Automated tracking and quantification of angiogenic vessel formation in 3D microfluidic devices

Vascularized organoid-on-a-chip: design, imaging, and analysis

Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.

Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation

Spinal cord injury is linked to the interruption of neural pathways, which results in irreversible neural dysfunction. Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury, which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies. Besides the involvement of endogenous stem cells in neurogenesis and neural repair, exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases. However, to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury, appropriate interventional measures (e.g., neuromodulation) should be adopted. Neuromodulation techniques, such as noninvasive magnetic stimulation and electrical stimulation, have been safely applied in many neuropsychiatric diseases. There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system; namely, by exciting, inhibiting, or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury. Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth, encourages the formation of new synaptic connections to promote neural plasticity, and improves motor function recovery in patients with spinal cord injury. With the development of biomaterial technology and biomechanical engineering, several emerging treatments have been developed, such as robots, brain-computer interfaces, and nanomaterials. These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury. However, large-scale clinical trials need to be conducted to validate their efficacy. This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence, to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.

3D Active Vessel Tracking Using an Elliptical Prior

In this paper, we propose a novel vessel tracking method, called active vessel tracking (AVT). The proposed method retains the major advantages that most 2D segmentation methods have demonstrated for 3D tracking while overcoming the drawbacks of previous 3D vessel tracking methods. Under the assumption that the vessel is cylindrical, thereby making its cross-section elliptical, the AVT finds a plane perpendicular to the vessel axis while tracking the vessel along its length. Also, We propose a method for vessel branch detection to automatically track complete vascular networks from a single starting point, whereas the previously proposed solutions have usually been limited in handling vessel bifurcations precisely on 3D or have required considerable user interaction. Our results show that the method is robust and accurate in both synthetic and clinical cases. In an experiment on synthetic data sets, the proposed method achieved a tracking accuracy of 96.1±0.5, detecting 99.1% of the branches. In an experiment on abdominal CTA data sets, it achieved a tracking accuracy of 98.4±0.5 for six target vessels, detecting 98.3% of the branches. These results show that the proposed method can outperform previous methods for vessel tracking.

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering

Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.