Comprehensive monitoring of tissue composition using in vivo imaging of cell nuclei and deep learning
Comprehensive analysis of tissue composition has so far been limited to ex vivo approaches. Here, we introduce NuClear (Nucleus-instructed tissue composition using deep learning), an approach combining in vivo two-photon imaging of histone 2B-eGFP-labeled cell nuclei with subsequent deep learning-ba...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
October 05, 2022
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| In: |
bioRxiv beta
Year: 2022, Pages: 1-41 |
| DOI: | 10.1101/2022.10.03.510670 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1101/2022.10.03.510670 Verlag, kostenfrei, Volltext: https://www.biorxiv.org/content/10.1101/2022.10.03.510670v1 
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| Verfasserangaben: | Amrita Das Gupta, Livia Asan, Jennifer John, Carlo A. Beretta, Thomas Kuner, Johannes Knabbe |
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| 520 | |a Comprehensive analysis of tissue composition has so far been limited to ex vivo approaches. Here, we introduce NuClear (Nucleus-instructed tissue composition using deep learning), an approach combining in vivo two-photon imaging of histone 2B-eGFP-labeled cell nuclei with subsequent deep learning-based identification of cell types from structural features of the respective cell nuclei. This allowed us to classify all cells per imaging volume (0.25 mm3 containing ~25000 cells) and identify their position in 3D space in a non-invasive manner using only a single label. NuClear opens a window to study changes in relative abundance and location of all major brain cell types in individual mice over extended time periods, enabling comprehensive studies of changes in cellular composition in physiological and pathophysiological conditions. | ||
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