Deepcell, a life science company pioneering AI-powered cell classification and isolation for cell biology and translational research, today announced a collaboration with Dr. Jianyu Rao, MD, a vice chair of the department of pathology and chief of cytopathology at the University of California at Los Angeles (UCLA), to advance cancer research through an innovative approach to studying cytology samples.
The collaboration with Dr. Rao and his team of researchers is focused on using Deepcell’s AI-powered platform to identify and sort cancer cells from clinical cytology samples of body fluids (e.g. ascitic or pleural fluid) based entirely on morphological distinctions ‒ or the visual features of cells ‒ rather than on labeling or using biomarkers. This collaboration aims to enable increased accuracy of cell classification and deliver intact cells for molecular analysis. Deepcell’s platform for cell analysis includes a microfluidic-based imaging, real-time sorting system, powered by deep learning. The collaboration allows Deepcell to continue to expand their Cell Morphology Atlas and to demonstrate the performance of the platform.
“Cytologic analysis on a self-learning AI platform for cancer research could lead to a better understanding of the biology of malignant cells and contribute to future diagnosis and improved patient outcomes,” said Dr. Rao, who is an internationally renowned cytopathologist and the director of Cytopathology, the director of gynecological pathology, and the medical director of the cytotechnology school. “Before Deepcell, cell morphology was limited by human interpretation and a lack of adequate tools for capturing and studying abnormal cells. Through the combination of AI, microfluidics, and single-cell analysis, the Deepcell platform provides a new way of understanding these cells.”
This collaboration is the first to be announced as part of Deepcell’s new collaboration program with scientific researchers and medical experts. This initiative is designed to form a series of partnerships that will unlock the potential of single-cell, morphology-based sorting and analysis. The goals of the program are to demonstrate the performance of the Deepcell platform, continue to expand a unique Cell Morphology Atlas for the benefit of the scientific and medical communities, and co-develop AI models for specific applications.
“The launch of Deepcell’s collaboration program formalizes our engagement with leaders across basic and translational research and opens up a new phase for Deepcell,” said Maddison Masaeli, Co-founder and CEO of Deepcell. “It is by focusing on the science and making Dr. Rao and future collaborators successful in their particular field that we will realize our vision to create a new lexicon for single cell morphology, paving the path that connects the genome to the patient.”
Deepcell was spun out of Stanford in 2017 to re-invent single cell analysis by creating a new quantitative dimension of cell morphology. Since then, the company has developed its AI-powered technology to characterize, identify and sort cells relying solely on cell morphology.
Deepcell’s technology is able to differentiate among cell types with a novel approach compared to traditional cell isolation techniques that rely on antibody staining or similar methods. Unlike other approaches, Deepcell’s technology was developed to profile samples, and isolate and collect label-free cells of any type, keeping the cell intact for downstream biological characterization. The technology combines advances in AI, cell capture, and single-cell analysis to identify and sort cells based on detailed visual features.
For more information about Deepcell or future collaborations with Deepcell, go to www.deepcell.com.
About Deepcell
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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