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Local generation and efficient evaluation of numerous drug combinations in a single sample
We develop a method that allows one to test a large number of drug combinations in a single-cell culture sample. We rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171870/ https://www.ncbi.nlm.nih.gov/pubmed/37039628 http://dx.doi.org/10.7554/eLife.85439 |
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author | Elgart, Vlad Loscalzo, Joseph |
author_facet | Elgart, Vlad Loscalzo, Joseph |
author_sort | Elgart, Vlad |
collection | PubMed |
description | We develop a method that allows one to test a large number of drug combinations in a single-cell culture sample. We rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combination of fluorescently barcoded drugs. We create independent transient drug gradients across the cell culture sample to produce heterogeneous local drug combinations. After the incubation period, the ensuing phenotype and corresponding drug barcodes for each cell are recorded. We use these data for statistical prediction of the treatment response to the drugs in a macroscopic population of cells. To further application of this technology, we developed a fluorescent barcoding method that does not require any chemical drug(s) modifications. We also developed segmentation-free image analysis capable of handling large optical fields containing thousands of cells in the sample, even in confluent growth condition. The technology necessary to execute our method is readily available in most biological laboratories, does not require robotic or microfluidic devices, and dramatically reduces resource needs and resulting costs of the traditional high-throughput studies. |
format | Online Article Text |
id | pubmed-10171870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-101718702023-05-11 Local generation and efficient evaluation of numerous drug combinations in a single sample Elgart, Vlad Loscalzo, Joseph eLife Computational and Systems Biology We develop a method that allows one to test a large number of drug combinations in a single-cell culture sample. We rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combination of fluorescently barcoded drugs. We create independent transient drug gradients across the cell culture sample to produce heterogeneous local drug combinations. After the incubation period, the ensuing phenotype and corresponding drug barcodes for each cell are recorded. We use these data for statistical prediction of the treatment response to the drugs in a macroscopic population of cells. To further application of this technology, we developed a fluorescent barcoding method that does not require any chemical drug(s) modifications. We also developed segmentation-free image analysis capable of handling large optical fields containing thousands of cells in the sample, even in confluent growth condition. The technology necessary to execute our method is readily available in most biological laboratories, does not require robotic or microfluidic devices, and dramatically reduces resource needs and resulting costs of the traditional high-throughput studies. eLife Sciences Publications, Ltd 2023-04-11 /pmc/articles/PMC10171870/ /pubmed/37039628 http://dx.doi.org/10.7554/eLife.85439 Text en © 2023, Elgart and Loscalzo https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Elgart, Vlad Loscalzo, Joseph Local generation and efficient evaluation of numerous drug combinations in a single sample |
title | Local generation and efficient evaluation of numerous drug combinations in a single sample |
title_full | Local generation and efficient evaluation of numerous drug combinations in a single sample |
title_fullStr | Local generation and efficient evaluation of numerous drug combinations in a single sample |
title_full_unstemmed | Local generation and efficient evaluation of numerous drug combinations in a single sample |
title_short | Local generation and efficient evaluation of numerous drug combinations in a single sample |
title_sort | local generation and efficient evaluation of numerous drug combinations in a single sample |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171870/ https://www.ncbi.nlm.nih.gov/pubmed/37039628 http://dx.doi.org/10.7554/eLife.85439 |
work_keys_str_mv | AT elgartvlad localgenerationandefficientevaluationofnumerousdrugcombinationsinasinglesample AT loscalzojoseph localgenerationandefficientevaluationofnumerousdrugcombinationsinasinglesample |