Researchers find a more efficient system to analyze blood cells’ data

Researchers have been using flow cytometry to examine the blood and any other body fluids till now. Cytometry uses the laser tech to read the nature of the particles present in blood like how particles show light-scattering properties. A computer processes those light signals and examines the physical and chemical properties of the fluids.

For more than five decades, there has been no other way to do so and researchers are looking for a better way. However, a team of researchers has come up with a more efficient method to visually sort blood cell data called as Image-Activated Cell Sorting (IACS). It is somewhat similar to flow cytometry but it uses deep learning to create 2D images. IACS, based on the global phenotypic profiles classifies cells by spatial and morphological properties; something that is not possible to achieve using the traditional method. It took more than six years to create the system and then test the process involving blood cells and algae.

Each cell is studied individually under the microscope. The intelligent decision-making abilities of the IACS are utilized to analyze data from the cells. The cell-sorting process starts with selecting the desired criteria and inserting a tube filled with cells into the IACS injection point. An IACS process has two tubes, one contains sorted cells, and the other has unsorted cells. The last step is examining the quality of each cell using an optical microscope. The system is faster; it does the processing within 32 milliseconds.