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

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 direc...

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Veröffentlicht in:Nature communications 2022-08, Vol.13 (1), p.4450-15, Article 4450
Hauptverfasser: Mathur, L., Szalai, B., Du, N. H., Utharala, R., Ballinger, M., Landry, J. J. M., Ryckelynck, M., Benes, V., Saez-Rodriguez, J., Merten, C. A.
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Sprache:eng
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Zusammenfassung: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. Current screens to assess tumour drug resistance require a large amount of material, normally not available from patients. Here the authors report CombiSeq, a scalable microfluidic workflow to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-32197-0