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Abstract Measurements and imaging of the mechanical response of biological cells are critical for understanding the mechanisms of many diseases, and for fundamental studies of energy, signal and force transduction. The recent emergence of Brillouin microscopy as a powerful non-contact, label-free way to non-invasively and non-destructively assess local viscoelastic properties provides an opportunity to expand the scope of biomechanical research to the sub-cellular level. Brillouin spectroscopy has recently been validated through static measurements of cell viscoelastic properties, however, fast (sub-second) measurements of sub-cellular cytomechanical changes have yet to be reported. In this report, we utilize a custom multimodal spectroscopy system to monitor for the very first time the rapid viscoelastic response of cells and subcellular structures to a short-duration electrical impulse. The cytomechanical response of three subcellular structures - cytoplasm, nucleoplasm, and nucleoli - were monitored, showing distinct mechanical changes despite an identical stimulus. Through this pioneering transformative study, we demonstrate the capability of Brillouin spectroscopy to measure rapid, real-time biomechanical changes within distinct subcellular compartments. Our results support the promising future of Brillouin spectroscopy within the broad scope of cellular biomechanics.
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Mechanical Evolution of Metastatic Cancer Cells in 3D Microenvironment
Abstract Cellular biomechanics plays a critical role in cancer metastasis and tumor progression. Existing studies on cancer cell biomechanics are mostly conducted in flat 2D conditions, where cells’ behavior can differ considerably from those in 3D physiological environments. Despite great advances in developing 3D in vitro models, probing cellular elasticity in 3D conditions remains a major challenge for existing technologies. In this work, optical Brillouin microscopy is utilized to longitudinally acquire mechanical images of growing cancerous spheroids over the period of 8 days. The dense mechanical mapping from Brillouin microscopy enables us to extract spatially resolved and temporally evolving mechanical features that were previously inaccessible. Using an established machine learning algorithm, it is demonstrated that incorporating these extracted mechanical features significantly improves the classification accuracy of cancer cells, from 74% to 95%. Building on this finding, a deep learning pipeline capable of accurately differentiating cancerous spheroids from normal ones solely using Brillouin images have been developed, suggesting the mechanical features of cancer cells can potentially serve as a new biomarker in cancer classification and detection.
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- Award ID(s):
- 2339278
- PAR ID:
- 10578693
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Small
- Volume:
- 21
- Issue:
- 18
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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