Xiaoxing Xing

Intracytoplasmic sperm injection (ICSI) represents a critical technique for addressing male infertility, whose success relies on the retrieval of high-quality spermatozoa from specimens containing compromised or nonviable sperm. Consequently, sperm selection constitutes a pivotal step in in vitro fertilization. Conventional selection methodologies—such as density gradient centrifugation (DGC), which may induce sperm DNA fragmentation, and the swim-up(SU) method, which is time-consuming with low sperm recovery rates—present significant challenges for optimal sperm selection. In recent years, microfluidic-based sperm selection platforms have emerged as a promising alternative. These systems recapitulate the physiological microenvironment of the female reproductive tract while eliminating the need for aggressive manipulations such as centrifugation, thereby significantly enhancing both the efficiency and safety of sperm selection. To tackle the existing issues, this study developed a microfluidic-based sperm sorting device that makes use of the two-dimensional swimming characteristics of sperm in high-viscosity environments as well as the boundary-following behaviour induced by a ratchet-structured channel for enhanced sperm sorting efficiency. Experimental results demonstrated that sperm exhibited boundary-following and directional motility within the device, preliminarily validating the feasibility of the proposed sorting mechanism.

Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.

An easily-fabricated device mapping anticancer drug-treated HeLa cells to progressive states from G₂/M arrest to apoptosis by impedance characteristics.

Chemotherapeutic drugs such as paclitaxel and vinblastine interact with the microtubules, and thus induce complex cell states of mitosis arrest at the G2/M phase followed by apoptosis dependent on drug exposure time and concentration. Microfluidic impedance cytometry (MIC) as a label-free and high-throughput technology for single-cell analysis, has been applied for viability assay of cancer cells post drug exposure at fixed time and dosage, yet verification of this technique for varied tumor cell states after anticancer drug treatment remains vacant. Here we present a novel MIC device and for the first time perform impedance cytometry on carcinoma cells exhibiting progressive states of G2/M arrest followed by apoptosis related to drug concentration and exposure time, after treatments by paclitaxel and vinblastine, respectively. Our results from impedance cytometry reveal increased amplitude and negative phase shift at low frequency, as well as higher opacity for the Hela cells under G2/M mitotic arrest compared to the untreated cells. The cells under apoptosis, on the other hand, exhibit opposite changes in these electrical parameters. Therefore, the impedance features differentiate the Hela cells under progressive states post anticancer drug treatment. We also demonstrate that vinblastine poses a more potent drug effect than paclitaxel especially at low concentrations. Our device is fabricated with a unique sacrificial layer-free soft lithography process as compared to the existing MIC device, which gives rise to readily aligned parallel microelectrodes made of silver-PDMS embedded in PDMS channel sidewalls with one molding step. Our results uncover the potential of the MIC device, with a fairly simple and low-cost fabrication process, for cellular state screening in anticancer drug therapy.

Bioelectronics, an emerging discipline formed by the biology and electronic information disciplines, has maintained a state of rapid development since its birth. Amongst the various functional bioelectronics materials, bacteriorhodopsin (bR), with its directional proton pump function and favorable structural stability properties, has drawn wide attention. The main contents of the paper are as follows: Inspired by the capacitive properties of natural protoplast cell membranes, a new bio-capacitor based on bR and artificial nanochannels was constructed. As a point of innovation, microfluidic chips were integrated into our device as an ion transport channel, which made the bio-capacitor more stable. Meanwhile, a single nanopore structure was integrated to improve the accuracy of the device structure. Experiments observed that the size of the nanopore affected the ion transmission rate. Consequently, by making the single nanopore’s size change, the photocurrent duration time (PDT) of bR was effectively regulated. By using this specific phenomenon, the original transient photocurrent was successfully transformed into a square-like wave.

This work for the first time demonstrates blood coagulation measurement by electrical resistance sensing of a three-dimensional (3D) elastic beam deformed by flowing blood undergoing coagulation process, as enabled by a novel microfluidic strain sensor with 3D AgPDMS beam fabricated using low-cost molding process. The chip has been showcased here for the electric current change when the chip is infused with glycerol solutions at different flow rates and varied viscosities by adjusting glycerol concentrations in water. Primary testing on blood coagulation using the chip are conducted by measuring the current change when injecting the blood at increased time intervals post initiation of coagulation.

Microelectrodes and flow sidewalls entirely made of AgPDMS by one-step replica molding process for dielectrophoretic cell separation and viability assay.

This is the first account of a single-cell impedance flow cytometry microsystem based on 3D silicon microelectrodes. Such microelectrodes emerge being readily aligned during lithographic patterning of silicon and thus relieve the burden of alignment that comes with planar microelectrodes and their opposing arrangement for a maximum sensitivity. The opposing arrangement of the microelectrodes generate homogeneous electric field and thus maintain maximum sensitivity while individual cells passing by. The microsystem has been showcased here for the flow impedance measurement of polystyrene microspheres and cells at 10 MHz. Successful classification of human colorectal cancer cells and red blood cells has also been demonstrated using this device.