Local generation and efficient evaluation of numerous drug combinations in a single sample

  1. Vlad Elgart
  2. Joseph Loscalzo  Is a corresponding author
  1. Brigham and Women's Hospital, United States

Abstract

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 barcodingmethod 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.

Data availability

Imaging, flow cytometry data, and custom Wolfram Mathematica computer code use for data analysis were deposited in Dryad database.

The following data sets were generated

Article and author information

Author details

  1. Vlad Elgart

    Department of Medicine, Brigham and Women's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Joseph Loscalzo

    Department of Medicine, Brigham and Women's Hospital, Boston, United States
    For correspondence
    jloscalzo@rics.bwh.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1153-8047

Funding

National Institutes of Health (HGHG007690)

  • Joseph Loscalzo

National Institutes of Health (HL108630)

  • Joseph Loscalzo

National Institutes of Health (HL155107)

  • Joseph Loscalzo

National Institutes of Health (HL155096)

  • Joseph Loscalzo

National Institutes of Health (HL119145)

  • Joseph Loscalzo

American Heart Association (D700382 and CV-19)

  • Joseph Loscalzo

American Heart Association (AHA957729)

  • Joseph Loscalzo

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Clifford J Rosen, Maine Medical Center Research Institute, United States

Version history

  1. Received: December 8, 2022
  2. Preprint posted: December 15, 2022 (view preprint)
  3. Accepted: April 10, 2023
  4. Accepted Manuscript published: April 11, 2023 (version 1)
  5. Version of Record published: May 10, 2023 (version 2)

Copyright

© 2023, Elgart & Loscalzo

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Vlad Elgart
  2. Joseph Loscalzo
(2023)
Local generation and efficient evaluation of numerous drug combinations in a single sample
eLife 12:e85439.
https://doi.org/10.7554/eLife.85439

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https://doi.org/10.7554/eLife.85439

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