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Distinguishing Between Healthy and Cancerous Cells Through Motion Analysis
Researchers at Tokyo Metropolitan University have made significant strides in differentiating cancerous cells from healthy ones by studying their movements. By analyzing the behavior of malignant fibrosarcoma cells alongside healthy fibroblasts, they demonstrated that it is possible to classify these cells with an impressive accuracy rate of up to 94%.
This innovative technique not only holds promise for diagnostics but may also provide insights into cell motility functions, which are vital for processes like tissue repair. The findings from this research are documented in the journal PLOS ONE.
Historically, cell studies have primarily focused on examining their morphology, internal structures, and composition under the microscope. However, cells are dynamic entities that change over time and are capable of movement. Understanding their motion is crucial in distinguishing between cells with varying functions, particularly in the context of cancer metastasis, where cancerous cells exploit their mobility to spread throughout the body.
Conducting such motion analysis poses challenges, primarily due to the limitations of studying small cell samples, which can skew results. Accurate diagnostic tools necessitate automated, large-scale tracking of cells to avoid these biases. While many existing methods rely on fluorescent labeling to enhance visibility under microscopes, this approach can alter cell properties, complicating accurate assessments.
To address these challenges, a research team led by Professor Hiromi Miyoshi has utilized phase-contrast microscopy, a widely employed technique that allows for non-invasive observation of cells. This method preserves the natural state of the cells as they move on a petri dish, avoiding interference from the optical characteristics of the plastic containers.
Employing advanced image analysis techniques, the researchers successfully traced the trajectories of individual cells. They focused on key characteristics of these movements, such as migration rates and the curvature of paths, which provided nuanced insights into the differences in cell behaviors.
In their comparative studies, the researchers observed that healthy fibroblast cells, which are fundamental to animal tissue structure, behaved differently from malignant fibrosarcoma cells. They characterized this differentiation by analyzing metrics such as the total of turn angles—indicative of path curvature—and the frequency of shallow turns. Their findings revealed subtle disparities in the way these cells migrated.
By synthesizing both the sum of turn angles and the occurrence of shallow turns, the researchers achieved a remarkable accuracy in assessing whether a cell was cancerous, reaching a level of precision at 94%.
This breakthrough not only introduces a new methodology for identifying cancer cells but also paves the way for further applications in studying various biological processes linked to cell motility, which may include insights into wound healing and tissue regeneration.
More information: Sota Endo et al., Development of label-free cell tracking for discrimination of heterogeneous mesenchymal migration, PLOS ONE (2025). DOI: 10.1371/journal.pone.0320287
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phys.org