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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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    References listed on IDEAS

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    1. Michael Schubert & Bertram Klinger & Martina Klünemann & Anja Sieber & Florian Uhlitz & Sascha Sauer & Mathew J. Garnett & Nils Blüthgen & Julio Saez-Rodriguez, 2018. "Perturbation-response genes reveal signaling footprints in cancer gene expression," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    2. Dmitry Kobak & Philipp Berens, 2019. "The art of using t-SNE for single-cell transcriptomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Arif E. Cetin & Mark M. Stevens & Nicholas L. Calistri & Mariateresa Fulciniti & Selim Olcum & Robert J. Kimmerling & Nikhil C. Munshi & Scott R. Manalis, 2017. "Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation," Nature Communications, Nature, vol. 8(1), pages 1-12, December.
    4. Michael P. Menden & Dennis Wang & Mike J. Mason & Bence Szalai & Krishna C. Bulusu & Yuanfang Guan & Thomas Yu & Jaewoo Kang & Minji Jeon & Russ Wolfinger & Tin Nguyen & Mikhail Zaslavskiy & In Sock J, 2019. "Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
    5. Philipp Gruner & Birte Riechers & Benoît Semin & Jiseok Lim & Abigail Johnston & Kathleen Short & Jean-Christophe Baret, 2016. "Controlling molecular transport in minimal emulsions," Nature Communications, Nature, vol. 7(1), pages 1-9, April.
    6. Mathew J. Garnett & Elena J. Edelman & Sonja J. Heidorn & Chris D. Greenman & Anahita Dastur & King Wai Lau & Patricia Greninger & I. Richard Thompson & Xi Luo & Jorge Soares & Qingsong Liu & Francesc, 2012. "Systematic identification of genomic markers of drug sensitivity in cancer cells," Nature, Nature, vol. 483(7391), pages 570-575, March.
    7. James M. McFarland & Brenton R. Paolella & Allison Warren & Kathryn Geiger-Schuller & Tsukasa Shibue & Michael Rothberg & Olena Kuksenko & William N. Colgan & Andrew Jones & Emily Chambers & Danielle , 2020. "Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    8. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    9. Erin C. Bush & Forest Ray & Mariano J. Alvarez & Ronald Realubit & Hai Li & Charles Karan & Andrea Califano & Peter A. Sims, 2017. "PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
    10. Sébastien Sart & Raphaël F.-X. Tomasi & Gabriel Amselem & Charles N. Baroud, 2017. "Multiscale cytometry and regulation of 3D cell cultures on a chip," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
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