AI helps to spot single diseased cells

Date:
August 30, 2021
Source:
Helmholtz Zentrum Mu"nchen - German Research Center for
Environmental Health
Summary:
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.



FULL STORY ==========================================================================
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 Mu"nchen 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_Mu"nchen_-_German_Research_Center_for
Environmental_Health. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Lotfollahi et al. Mapping single-cell data to reference atlases by
transfer learning. Nature Biotechnology, 2021 DOI:
10.1038/s41587-021- 01001-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210830113325.htm

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