Press Releases
Alithea Genomics and Revvity Announce Distribution Agreement to Expand Access to MERCURIUSTM DRUG-seq Solutions
Lausanne, Switzerland and Frederick, MD USA – [June 8, 2026] – Life Science Newswire – Alithea Genomics, a pioneering biotech company specializing in scalable RNA sequencing solutions, today announced a new agreement with Revvity Inc. to broaden access to MERCURIUS™ DRUG-seq and MERCURIUS™ Total DRUG-seq solutions through Revvity’s global commercial network.
The collaboration brings together Alithea Genomics’ scalable high-throughput transcriptomics technologies with Revvity’s comprehensive drug discovery and screening portfolio, enabling researchers to integrate transcriptomic profiling into large-scale phenotypic and functional genomics workflows, unlocking richer biological insight at scale.
MERCURIUS™ DRUG-seq complements Revvity’s screening workflows by adding scalable 3′ mRNA expression profiling to phenotypic and perturbation-driven screening readouts. High-content imaging and cell painting approaches can reveal how cells respond to perturbations, while DRUG-seq helps characterize the gene expression changes associated with those responses.
MERCURIUS™ Total DRUG-seq expands these capabilities with full-length, total RNA-seq at screening scale, supporting pathway-level analysis, target hypothesis generation, and follow-up studies where transcript structure, isoforms, or broader RNA biotypes are important. “By collaborating with Revvity, we are significantly expanding global access to our MERCURIUS™ technologies and enabling researchers to combine scalable transcriptomics with advanced phenotypic and functional genomics workflows,” said Riccardo Dainese, CBO and Co-founder of Alithea Genomics.
“Revvity is focused on helping researchers build more complete discovery workflows by connecting phenotypic, imaging, and molecular readouts,” said Craig Monell, SVP Life Science Reagents at Revvity. “Adding MERCURIUS™ DRUG-seq and MERCURIUS™ Total DRUG-seq to our portfolio gives customers a scalable way to add transcriptomic context to screening studies, helping them prioritize hits, explore the mechanism of action, and generate richer datasets for downstream analysis.”
The collaboration reflects growing demand for scalable multi-omics approaches that generate large, high-quality datasets for modern drug discovery, including AI- and machine learning-enabled applications.
