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Cell Painting

From Wikipedia, the free encyclopedia


The Cell Painting assay is a high-content, high-throughput imaging technique used to capture a wide array of cellular phenotypes in response to diverse perturbations.[1] 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.[2] This has been adopted by many pharmaceutical companies in profiling compounds including Recursion Pharmaceutical[3] and AstraZeneca[4]

Methodology

In the Cell Painting assay, cells are stained with six fluorescent dyes that mark different cellular compartments, including nuclei, cytoplasm, endoplasmic reticulum, Golgi apparatus, mitochondria, and actin. High-resolution images are then captured using automated fluorescence microscopy, and image analysis algorithms are applied to extract thousands of morphological features.[5] These features form the basis of the morphological profile for each perturbation.[6]

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.[7] By comparing the morphological profiles of cells treated with different compounds, researchers can identify potential drug candidates, toxicity[8] or understand the mechanism of action of existing drugs.[9][10] 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.[11] 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.[12]

Limitations and Challenges

While the Cell Painting assay offers a wealth of information, it's 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.[13] 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. Additionally, 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.[14]

Notable Scientists and Contributions

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.

References

  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. PMC 5223290. PMID 27560178.
  2. ^ Kohler, Makenzie; MIT, Broad Institute of; Harvard. "New image-based cellular profiling tool peers deeply into metabolic biology". phys.org. Retrieved 2023-08-13.
  3. ^ "The subtle art of really big data: Recursion Pharma maps the body". ZDNET. Retrieved 2023-08-13.
  4. ^ Trapotsi, Maria-Anna; Mouchet, Elizabeth; Williams, Guy; Monteverde, Tiziana; Juhani, Karolina; Turkki, Riku; Miljković, Filip; Martinsson, Anton; Mervin, Lewis (2022-01-18). "Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature". doi:10.1101/2022.01.17.476610. Retrieved 2023-08-13.
  5. ^ Bray, Mark-Anthony; Singh, Shantanu; Han, Han; Davis, Chadwick T.; Borgeson, Blake; Hartland, Cathy; Kost-Alimova, Maria; Gustafsdottir, Sigrun M.; Gibson, Christopher C. (2016-04-25). "Cell Painting, an image-based assay for morphological profiling". doi:10.1101/049817. Retrieved 2023-08-13.
  6. ^ Bray, Mark-Anthony; Gustafsdottir, Sigrun M; Rohban, Mohammad H; Singh, Shantanu; Ljosa, Vebjorn; Sokolnicki, Katherine L; Bittker, Joshua A; Bodycombe, Nicole E; Dančík, Vlado; Hasaka, Thomas P; Hon, Cindy S; Kemp, Melissa M; Li, Kejie; Walpita, Deepika; Wawer, Mathias J (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. PMC 5721342.
  7. ^ "A technique called Cell Painting could speed drug discovery". MIT Technology Review. Retrieved 2023-08-13.
  8. ^ Tian, Guangyan; Harrison, Philip J; Sreenivasan, Akshai P; Puigvert, Jordi Carreras; Spjuth, Ola (2022-10-07). "Combining molecular and cell painting image data for mechanism of action prediction". doi:10.1101/2022.10.04.510834. Retrieved 2023-08-13.
  9. ^ 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.
  10. ^ Tian, Guangyan; Harrison, Philip J; Sreenivasan, Akshai P; Puigvert, Jordi Carreras; Spjuth, Ola (2022-10-07). "Combining molecular and cell painting image data for mechanism of action prediction". doi:10.1101/2022.10.04.510834. Retrieved 2023-08-13.
  11. ^ Settleman, Jeffrey, ed. (2017-01-23). "Decision letter: Systematic morphological profiling of human gene and allele function via Cell Painting". doi:10.7554/elife.24060.021. {{cite journal}}: Cite journal requires |journal= (help)
  12. ^ Caicedo, Juan C.; Arevalo, John; Piccioni, Federica; Bray, Mark-Anthony; Hartland, Cathy L.; Wu, Xiaoyun; Brooks, Angela N.; Berger, Alice H.; Boehm, Jesse S. (2021-11-20). "Cell Painting predicts impact of lung cancer variants". doi:10.1101/2021.11.18.469171. Retrieved 2023-08-13.
  13. ^ 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. PMC 7754181.
  14. ^ Rahman, Lu (2021-04-15). "Cell painting: a vibrant future for phenotypic drug discovery". Drug Discovery World (DDW). Retrieved 2023-08-13.
This page was last edited on 22 January 2024, at 03:22
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