A platform for automated and label-free monitoring of morphological features and kinetics of spheroid fusion

Thomas Deckers; Gabriella Nilsson Hall; Ioannis Papantoniou; Jean-Marie Aerts; Veerle Bloemen

doi:10.3389/fbioe.2022.946992

A platform for automated and label-free monitoring of morphological features and kinetics of spheroid fusion

Front Bioeng Biotechnol. 2022 Aug 26:10:946992. doi: 10.3389/fbioe.2022.946992. eCollection 2022.

Authors

Thomas Deckers^{1

2

3}, Gabriella Nilsson Hall^{3

4}, Ioannis Papantoniou^{3

4

5}, Jean-Marie Aerts^{1

3}, Veerle Bloemen^{2

3}

Affiliations

¹ Measure, Model and Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, Leuven, Belgium.
² Surface and Interface Engineered Materials (SIEM), Group T Leuven Campus, KU Leuven, Leuven, Belgium.
³ Prometheus, Division of Skeletal Tissue Engineering Leuven, KU Leuven, Leuven, Belgium.
⁴ Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium.
⁵ Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH), Patras, Greece.

Abstract

Spheroids are widely applied as building blocks for biofabrication of living tissues, where they exhibit spontaneous fusion toward an integrated structure upon contact. Tissue fusion is a fundamental biological process, but due to a lack of automated monitoring systems, the in-depth characterization of this process is still limited. Therefore, a quantitative high-throughput platform was developed to semi-automatically select doublet candidates and automatically monitor their fusion kinetics. Spheroids with varying degrees of chondrogenic maturation (days 1, 7, 14, and 21) were produced from two different cell pools, and their fusion kinetics were analyzed via the following steps: (1) by applying a novel spheroid seeding approach, the background noise was decreased due to the removal of cell debris while a sufficient number of doublets were still generated. (2) The doublet candidates were semi-automatically selected, thereby reducing the time and effort spent on manual selection. This was achieved by automatic detection of the microwells and building a random forest classifier, obtaining average accuracies, sensitivities, and precisions ranging from 95.0% to 97.4%, from 51.5% to 92.0%, and from 66.7% to 83.9%, respectively. (3) A software tool was developed to automatically extract morphological features such as the doublet area, roundness, contact length, and intersphere angle. For all data sets, the segmentation procedure obtained average sensitivities and precisions ranging from 96.8% to 98.1% and from 97.7% to 98.8%, respectively. Moreover, the average relative errors for the doublet area and contact length ranged from 1.23% to 2.26% and from 2.30% to 4.66%, respectively, while the average absolute errors for the doublet roundness and intersphere angle ranged from 0.0083 to 0.0135 and from 10.70 to 13.44°, respectively. (4) The data of both cell pools were analyzed, and an exponential model was used to extract kinetic parameters from the time-series data of the doublet roundness. For both cell pools, the technology was able to characterize the fusion rate and quality in an automated manner and allowed us to demonstrate that an increased chondrogenic maturity was linked with a decreased fusion rate. The platform is also applicable to other spheroid types, enabling an increased understanding of tissue fusion. Finally, our approach to study spheroid fusion over time will aid in the design of controlled fabrication of "assembloids" and bottom-up biofabrication of living tissues using spheroids.

Keywords: automated monitoring; biofabrication; bright-field microscopy; image analysis; machine learning; morphological features; spheroid fusion kinetics.