Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Abstract

In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, which is a morphological profiling assay that multiplexes six fluorescent dyes, imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multiwell plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Next, an automated image analysis software identifies individual cells and measures 1,500 morphological features (various measures of size, shape, texture, intensity, and so on) to produce a rich profile that is suitable for the detection of subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes 2 weeks; feature extraction and data analysis take an additional 1–2 weeks.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The Cell Painting assay in U2OS and A549 cells.
Figure 2: Overview of the strategy of morphological profiling using an image-based assay.
Figure 3: Sample images from a small-molecule Cell Painting experiment using HUVEC cells.

References

  1. 1

    Swinney, D.C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    CAS  Article  Google Scholar 

  2. 2

    Swinney, D.C. The contribution of mechanistic understanding to phenotypic screening for first-in-class medicines. J. Biomol. Screen. 18, 1186–1192 (2013).

    Article  Google Scholar 

  3. 3

    Moffat, J.G., Joachim, R. & David, B. Phenotypic screening in cancer drug discovery — past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).

    CAS  Article  Google Scholar 

  4. 4

    Johannessen, C.M., Clemons, P.A. & Wagner, B.K. Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery. Trends Genet. 31, 16–23 (2015).

    CAS  Article  Google Scholar 

  5. 5

    Bickle, M. The beautiful cell: high-content screening in drug discovery. Anal. Bioanal. Chem. 398, 219–226 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Singh, S., Carpenter, A.E. & Genovesio, A. Increasing the content of high-content screening: an overview. J. Biomol. Screen. 19, 640–650 (2014).

    Article  Google Scholar 

  7. 7

    Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    CAS  Article  Google Scholar 

  8. 8

    Danuser, G. Computer vision in cell biology. Cell 147, 973–978 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Altschuler, S.J. & Wu, L.F. Cellular heterogeneity: do differences make a difference? Cell 141, 559–563 (2010).

    CAS  Article  Google Scholar 

  10. 10

    Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119–125 (2011).

    CAS  Article  Google Scholar 

  11. 11

    Eliceiri, K.W. et al. Biological imaging software tools. Nat. Methods 9, 697–710 (2012).

    CAS  Article  Google Scholar 

  12. 12

    Paull, K.D. et al. Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J. Natl. Cancer Inst. 81, 1088–1092 (1989).

    CAS  Article  Google Scholar 

  13. 13

    Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  Article  Google Scholar 

  14. 14

    Adams, C.L. et al. Compound classification using image-based cellular phenotypes. Methods Enzymol. 414, 440–468 (2006).

    CAS  Article  Google Scholar 

  15. 15

    Loo, L.-H., Wu, L.F. & Altschuler, S.J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445–453 (2007).

    CAS  Article  Google Scholar 

  16. 16

    Young, D.W. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 59–68 (2008).

    CAS  Article  Google Scholar 

  17. 17

    Ljosa, V. et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J. Biomol. Screen. 18, 1321–1329 (2013).

    CAS  Article  Google Scholar 

  18. 18

    Reisen, F. et al. Linking phenotypes and modes of action through high-content screen fingerprints. Assay Drug Dev. Technol. 13, 415–427 (2015).

    CAS  Article  Google Scholar 

  19. 19

    Futamura, Y. et al. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem. Biol. 19, 1620–1630 (2012).

    CAS  Article  Google Scholar 

  20. 20

    Sundaramurthy, V. et al. Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe 13, 129–142 (2013).

    CAS  Article  Google Scholar 

  21. 21

    Castoreno, A.B. et al. Small molecules discovered in a pathway screen target the Rho pathway in cytokinesis. Nat. Chem. Biol. 6, 457–463 (2010).

    CAS  Article  Google Scholar 

  22. 22

    Loo, L.-H. et al. An approach for extensibly profiling the molecular states of cellular subpopulations. Nat. Methods 6, 759–765 (2009).

    CAS  Article  Google Scholar 

  23. 23

    Fuchs, F. et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol. Syst. Biol. 6, 370 (2010).

    Article  Google Scholar 

  24. 24

    Collinet, C. et al. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243–249 (2010).

    CAS  Article  Google Scholar 

  25. 25

    Laufer, C., Fischer, B., Billmann, M., Huber, W. & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat. Methods 10, 427–431 (2013).

    CAS  Article  Google Scholar 

  26. 26

    Liberali, P., Snijder, B. & Pelkmans, L. A hierarchical map of regulatory genetic interactions in membrane trafficking. Cell 157, 1473–1487 (2014).

    CAS  Article  Google Scholar 

  27. 27

    Fischer, B. et al. A map of directional genetic interactions in a metazoan cell. Elife 4, e05464 (2015).

    Article  Google Scholar 

  28. 28

    Yin, Z. et al. A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nat. Cell Biol. 15, 860–871 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Gustafsdottir, S.M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 8, e80999 (2013).

    Article  Google Scholar 

  30. 30

    Wawer, M.J. et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc. Natl. Acad. Sci. USA 111, 10911–10916 (2014).

    CAS  Article  Google Scholar 

  31. 31

    Singh, S. et al. Morphological profiles of RNAi-induced gene knockdown are highly reproducible but dominated by seed effects. PLoS One 10, e0131370 (2015).

    Article  Google Scholar 

  32. 32

    Gibson, C.C. et al. Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation 131, 289–299 (2015).

    CAS  Article  Google Scholar 

  33. 33

    MacRae, C.A. A new phenotypic lexicon for accelerated translation: rise of the machines. Circulation 131, 234–236 (2015).

    Article  Google Scholar 

  34. 34

    Petrone, P.M. et al. Biodiversity of small molecules--a new perspective in screening set selection. Drug Discov. Today 18, 674–680 (2013).

    CAS  Article  Google Scholar 

  35. 35

    Peck, D. et al. A method for high-throughput gene expression signature analysis. Genome Biol. 7, R61 (2006).

    Article  Google Scholar 

  36. 36

    Rajaram, S., Pavie, B., Wu, L.F. & Altschuler, S.J. PhenoRipper: software for rapidly profiling microscopy images. Nat. Methods 9, 635–637 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Hartwell, K.A. et al. Niche-based screening identifies small-molecule inhibitors of leukemia stem cells. Nat. Chem. Biol. 9, 840–848 (2013).

    CAS  Article  Google Scholar 

  38. 38

    Uhlmann, V., Singh, S. & Carpenter, A.E. CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics 17, 51 (2016).

    Article  Google Scholar 

  39. 39

    Bray, M.-A. & Carpenter, A. in Assay Guidance Manual (eds. Sittampalam, G.S. et al.) (Eli Lilly & Company and the National Center for Advancing Translational Sciences, 2013).

  40. 40

    Iversen, P.W. et al. in Assay Guidance Manual (eds. Sittampalam, G.S. et al.) (Eli Lilly & Company and the National Center for Advancing Translational Sciences, 2012).

  41. 41

    Singh, S., Bray, M.-A., Jones, T.R. & Carpenter, A.E. Pipeline for illumination correction of images for high-throughput microscopy. J. Microsc. 256, 231–236 (2014).

    CAS  Article  Google Scholar 

  42. 42

    Bray, M.-A., Fraser, A.N., Hasaka, T.P. & Carpenter, A.E. Workflow and metrics for image quality control in large-scale high-content screens. J. Biomol. Screen. 17, 266–274 (2012).

    CAS  Article  Google Scholar 

  43. 43

    Clarke, R. et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8, 37–49 (2008).

    CAS  Article  Google Scholar 

  44. 44

    Feng, Y., Mitchison, T.J., Bender, A., Young, D.W. & Tallarico, J.A. Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nat. Rev. Drug Discov. 8, 567–578 (2009).

    CAS  Article  Google Scholar 

  45. 45

    Janzen, W.P. & Popa-Burke, I.G. Advances in improving the quality and flexibility of compound management. J. Biomol. Screen. 14, 444–451 (2009).

    CAS  Article  Google Scholar 

  46. 46

    Lundholt, B.K., Scudder, K.M. & Pagliaro, L. A simple technique for reducing edge effect in cell-based assays. J. Biomol. Screen. 8, 566–570 (2003).

    CAS  Article  Google Scholar 

  47. 47

    Ljosa, V., Sokolnicki, K.L. & Carpenter, A.E. Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 (2012).

    CAS  Article  Google Scholar 

  48. 48

    Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003).

    Google Scholar 

  49. 49

    Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  Google Scholar 

Download references

Acknowledgements

Research reported in this publication was supported in part by NIH R44 TR001197 (C.C.G.), NSF RIG DBI 1119830 (M.-A.B.), NIH R01 GM089652 (A.E.C.), and NSF CAREER DBI 1148823 (A.E.C.). The RNAi Cell Painting knockdown experiment used in this paper was previously published31 and was supported in part by the Slim Initiative for Genomic Medicine, a project funded by the Carlos Slim Foundation in Mexico. The authors thank the original developers of earlier versions of the protocol, who are the authors of the original paper describing the assay29; these authors are S.M. Gustafsdottir, V. Ljosa, K.L. Sokolnicki, J.A. Wilson, D. Walpita, M.M. Kemp, K. Petri Seiler, H.A. Carrel, T.R. Golub, S.L. Schreiber, P.A. Clemons, A.E. Carpenter, and A.F. Shamji. For enabling this work, Recursion Pharmaceuticals thanks the University of Utah Core Facilities (J. Phillips), specifically the Drug Discovery Core (B. Luo) and the Fluorescent Imaging Core (C. Rodesch). We thank members of the Broad Institute's Center for the Development of Therapeutics, especially T.P. Hasaka, for technical assistance. We also thank A. Kozol for proofreading the Equipment and Reagents sections and for testing the image analysis workflow, and A. Berger and X. Wu for offering helpful comments and suggestions during manuscript preparation.

Author information

Affiliations

Authors

Contributions

All authors contributed to writing this protocol. C.T.D., H.H., and S.M.G. contributed to describing the benchwork aspects of the protocol. C.H., M.K.-A., M.-A.B., and C.T.D. contributed to updates to the experimental design. M.-A.B. and S.S. contributed most heavily to describing the computational aspects of the protocol. A.E.C., C.C.G., B.B., and S.S. contributed most heavily to describing the rationale and the experimental design.

Corresponding authors

Correspondence to Christopher C Gibson or Anne E Carpenter.

Ethics declarations

Competing interests

Recursion Pharmaceuticals is a biotechnology company in which C.C.G., B.B., C.T.D., H.H., and A.E.C. have real or optional ownership interest.

Supplementary information

Supplementary Method

Steps for producing per-well morphological profiles from single-cell measurements (PDF 163 kb)

Supplementary Note

Imaging considerations for wide-field versus confocal microscopes (PDF 308 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bray, MA., Singh, S., Han, H. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc 11, 1757–1774 (2016). https://doi.org/10.1038/nprot.2016.105

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing