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

Journal name:
Nature Protocols
Volume:
11,
Pages:
1757–1774
Year published:
DOI:
doi:10.1038/nprot.2016.105
Published online

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.

At a glance

Figures

  1. The Cell Painting assay in U2OS and A549 cells.
    Figure 1: The Cell Painting assay in U2OS and A549 cells.

    The columns display the five channels imaged in the Cell Painting assay protocol (left to right) as imaged using the ImageXpress XLS microscope: Hoechst 33342 (DNA), concanavalin A (endoplasmic reticulum), SYTO 14 (nucleoli and cytoplasmic RNA), phalloidin (actin) and WGA (Golgi and plasma membrane), and MitoTracker Deep Red (mitochondria). Scale bars, 20 μm. See Table 1 for additional details about the stains and channels imaged.

  2. Overview of the strategy of morphological profiling using an image-based assay.
    Figure 2: Overview of the strategy of morphological profiling using an image-based assay.

    After perturbing, staining and imaging cells, the open-source software CellProfiler is used to extract ~1,500 morphological features of each cell. The collection of features is known as a profile: it reflects the phenotypic state of the cells in that sample, and it can be compared with other profiles to make inferences.

  3. Sample images from a small-molecule Cell Painting experiment using HUVEC cells.
    Figure 3: Sample images from a small-molecule Cell Painting experiment using HUVEC cells.

    Images are shown from a well treated with transfection reagent alone (negative control, top row) and an siRNA-treated well (bottom row), as imaged on an ImageXpress XLS microscope. The first five columns display the five channels imaged in the Cell Painting assay protocol, whereas the last column illustrates a merged image of the DNA stain (blue) and the nucleolar/cytoplasmic RNA stain (red), with the identified (i.e., segmented) nuclei and cell body outlines resulting from image analysis overlaid in white and magenta, respectively. Scale bars, 20 μm. See Table 1 for details about the stains and channels imaged. Each panel is 6% of a full image; for each well, nine images are acquired in a 3 × 3 grid, representing ~52% of the well area in total.

References

  1. Swinney, D.C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507519 (2011).
  2. Swinney, D.C. The contribution of mechanistic understanding to phenotypic screening for first-in-class medicines. J. Biomol. Screen. 18, 11861192 (2013).
  3. Moffat, J.G., Joachim, R. & David, B. Phenotypic screening in cancer drug discovery — past, present and future. Nat. Rev. Drug Discov. 13, 588602 (2014).
  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, 1623 (2015).
  5. Bickle, M. The beautiful cell: high-content screening in drug discovery. Anal. Bioanal. Chem. 398, 219226 (2010).
  6. Singh, S., Carpenter, A.E. & Genovesio, A. Increasing the content of high-content screening: an overview. J. Biomol. Screen. 19, 640650 (2014).
  7. Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 11941198 (2004).
  8. Danuser, G. Computer vision in cell biology. Cell 147, 973978 (2011).
  9. Altschuler, S.J. & Wu, L.F. Cellular heterogeneity: do differences make a difference? Cell 141, 559563 (2010).
  10. Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119125 (2011).
  11. Eliceiri, K.W. et al. Biological imaging software tools. Nat. Methods 9, 697710 (2012).
  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, 10881092 (1989).
  13. Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 19291935 (2006).
  14. Adams, C.L. et al. Compound classification using image-based cellular phenotypes. Methods Enzymol. 414, 440468 (2006).
  15. Loo, L.-H., Wu, L.F. & Altschuler, S.J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445453 (2007).
  16. Young, D.W. et al. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 4, 5968 (2008).
  17. Ljosa, V. et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J. Biomol. Screen. 18, 13211329 (2013).
  18. Reisen, F. et al. Linking phenotypes and modes of action through high-content screen fingerprints. Assay Drug Dev. Technol. 13, 415427 (2015).
  19. Futamura, Y. et al. Morphobase, an encyclopedic cell morphology database, and its use for drug target identification. Chem. Biol. 19, 16201630 (2012).
  20. Sundaramurthy, V. et al. Integration of chemical and RNAi multiparametric profiles identifies triggers of intracellular mycobacterial killing. Cell Host Microbe 13, 129142 (2013).
  21. Castoreno, A.B. et al. Small molecules discovered in a pathway screen target the Rho pathway in cytokinesis. Nat. Chem. Biol. 6, 457463 (2010).
  22. Loo, L.-H. et al. An approach for extensibly profiling the molecular states of cellular subpopulations. Nat. Methods 6, 759765 (2009).
  23. Fuchs, F. et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol. Syst. Biol. 6, 370 (2010).
  24. Collinet, C. et al. Systems survey of endocytosis by multiparametric image analysis. Nature 464, 243249 (2010).
  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, 427431 (2013).
  26. Liberali, P., Snijder, B. & Pelkmans, L. A hierarchical map of regulatory genetic interactions in membrane trafficking. Cell 157, 14731487 (2014).
  27. Fischer, B. et al. A map of directional genetic interactions in a metazoan cell. Elife 4, e05464 (2015).
  28. Yin, Z. et al. A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nat. Cell Biol. 15, 860871 (2013).
  29. Gustafsdottir, S.M. et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One 8, e80999 (2013).
  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, 1091110916 (2014).
  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).
  32. Gibson, C.C. et al. Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation. Circulation 131, 289299 (2015).
  33. MacRae, C.A. A new phenotypic lexicon for accelerated translation: rise of the machines. Circulation 131, 234236 (2015).
  34. Petrone, P.M. et al. Biodiversity of small molecules--a new perspective in screening set selection. Drug Discov. Today 18, 674680 (2013).
  35. Peck, D. et al. A method for high-throughput gene expression signature analysis. Genome Biol. 7, R61 (2006).
  36. Rajaram, S., Pavie, B., Wu, L.F. & Altschuler, S.J. PhenoRipper: software for rapidly profiling microscopy images. Nat. Methods 9, 635637 (2012).
  37. Hartwell, K.A. et al. Niche-based screening identifies small-molecule inhibitors of leukemia stem cells. Nat. Chem. Biol. 9, 840848 (2013).
  38. Uhlmann, V., Singh, S. & Carpenter, A.E. CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics 17, 51 (2016).
  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. 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. Singh, S., Bray, M.-A., Jones, T.R. & Carpenter, A.E. Pipeline for illumination correction of images for high-throughput microscopy. J. Microsc. 256, 231236 (2014).
  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, 266274 (2012).
  43. Clarke, R. et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8, 3749 (2008).
  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, 567578 (2009).
  45. Janzen, W.P. & Popa-Burke, I.G. Advances in improving the quality and flexibility of compound management. J. Biomol. Screen. 14, 444451 (2009).
  46. Lundholt, B.K., Scudder, K.M. & Pagliaro, L. A simple technique for reducing edge effect in cell-based assays. J. Biomol. Screen. 8, 566570 (2003).
  47. Ljosa, V., Sokolnicki, K.L. & Carpenter, A.E. Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 (2012).
  48. Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 11571182 (2003).
  49. Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

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Author information

Affiliations

  1. Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Mark-Anthony Bray,
    • Shantanu Singh &
    • Anne E Carpenter
  2. Recursion Pharmaceuticals, Salt Lake City, Utah, USA.

    • Han Han,
    • Chadwick T Davis,
    • Blake Borgeson &
    • Christopher C Gibson
  3. Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Cathy Hartland,
    • Maria Kost-Alimova &
    • Sigrun M Gustafsdottir

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.

Competing financial 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.

Corresponding authors

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Supplementary information

PDF files

  1. Supplementary Method (167 KB)

    Steps for producing per-well morphological profiles from single-cell measurements

  2. Supplementary Note (316 KB)

    Imaging considerations for wide-field versus confocal microscopes

Additional data