KymoButler, a deep learning software for automated kymograph analysis.

Kristian Franze,Maximilian Ah Jakobs,Andrea Dimitracopoulos

doi:10.7554/elife.42288

Abstract

Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based 'one-click' application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis.

Highlights

Many processes in living cells are highly dynamic, and molecules, vesicles, and organelles diffuse or are transported along complex trajectories
Multiple particles move along the same stationary path with little to no deviations, making kymographs a very useful representation of their dynamics
For our Fully Convolutional Deep Neural Networks (FCNs)-based kymograph analysis software, we implemented a customised architecture based on the U-Net (Ronneberger et al 2015)

Summary

Introduction

Many processes in living cells are highly dynamic, and molecules, vesicles, and organelles diffuse or are transported along complex trajectories. Particle tracking algorithms represent powerful approaches to track the dynamics of such particles ((Jaqaman et al 2008; Sbalzarini & Koumoutsakos 2005; Lee & Park 2018)). In scenarios where particles follow a stationary path and move much faster than the confounding cell (e.g., as in molecular transport along neuronal axons and dendrites, retrograde actin flow, or cilia transport), kymographs provide an elegant solution to the visualisation and analysis of particle dynamics. Multiple particles move along the same stationary path with little to no deviations, making kymographs a very useful representation of their dynamics In the resulting space-time image, each (usually fluorescently) labelled particle is shown as a line, whose slope, for example, represents the velocity of that particle (Figure 1A).

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