🦠 Researchers gain free access to 100 million cancer cells to accelerate drug development
The dataset maps 60,000 drug-cell interactions across 50 cancer cell lines, enabling faster and more accurate development of new cancer drugs. Researchers can now for the first time study both natural cell states and how cells respond to 1,200 different drug treatments.
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- Vevo Therapeutics has made 100 million cancer cells freely available to researchers worldwide, which can reduce years of laboratory work to minutes of calculations.
- The dataset maps 60,000 drug-cell interactions across 50 cancer cell lines, enabling faster and more accurate development of new cancer drugs.
- Researchers can now for the first time study both natural cell states and how cells respond to 1,200 different drug treatments in a single consolidated dataset.
Major step for cell biology research
Arc Institute and Vevo Therapeutics have initiated a collaboration to launch the Arc Virtual Cell Atlas - the largest and most biologically diverse public resource for transcriptomic data at the single-cell level. This resource includes data from over 300 million unique cells and became freely available via Arc Institute's website on February 25, 2025.
The atlas currently contains data from two extensive datasets. Vevo's Tahoe-100M is the world's largest single-cell dataset, 50 times larger than all public drug-perturbed data combined. It encompasses 100 million cells and maps 60,000 drug-cell interactions, measuring cellular responses across 50 cancer cell lines with 1,200 different drug treatments.
Tahoe-100M was generated using Vevo's Mosaic technology, the first platform that makes large-scale testing of drugs at the single-cell level possible. The work was conducted with support from Parse Biosciences' GigaLab, which contributed its single-cell RNA sequencing capabilities.
Combines natural and manipulated cell states
The second part of the atlas consists of Arc Institute's scBaseCamp, which is the first RNA sequencing data archive for single cells from public data that has been curated and processed at scale using AI agents. This gene expression data from an additional 200 million cells from 21 different species has been standardized to ensure compatibility for optimal use by machine learning models.
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