Deepcell’s New Data at CYTO 2021 Shows Potential in Understanding Cancer Cell Biological Heterogeneity
Deepcell to present new data analyzing cell morphology using a high-throughput, quantitative approach and linking morphological features with DNA copy number variance
MOUNTAIN VIEW, Calif. – June 2, 2021 − Deepcell, a life science company pioneering AI-powered cell classification and isolation for cell biology and translational research, will showcase new data in a video poster presentation at the CYTO 2021 virtual conference, conducted by the International Society for Advancement of Cytometry. The data, generated on Deepcell’s deep learning and microfluidics platform, shows that morphology alone can successfully distinguish malignant from non-malignant cells.
“The new data is exciting because it reveals morphological distinctions that correspond with real biological differences, reinforcing what the Deepcell platform has been demonstrating with large populations of cells,” said Dr. Christina Chang PhD, Head of Application Development at Deepcell. “In our study we compared morphology and genomic profile and found that our artificial intelligence-powered approach successfully distinguished between cells with and without copy number variance aberrations. These results are another step towards adding a new quantitative dimension of cell morphology to the multi-omic view of biology.”
The CYTO 2021 conference will showcase cutting-edge science, including new capabilities for cell analysis. Among them is Deepcell’s unique AI-powered technology, which transforms cell morphology into a precise, reproducible and unbiased analyte that enables highly accurate cell classification while maintaining cell viability. By applying deep learning capabilities to cell biology, the Deepcell platform combines high-resolution imaging of cells in flow with real-time cell classification and sorting using cell morphology as the single analyte.
This label-free, target-agnostic approach overcomes some of the limitations of cell surface marker-based classification and enrichment, including the small number of available markers and channels for detection and prior knowledge or guesswork required to select surface proteins. In addition, Deepcell’s label-free approach does not alter the isolated cell’s gene expression or viability.
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 Mountain View, CA. For more information, please visit deepcellbio.com.
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