RESEARCH

We utilize a micron-sized fluid channel encapsulated resonator known as the suspended microchannel resonator (SMR). SMR measures the buoyant mass of a living cell by detecting changes in resonant frequency. The resolution of SMR for typical mammalian cell mass measurement surpasses 10 femtograms—a value corresponding to <5 nanometers in cell diameter or <0.1% of the whole cell size.

Through feedback-controlled fluidics and optical integration, SMR-based platforms can simultaneously monitor an array of cellular physical attributes beyond mass, including cell volume, stiffness, and even bioenergetic properties in the single-cell level. These precision instruments find application across a wide spectrum, spanning from fundamental cell biology research to the healthcare industry.

We develop an optical biochip platform that can non-invasively monitor the 3D morphology of individual living cells—both in isolation and within compact monolayer. The platform reconstructs 3D topography of live cells through the exclusion of fluorescence signals within the confined microfluidic channel. Unlike SMR, this platform does not require cells to be in suspension, and thus can monitor cells that are adhered to the substrate or neighboring cells. This approach allows us to model emergent collective behaviors, such as the epithelial-to-mesenchymal transition (EMT), and extend our understanding of tissue mechanics from 2D layers to 3D architectures.

Using this technology, we are actively exploring mechanical changes associated with diverse diseases, ranging from cancer metastasis to neuroinflammation. Our ultimate goal is to potentially identify novel, label-free therapeutic biomarkers capable of accurately indicating cellular states during disease progression or medical treatment. 

Our research extends to the advancements of analytical methodologies and computational techniques within single-cell analysis. We aim to model and infer dynamics fluctuations of cell's physical and mechanical state (e.g. mass, stiffness, density, and aspect ratio, convexity, compactness) from the static snapshot. We employ machine learning and computational modeling to interpret these complex datasets, enabling a shift form correlative observations to predictive models of cellular behavior and heterogeneity. 

Furthermore, we are actively exploring artificial intelligence (AI) driven strategies to refine the categorization and classification of single-cell datasets, potentially revealing new insights into the heterogeneity of cellular responses during therapeutic interventions.