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Atwell, S. ; Waibel, D.J.E. ; Shetab Boushehri, S. ; Wiedenmann, S. ; Marr, C. ; Meier, M.

Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip.

Cell Rep. Methods 3:100523 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.
Massive, parallelized 3D stem cell cultures for engineering in vitro human cell types require imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture technologies. Here, we introduce a large-scale integrated microfluidic chip platform for automated 3D stem cell differentiation. To fully enable dynamic high-content imaging on the chip platform, we developed a label-free deep learning method called Bright2Nuc to predict in silico nuclear staining in 3D from confocal microscopy bright-field images. Bright2Nuc was trained and applied to hundreds of 3D human induced pluripotent stem cell cultures differentiating toward definitive endoderm on a microfluidic platform. Combined with existing image analysis tools, Bright2Nuc segmented individual nuclei from bright-field images, quantified their morphological properties, predicted stem cell differentiation state, and tracked the cells over time. Our methods are available in an open-source pipeline, enabling researchers to upscale image acquisition and phenotyping of 3D cell culture.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords 3d Cell Culture Technology ; Ai Imaging ; Cell State Prediction ; Microfluidics ; Stem Cells-on-chip ; Tracking Single Cells
ISSN (print) / ISBN 2667-2375
e-ISSN 2667-2375
Journal Cell Reports Methods
Quellenangaben Volume: 3, Issue: 7, Pages: , Article Number: 100523 Supplement: ,
Publisher Elsevier
Publishing Place 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Helmholtz Pioneer Campus (HPC)
Institute of AI for Health (AIH)
Grants Hightech Agenda Bayern
Helmholtz Association under the joint research school "Munich School for Data Science-MUDS
F. Hoffmann-La Roche
Helmholtz Pioneer Campus
European Research Council (ERC) under the European Union