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Biology subjects

Shen, A. Q.

Publications and source records attributed to Shen, A. Q..

3 recordsLinked to original sources

Enzyme Activity Regulates Substrate Diffusion by Modulating Viscosity in Crowded Milieu

Enzymatic activity and its tight regulation are fundamental to life, yet how enzymes maintain efficient substrate availability within the highly crowded, heterogeneous native cytosol remains poorly understood. Here, we harness liquid-liquid phase separation (LLPS) to generate controlled in vitro protein droplets that mimic the crowding of cytosolic proteins and demonstrate that enzymatic activity enhances substrate diffusion within the crowded environment by up to three-fold. Using fluorescence microscopy, fluorescence recovery after photobleaching (FRAP), molecular docking, and particle-tracking microrheology, we show that this enhanced diffusion arises from a reduction in apparent shear viscosity driven by enzymatic disruption of transient non-covalent interactions between substrate and protein crowder. Our findings reveal a physical feedback mechanism by which enzymatic activity actively reshapes its local environment to sustain substrate availability, providing new insight into how enzymatic activity creates a positive physical feedback on its own substrate supply under the diffusion constraints of the crowded cell.

biochemistry↗

Voltage-gated ion channels mediate the electrotaxis of glioblastoma cells in a hybrid PMMA/PDMS microdevice

Transformed astrocytes in the most aggressive form cause glioblastoma, the most common cancer in central nervous system with high mortality. The physiological electric field by neuronal local field potentials and tissue polarity may guide the infiltration of glioblastoma cells through the electrotaxis process. However, microenvironments with multiplex gradients are difficult to create. In this work, we have developed a hybrid microfluidic platform to study glioblastoma electrotaxis in controlled microenvironments with high through-put quantitative analysis by a machine learning-powered single cell tracking software. By equalizing the hydrostatic pressure difference between inlets and outlets of the microchannel, uniform single cells can be seeded reliably inside the microdevice. The electrotaxis of two glioblastoma models, T98G and U-251MG, require optimal laminin-containing extracellular matrix and exhibits opposite directional and electro-alignment tendencies. Calcium signaling is a key contributor in glioblastoma pathophysiology but its role in glioblastoma electrotaxis is still an open question. Anodal T98G electrotaxis and cathodal U-251MG electrotaxis require the presence of extracellular calcium cations. U-251MG electrotaxis is dependent on the P/Q-type voltage-gated calcium channel (VGCC) and T98G is dependent on the R-type VGCC. U-251MG and T98G electrotaxis are also mediated by A-type (rapidly inactivating) voltage-gated potassium channels and acid-sensing sodium channels. The involvement of multiple ion channels suggests that the glioblastoma electrotaxis is complex and patient-specific ion channel expression can be critical to develop personalized therapeutics to fight against cancer metastasis. The hybrid microfluidic design and machine learning-powered single cell analysis provide a simple and flexible platform for quantitative investigation of complicated biological systems.

cell biology↗

Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

Stain-free, single-cell segmentation and tracking is tantamount to the holy grail of microscopic cell migration analysis. Phase contrast microscopy (PCM) images with cells at high density are notoriously difficult to segment accurately; thus, manual segmentation remains the de facto standard practice. In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. Stain-free, instance-aware segmentation is accomplished using a mask regional convolutional neural network (Mask R-CNN). A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. Usiigaci provides highly accurate cell movement and morphological information for quantitative cell migration analysis.

bioinformatics↗