Speaker
Description
This paper reports a novel microfluidic approach for characterizing and sorting breast cancer cell subpopulations based on size and mechanical properties, using deterministic lateral displacement (DLD). The metastatic potential of cancer cells is closely linked to their mechanical characteristics, which evolve with tumor progression and reflect cellular heterogeneity. Small and large tumor cells serve distinct roles: smaller cells often exhibit higher proliferative capacity and initiate new tumors, while larger cells may be more differentiated or adapted to specific microenvironments [1]. Characterizing these differences is critical to understanding cancer progression and metastasis [2]. While previous research has shown that cell mechanics can be probed through deformation-based methods, including our earlier work with blood and skeletal stem cells using DLD [3, 4], the application of such techniques to aggressive cancer cell types remains underexplored.
Here, we present a DLD-based microfluidic device designed to sort MDA-MB-231 breast cancer cells into size-based subpopulations. The device features three inlets and outlets to fractionate cells into small, medium, and large groups. The medium outlet serves as a transitional fraction containing a mix of cell sizes. Microscopic imaging confirmed that the small outlet contains uniform small cells, whereas the large outlet includes both individual large cells and clusters spatially separated within the outlet. Sorting efficiency was validated using inverted microscopy, and size distributions were quantified via a custom Python script.
To explore functional differences among the sorted populations, we assessed both proliferation and migration behavior. All subpopulations maintained proliferative capacity over a seven-day period. Migration assays were performed by seeding cells on wells coated with different extracellular matrix proteins—Basement Membrane Extract (BME), fibronectin, and collagen—and capturing time-lapse images over 20 hours. Small cells consistently displayed significantly greater motility across all matrix conditions compared to large cells and unsorted controls-inlet, supporting the hypothesis that smaller cells possess enhanced metastatic potential. To evaluate invasiveness in a 3D environment,
spheroids were formed from small, large, and inlet populations. After seven days, the spheroids were embedded in Matrigel to generate 3D invasion models. Small-cell spheroids exhibited invasive outgrowth originating from the core, while large-cell spheroids remained compact with well-defined boundaries. Quantification of the spheroid spread area further confirmed significant differences in invasiveness between subpopulations, with small-cell spheroids covering a larger area over time.
This study demonstrates that DLD-based microfluidics is a robust, label-free, high-throughput method for sorting and studying cancer cell heterogeneity. It enables the identification of aggressive subpopulations based on physical properties and behavior. Ongoing work focuses on extending this approach to include deformability-based separation using Multi-Dc devices, with the ultimate goal of gaining deeper insight into tumor progression, drug resistance, and invasion mechanisms. These findings contribute to the development of precision strategies for treating aggressive breast cancer.
- Celià-Terrassa T, Kang Y. Distinctive properties of metastasis initiating cells. Genes Dev. 2016;30(8).
- Wullkopf L, et al. Cancer cell mechanical adaptation to ECM stiffness correlates with invasiveness. Mol Biol Cell. 2018;29(20).
- Beech JP, et al. Sorting cells by size, shape, and deformability. Lab Chip. 2012;12(6).
- Xavier M, et al. Label-free enrichment of skeletal progenitor cells via DLD. Lab Chip. 2019;19(3).
| Broad physics domain | Biophysics |
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| AI/ML technique(s) to be presented | I’m particularly interested in learning more about supervised learning techniques such as decision trees, support vector machines, and neural networks, especially in the context of biological data like cell images or time-lapse migration experiments. I have previously used dimensionality reduction methods like PCA and UMAP to explore heterogeneity in my datasets, and I would love to deepen my understanding of how these and other techniques can be combined with classification or clustering approaches. Since I’m still developing my experience in this field, practical overviews and applied examples would be especially valuable. |
