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| Cells. 2021 Sep 25. pii: cells10102539. doi: 10.3390/cells10102539 |
| Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration. |
| Palumbo A1, Grüning P2, Landt SK3, Heckmann LE4, Bartram L5, Pabst A6, Flory C7, Ikhsan M8, Pietsch S9, Schulz R10, Kren C11, Koop N12, Boltze J13, Madany Mamlouk A14, Zille M15 |
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Abstract Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context. |
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KEYWORDS: axon, brain hemorrhage, cell culture, cortical neurons, machine learning, microfluidic, microscopy, stroke, time-lapse |
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Publikations ID: 34685519 Quelle: öffnen |
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