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Ryckelynck, M.

Publications and source records attributed to Ryckelynck, M..

2 recordsLinked to original sources

Ultrahigh-throughput discovery of modified aptamers as specific and potent enzyme inhibitors

Enzymes are instrumental to life and key actors of pathologies, making them relevant drug targets. Most enzyme inhibitors consist of small molecules. Although efficient, their development is long, costly and can come with unwanted off-targeting. Substantial gain in specificity and discovery efficiency is possible using biologicals. Best exemplified by antibodies, these drugs derived from living systems display high specificity and their development is eased by harnessing natural evolution. Aptamers are nucleic acids sharing functional similarities with antibodies while being deprived of many of their limitations. Yet, the success rate of inhibitory aptamer discovery remained hampered by the lack of an efficient discovery pipeline. In this work, we addressed this issue by introducing an ultrahigh-throughput strategy combining in vitro selection, microfluidic screening and bioinformatics. We demonstrate its efficiency by discovering a modified aptamer that specifically and strongly inhibits SPM-1, a beta-lactamase that remained recalcitrant to the development of potent inhibitors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/608213v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@b15e8forg.highwire.dtl.DTLVardef@b7259forg.highwire.dtl.DTLVardef@6f24borg.highwire.dtl.DTLVardef@d4f48_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

Combi-Seq: Multiplexed transcriptome-based profiling of drug combinations using 1deterministic 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 directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we developed a scalable microfluidic workflow to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devised 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 applied our method 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.

systems biology↗