scArches Makes Single-Cell Reference Atlases Easier to Use

In an effort to reduce the challenges associated with the use of reference atlases, researchers from Helmholtz Zentrum München and the Technical University of Munich have developed a novel algorithm called scArches, short for single-cell architecture surgery. The biggest advantage of scArches is that 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, according to Mohammad Lotfollahi, first author on the paper published in Nature Biotechnology today.

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.