Real-time image-based cell identification

Identification of different cell types is an indispensable task of biomedical research and clinical application. During the last decades, much attention was given to molecular characterization, and many cell types can now be identified using established markers that bind to cell-specific antigens. The required staining process is a lengthy and costly treatment, which can cause alterations of cellular properties, contaminate the sample and therefore limit its subsequent use. For example, for photoreceptor transplantations, highly pure samples of photoreceptor cells are required, which can currently only be obtained using molecular labelling, rendering the resulting sample incompatible for clinical application. A promising alternative to molecular markers is the label-free identification of cells using mechanical or morphological features. Real-time deformability cytometry (RT DC) is a microfluidic technique, which allows capturing both types of information simultaneously for single cells at high-throughput. In this thesis, I present machine learning methods which allow identifying different cell types, based on bright-field images from RT DC. In particular, I introduce algorithms that are fast enough to be applied in real-time during the measurement (at >1000 cells/s), which can be used for image-based cell sorting. The performance of the algorithms is shown for the identification of rod precursor cells in retina-samples, indicating that image-based sorting based on those algorithms would allow enriching photoreceptors to a final concentration, applicable for transplantation purposes.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Technische Universität Dresden

Year

2020

Author dc:creator

• Herbig, Maik

Contributors dc:contributor

• Guck, Jochen
• Schlierf, Michael

• Machine learning
• deep learning
• microfluidics
• flow cytometry
• statistics