A university spin-out backed by US$2.5 m in funding is harnessing AI to develop software that could speed up cell-based immunotherapy in cancer treatment.
CellChorus, a spin-out from the University of Houston, has developed a platform for the dynamic analysis of single cells called TIMING (time-lapse imaging microscopy in nanowell grids).
The specialised tool enables the studying of single cells over time. Using a video-array-based technology, it observes cell interactions and produces tens of thousands of videos; but analysing these massive video arrays requires automated computer vision systems.
The analysis is enabled by new AI and machine learning models trained on tens of millions of images of cells and will be optimised for fast, high-throughput single-cell analysis by customers.
Rebecca Berdeaux, chief scientific officer, says: “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.”
The startup has received a US$2.5m 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.
Credit: University of Houston
Badri Roysam (pictured above), professor and chair of the University of Houston’s electrical and computer engineering department, which is collaborating with CellChorus, says: “This is an opportunity to leverage artificial intelligence methods for advancing the life sciences.
“We are especially excited about its applications to advancing cell-based immunotherapy to treat cancer and other diseases.”
The firm’s new grant will be applied to quantify the behaviour of cells without the need to fluorescently stain them.
This ‘label-free analysis’ 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.
