Abstract
Knowledge about the dynamics of cytoskeletal proteins, such as actin filaments and microtubules, is key to understanding numerous active cellular processes. Automated tracking algorithms nowadays allow to follow the motion of fluorescently labelled cytoskeleton-associated proteins to some extent. However, these algorithms often require human supervision and are less accurate than manual analysis, which on the other hand is time-consuming and prone to unconscious bias. As an alternative, kymographs, which are images depicting dynamic processes along a predefined axis, offer a convenient approach to visualise and track fluorescent proteins. However, kymographs are currently almost exclusively analysed manually, again limiting throughput. We here developed and trained KymoButler, a deep neural network to trace dynamic processes in kymographs. We demonstrate that KymoButler performs at least as well as manual tracking and outperforms currently available automated tracking packages. Additionally, we successfully applied KymoButler to a variety of different kymograph tracing problems. Finally, the network was packaged in a web-based “one-click” software for use by the wider scientific community. Our approach significantly speeds up data analysis, avoids unconscious bias, and represents a step towards the widespread adaptation of Artificial Intelligence techniques in biological data analysis.