A microfluidic platform with cell-scale precise temperature control for simultaneous investigation of the osmotic responses of multiple oocytes.

The temperature-dependent oocyte membrane permeability plays a significant role in oocyte cryopreservation, such as optimizing the addition/removal of cryoprotective agents and the rate of cooling/rewarming. However, the systems for studying the temperature dependence of oocyte membrane permeability are either too complicated or unable to achieve wide-range precise temperature control. In addition, these systems cannot achieve the simultaneous observation of multiple oocytes. Here, we report a novel microfluidic platform that combines a precise local temperature heater/detector and a simple global water bath to achieve wide-range accurate temperature control without increasing the difficulty of fabrication, and it also realizes non-interfering, position-controllable and non-missing capture of multiple oocytes for parallel experiments to increase throughput. The permeability coefficients (Lp, Ps) of the mouse oocyte membrane exposed to cryoprotective agents (1.5 M EG and 1.5 M PG) at four temperatures (4, 15, 25 and 37 °C) are consistent with those reported in previous works, which proves the feasibility and practicality of the microfluidic platform in this study.

Microencapsulation Facilitates Low-Cryoprotectant Vitrification of Human Umbilical Vein Endothelial Cells.

Vitrification has become one of the promising cryopreservation methods for biosamples including cells and tissues because the vitreous state reduces the damage of ice crystals to cells. However, besides extremely high cooling rates, routine vitrification protocols require a high concentration of penetrating cryoprotectants (pCPAs, ∼6-8 M), which is toxic for cells and brings trouble when removing pCPAs. Therefore, reducing the concentration of toxic pCPAs in vitrification remains a challenge, and advanced strategies are urgently needed. Hydrogel encapsulation has become one effective method to achieve low-cryoprotectant (CPA) concentration preservation of stem cells with rapid cooling, but there are very few related studies about endothelial cells (ECs). In this study, we achieved pCPA concentration (up to 3 M) vitrification by encapsulating human umbilical vein endothelial cells (HUVECs) into core-shell alginate hydrogel microcapsules. Alginate encapsulation increased HUVEC cryosurvival up to 80%, which is 60% improvement compared to control without encapsulation. Furthermore, two different sizes of capsules (diameter: ∼900 and 400 µm) were produced to explore the effects of microcapsule volume on the cell preservation results, and it was found that larger capsules (∼900 µm) have no significant effect on cell survival while improving encapsulation efficiency. This encapsulation method provides a new strategy for EC preservation and serves as an improvement to optimize the preservation of biosamples.

The application of convolution neural network based cell segmentation during cryopreservation.

For most of the cells, water permeability and plasma membrane properties play a vital role in the optimal protocol for successful cryopreservation. Measuring the water permeability of cells during subzero temperature is essential. So far, there is no perfect segmentation technique to be used for the image processing task on subzero temperature accurately. The ice formation and variable background during freezing posed a significant challenge for most of the conventional segmentation algorithms. Thus, a robust and accurate segmentation approach that can accurately extract cells from extracellular ice that surrounding the cell boundary is needed. Therefore, we propose a convolutional neural network (CNN) architecture similar to U-Net but differs from those conventionally used in computer vision to extract all the cell boundaries as they shrank in the engulfing ice. The images used was obtained from the cryo-stage microscope, and the data was validated using the Hausdorff distance, means ±â€¯standard deviation for different methods of segmentation result using the CNN model. The experimental results prove that the typical CNN model extracts cell borders contour from the background in its subzero state more coherent and effective as compared to other traditional segmentation approaches.