Explore publications, scientific posters, application notes, webinars, and other resources related to our technologies.
A 33-compound study demonstrating reproducible transcriptomic profiling at 1536-well scale, with gene-level resolution of mechanisms of action, toxicity pathways, and dose-dependent responses.
Max Blanck, Anna Habryka-Pawlowska, Joanna Bird, David Sorrell, Silja Placzek, Vincent Hahaut
In this tech note, we showcase the technical performance of MERCURIUS™ 1536 DRUG-seq and suggest that this AI-ready, automatable, transcriptomic data generation technology can help AI-enabled drug discovery teams build extensive perturbation data moats that give adopters the competitive advantage needed to improve foundational model performance and experimentally assess AI-native predictions.
This paper demonstrates how paired-end sequencing of multiplexed RNA-seq libraries that is currently only possible on AVITI™ systems, boosts transcript detection rate while uncovering physiologically-relevant promoter switching events.
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