Deepcell provides the REM-I platform, which utilizes label-free imaging, deep learning, and microfluidics for high-dimensional analysis of single-cell morphology. This technology enables researchers to quantitatively characterize cell phenotypes and functions, facilitating advancements in biological research and diagnostics.
Funding
$98M 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.
Founders
Product
Problem
Traditional methods of single-cell analysis often require fluorescent labels or genetic modification, which can alter cell behavior and limit the scope of observable characteristics. These methods can be time-consuming, expensive, and may not be suitable for all cell types or research questions. The reliance on pre-selected markers also restricts the discovery of novel cell phenotypes and functions.
Solution
Deepcell's REM-I platform utilizes label-free imaging, deep learning, and microfluidics to provide high-dimensional analysis of single-cell morphology. The platform captures high-resolution images of cells as they flow through a microfluidic channel, using AI to identify and sort cells based on morphological features alone. This approach enables researchers to quantitatively characterize cell phenotypes and functions without the need for labels or genetic modification, preserving the native state of the cells. The REM-I platform facilitates advancements in biological research and diagnostics by providing a more comprehensive and unbiased view of cellular characteristics.
Target Audience
The primary users are researchers in cell biology, immunology, cancer research, and drug discovery, as well as clinical labs needing advanced cell characterization tools.
Features
- High-resolution, label-free imaging of single cells in flow
- Microfluidic cell sorting based on real-time morphological analysis
- Deep learning algorithms for automated cell identification and classification
- High-dimensional data analysis and visualization tools
- Preservation of native cell state without labels or genetic modification
- Integration of imaging, sorting, and analysis into a single platform