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Detection and Spatiotemporal Analysis of In-vitro 3D Migratory Triple-Negative Breast Cancer Cells

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Abstract

The invasion of cancer cells into the surrounding tissues is one of the hallmarks of cancer. However, a precise quantitative understanding of the spatiotemporal patterns of cancer cell migration and invasion still remains elusive. A promising approach to investigate these patterns are 3D cell cultures, which provide more realistic models of cancer growth compared to conventional 2D monolayers. Quantifying the spatial distribution of cells in these 3D cultures yields great promise for understanding the spatiotemporal progression of cancer. In the present study, we present an image processing and segmentation pipeline for the detection of 3D GFP-fluorescent triple-negative breast cancer cell nuclei, and we perform quantitative analysis of the formed spatial patterns and their temporal evolution. The performance of the proposed pipeline was evaluated using experimental 3D cell culture data, and was found to be comparable to manual segmentation, outperforming four alternative automated methods. The spatiotemporal statistical analysis of the detected distributions of nuclei revealed transient, non-random spatial distributions that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the implementation of the proposed framework revealed the spatial organization of cellular nuclei with improved accuracy, providing insights into the 3 dimensional inter-cellular organization and its progression through time.

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Acknowledgments

N. M. D. thanks Stavros Niarchos Foundation (F237055R00), Werner Graupe (F202955R00) and McGill University (90025) for the scholarships. S. F. T. thanks McGill University for the McGill Engineering Doctoral Award (90025) and the FRQNT (291010) for the scholarships. This work was supported by Cyprus Research and Innovation Foundation (Project: INTERNATIONAL/OTHER/0118/0018), Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06638 (G. D. M.).

Data Availability

Raw data https://figshare.com/projects/3D-GROWTH-MDA-MB-231-SERIES-12/118989, Code for Image processing https://github.com/NMDimitriou/3D-Preprocessing-Nuclei-Segmentation.git, and Spatial analysis https://github.com/NMDimitriou/3D-spatial-analysis-cell-nuclei.git.

Conflict of interest

The authors have no conflicts of interest to declare.

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Authors and Affiliations

  1. Department of Bioengineering, McGill University, Montreal, QC, H3A 0E9, Canada

    Nikolaos M. Dimitriou, Salvador Flores-Torres, Joseph Matthew Kinsella & Georgios D. Mitsis

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  1. Nikolaos M. Dimitriou
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  2. Salvador Flores-Torres
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  3. Joseph Matthew Kinsella
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  4. Georgios D. Mitsis
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Correspondence to Nikolaos M. Dimitriou.

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Associate Editor Stefan M. Duma oversaw the review of this article.

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Dimitriou, N.M., Flores-Torres, S., Kinsella, J.M. et al. Detection and Spatiotemporal Analysis of In-vitro 3D Migratory Triple-Negative Breast Cancer Cells. Ann Biomed Eng 51, 318–328 (2023). https://doi.org/10.1007/s10439-022-03022-y

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