01-Sep-2021 - Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt GmbH

AI helps to spot single diseased cells

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.

The Human Cell Atlas is the world’s largest, growing single-cell reference atlas. It contains references of millions of cells across tissues, organs and developmental stages. These references help physicians to understand the influences of aging, environment and disease on a cell – and ultimately diagnose and treat patients better. Yet, reference atlases do not come without challenges. Single-cell datasets may contain measurement errors (batch effect), the global availability of computational resources is limited and the sharing of raw data is often legally restricted.

Researchers from Helmholtz Zentrum München 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.”

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt GmbH

Recommend news PDF version / Print Add news to watchlist

Share on

Facts, background information, dossiers

  • artificial intelligence
  • cells
  • personalized medicine
  • Covid-19

More about Helmholtz Zentrum München

  • News

    The oat genome unlocks the unique health benefits of oats

    Researchers have succeeded in sequencing and characterizing the entire genome of oat. Compared to other cereals and humans, the oat genome architecture is very complex. Scientists from Helmholtz Munich, Lund University and the ScanOats network finally elucidated at the genetic level why oat ... more

    A Speed Limit Could Be a Breakthrough for Stem Cell Therapy

    Replacing sick or damaged cells with healthy cells: this is a major goal of regenerative medicine. One of the most promising approaches is cellular reprogramming, whereby one cell type in our body converts to another cell type. Research carried out at Helmholtz Munich and Ludwig-Maximilians ... more

    COVID-19: Breakthrough infection can substitute for a third vaccine shot

    According to a new study led by Ulrike Protzer, a breakthrough infection after two vaccinations achieves the same protective effect as an additional booster vaccination. According to the study by Helmholtz Munich, LMU and TUM, the decisive factor for immunity is that the immune system has h ... more

  • q&more articles

    Using deep learning to better understand blood disorders

    For a long time, doctors have been diagnosing disorders of the body’s hematopoietic system using a light microscope. The analysis of individual blood cells is largely performed manually. Now, artificial intelligence can lend them a digital hand. more

  • Authors

    Dr. Carsten Marr

    Carsten Marr, born in 1977, received his diploma in general physics from the Technische Universität München in 2002. He wrote his diploma thesis at the Max-Planck-Institute for Quantum Optics, Garching, Germany, and in 2003 visited the Quantum Information and Quantum Optics Theory Group at ... more

    Dr. Christian Matek

    Christian Matek, born in 1986, received undergraduate degrees in both Physics and Medicine in Munich. He then moved to the UK and finished his DPhil in Theoretical Physics at Oxford University in 2014. Since 2017, his main research interest has been applying artificial intelligence and mach ... more

q&more – the networking platform for quality excellence in lab and process

The q&more concept is to increase the visibility of recent research and innovative solutions, and support the exchange of knowledge. In the broad spectrum of subjects covered, the focus is on achieving maximum quality in highly innovative sectors. As a modern knowledge platform, q&more offers market participants one-of-a-kind networking opportunities. Cutting-edge research is presented by authors of international repute. Attractively presented in a high-quality context, and published in German and English, the original articles introduce new concepts and highlight unconventional solution strategies.

> more about q&more

q&more is supported by: