An automated pipeline for tracking and measuring cell spheroids encapsulated in 3D hydrogel systems

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Abstract
Cell spheroids have been exploited as fundamental engineering units applied as screening platforms or assembled as building blocks for tissue engineering applications. While spheroid encapsulation into hydrogels creates more reliable 3D models, it also brings several constraints, e.g., hydrogel swelling and dynamicity, shading, limitations on depth-resolution, and cell staining strategies for monitoring long real-time imaging. Hence, the objective of this work was to develop a post-imaging automated pipeline for the accurate tracking and measuring of spheroids encapsulated in 3D hydrogels. Using NIS Elements ARv5.30 (Nikon) software, we created a sequence of functions for enhancing spheroid borders, extending the depth of focus, reducing hydrogel shading, and identifying coordinates in an automated manner for time-lapse microscopy analysis up to 70 h. Additionally, we established a method for identifying and tracking migration trajectories of protruded cell clusters that detached from spheroids into the hydrogel. For a comparative hydrogel analysis, fluorescent beads were encapsulated in the ionically crosslinked xanthan gum–alginate (XG–Alg) and photocrosslinked methacrylate hyaluronic acid (HAMA). For pipeline validation, human mesenchymal stem cell spheroids were encapsulated in XG–Alg hydrogel. By employing our pipeline, a high dynamicity and intense swelling effect were detected within XG–Alg, while HAMA remained stable, without noticeable movements up to 60 h. Accurate imaging and tracking detected several spheroid morphological changes, including reversible spheroid-ellipsoid shapes, axis rotational motion, outermost layer movements, spheroid fusion, and a spheroid migration speed of approximately 1.3 µ m h −1 . Protruded cell clusters were detected in high numbers (83–173 per spheroid), migrating arbitrarily into the hydrogel (16°–311°), with an average speed of approximately 11.4 µ m h −1 . Our results indicate that this automated pipeline can facilitate the understanding of several cellular dynamic events with high accuracy and low manual interference, which are essential for scaling up tissue engineering and other advanced applications such as drug screening platforms.
Funding Information
  • European Union’s Horizon 2020 Framework Program (874 837)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (88 887.310405/2018-00)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (142 050/2018-0)
  • Interreg North-West Europe (495)
  • Fundo de Apoio ao Ensino, Pesquisa e Extensão, Universidade Estadual de Campinas (2324/21)

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