Ghost Cytometry: Visually Identifying Cells Without Images

Ghost Cytometry is an image-free visual system enabled with artificial intelligence to quickly identify and sort cells. (Credit: Sacco Fujishima)

A team of researchers have invented a new cell identification and sorting system that combines a novel imaging technique with artificial intelligence, to identify and sort cells with unprecedented high-throughput speed. 

“Ghost Cytometry will help researchers who need to classify cells in the lab, and benefit clinicians and patients who need fast and accurate isolation and diagnosis of cell samples,” said Associate Professor Sadao Ota, from the University of Tokyo. 

The scientists hope that their method will be used to identify and sort cancer cells circulating in patients’ blood, enable faster drug discovery, and improve the quality of cell-based medical therapies.

In an article published today in Science, they demonstrated that Ghost Cytometry can sort at least two different types of cells with similar sizes and structures with very few misidentified cells. 

Ghost Cytometry can identify cells at a rate of more than 10,000 cells per second and sort cells into appropriate groups at a rate of multiple thousands of cells per second. 

Existing cell-sorting machines cannot distinguish between cell types with such similar appearances. Human exerts using microscopy routinely identify and sort fewer than 10 cells per second, sometimes with less accuracy. 

The name — Ghost Cytometry — refers to how technique analyses minimal light wave data without transforming any of that light data into a picture; it is image-free imaging technology. 

 

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Current methods to identify different types of cells rely on microscope images in the cells, which are then classified by either a computer image recognition program or human observer. Relying on full images has made real-time, high-throughput cell sorting an elusive goal. 

“Sometimes there are no stains, dyes, or other biomarkers that can effectively label different types of cells or different activation states of the same cell,” said Ota. “That is one time when Ghost Cytometry can be especially valuable for clinicians, patients, and researchers.” 

How It Works

Cells rush one at a time through a narrow channel underneath a single pixel detector camera that senses the fluorescent light waves emitted by each cell. 

This interpretation of light waves without needing to transform them into a full image is what makes Ghost Cytometry an image-free visual system. An electrical circuit equipped with machine learning algorithms is attached to the single pixel detector camera and learns the unique light wave pattern of each cell type to identify cells within 10 microseconds. The circuit then sends an electrical signal to push cells into the correct sorting pathway for their type as they flow past. 

The machine learning system does not need images to analyse the cells, but if researchers require images for additional analysis, the single pixel detector camera does capture enough information to digitally reconstruct traditional, two-dimensional pictures of cells that pass through the cytometry system if researchers require images for additional analysis. 

This is the first ultrafast fluorescence-activated cell sorting technology and it can isolate a specific cell type from a mix of physically similar cells at high-throughput. 

Company ThinkCyte plans to start oncology and regenerative medicine clinical research project using the technology in collaboration with research institutes this year, as well as developing a research prototype of the equipment. They plan to commercialise a research-use beta product next year. 

 


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