ThinkCyte utilizes VisionSort, a technology that combines fluorescence flow cytometry with high-dimensional morphological profiling and AI for label-free cell sorting and unbiased single-cell profiling. This approach enables rapid identification and selection of therapeutically relevant cells, enhancing efficiency in cell therapy research and drug discovery.
Funding
$97.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JGSVFounders
Product
Problem
Traditional cell sorting methods often rely on fluorescent labels, which can alter cell behavior and limit downstream applications. Existing label-free methods may lack the throughput and resolution needed for comprehensive cell profiling and selection. This creates a need for unbiased, high-throughput methods to identify and isolate therapeutically relevant cells.
Solution
ThinkCyte's VisionSort platform combines high-dimensional morphological profiling with fluorescence flow cytometry and AI to enable label-free cell sorting and unbiased single-cell analysis. The system uses advanced image analysis and machine learning algorithms to characterize cells based on their morphological features, allowing for the identification and isolation of specific cell populations without the use of labels. This approach enables researchers to select therapeutically relevant cells, enhance efficiency in cell therapy research, accelerate drug discovery, and gain deeper insights into disease mechanisms. VisionSort offers a faster and more cost-effective alternative to traditional plate-based phenotypic screening, while also being compatible with small molecule and CRISPR-based approaches.
Target Audience
The primary target audience includes researchers and scientists in cell therapy, drug discovery, and disease profiling, as well as pharmaceutical and biotechnology companies, and academic research institutions.
Features
- Integrates fluorescence flow cytometry with high-dimensional morphological profiling
- Employs AI-driven analysis for label-free cell sorting
- Enables unbiased single-cell profiling
- Facilitates the selection of therapeutically relevant cells
- Compatible with cell therapy R&D, drug discovery, and disease profiling applications
- Provides a faster and cheaper alternative to plate-based phenotypic screening
- Compatible with small molecule & CRISPR-based approaches
- Can uncover novel cell populations and identify new targets for disease characterization