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Three-dimensional (3D) tumor spheroid models have gained increased recognition as important tools in cancer research and anti-cancer drug development. However, currently available imaging approaches employed in high-throughput screening drug discovery platforms e.g. bright field, phase contrast, and fluorescence microscopies, are unable to resolve 3D structures deep inside (>50 μm) tumor spheroids. In this study, we established a label-free, non-invasive optical coherence tomography (OCT) imaging platform to characterize 3D morphological and physiological information of multicellular tumor spheroids (MCTS) growing from ~250 μm up to ~600 μm in height over 21 days. In particular, tumor spheroids of two cell lines glioblastoma (U-87 MG) and colorectal carcinoma (HCT 116) exhibited distinctive evolutions in their geometric shapes at late growth stages. Volumes of MCTS were accurately quantified using a voxel-based approach without presumptions of their geometries. In contrast, conventional diameter-based volume calculations assuming perfect spherical shape resulted in large quantification errors. Furthermore, we successfully detected necrotic regions within these tumor spheroids based on increased intrinsic optical attenuation, suggesting a promising alternative of label-free viability tests in tumor spheroids. Therefore, OCT can serve as a promising imaging modality to characterize morphological and physiological features of MCTS, showing great potential for high-throughput drug screening.
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Towards a more objective and high-throughput spheroid invasion assay quantification method
Abstract Multicellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high‐throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high‐throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis.
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- Award ID(s):
- 1761432
- PAR ID:
- 10562536
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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