Researchers developed a novel artificial intelligence algorithm for clinical applications called ‘scArches’. It efficiently compares patients’ cells with a reference atlas of cells of healthy individuals. This enables physicians to pinpoint cells in disease and prioritize them for personalized treatment in each patient.

Researchers from Helmholtz Zentrum Munchen and the Technical University of Munich (TUM) developed a novel algorithm called «scArches,» short for single-cell architecture surgery. The biggest advantage: «Instead of sharing raw data between clinics or research centers, the algorithm uses transfer learning to compare new datasets from single-cell genomics with existing references and thus preserves privacy and anonymity. This also makes annotating and interpreting of new data sets very easy and democratizes the usage of single-cell reference atlases dramatically,» says Mohammad Lotfollahi, the leading scientist of the algorithm.

Example COVID-19

The researchers applied scArches to study COVID-19 in several lung bronchial samples. They compared the cells of COVID-19 patients to healthy references using single-cell transcriptomics. The algorithm was able to separate diseased cells from the references and thus enabled the user to pinpoint the cells in need for treatment, for both mild and severe COVID-19 cases. Biological variation between patients did not affect the quality of the mapping process.

Fabian Theis: «Our vision is that in the future we will use cell references as easily as we nowadays do for genome references. In other word, if you want to bake a cake, you usually do not want to try coming up with your own recipe — instead you just look one up in a cookbook. With scArches, we formalize and simplify this lookup process.»

Story Source: Materials provided by Helmholtz Zentrum Munchen — German Research Center for Environmental Health. Note: Content may be edited for style and length.