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HCS Methodology for Helping in Lab Scale Image-Based Assays

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Computer Optimized Microscopy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2040))

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Abstract

High-content screening (HCS) automates image acquisition and analysis in microscopy. This technology considers the multiple parameters contained in the images and produces statistically significant results. The recent improvements in image acquisition throughput, image analysis, and machine learning (ML) have popularized this kind of experiments, emphasizing the need for new tools and know-how to help in its design, analysis, and data interpretation. This chapter summarizes HCS recommendations for lab scale assays and provides both macros for HCS-oriented image analysis and user-friendly tools for data mining processes. All the steps described herein are oriented to a wide variety of image cell-based experiments. The workflows are illustrated with practical examples and test images. Their use is expected to help analyze thousands of images, create graphical representations, and apply machine learning models on HCS.

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References

  1. Hartig SM, Newberg JY, Bolt MJ, Szafran AT, Marcelli M, Mancini MA (2011) Automated microscopy and image analysis for androgen receptor function. Methods Mol Biol 776:313–331. https://doi.org/10.1007/978-1-61779-243-4_18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Starkuviene V, Pepperkok R (2007) The potential of high-content high-throughput microscopy in drug discovery. Br J Pharmacol 152(1):62–71. https://doi.org/10.1038/sj.bjp.0707346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Martinez NJ, Titus SA, Wagner AK, Simeonov A (2015) High-throughput fluorescence imaging approaches for drug discovery using in vitro and in vivo three-dimensional models. Expert Opin Drug Discov 10(12):1347–1361. https://doi.org/10.1517/17460441.2015.1091814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Westhoff JH, Giselbrecht S, Schmidts M, Schindler S, Beales PL, Tonshoff B, Liebel U, Gehrig J (2013) Development of an automated imaging pipeline for the analysis of the zebrafish larval kidney. PLoS One 8(12):e82137. https://doi.org/10.1371/journal.pone.0082137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Garvey CM, Spiller E, Lindsay D, Chiang CT, Choi NC, Agus DB, Mallick P, Foo J, Mumenthaler SM (2016) A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Sci Rep 6:29752. https://doi.org/10.1038/srep29752

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fang Y, Eglen RM (2017) Three-dimensional cell cultures in drug discovery and development. SLAS Discov 22(5):456–472. https://doi.org/10.1177/1087057117696795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Henser-Brownhill T, Monserrat J, Scaffidi P (2017) Generation of an arrayed CRISPR-Cas9 library targeting epigenetic regulators: from high-content screens to in vivo assays. Epigenetics 12(12):1065–1075. https://doi.org/10.1080/15592294.2017.1395121

    Article  PubMed  Google Scholar 

  8. de Groot R, Luthi J, Lindsay H, Holtackers R, Pelkmans L (2018) Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens. Mol Syst Biol 14(1):e8064

    Article  PubMed  PubMed Central  Google Scholar 

  9. Boutros M, Bras LP, Huber W (2006) Analysis of cell-based RNAi screens. Genome Biol 7(7):R66. https://doi.org/10.1186/gb-2006-7-7-R66

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682. https://doi.org/10.1038/nmeth.2019

    Article  CAS  PubMed  Google Scholar 

  11. Demsar JCT, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: data mining toolbox in python. J Mach Learn Res 14:2349–2353

    Google Scholar 

  12. Materials can be downloaded from. https://github.com/ConfocalMicroscopyUnit/HCSmaterials

  13. Link W, Oyarzabal J, Serelde BG, Albarran MI, Rabal O, Cebria A, Alfonso P, Fominaya J, Renner O, Peregrina S, Soilan D, Ceballos PA, Hernandez AI, Lorenzo M, Pevarello P, Granda TG, Kurz G, Carnero A, Bischoff JR (2009) Chemical interrogation of FOXO3a nuclear translocation identifies potent and selective inhibitors of phosphoinositide 3-kinases. J Biol Chem 284(41):28392–28400. https://doi.org/10.1074/jbc.M109.038984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fiji download website. https://ImageJ.net/Fiji/Downloads

  15. Orange download website. https://Orange.biolab.si/download/

  16. Bray MA, Carpenter A (2004) Advanced assay development guidelines for image-based high content screening and analysis. doi:NBK126174 [bookaccession]

    Google Scholar 

  17. Nierode G, Kwon PS, Dordick JS, Kwon SJ (2016) Cell-based assay design for high-content screening of drug candidates. J Microbiol Biotechnol 26(2):213–225. https://doi.org/10.4014/jmb.1508.08007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Johnston PA, Shinde SN, Hua Y, Shun TY, Lazo JS, Day BW (2012) Development and validation of a high-content screening assay to identify inhibitors of cytoplasmic dynein-mediated transport of glucocorticoid receptor to the nucleus. Assay Drug Dev Technol 10(5):432–456. https://doi.org/10.1089/adt.2012.456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jackson D, Lenard M, Zelensky A, Shaikh M, Scharpf JV, Shaginaw R, Nawade M, Agler M, Cloutier NJ, Fennell M, Guo Q, Wardwell-Swanson J, Zhao D, Zhu Y, Miller C, Gill J (2010) HCS road: an enterprise system for integrated HCS data management and analysis. J Biomol Screen 15(7):882–891. https://doi.org/10.1177/1087057110374233

    Article  PubMed  Google Scholar 

  20. Voskuil JL (2017) The challenges with the validation of research antibodies. F1000Res 6:161. https://doi.org/10.12688/f1000research.10851.1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Singh S, Carpenter AE, Genovesio A (2014) Increasing the content of high-content screening: an overview. J Biomol Screen 19(5):640–650. https://doi.org/10.1177/1087057114528537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sommer C, Hoefler R, Samwer M, Gerlich DW (2017) A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol Biol Cell 28(23):3428–3436. https://doi.org/10.1091/mbc.E17-05-0333

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fuchs F, Pau G, Kranz D, Sklyar O, Budjan C, Steinbrink S, Horn T, Pedal A, Huber W, Boutros M (2010) Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol Syst Biol 6:370. https://doi.org/10.1038/msb.2010.25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver SJ, Grenier JK, Castoreno AB, Eggert US, Root DE, Golland P, Sabatini DM (2009) Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A 106(6):1826–1831. https://doi.org/10.1073/pnas.0808843106

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rijsbergen CJV (1979) Information retrieval. Butterworth-Heinemann, Newton, MA

    Google Scholar 

  26. Jolliffe IT (1986) Principal component analysis and factor analysis. In: Principal component analysis. Springer New York, New York, NY, pp 115–128. https://doi.org/10.1007/978-1-4757-1904-8_7

    Chapter  Google Scholar 

  27. Brideau C, Gunter B, Pikounis B, Liaw A (2003) Improved statistical methods for hit selection in high-throughput screening. J Biomol Screen 8(6):634–647. https://doi.org/10.1177/1087057103258285

    Article  PubMed  Google Scholar 

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Acknowledgments

We acknowledge Pablo J. Fernandez-Marcos, PhD, Madrid Institute of Advanced Studies IMDEA-Food, for his scientific contribution. We are grateful to Elena Rebollo and Manel Bosch for their help and critical reading.

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Author notes

  1. J. Soriano and G. Mata contributed equally to this work.

Authors and Affiliations

  1. Confocal Microscopy Unit, Spanish National Cancer Research Centre-CNIO, Madrid, Spain

    Joaquim Soriano, Gadea Mata & Diego Megias

Authors
  1. Joaquim Soriano
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  2. Gadea Mata
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  3. Diego Megias
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Corresponding author

Correspondence to Diego Megias .

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Editors and Affiliations

  1. Molecular Imaging Platform, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Spain

    Elena Rebollo

  2. Scientific and Technological Centers (CCiTUB), Universitat de Barcelona, Barcelona, Spain

    Manel Bosch

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Soriano, J., Mata, G., Megias, D. (2019). HCS Methodology for Helping in Lab Scale Image-Based Assays. In: Rebollo, E., Bosch, M. (eds) Computer Optimized Microscopy. Methods in Molecular Biology, vol 2040. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9686-5_15

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  • DOI: https://doi.org/10.1007/978-1-4939-9686-5_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9685-8

  • Online ISBN: 978-1-4939-9686-5

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