Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research

Nikita Konshin; Marta Garcia Valverde; Danila Solodennikov; Koen Minartz; Vlado Menkovski; Rosalinde Masereeuw; Shantanu Singh; Silvia M. Mihăilă; Jan de Boer

doi:10.1016/j.mtbio.2025.102352

Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research

Nikita Konshin (Corresponding author)
, Marta Garcia Valverde
, Danila Solodennikov
, Koen Minartz
, Vlado Menkovski
, Rosalinde Masereeuw
, Shantanu Singh
, Silvia M. Mihăilă
, Jan de Boer (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

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We present a large-scale, standardized image dataset and analysis pipeline designed to enable in silico discovery of cell–material interactions. This resource paper introduces an open, FAIR-aligned framework for acquiring, curating, and analyzing high-content imaging data of kidney podocytes cultured on 2176 micro-topographical surfaces using the TopoChip platform. Our workflow includes automated imaging, tilt correction, object segmentation, and multi-tiered quality control, resulting in over 5500 morphological features for >1.2 million cells. Structured metadata, standardized file architectures, and ontological annotations ensure that the dataset is fully interoperable and ready for reuse. To illustrate its versatility, we provide examples of how this resource supports machine learning model development, reproducible benchmarking, and hypothesis testing in cell biology and biomaterials science. This dataset and accompanying tools are designed as a foundational reference for the community, enabling scalable, quantitative, and reproducible exploration of how microenvironments shape cell behavior.

Originele taal-2	Engels
Artikelnummer	102352
Aantal pagina's	18
Tijdschrift	Materials Today Bio
Volume	35
Vroegere onlinedatum	7 okt. 2025
DOI's	https://doi.org/10.1016/j.mtbio.2025.102352
Status	Gepubliceerd - dec. 2025

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Publisher Copyright:
© 2025 The Authors

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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Nikita Konshin reports financial support was provided by Eindhoven University of Technology. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.This research was financially supported by Chemelot Institute for Science & Technology (InSciTe) under grant agreement BM3.03 SEAMS: Smart, Easy, and Accurate Minimally Invasive Glaucoma Surgery. We express our sincere gratitude to Phani Krishna Sudarsanam for his theoretical contributions to the project. Discussions with Phani significantly enhanced our understanding of the principles underlying the TopoChip analysis. We are grateful to Linda Chen-Zijlstra for assembling the high-performance computing resources, including a powerful workstation, and for facilitating access to the High-Performance Computing cluster essential for this analysis. Finally, we extend our appreciation to all participants of the Biomaterials Screening meetings hosted by the Patricia Dankers, Lorenzo Albertazzi, and Jan de Boer labs. These recurring discussions and valuable feedback have considerably strengthened our methodological approach and clarified the vision for this work. This research was financially supported by Chemelot Institute for Science & Technology (InSciTe) under grant agreement BM3.03 SEAMS: Smart, Easy, and Accurate Minimally Invasive Glaucoma Surgery. We express our sincere gratitude to Phani Krishna Sudarsanam for his theoretical contributions to the project. Discussions with Phani significantly enhanced our understanding of the principles underlying the TopoChip analysis. We are grateful to Linda Chen-Zijlstra for assembling the high-performance computing resources, including a powerful workstation, and for facilitating access to the High-Performance Computing cluster essential for this analysis. Finally, we extend our appreciation to all participants of the Biomaterials Screening meetings hosted by the Patricia Dankers, Lorenzo Albertazzi, and Jan de Boer labs. These recurring discussions and valuable feedback have considerably strengthened our methodological approach and clarified the vision for this work.

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abstract = "We present a large-scale, standardized image dataset and analysis pipeline designed to enable in silico discovery of cell–material interactions. This resource paper introduces an open, FAIR-aligned framework for acquiring, curating, and analyzing high-content imaging data of kidney podocytes cultured on 2176 micro-topographical surfaces using the TopoChip platform. Our workflow includes automated imaging, tilt correction, object segmentation, and multi-tiered quality control, resulting in over 5500 morphological features for >1.2 million cells. Structured metadata, standardized file architectures, and ontological annotations ensure that the dataset is fully interoperable and ready for reuse. To illustrate its versatility, we provide examples of how this resource supports machine learning model development, reproducible benchmarking, and hypothesis testing in cell biology and biomaterials science. This dataset and accompanying tools are designed as a foundational reference for the community, enabling scalable, quantitative, and reproducible exploration of how microenvironments shape cell behavior.",

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Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research

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