AI Drives Development of Cancer Fighting Software

University of Houston researchers and their students are developing a new software technology, based on artificial intelligence, for advancing cell-based immunotherapy to treat cancer and other diseases.

CellChorus Inc., a spinoff 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've received a $2.5 million grant from the National Center for Advancing Translational Sciences of the National Institutes of Health to fast-track the development of an advanced "label-free" version of this technology in partnership with the University of Houston.

Badri Roysam, Hugh Roy and Lillie Cranz Cullen University Professor of Electrical and Computer Engineering at the University of Houston, is collaborating with Professor Navin Varadarajan on the project. Varadarjan is an M.D. Anderson Professor, Chemical and Biomolecular Engineering also at UH and co-founder of CellChorus.

"This is an opportunity to leverage artificial intelligence methods for advancing the life sciences," said Roysam. "We are especially excited about its applications to advancing cell-based immunotherapy to treat cancer and other diseases."

TIMING™ is a specialized tool for studying single cells over time. Because it is 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 yield deep insight into cellular behaviors that directly impact human disease and new classes of therapeutics," said Rebecca Berdeaux, chief scientific officer at CellChorus and co-Principal Investigator on the grant. "The generous support of NCATS 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 fluorescently stain them. Label-free analysis, or analysis without fluorescent dyes, allows scientists to watch 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 in studying diseases like cancer or how cells react to treatments.

The label-free analysis is enabled by new artificial intelligence and machine learning models trained on tens of millions of images of cells and will be optimized for fast, high-throughput single-cell analysis by customers.

This grant is under Award Number 1R42TR005299. The content of this release is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Most Popular Now

Integrating Care Records is Good. Using …

Opinion Article by Dr Paul Deffley, Chief Medical Officer, Alcidion. A single patient record already exists in the NHS. Or at least, that’s a perception shared by many. A survey of...

Should AI Chatbots Replace Your Therapis…

The new study exposes the dangerous flaws in using artificial intelligence (AI) chatbots for mental health support. For the first time, the researchers evaluated these AI systems against clinical standards...

AI could Help Pathologists Match Cancer …

A new study by researchers at the Icahn School of Medicine at Mount Sinai, Memorial Sloan Kettering Cancer Center, and collaborators, suggests that artificial intelligence (AI) could significantly improve how...

AI Detects Early Signs of Osteoporosis f…

Investigators have developed an artificial intelligence-assisted diagnostic system that can estimate bone mineral density in both the lumbar spine and the femur of the upper leg, based on X-ray images...

AI Model Converts Hospital Records into …

UCLA researchers have developed an AI system that turns fragmented electronic health records (EHR) normally in tables into readable narratives, allowing artificial intelligence to make sense of complex patient histories...

AI Sharpens Pathologists' Interpret…

Pathologists' examinations of tissue samples from skin cancer tumours improved when they were assisted by an AI tool. The assessments became more consistent and patients' prognoses were described more accurately...

AI Tool Detects Surgical Site Infections…

A team of Mayo Clinic researchers has developed an artificial intelligence (AI) system that can detect surgical site infections (SSIs) with high accuracy from patient-submitted postoperative wound photos, potentially transforming...

Forging a Novel Therapeutic Path for Pat…

Rett syndrome is a devastating rare genetic childhood disorder primarily affecting girls. Merely 1 out of 10,000 girls are born with it and much fewer boys. It is caused by...

Mayo Clinic's AI Tool Identifies 9 …

Mayo Clinic researchers have developed a new artificial intelligence (AI) tool that helps clinicians identify brain activity patterns linked to nine types of dementia, including Alzheimer's disease, using a single...

AI Detects Fatty Liver Disease with Ches…

Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications...

AI Matches Doctors in Mapping Lung Tumor…

In radiation therapy, precision can save lives. Oncologists must carefully map the size and location of a tumor before delivering high-dose radiation to destroy cancer cells while sparing healthy tissue...

Meet Your Digital Twin

Before an important meeting or when a big decision needs to be made, we often mentally run through various scenarios before settling on the best course of action. But when...