Physics:Cell painting

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Short description: Cell imaging
Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

The cell painting assay[1] is a high-content, high-throughput imaging technique used to capture a wide array of cellular phenotypes in response to diverse perturbations.[2] These phenotypes, often termed "morphological profiles", can be used to understand various biological phenomena, including cellular responses to genetic changes, drug treatments, and other environmental changes.[3] This has been adopted by many pharmaceutical companies in profiling compounds, including Recursion Pharmaceuticals,[4] Bayer,[5] and AstraZeneca[6]

Methodology

In the cell painting assay, cells are stained with six fluorescent dyes imaged in five channels to reveal eight different cellular compartments: nuclei, cytoplasm, endoplasmic reticulum, Golgi apparatus, mitochondria, actin filaments, nucleoli and plasma membrane. Because only five channels can be imaged without significant spectral overlap, two of the dyes share a channel: phalloidin, which stains actin, and wheat germ agglutinin, which stains the Golgi apparatus and plasma membrane, are imaged together.[7] High-resolution images are then captured using automated fluorescence microscopy, and automated image analysis software (typically CellProfiler) identifies individual cells and extracts approximately 1,500 morphological features, including measures of size, shape, texture, intensity, and spatial relationships between structures.[8][7] These features form the basis of the morphological profile for each perturbation.[9]

Variants and Extensions

Cell Painting PLUS (CPP) extends the assay using iterative staining and elution cycles, in which fluorescent dyes are removed after imaging so that the same cells can be stained and imaged again. The method allows at least seven dyes labelling nine cellular compartments, including lysosomes, to be imaged in separate channels. This can improve the specificity of morphological measurements for individual organelles and allows the choice of dyes to be adapted to particular experimental applications.[10]

Virtual cell painting uses deep learning models to predict Cell Painting fluorescence images from unlabelled brightfield images of the same cells, potentially avoiding fluorescent staining.[11] Although the predicted images can visually resemble experimentally stained images, morphological features derived from synthetic images may correlate less strongly with those obtained from fluorescence microscopy. Performance can also decrease when models are applied to cell lines not represented during training.[11][12]

Applications

Given its ability to capture a wide array of cellular responses, the cell painting assay has become a powerful tool in the field of drug discovery.[13] By comparing the morphological profiles of cells treated with different compounds, researchers can identify potential drug candidates, toxicity[14][15] or understand the mechanism of action of existing drugs.[16][15] In combination with genetic perturbations, the assay can be used to determine the function of genes or to understand the underlying mechanisms of genetic diseases.[17] By observing how cells from disease models differ in their morphological profiles from healthy cells, researchers can gain insights into disease mechanisms and potential therapeutic interventions.[18]

Limitations and challenges

While the cell painting assay offers a wealth of information, it is not without its challenges. The high dimensionality of the data requires sophisticated computational tools for analysis. Additionally, the interpretation of morphological profiles in terms of underlying biology can sometimes be non-trivial.[19] With advancements in imaging technology and machine learning, the resolution and depth of morphological profiles are expected to increase, allowing for even more detailed insights into cellular biology. As the scientific community continues to generate data using the cell painting assay, there's a push towards creating shared repositories to facilitate collaborative research and data-driven discoveries.[20]

Notable scientists and contributions

  • Anne E. Carpenter
  • Thouis (Ray) Jones
  • Shantanu Singh
  • Beth A Cimini
  • Sigrun M Gustafsdottir

See also

Notable works

  1. Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M; Gibson, Christopher C; Carpenter, Anne E (2016-08-25). "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes". Nature Protocols. 11 (9): 1757–1774. doi:10.1038/nprot.2016.105. ISSN 1754-2189.
  2. Seal, Srijit; Trapotsi, Maria-Anna; Spjuth, Ola; Singh, Shantanu; Carreras-Puigvert, Jordi; Greene, Nigel; Bender, Andreas; Carpenter, Anne E. (2024-12-05). "Cell Painting: a decade of discovery and innovation in cellular imaging". Nature Methods: 1–15. doi:10.1038/s41592-024-02528-8. ISSN 1548-7105.

References

  1. ↑ Seal, Srijit; Trapotsi, Maria-Anna; Spjuth, Ola; Singh, Shantanu; Carreras-Puigvert, Jordi; Greene, Nigel; Bender, Andreas; Carpenter, Anne E. (2024-12-05). "Cell Painting: a decade of discovery and innovation in cellular imaging" (in en). Nature Methods 22 (2): 254–268. doi:10.1038/s41592-024-02528-8. ISSN 1548-7105. PMID 39639168. 
  2. ↑ Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M et al. (2016-08-25). "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes". Nature Protocols 11 (9): 1757–1774. doi:10.1038/nprot.2016.105. ISSN 1754-2189. PMID 27560178. PMC 5223290. http://dx.doi.org/10.1038/nprot.2016.105. 
  3. ↑ Kohler, Makenzie; MIT, Broad Institute of; Harvard. "New image-based cellular profiling tool peers deeply into metabolic biology" (in en). https://phys.org/news/2023-07-image-based-cellular-profiling-tool-peers.html. 
  4. ↑ "The subtle art of really big data: Recursion Pharma maps the body" (in en). 25 July 2019. https://www.zdnet.com/article/the-subtle-art-of-really-big-data-recursion-maps-the-body/. 
  5. ↑ Zapata, Paula A. Marin; Méndez-Lucio, Oscar; Le, Tuan; Jörn Beese, Carsten; Wichard, Jörg; Rouquié, David; Clevert, Djork-Arné (2023). "Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features" (in en). Digital Discovery 2 (1): 91–102. doi:10.1039/D2DD00081D. 
  6. ↑ Trapotsi, Maria-Anna; Mouchet, Elizabeth; Williams, Guy; Monteverde, Tiziana; Juhani, Karolina; Turkki, Riku; Miljković, Filip; Martinsson, Anton et al. (2022). "Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature". ACS Chemical Biology 17 (7): 1733–1744. doi:10.1021/acschembio.2c00076. PMID 35793809. 
  7. ↑ 7.0 7.1 Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T.; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M. et al. (2016-08-25). "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes". Nature Protocols 11 (9): 1757–1774. doi:10.1038/nprot.2016.105. ISSN 1754-2189. PMID 27560178. 
  8. ↑ Carpenter, Anne E.; Jones, Thouis R.; Lamprecht, Michael R.; Clarke, Colin; Kang, In Han; Friman, Ola; Guertin, David A.; Chang, Joo Han et al. (2006). "CellProfiler: image analysis software for identifying and quantifying cell phenotypes". Genome Biology 7 (10): R100. doi:10.1186/gb-2006-7-10-r100. ISSN 1474-760X. PMID 17076895. 
  9. ↑ Bray, Mark-Anthony; Gustafsdottir, Sigrun M; Rohban, Mohammad H; Singh, Shantanu; Ljosa, Vebjorn; Sokolnicki, Katherine L; Bittker, Joshua A; Bodycombe, Nicole E et al. (2017-01-07). "A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay". GigaScience 6 (12). doi:10.1093/gigascience/giw014. ISSN 2047-217X. PMID 28327978. 
  10. ↑ von Coburg, Elena; Wedler, Marlene; Muino, Jose M.; Wolff, Christopher; Körber, Nils; Dunst, Sebastian; Liu, Shu (2025-04-24). "Cell Painting PLUS: expanding the multiplexing capacity of Cell Painting-based phenotypic profiling using iterative staining-elution cycles". Nature Communications 16 (1). doi:10.1038/s41467-025-58765-8. PMID 40274798. Bibcode: 2025NatCo..16.3857V. 
  11. ↑ 11.0 11.1 Cross-Zamirski, Jan Oscar; Mouchet, Elizabeth; Williams, Guy; Schönlieb, Carola-Bibiane; Turkki, Riku; Wang, Yinhai (2022-06-15). "Label-free prediction of cell painting from brightfield images". Scientific Reports 12 (1). doi:10.1038/s41598-022-12914-x. PMID 35705591. Bibcode: 2022NatSR..1210001C. 
  12. ↑ Xing, Xiaodan; Murdoch, Siofra; Tang, Chunling; Papanastasiou, Giorgos; Cross-Zamirski, Jan; Guo, Yunzhe; Xiao, Xianglu; Schönlieb, Carola-Bibiane et al. (2024-09-09). "Can generative AI replace immunofluorescent staining processes? A comparison study of synthetically generated cellpainting images from brightfield". Computers in Biology and Medicine 182. doi:10.1016/j.compbiomed.2024.109102. PMID 39255659. 
  13. ↑ "A technique called Cell Painting could speed drug discovery" (in en). https://www.technologyreview.com/2023/03/03/1069261/a-technique-called-cell-painting-could-speed-drug-discovery/. 
  14. ↑ Nyffeler, Johanna; Willis, Clinton; Lougee, Ryan; Richard, Ann; Paul-Friedman, Katie; Harrill, Joshua A. (2020-01-15). "Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling". Toxicology and Applied Pharmacology 389. doi:10.1016/j.taap.2019.114876. ISSN 0041-008X. PMID 31899216. Bibcode: 2020ToxAP.38914876N. 
  15. ↑ 15.0 15.1 Tian, Guangyan; Harrison, Philip J; Sreenivasan, Akshai P; Puigvert, Jordi Carreras; Spjuth, Ola (2023). "Combining molecular and cell painting image data for mechanism of action prediction". Artificial Intelligence in the Life Sciences 3. doi:10.1016/j.ailsci.2023.100060. https://uu.diva-portal.org/smash/get/diva2:1910593/FULLTEXT01. 
  16. ↑ Pennisi, E. (2016-05-19). "'Cell painting highlights responses to drugs and toxins". Science 352 (6288): 877–878. doi:10.1126/science.352.6288.877. ISSN 0036-8075. PMID 27199393. http://dx.doi.org/10.1126/science.352.6288.877. 
  17. ↑ Rohban, Mohammad Hossein; Singh, Shantanu; Wu, Xiaoyun; Berthet, Julia B; Bray, Mark-Anthony; Shrestha, Yashaswi; Varelas, Xaralabos; Boehm, Jesse S et al. (2017-03-18). "Systematic morphological profiling of human gene and allele function via Cell Painting" (in en). eLife 6. doi:10.7554/eLife.24060. ISSN 2050-084X. PMID 28315521. PMC 5386591. https://elifesciences.org/articles/24060. 
  18. ↑ Caicedo, Juan C.; Arevalo, John; Piccioni, Federica; Bray, Mark-Anthony; Hartland, Cathy L.; Wu, Xiaoyun; Brooks, Angela N.; Berger, Alice H. et al. (2022). "Cell Painting predicts impact of lung cancer variants". Molecular Biology of the Cell 33 (6). doi:10.1091/mbc.E21-11-0538. PMID 35353015. 
  19. ↑ Chandrasekaran, Srinivas Niranj; Ceulemans, Hugo; Boyd, Justin D.; Carpenter, Anne E. (2020-12-22). "Image-based profiling for drug discovery: due for a machine-learning upgrade?". Nature Reviews Drug Discovery 20 (2): 145–159. doi:10.1038/s41573-020-00117-w. ISSN 1474-1776. PMID 33353986. 
  20. ↑ Rahman, Lu (2021-04-15). "Cell painting: a vibrant future for phenotypic drug discovery" (in en-US). https://www.ddw-online.com/cell-painting-a-vibrant-future-for-phenotypic-drug-discovery-10813-202104/. 



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