.ncbi.nlm.nih.gov/pubmed/29082488

 

 

Image analysis and phenotypic profiling

Eliceiri KW, Berthold MR, Goldberg IG et al. Biological imaging software tools. Nature methods 2012; 9: 697-710. doi: 10.1038/nmeth.2084. https://www.ncbi.nlm.nih.gov/pubmed/22743775

 

Caicedo JC, Cooper S, Heigwer F et al. Data-analysis strategies for image-based cell profiling. Nature methods 2017; 14: 849-863. doi: 10.1038/nmeth.4397. https://www.ncbi.nlm.nih.gov/pubmed/28858338

 

Grys BT, Lo DS, Sahin N et al. Machine learning and computer vision approaches for phenotypic profiling. The Journal of cell biology 2017; 216: 65-71. doi: 10.1083/jcb.201610026. https://www.ncbi.nlm.nih.gov/pubmed/27940887

 

Sommer C, Gerlich DW. Machine learning in cell biology - teaching computers to recognize phenotypes. J Cell Sci 2013; 126: 5529-5539. doi: 10.1242/jcs.123604. https://www.ncbi.nlm.nih.gov/pubmed/24259662

 

Evans L, Sailem H, Vargas PP, Bakal C. Inferring signalling networks from images. J Microsc 2013; 252: 1-7. doi: 10.1111/jmi.12062. https://www.ncbi.nlm.nih.gov/pubmed/23841886

Caicedo JC, Singh S, Carpenter AE. Applications in image-based profiling of perturbations. Curr Opin Biotechnol 2016; 39: 134-142. doi: 10.1016/j.copbio.2016.04.003. https://www.ncbi.nlm.nih.gov/pubmed/27089218

 

 

Data presentation

Sailem HZ, Cooper S, Bakal C. Visualizing quantitative microscopy data: History and challenges. Crit Rev Biochem Mol Biol 2016; 51: 96-101. doi: 10.3109/10409238.2016.1146222. https://www.ncbi.nlm.nih.gov/pubmed/26906253

 

 

Data management

Kozak K, Eshun B. The handling and analysis of large scale high content screening data. European Pharmaceutical Review 2007. https://www.europeanpharmaceuticalreview.com/article/2273/the-handling-and-analysis-of-large-scale-high-content-screening-data/

 

Author

Sonja Aits's picture
Sonja Aits
Contact:
Lund University
Department of Experimental Medical Science
Cell Death and Lysosomes Group

BMC B11
221 84 Lund
Sweden

Web:
http://research.med.lu.se/sonja-aits
http://www.aitslab.org
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