Deepcell, a pioneer in artificial intelligence (AI)-powered single cell analysis to fuel deep biological discoveries, announced the launch of its Beta Program for the REM-I platform. Sites testing the REM-I platform are a selected number of leading academic institutions and medical centers in the U.S. and Europe including Newcastle University, the European Molecular Biology Laboratory (EMBL) in Heidelberg, and a third U.S.-based academic health science center. The participants will use the technology to study plasma disorders, blood cancers, immunological functions, genetic perturbations, and other cellular processes.
The REM-I platform is a high-dimensional cell morphology analysis and sorting platform which comprises the REM-I benchtop instrument, an AI Foundation Model, and Axon data suite. By bringing together single cell imaging, sorting, and high-dimensional analysis, the REM-I platform will catalyze new methods of discovery in a wide range of fields including cancer biology, developmental biology, stem cell biology, gene therapy, and functional screening, among others.
“Deepcell’s method of integrating artificial intelligence and single cell analysis remains unique in the industry,” said Maddison Masaeli, Ph.D., co-founder and chief executive officer at Deepcell. “Beta testing is our final opportunity to hone our customer experience as we make final preparations for full commercialization next quarter, ensuring that we can ship, install, and support the global base of customers who have ordered REM-I instruments.”
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Deepcell’s technology has been used to capture and characterize more than two billion images of single cells across a large variety of cell types. The company’s Human Foundation Model, a self-supervised deep learning model trained on a subset of these unlabeled cellular images from a range of carefully selected biological samples, characterizes brightfield single cell images captured on the REM-I instrument and generates high-dimensional embedding data.
“Deepcell technology offers the potential to better visualize cells in a way that allows us to link genotype to form and function without the need to use expensive probes that alter the underlying cell biology. We plan to use the REM-I platform to advance our study of immune cells, leveraging data on the morphological characteristics of eosinophils and neutrophils to further understand areas such as liver cancer and allergy,” said Andrew Filby, Ph.D., professor of enabling biomedical technologies at Newcastle University.
“Existing research methods make it difficult to study the effects of particular biological characteristics, such as genes or gene expression, on cell morphology. Until now, pretty much any cell morphology analysis method required human interpretation and was subject to the limits of human cognition and pattern recognition,” said Daniel Gimenes, Ph.D., flow cytometry senior specialist at EMBL. “Deepcell applies artificial intelligence and machine learning to cell morphology analysis, and not only enables a historically qualitative data type to be turned into a quantitative one, but also enhances the type of data we can study.”
SOURCE: BusinessWire
