Theis and colleagues have shown how data from individual blood stem cells can be used to determine their fate...
Scientists at the DZL Site CPC-M (Munich) have received the 2017 Erwin Schrödinger Prize: Prof. Dr. Dr. Fabian Theis (Director of the Institute of Computational Biology (ICB) at the Helmholtz Zentrum München (HMGU) and professor at the Technical University of Munich), Prof. Dr. Timm Schroeder (Department of Biosystems Science and Engineering of ETH Zürich in Basel, previously HMGU), Dr. Carsten Marr (ICB) and Dr. Laleh Haghverdi (EMBL-EBI Hinxton, previously ICB). In an interdisciplinary collaboration they have shown how data from individual blood stem cells can be used to determine their fate.
Haghverdi, Marr, Schroeder and Theis received the 2017 Erwin Schrödinger Prize, endowed with 50,000 euros, at the Helmholtz Association’s annual conference on September 14. With this award, the Helmholtz Association and the Stifterverband für die Deutsche Wissenschaft, a donors' association for the promotion of humanities and sciences in Germany, honor outstanding scientific or technically innovative achievements in areas bordering on various disciplines in medicine, the natural sciences, and engineering in which representatives of at least two disciplines have participated.
Insights into blood cell differentiation processes are important for a better understanding of autoimmune diseases and leukemia. Scientists often work with cell populations that are expected to be homogenous after they have been sorted in the laboratory. “It is becoming increasingly evident, however, that this view of clearly defined cell types does not adequately describe the biological reality,” states DZL scientistc Prof. Dr. Dr. Fabian Theis.
The team developed a number of methods to acquire a better description of cell populations and forecast the fate of individual cells. The team generated accurate single-cell microscopy movies, analyzed genomic or proteomic data from individual cells, and applied algorithms and methods from mathematics and machine learning.
This interdisciplinary approach has already provided spectacular insights into the dynamic heterogeneity of individual blood cells. The scientists have additionally corrected the assumption that two particular transcription factors are responsible for a specific lineage decision. They also found a sequence of gene expression patterns that allow insights into early differentiation processes in blood cells.
“Our work provides important foundations for the Human Cell Atlas,” adds Theis. This international project is intended to compile information on all human cells.
Prof. Dr. Dr. Fabian Theis
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
Institute of Computational Biology (ICB)
Ingolstädter Landstr. 1
Tel.: +49 89 3187 4030
Press release of the "Stifterverband für die Deutsche Wissenschaft" (in German)
Press release from the Helmholtz Zentrum München (in English)
YouTube Video presenting the team and its project (in German)
Buggenthin, F. et al. (2017): Prospective identification of hematopoietic lineage choice by deep learning. Nature Methods, DOI:10.1038/nmeth.4182
Haghverdi, L. et al. (2016): Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods, DOI: 10.1038/nmeth.3971
Hilsenbeck, O. et al. (2016): Software tools for single-cell tracking and quantification of cellular and molecular properties. Nature Biotechnology, DOI:10.1038/nbt.3626
Hoppe, PS. et al. (2016): Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature DOI: 10.1038/nature18320
Haghverdi, L. et al. (2015): Diffusion maps for high-dimensional single-cell analysis. Bioinformatics, DOI: 10.1093/bioinformatics/btv325