University of Houston researchers and their students are developing a new software technology, based on artificial intelligence, to advance cell-based immunotherapy to treat cancer and other diseases.
CellChorus Inc., a spin-off from the University of Houston, is commercializing the UH-developed Time-lapse Imaging Microscopy In Nanowell Grids™ platform for dynamic single-cell analysis with label-free analysis. Now they have received a $2.5 million grant from the National Center for Advancing Translational Sciences at the National Institutes of Health to accelerate the development of an advanced “label-free” version of this technology, in collaboration with the University of Houston .
Badri Roysam, the Hugh Roy and Lillie Cranz Cullen University Professor of Electrical and Computer Engineering at the University of Houston, is working with Professor Navin Varadarajan on the project. Varadarjan is MD Anderson Professor, Chemical and Biomolecular Engineering also at UH and co-founder of CellChorus.
“This is an opportunity to use artificial intelligence methods to advance the life sciences,” Roysam said. “We are especially excited about its applications in advancing cell-based immunotherapy for the treatment of cancer and other diseases.”
TIMING™ is a specialized tool for studying single cells over time. Being a video array-based technology, it observes cell interactions and produces tens of thousands of videos. Analyzing these massive video arrays requires automated computer vision systems.
“By combining AI, microscale manufacturing and advanced microscopy, the label-free TIMING platform will deliver deep insights into cellular behavior that directly impacts human disease and new classes of therapies,” said Rebecca Berdeaux, Chief Scientific Officer at CellChorus and co-director. Researcher on the grant. “NCATS’ generous support enables our development of computational tools that will ultimately integrate single-cell dynamic functional analysis of cell behavior with intracellular signaling events.
The goal of the grant, a Small Business Technology Transfer Fast-Track award, is to quantify the behavior of cells without the need to stain them fluorescently. Label-free analysis, or analysis without fluorescent dyes, allows scientists to view cells in their natural state and gather important information about their movement, interactions and changes. It will also allow them to use selective fluorescent staining to observe new molecules of interest. This is useful when studying diseases such as cancer or how cells respond to treatments.
The label-free analysis is powered by new artificial intelligence and machine learning models trained on tens of millions of images of cells and will be optimized for rapid, high-throughput single-cell analysis by customers.
This grant falls under award number 1R42TR005299. The contents of this press release are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
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