M.Sc.

Nießing, Bastian

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  • Publication
    Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status
    ( 2021)
    Piotrowski, Tobias
    ;
    Rippel, Oliver
    ;
    Elanzew, Andreas
    ;
    Nießing, Bastian
    ;
    Stucken, Sebastian
    ;
    Jung, Sven
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    König, Niels orcid-logo
    ;
    Haupt, Simone
    ;
    Stappert, Laura
    ;
    Brüstle, Oliver
    ;
    Schmitt, Robert
    ;
    Jonas, Stephan
    Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of highquality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an auto mated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.
  • Publication
    The StemCellFactory: A Modular System Integration for Automated Generation and Expansion of Human Induced Pluripotent Stem Cells
    ( 2020)
    Elanzew, Andreas
    ;
    Nießing, Bastian
    ;
    Langendoerfer, Daniel
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    Rippel, Oliver
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    Piotrowski, Tobias
    ;
    Schenk, Friedrich
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    Kulik, Michael
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    Peitz, Michael
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    Breitkreuz, Yannik
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    Jung, Sven
    ;
    Wanek, Paul
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    Stappert, Laura
    ;
    Schmitt, Robert H.
    ;
    Haupt, Simone
    ;
    Zenke, Martin
    ;
    König, Niels orcid-logo
    ;
    Brüstle, Oliver
    While human induced pluripotent stem cells (hiPSCs) provide novel prospects for disease-modeling, the high phenotypic variability seen across different lines demands usage of large hiPSC cohorts to decipher the impact of individual genetic variants. Thus, a much higher grade of parallelization, and throughput in the production of hiPSCs is needed, which can only be achieved by implementing automated solutions for cell reprogramming, and hiPSC expansion. Here, we describe the StemCellFactory, an automated, modular platform covering the entire process of hiPSC production, ranging from adult human fibroblast expansion, Sendai virus-based reprogramming to automated isolation, and parallel expansion of hiPSC clones. We have developed a feeder-free, Sendai virus-mediated reprogramming protocol suitable for cell culture processing via a robotic liquid handling unit that delivers footprint-free hiPSCs within 3 weeks with state-of-the-art efficiencies. Evolving hiPSC colonies are automatically detected, harvested, and clonally propagated in 24-well plates. In order to ensure high fidelity performance, we have implemented a high-speed microscope for in-process quality control, and image-based confluence measurements for automated dilution ratio calculation. This confluence-based splitting approach enables parallel, and individual expansion of hiPSCs in 24-well plates or scale-up in 6-well plates across at least 10 passages. Automatically expanded hiPSCs exhibit normal growth characteristics, and show sustained expression of the pluripotency associated stem cell marker TRA-1-60 over a t least 5 weeks (10 passages). Our set-up enables automated, user-independent expansion of hiPSCs under fully defined conditions, and could be exploited to generate a large number of hiPSC lines for disease modeling, and drug screening at industrial scale, and quality.