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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)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 3d Cell Culture Technology ; Ai Imaging ; Cell State Prediction ; Microfluidics ; Stem Cells-on-chip ; Tracking Single Cells
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2667-2375
e-ISSN 2667-2375
Zeitschrift Cell Reports Methods
Quellenangaben Band: 3, Heft: 7, Seiten: , Artikelnummer: 100523 Supplement: ,
Verlag Elsevier
Verlagsort 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Pioneer Campus (HPC)
Institute of AI for Health (AIH)
POF Topic(s) 30201 - Metabolic Health
30205 - Bioengineering and Digital Health
Forschungsfeld(er) Pioneer Campus
Enabling and Novel Technologies
PSP-Element(e) G-510002-001
G-540007-001
Förderungen 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
DOI 10.1016/j.crmeth.2023.100523
WOS ID 001048996900001
Scopus ID 85165505618
PubMed ID 37533640
Erfassungsdatum 2023-10-06
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