Parse Biosciences, a QIAGEN company and a provider of scalable and accessible single-cell sequencing, and Nomic Bio, focused on high-throughput proteomic profiling, today announced their partnership to generate Cascade, a 100 Million cell foundational dataset to understand the human immune response. The dataset’s scale and depth will offer pharma partners cellular- and protein-level insight into immune biology, with the goal to accelerate their ability to identify and validate new drug targets.

Cascade is a multimodal, perturbation dataset that captures how immune cells respond to treatment across multiple timepoints. The dataset will combine 100 million single-cell RNA-seq profiles with more than 11,000 proteomic secretome profiles, capturing how different immune cell types respond to over 150,000 individual and combinatorial cytokine and drug perturbation conditions. By pairing transcriptomic and proteomic readouts of the same immune responses over time, Cascade will provide researchers a view into the mechanisms driving human immune signaling, filling a critical data gap for building predictive, virtual cell models.

Cascade has already drawn foundational partner commitments from leading pharmaceutical and biotechnology companies, including GSK, Pfizer, Sanofi, Graph Therapeutics, and Turbine, underscoring the demand for large-scale, multimodal immune data in drug discovery.

The single-cell RNA-seq data generation will be performed in Parse’s GigaLab, a high throughput service lab capable of profiling billions of cells a year. These data will be paired with proteomic secretome data that will be generated through Nomic’s Omini 1000 platform, which will measure more than 1,000 secreted proteins across all samples.

“Biological AI modeling has hit a data bottleneck. Despite their central role in drug discovery, there is a surprisingly small amount of available data measuring the human’s immune response to key perturbations like cytokines and other drugs,” notes Charlie Roco, PhD, co-founder and chief technology officer at Parse Biosciences. “Cascade is a critical step to revealing these interactions, and we expect to dramatically improve the predictive power of our partner’s AI models as a result.”

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