CCB offers a computational platform that generates neural model evidence for human biology at pheno‑multiomic resolution. The service provides detailed analyses across central nervous system biology, neuro‑oncology, and peripheral nervous system biology, enabling researchers to integrate multi‑omic data for deeper insight into neurological conditions.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers studying the central nervous system, neuro‑oncology, and peripheral nervous system face challenges integrating high‑dimensional phenotypic and multi‑omic data from complex 3D human tissue models, which limits the speed and reliability of therapeutic target discovery.
Solution
CellCircuit provides an AI‑native discovery platform that combines engineered 3D human biology (organoids and spheroids) with scalable automated manufacturing, high‑throughput imaging, and integrated pheno‑multiomic profiling. Spatial phenomics are linked with genomics, transcriptomics, proteomics, metabolomics, lipidomics, and phosphoproteomics to generate a structured, high‑resolution view of cell states and disease mechanisms. Continuous data ingestion feeds a machine‑learning engine that identifies disease pathways, prioritizes therapeutic opportunities, and produces actionable discovery intelligence. The platform’s automation ensures reproducible model generation and consistent data across large sample sets, enabling researchers to accelerate hypothesis generation and validation at scale.
Target Audience
Primary customers are pharmaceutical and biotech research teams focused on neuroscience and oncology who require scalable, high‑resolution biological data to inform drug discovery programs.
Features
- Engineered 3D human tissue models (organoids, spheroids) tailored for CNS, neuro‑oncology, and PNS indications
- Automated, high‑throughput manufacturing pipelines that produce thousands of reproducible models with minimal manual variability
- Integrated imaging, liquid‑handling, and analysis workflows for rapid processing of large sample cohorts
- Pheno‑multiomic integration linking spatial phenomics with genomics, transcriptomics, proteomics, metabolomics, lipidomics, and phosphoproteomics
- AI‑native discovery engine that continuously learns from accumulated datasets to uncover disease mechanisms and rank therapeutic targets
- Structured data output compatible with downstream computational analysis and modeling pipelines