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.
ABSTRACT
Studies on monolayer cultures and whole-animal models for the prediction of the response of native human tissue are associated with limitations. Therefore, more and more laboratories are tending towards multicellular spheroids grown in vitro as a model of native tissues. In addition, they are increasingly used in a wide range of biofabrication methodologies. These 3D microspheroids are generated through a self-assembly process that is still poorly characterised, called cellular aggregation. Here, a system is proposed for the automated, non-invasive and high throughput monitoring of the morphological changes during cell aggregation. Microwell patterned inserts were used for spheroid formation while an automated microscope with 4x bright-field objective captured the morphological changes during this process. Subsequently, the acquired time-lapse images were automatically segmented and several morphological features such as minor axis length, major axis length, roundness, area, perimeter and circularity were extracted for each spheroid. The method was quantitatively validated with respect to manual segmentation on four sets of ± 60 spheroids. The average sensitivities and precisions of the proposed segmentation method ranged from 96.67-97.84% and 96.77-97.73%, respectively. In addition, the different morphological features were validated, obtaining average relative errors between 0.78-4.50%. On average, a spheroid was processed 73 times faster than a human operator. As opposed to existing algorithms, our methodology was not only able to automatically monitor compact spheroids but also the aggregation process of individual spheroids, and this in an accurate and high-throughput manner. In total, the aggregation behaviour of more than 700 individual spheroids was monitored over a duration of 16 hours with a time interval of 5 minutes, and this could be increased up to 48,000 for the described culture format. In conclusion, the proposed system has the potential to be used for unravelling the mechanisms involved in spheroid formation and monitoring their formation during large-scale manufacturing protocols.
