Deepcell Collaborates with the University of Zurich to Deepen the Understanding of Cancer Biology
Deepcell’s AI-powered platform will be used to isolate cancer cells, profile the tumor microenvironment, and highlight relationships between cell phenotype and function
MENLO PARK, Calif. — October 12, 2021 — Deepcell, a life science company pioneering AI-powered cell classification and isolation for basic and translational research, today announced a collaboration with the Levesque Lab at the University of Zurich. The goals of the collaboration are to use Deepcell’s technology to identify and sort rare melanoma cells, profile melanoma tissues to gain a deeper understanding about tumor microenvironment, and enable molecular analyses of the sorted cells.
This collaboration brings together one of the leading scientific research institutions in Europe and one of the most innovative life science companies. Deepcell’s AI-powered platform analyzes, classifies and isolates viable cells in a label-free manner based on morphology alone. Through its deep learning-based algorithms and unique cell sorting approach, Deepcell supports a quantitative and mechanistic understanding of cell biology.
“Melanoma cells are difficult to isolate with conventional sorting methods because they lack reliable cell surface markers. By isolating and sorting cells using morphology, we may deepen our understanding of the biology of melanoma progression and, in particular, of cell phenotypes and molecular features of cancerous cells.” said Dr Levesque, Associate Professor at the University of Zurich.
The Deepcell platform combines high-resolution imaging of cells in flow with real-time cell classification and sorting, using cell morphology as the only analyte. This label-free, target-agnostic approach overcomes some of the limitations of cell surface marker-based classification and enrichment, including the limited number of available markers and channels for detection, prior knowledge or guesswork required to select surface proteins, and availability of protein-specific antibodies. Importantly, the technology not only analyzes the cellular phenotype, but also enables the isolation of viable, unperturbed cells, allowing for the linkage of cell morphology with molecular data and functional assays.
“We continue to expand our collaborations with world-class researchers, such as teaming up with Dr Levesque and his team at the University of Zurich,” said Maddison Masaeli, Co-founder and CEO of Deepcell. “Our unique AI-powered technology transforms cell morphology into a precise, reproducible and unbiased analyte that enables highly accurate cell classification while maintaining cell viability. With the researchers at the University of Zurich, we will be able to generate rich data to help elucidate the complex tumor microenvironment.”
For more information about Deepcell, go to www.deepcell.com.
Deepcell is helping to advance precision medicine by combining advances in AI, cell classification and capture, and single-cell analysis to deliver novel insights through an unprecedented view of cell biology. Spun out of Stanford University in 2017, the company has created unique, microfluidics-based technology that uses continuously learning AI to classify cells based on detailed visual features and sort them without inherent bias. The Deepcell platform maintains cell viability for downstream single-cell analysis and can be used to isolate virtually any type of cell, even those occurring at frequencies as low as one in a billion. The technology will initially be available as a service for use in translational research as well as diagnostics and therapeutic development. Deepcell is privately held and based in Menlo Park, CA. For more information, please visit deepcellbio.com.
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