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Reliable identification and quantification of neural cells in microscopic images of neurospheres.

Authors
  • Förster, Nils1
  • Butke, Joshua2
  • Keßel, Hagen Eike3
  • Bendt, Farina3
  • Pahl, Melanie3
  • Li, Lu4, 5, 6
  • Fan, Xiaohui4
  • Leung, Ping-Chung6
  • Klose, Jördis3
  • Masjosthusmann, Stefan3
  • Fritsche, Ellen3
  • Mosig, Axel1
  • 1 Department of Bioinformatics, Center for Protein Diagnostics, Ruhr-University Bochum, Gesundheitscampus 4, Bochum, Germany. , (Germany)
  • 2 Bioinformatics, Faculty of Biology and Biotechnology, Ruhr-University Bochum, Universitätsstr 150, Bochum, Germany. , (Germany)
  • 3 IUF - Leibniz Research Institute for Environmental Medicine, Düsseldorf, North Rhine-Westphalia, Germany. , (Germany)
  • 4 College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. , (China)
  • 5 Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin New Town, Hong Kong. , (Hong Kong SAR China)
  • 6 Institute of Chinese Medicine, The Chinese University of Hong Kong, Shatin New Town, Hong Kong. , (Hong Kong SAR China)
Type
Published Article
Journal
Cytometry Part A
Publisher
Wiley (John Wiley & Sons)
Publication Date
May 01, 2022
Volume
101
Issue
5
Pages
411–422
Identifiers
DOI: 10.1002/cyto.a.24514
PMID: 34747115
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

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