Regensburg 2022 – wissenschaftliches Programm
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BP: Fachverband Biologische Physik
BP 10: Cell Adhesion and Multicellular Systems
BP 10.4: Vortrag
Dienstag, 6. September 2022, 10:45–11:00, H13
Spatiotemporally resolved single-cell growth in bacterial biofilms — •Eric Jelli1,2,3, Takuya Ohmura2,4, Niklas Netter2,3,4, Martin Abt2,3, Eva Jiménez-Siebert2,3,4, Konstantin Neuhaus2,3,4, Daniel Karl-Heinz Rode2,3,4, and Knut Drescher2,3,4 — 1Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany — 2Max Planck Institute for Terrestrial Microbiology, Marburg, Germany — 3Department of Physics, Philipps-Universität Marburg, Marburg, Germany — 4Biozentrum - University of Basel, Basel, Switzerland
Bacterial biofilms are dense multicellular communities that are embedded in a self-produced matrix. The high density of cells gives rise to nutrient, oxygen, and metabolite gradients in space and time. To understand the underlying spatio-temporal growth principles in biofilms, single-cell segmentation algorithms are required. Current Deep Learning algorithms provide the required accuracy for tracking-dependent investigations, yet depend on suitable large training datasets.
We used an iterative training pipeline to densely annotate complete biofilms with thousands of cells in 3D. The pipeline reduced the required manual labeling steps which would otherwise be prohibitive for a dataset of a similar size. The collected data enabled us to compare the single-cell segmentation accuracy of recent Deep Learning algorithms with the results of classical biofilm segmentation approaches. We used the trained algorithms for single-cell tracking in 3D time-lapse confocal microscopy data and identified regions with different division rates inside the microbial communities.