EntireAxon: Deep learning deciphers four distinct patterns of axonal degeneration

Alex Palumbo, Philipp Grüning, Svenja Kim Landt, Lara Eleen Heckmann, Luisa Bartram, Alessa Pabst, Charlotte Flory, Maulana Ikhsan, Sören Pietsch, Reinhard Schulz, Christopher Kren, Norbert Koop, Johannes Boltze, Amir Madany Mamlouk, Marietta Zille

Abstract

Different axonal degeneration (AxD) patterns have been described depending on the biological condition. Until now, it remains unclear whether they are restricted to one specific condition or can occur concomitantly. Here, we present a novel microfluidic device in combination with a deep learning tool, the EntireAxon, for the high-throughput analysis of AxD. We evaluated the progression of AxD in an in vitro model of hemorrhagic stroke and observed that axonal swellings preceded axon fragmentation. We further identified four distinct morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) that occur concomitantly. These findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The newly developed microfluidic device along with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.
Original languageEnglish
JournalbioRxiv
Pages (from-to)1-38
Number of pages38
ISSN2692-8205
Publication statusPublished - 2020

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

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