Wittgenbio offers a GenAI‑driven virtual cell platform that converts existing bulk RNA‑seq, FFPE, H&E, and IHC data into single‑cell‑level insights without additional wet‑lab experiments. By leveraging a curated atlas of over 1.3 billion single‑cell profiles, it provides regulator‑ready patient stratification, biomarker packages, and responder predictions, enabling pharma and biotech teams to de‑risk trials and accelerate precision‑medicine decisions.
Funding
Funding not disclosed
PAFounders
Product
Problem
Pharmaceutical and biotech teams often rely on bulk RNA sequencing or archived pathology images, which lack cellular resolution needed for precise patient stratification and biomarker discovery. Obtaining true single‑cell data is expensive, time‑consuming, and requires fresh samples, creating a bottleneck in trial design and increasing the risk of late‑stage failures.
Solution
WittGen provides a virtual cell platform that uses GenAI‑driven deconvolution to convert existing bulk RNA‑seq, FFPE, H&E, and IHC data into single‑cell–level insights without additional wet‑lab experiments. The system generates regulator‑ready stratification plans, responder predictions, and detailed biomarker packages, enabling faster go/no‑go decisions and more accurate enrollment criteria. By leveraging a curated atlas of over 1.3 billion single‑cell profiles, the platform delivers high‑precision cellular composition, gene expression, and spatial context at a fraction of traditional single‑cell costs. The output includes explainable documentation, model cards, and validation evidence to satisfy bioinformatics and regulatory reviewers. This approach allows clinical programs to retrospectively unlock hidden value from archived datasets and scale patient profiling across thousands of trial participants.
Target Audience
Primary customers are oncology and precision‑medicine leaders in pharma and biotech who need reliable patient stratification for high‑risk clinical trials, as well as bioinformatics and AI teams responsible for regulatory submissions.
Features
- GenAI‑powered bulk‑to‑single‑cell deconvolution that transforms $50 bulk RNA‑seq runs into $4,000+ single‑cell resolution profiles
- Multi‑modal integration of H&E, IHC, and imaging data to infer RNA, DNA, and protein layers at single‑cell granularity
- Access to a curated reference atlas of 1.3 billion cells spanning 6+ cancer types and healthy tissues for accurate cell‑type annotation
- Regulator‑ready deliverables including explainability, reproducibility, traceability documentation, and model cards
- Scalable profiling of up to 1,000 patients per trial for the cost of 150 traditional single‑cell samples
- Automated QC, data harmonization, and PHI safeguards for secure handling of historical datasets
- API and playbook toolkit for pipeline expansion, enabling parallel program execution and custom integration