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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...

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Detalles Bibliográficos
Autores principales: Elgart, Vlad, Loscalzo, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
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.
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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