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Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments

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Abstract

Organs On a Chip (OOCs) represent a sophisticated approach for exploring biological mechanisms and developing therapeutic agents. In conjunction with high-quality time-lapse microscopy (TLM), OOCs allow for the visualization of reconstituted complex biological processes, such as multi-cell-type migration and cell–cell interactions. In this context, increasing the frame rate is desirable to reconstruct accurately cell-interaction dynamics. However, a trade-off between high resolution and carried information content is required to reduce the overall data volume. Moreover, high frame rates increase photobleaching and phototoxicity. As a possible solution for these problems, we report a new hybrid-imaging paradigm based on the integration of OOC/TLMs with a Multi-scale Generative Adversarial Network (GAN) predicting interleaved video frames with the aim to provide high-throughput videos. We tested the performance of the predictive capability of GAN on synthetic videos, as well as on real OOC experiments dealing with tumor–immune cell interactions. The proposed approach offers the possibility to acquire a reduced number of high-quality TLM images without any major loss of information on the phenomena under investigation.

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Acknowledgements

GK is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR) – Projets blancs; ANR under the frame of E-Rare-2, the ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Chancelerie des universités de Paris (Legs Poix), Fondation pour la Recherche Médicale (FRM); a donation by Elior; European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR); Gustave Roussy Odyssea, the European Union Horizon 2020 Project Oncobiome; Fondation Carrefour; High-end Foreign Expert Program in China (GDW20171100085 and GDW20181100051), Institut National du Cancer (INCa); Inserm (HTE); Institut Universitaire de France; LeDucq Foundation; the LabEx Immuno-Oncology; the RHU Torino Lumière; the Seerave Foundation; the SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and the SIRIC Cancer Research and Personalized Medicine (CARPEM).

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Authors and Affiliations

  1. Department of Electronic Engineering, University of Rome Tor Vergata, via del Politecnico 1, 00133, Rome, Italy

    M. C. Comes, J. Filippi, A. Mencattini, P. Casti, D. Di Giuseppe, M. D’Orazio, C. Di Natale & E. Martinelli

  2. Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133, Rome, Italy

    M. C. Comes, J. Filippi, A. Mencattini, P. Casti, D. Di Giuseppe, M. D’Orazio & E. Martinelli

  3. Equipe labellisée par la Ligue Contre le Cancer, INSERM U1138, Centre de Recherche des Cordeliers, Université Paris Descartes, Université Sorbonne Paris Cité, Université Paris Diderot, Sorbonne Université, Paris, France

    G. Cerrato, A. Sauvat, E. Vacchelli & G. Kroemer

  4. Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France

    G. Cerrato, A. Sauvat, E. Vacchelli & G. Kroemer

  5. Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France

    G. Kroemer

  6. Suzhou Institute for Systems Biology, Chinese Academy of Sciences, Suzhou, China

    G. Kroemer

  7. Department of Women’s and Children’s Health, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden

    G. Kroemer

  8. Institute for Photonics and Nanotechnologies, Italian National Research Council, Via Cineto Romano 42, 00156, Rome, Italy

    A. De Ninno & L. Businaro

  9. Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161, Rome, Italy

    F. Mattei & G. Schiavoni

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  1. M. C. Comes
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  2. J. Filippi
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Correspondence to E. Martinelli.

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Supplementary Fig. 1

Evaluation performance on the single scenario, the negative case of the organ-on-chip experiments. In the upper panel the real histogram of the mean interaction time in comparison with the generated counterpart after subsampling at 0.1667 frames/min. In the lower panel the real histogram of the mean interaction time in comparison with the subsampled counterpart (0.1667 frames/min). P-values for the K–S are also specified. (TIF 110 kb)

Supplementary Fig. 2

Comparison between the generated and subsampled histograms of the mean interaction time for the negative and positive cases on the panels on the top and on the bottom, respectively. Videos are subsampled at 0.1667 frames/min. P-values for the K–S test are also specified. (TIF 109 kb)

Supplementary Video 1 Artificial movie of one out of 100 for the simulated control case. (AVI 21103 kb)

Supplementary Video 2 Subsampled counterpart of Supplementary Video 1 obtained by subsampling at temporal resolution of 0.25 frames/min (every 4 minutes). The red word Start indicates that the temporal subsampling starts from the frame 50, when all immune cells are appeared in the field of view. (AVI 16880 kb)

Supplementary Video 3 Hybrid counterpart of Supplementary Video 1 obtained by alternating sequences of GAN generated frames with original theoretical frames. (AVI 21103 kb)

Supplementary Video 4 Example of extracted Region of Interest (ROI) for the negative case of the Tumor-Immune On Chip real experiment. (AVI 21103 kb)

Supplementary Video 5 Hybrid counterpart of Supplementary Video 3 obtained by alternating sequences of GAN generated frames with original theoretical frames.(AVI 2503 kb)

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Comes, M.C., Filippi, J., Mencattini, A. et al. Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments. Neural Comput & Applic 33, 3671–3689 (2021). https://doi.org/10.1007/s00521-020-05226-6

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