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Chong, S. W.

Publications and source records attributed to Chong, S. W..

3 recordsLinked to original sources

Fluorescently Labeled Gradient Hydrogels Reveal Matrix-Dependent Cell Responses to Substrate Stiffness

Microfabricated stiffness gradient hydrogels hold significant value for advancing mechanobiology, tissue engineering, and in vitro tissue models. However, it remains challenging to design these materials given their broad processing parameter space. The continuum of stiffness values also makes it difficult to precisely correlate the local substrate properties and observed biological responses, often relying on cumbersome characterization methods such as atomic force microscopy. To address these bottlenecks, we present a straightforward thermophoresis-based fabrication strategy to pattern stiffness gradients in fluorescein isothiocyanate-labeled hydrogel network, which displays a polymer concentration-dependent fluorescence readout. This approach enables quantitative assessment of the gradient formation process and contactless stiffness mapping via standard microscopy imaging. Using gelatin methacryloyl and Gellan gum as model systems, it is shown that substrate stiffness and extracellular matrix protein composition work together to affect 3T3-L1 fibroblast cell morphology and migration, with the underlying hydrogel type also affecting the outcome. By offering a simple and reliable approach for characterizing stiffness gradient hydrogels, this work advances the thermophoretic fabrication platform, opening avenues for new biomaterial systems for understanding and controlling the cell-material interplay.

bioengineering↗

A Modular Microfluidic System to Generate Gradient Hydrogels with Simple-to-Complex Stiffness Profiles for Mechanobiology

Engineered stiffness gradient hydrogels offer exciting opportunities to probe fundamental mechanobiological processes in vitro. However, the need to spatially manipulate the properties of soft hydrogels at the micron scale poses challenges in developing fabrication platforms that can reliably modulate the gradient gel characteristics according to user needs. This study describes a modular approach leveraging thermophoresis in microfluidics to create high-fidelity stiffness gradients with linear and complex profiles, including periodic and anisotropic gradients. This study describes the platforms design and optimization, demonstrating achievable stiffness ranges and gradient slopes that correlate with many physiologic and diseased tissue types. This platform is also compatible with different hydrogel crosslinking chemistries, providing a versatile tool to engineer microenvironments with increased complexity. Directionally biased fibroblast cell proliferation and migration on fabricated stiffness gradient gelatin methacryloyl (GelMA) hydrogels indicate the effectiveness of this platform in modulating the mechanical microenvironment of cells. The results indicate that both the absolute stiffness range and the pattern of stiffness variation jointly affect cell behaviors. Considering its remarkable flexibility, the fabrication platform can significantly advance the development of biophysical gradient hydrogels that better replicate the intricacies of native tissues and help realize the next breakthroughs in mechanobiology.

bioengineering↗

Neuromorphic Imaging Cytometry on Human Blood Cells

AO_SCPLOWBSTRACTC_SCPLOWImage-enhanced cytometry and sorting are powerful technologies that provide single-cell resolution and, where possible, cell actuation based on spatial and fluorescence characterisation. With the emergence of deep learning (DL), numerous cytometry-related works incorporate DL to assist their research in handling data-intensive and repetitive workloads. The rich spatial information provided by single-cell images has exceptional use with DL models to classify cells, detect rare cell events, disclose irregularity and achieve higher sample purity than a conventional feature-gating strategy. One of the significant challenges in these image-enable technologies is the constrained throughput owing to the data-expensive image acquisition and balancing between speed and resolution. This work introduces a novel paradigm by adopting a bio-inspired neuromorphic photosensor to capture fast-moving cell events. It facilitates a data-efficient, fluorescence-sensitive, fast inference approach to establish a foundation for neuromorphic-enabled cytometry/sorting applications. We have also curated the first neuromorphic-encoded cell dataset, including human blood cells (red blood cells, neutrophils, lymphocytes, thrombocytes), endothelial cells and polystyrene-based microparticles. To evaluate the data quality and potential of DL-based gating, we have directly trained a hybrid classification model based on this dataset, accomplishing a promising performance of 97% accuracy and F1 score with a significant reduction in memory usage and power consumption. Combining neuromorphic imaging and DL holds substantial potential to develop into a next-generation AI-assisted cytometry and sorting application.

cell biology↗