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Dynamic microfluidic single-cell screening identifies pheno-tuning compounds to potentiate tuberculosis therapy

Author

Listed:
  • Maxime Mistretta

    (Microbial Individuality and Infection Laboratory)

  • Mena Cimino

    (Microbial Individuality and Infection Laboratory)

  • Pascal Campagne

    (Bioinformatics and Biostatistics Hub)

  • Stevenn Volant

    (Bioinformatics and Biostatistics Hub)

  • Etienne Kornobis

    (Bioinformatics and Biostatistics Hub
    Biomics Platform)

  • Olivier Hebert

    (CERMN)

  • Christophe Rochais

    (CERMN)

  • Patrick Dallemagne

    (CERMN)

  • Cédric Lecoutey

    (CERMN)

  • Camille Tisnerat

    (CERMN)

  • Alban Lepailleur

    (CERMN)

  • Yann Ayotte

    (Institut National de la Recherche Scientifique—Armand-Frappier Santé Biotechnologie Research Centre)

  • Steven R. LaPlante

    (Institut National de la Recherche Scientifique—Armand-Frappier Santé Biotechnologie Research Centre)

  • Nicolas Gangneux

    (Microbial Individuality and Infection Laboratory)

  • Monika Záhorszká

    (Comenius University in Bratislava)

  • Jana Korduláková

    (Comenius University in Bratislava)

  • Sophie Vichier-Guerre

    (Epigenetic Chemical Biology Unit)

  • Frédéric Bonhomme

    (Epigenetic Chemical Biology Unit)

  • Laura Pokorny

    (Microbial Individuality and Infection Laboratory)

  • Marvin Albert

    (Image Analysis Hub)

  • Jean-Yves Tinevez

    (Image Analysis Hub)

  • Giulia Manina

    (Microbial Individuality and Infection Laboratory)

Abstract

Drug-recalcitrant infections are a leading global-health concern. Bacterial cells benefit from phenotypic variation, which can suggest effective antimicrobial strategies. However, probing phenotypic variation entails spatiotemporal analysis of individual cells that is technically challenging, and hard to integrate into drug discovery. In this work, we develop a multi-condition microfluidic platform suitable for imaging two-dimensional growth of bacterial cells during transitions between separate environmental conditions. With this platform, we implement a dynamic single-cell screening for pheno-tuning compounds, which induce a phenotypic change and decrease cell-to-cell variation, aiming to undermine the entire bacterial population and make it more vulnerable to other drugs. We apply this strategy to mycobacteria, as tuberculosis poses a major public-health threat. Our lead compound impairs Mycobacterium tuberculosis via a peculiar mode of action and enhances other anti-tubercular drugs. This work proves that harnessing phenotypic variation represents a successful approach to tackle pathogens that are increasingly difficult to treat.

Suggested Citation

  • Maxime Mistretta & Mena Cimino & Pascal Campagne & Stevenn Volant & Etienne Kornobis & Olivier Hebert & Christophe Rochais & Patrick Dallemagne & Cédric Lecoutey & Camille Tisnerat & Alban Lepailleur , 2024. "Dynamic microfluidic single-cell screening identifies pheno-tuning compounds to potentiate tuberculosis therapy," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48269-2
    DOI: 10.1038/s41467-024-48269-2
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    References listed on IDEAS

    as
    1. Adam C. Palmer & Roy Kishony, 2014. "Opposing effects of target overexpression reveal drug mechanisms," Nature Communications, Nature, vol. 5(1), pages 1-8, September.
    2. Dmitry Sutormin & Alina Galivondzhyan & Olga Musharova & Dmitrii Travin & Anastasiia Rusanova & Kseniya Obraztsova & Sergei Borukhov & Konstantin Severinov, 2022. "Interaction between transcribing RNA polymerase and topoisomerase I prevents R-loop formation in E. coli," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. E. Hesper Rego & Rebecca E. Audette & Eric J. Rubin, 2017. "Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity," Nature, Nature, vol. 546(7656), pages 153-157, June.
    4. Hugo Varet & Loraine Brillet-Guéguen & Jean-Yves Coppée & Marie-Agnès Dillies, 2016. "SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-8, June.
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