Alithea Genomics, a biotech company focused on scalable RNA sequencing technologies, has struck a new agreement with Revvity to expand global access to its MERCURIUS DRUG-seq and MERCURIUS Total DRUG-seq platforms through Revvity’s commercial network.

 

The partnership combines Alithea’s high-throughput transcriptomics capabilities with Revvity’s drug discovery and screening portfolio, aiming to integrate gene expression profiling directly into large-scale phenotypic and functional genomics workflows. 

 

The goal: richer biological insight at screening scale.

 

MERCURIUS DRUG-seq will complement Revvity’s existing screening tools by adding scalable 3′ mRNA expression profiling alongside phenotypic and perturbation-based readouts. While high-content imaging and cell painting capture cellular responses, DRUG-seq provides the molecular layer—revealing how gene expression shifts under those conditions.

 

MERCURIUS Total DRUG-seq extends the platform further, delivering full-length, total RNA-seq at screening scale. It enables pathway-level analysis, target hypothesis generation, and deeper follow-up studies where transcript structure, isoforms, and broader RNA biotypes matter.

 

“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 companies said the collaboration reflects rising demand for scalable multi-omics approaches, particularly those generating large, high-quality datasets for AI- and machine learning-driven drug discovery.