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.
