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Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets

Author

Listed:
  • L. Mathur

    (European Molecular Biology Laboratory (EMBL)
    Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences)

  • B. Szalai

    (Semmelweis University
    Research Centre for Natural Sciences
    Turbine Simulated Cell Technologies Ltd)

  • N. H. Du

    (École Polytechnique Fédérale de Lausanne (EPFL))

  • R. Utharala

    (European Molecular Biology Laboratory (EMBL))

  • M. Ballinger

    (European Molecular Biology Laboratory (EMBL))

  • J. J. M. Landry

    (European Molecular Biology Laboratory (EMBL))

  • M. Ryckelynck

    (Université de Strasbourg, CNRS, Architecture et Réactivité de l’ARN, UPR)

  • V. Benes

    (European Molecular Biology Laboratory (EMBL))

  • J. Saez-Rodriguez

    (Faculty of Medicine and Heidelberg University Hospital, Institute of Computational Biomedicine, Heidelberg University
    RWTH Aachen University)

  • C. A. Merten

    (European Molecular Biology Laboratory (EMBL)
    École Polytechnique Fédérale de Lausanne (EPFL))

Abstract

Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we present a scalable microfluidic workflow, called Combi-Seq, to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devise a deterministic combinatorial DNA barcoding approach to encode treatment conditions, enabling the gene expression-based readout of drug effects in a highly multiplexed fashion. We apply Combi-Seq to screen the effect of 420 drug combinations on the transcriptome of K562 cells using only ~250 single cell droplets per condition, to successfully predict synergistic and antagonistic drug pairs, as well as their pathway activities.

Suggested Citation

  • L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32197-0
    DOI: 10.1038/s41467-022-32197-0
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