Reliable identification and quantification of neural cells in microscopic images of neurospheres
| Autor: | Nils Förster, Xiaohui Fan, Farina Bendt, Ellen Fritsche, Melanie Pahl, Joshua Butke, Hagen Eike Keßel, Axel Mosig, Ping-Chung Leung, Jördis Klose, Lu Li, Stefan Masjosthusmann |
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| Rok vydání: | 2021 |
| Předmět: |
Neurons
Cell type Histology business.industry Deep learning Neurogenesis Cell Cell Differentiation Cell Biology Biology Pathology and Forensic Medicine Extracellular matrix Organoids medicine.anatomical_structure Neural Stem Cells Neurosphere medicine Neural differentiation Artificial intelligence Progenitor cell business Neuroscience Neural development Cells Cultured |
| Zdroj: | Cytometry. Part A : the journal of the International Society for Analytical CytologyREFERENCES. 101(5) |
| ISSN: | 1552-4930 |
| Popis: | Neurosphere cultures consisting of primary human neural stem/progenitor cells (hNPC) are used for studying the effects of substances on early neurodevelopmental processes in vitro. Differentiating hNPCs migrate and differentiate into radial glia, neurons, astrocytes, and oligodendrocytes upon plating on a suitable extracellular matrix and thus model processes of early neural development. In order to characterize alterations in hNPC development, it is thus an essential task to reliably identify the cell type of each migrated cell in the migration area of a neurosphere. To this end, we introduce and validate a deep learning approach for identifying and quantifying cell types in microscopic images of differentiated hNPC. As we demonstrate, our approach performs with high accuracy and is robust against typical potential confounders. We demonstrate that our deep learning approach reproduces the dose responses of well-established developmental neurotoxic compounds and controls, indicating its potential in medium or high throughput in vitro screening studies. Hence, our approach can be used for studying compound effects on neural differentiation processes in an automated and unbiased process. |
| Databáze: | OpenAIRE |
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