Microphysiologically Engineered Vessel-Tumor Model to Investigate Vascular Transport Dynamics of Immune Cells.

Cancer immunotherapy has emerged as a promising therapeutic strategy to combat cancer effectively. However, it is hard to observe and quantify how this in vivo process happens. Three-dimensional (3D) microfluidic vessel-tumor models offer valuable capability to study how immune cells transport during cancer progression. We presented an advanced 3D vessel-supported tumor model consisting of the endothelial lumen and vessel network for the study of T cells' transportation. The process of T cell transport through the vessel network and interaction with tumor spheroids was represented and monitored in vitro. Specifically, we demonstrate that the endothelial glycocalyx serving in the T cells' transport can influence the endothelium-immune interaction. Furthermore, after vascular transport, how programmed cell death protein 1 (PD-1) immune checkpoint inhibition influences the delivered activated-T cells on tumor killing was evaluated. Our in vitro vessel-tumor model provides a microphysiologically engineered platform to represent T cell vascular transportation during tumor immunotherapy. The reported innovative vessel-tumor platform is believed to have the potential to explore the tumor-induced immune response mechanism and preclinically evaluate immunotherapy's effectiveness.

Rapid prototyping of high-resolution large format microfluidic device through maskless image guided in-situ photopolymerization.

Microfluidic devices have found extensive applications in mechanical, biomedical, chemical, and materials research. However, the high initial cost, low resolution, inferior feature fidelity, poor repeatability, rough surface finish, and long turn-around time of traditional prototyping methods limit their wider adoption. In this study, a strategic approach to a deterministic fabrication process based on in-situ image analysis and intermittent flow control called image-guided in-situ maskless lithography (IGIs-ML), has been proposed to overcome these challenges. By using dynamic image analysis and integrated flow control, IGIs-ML provides superior repeatability and fidelity of densely packed features across a large area and multiple devices. This general and robust approach enables the fabrication of a wide variety of microfluidic devices and resolves critical proximity effect and size limitations in rapid prototyping. The affordability and reliability of IGIs-ML make it a powerful tool for exploring the design space beyond the capabilities of traditional rapid prototyping.

Hierarchical Vessel Network-Supported Tumor Model-on-a-Chip Constructed by Induced Spontaneous Anastomosis.

The vascular system in living tissues is a highly organized system that consists of vessels with various diameters for nutrient delivery and waste transport. In recent years, many vessel construction methods have been developed for building vascularized on-chip tissue models. These methods usually focused on constructing vessels at a single scale. In this work, a method that can build a hierarchical and perfusable vessel networks was developed. By providing flow stimuli and proper HUVEC concentration, spontaneous anastomosis between endothelialized lumens and the self-assembled capillary network was induced; thus, a perfusable network containing vessels at different scales was achieved. With this simple method, an in vivo-like hierarchical vessel-supported tumor model was prepared and its application in anticancer drug testing was demonstrated. The tumor growth rate was predicted by combining computational fluid dynamics simulation and a tumor growth mathematical model to understand the vessel perfusability effect on tumor growth rate in the hierarchical vessel network. Compared to the tumor model without capillary vessels, the hierarchical vessel-supported tumor shows a significantly higher growth rate and drug delivery efficiency.

Protocatechuic Acid Ameliorates High Fat Diet-Induced Obesity and Insulin Resistance in Mice.

SCOPE: Insulin resistance is a common feature of obesity and type 2 diabetes and partly results from an imbalance between food intake and energy expenditure. Therefore, efficient and safe insulin resistance treatment therapies are warranted. This work is aim to access the impact of protocatechuic acid (PCA), a catechol-type O-diphenol phenolic acid, in high fat diet (HFD)-induced glucose, and lipid dysregulation. METHODS AND RESULTS: Five-week-old male C57BL/6 mice are fed with HFD for 4 weeks and then are randomly divided into two cohorts: one cohort feed with HFD is free access to sterile water for 4 weeks, another cohort is free access to PCA-containing water (2.7 mM) for 4 weeks with HFD. In this study, using a hyperinsulinemic-euglycemic mouse clamp, it is showed that PCA-treated mice display improved systemic insulin resistance via enhanced fatty acid mobilization and utilization, thereby reducing ectopic lipid accumulation and promoting hepatic and peripheral insulin action. CONCLUSIONS: This study provides insights on the potent pharmacological effects of PCA from food sources on improving high fat diet (HFD)-induced whole-body insulin resistance and type 2 diabetes.

Multiplex solid-phase RPA coupled CRISPR-based visual detection of SARS-CoV-2.

The COVID-19 pandemic has presented a significant challenge to the world's public health and led to over 6.9 million deaths reported to date. A rapid, sensitive, and cost-effective point-of-care virus detection device is essential for the control and surveillance of the contagious severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic. The study presented here aimed to demonstrate a solid-phase isothermal recombinase polymerase amplification coupled CRISPR-based (spRPA-CRISPR) assay for on-chip multiplexed, sensitive and visual COVID-19 DNA detection. The assay targets the SARS-CoV-2 structure protein encoded genomes and can simultaneously detect two specific genes without cross-interaction. The amplified target sequences were immobilized on the one-pot device surface and detected using the mixed Cas12a-crRNA collateral cleavage of reporter-released fluorescent signal when specific genes were recognized. The endpoint signal can be directly visualized for rapid detection of COVID-19. The system was tested with samples of a broad range of concentrations (20 to 2 × 104 copies) and showed analytical sensitivity down to 20 copies per microliter. Furthermore, a low-cost blue LED flashlight (~$12) was used to provide a visible SARS-CoV-2 detection signal of the spRPA-CRISPR assay which could be purchased online easily. Thus, our platform provides a sensitive and easy-to-read multiplexed gene detection method that can specifically identify low concentration genes.

Adaptable Microfluidic Vessel-on-a-Chip Platform for Investigating Tumor Metastatic Transport in Bloodstream.

Cancer metastasis counts for 90% of cancer fatalities, and its development process is still a mystery. The dynamic process of tumor metastatic transport in the blood vessel is not well understood, in which some biomechanical factors, such as shear stress and various flow patterns, may have significant impacts. Here, we report a microfluidic vessel-on-a-chip platform for recapitulating several key metastatic steps of tumor cells in blood vessels on the same chip, including intravasation, circulating tumor cell (CTC) vascular adhesion, and extravasation. Due to its excellent adaptability, our system can reproduce various microenvironments to investigate the specific interactions between CTCs and blood vessels. On the basis of this platform, effects of important biomechanical factors on CTC adhesion such as vascular surface properties and vessel geometry-dependent hemodynamics were specifically inspected. We demonstrated that CTC adhesion is more likely to occur under certain mechano-physiological situations, such as vessels with vascular glycocalyx (VGCX) shedding and hemodynamic disturbances. Finally, computational models of both the fluidic dynamics in vessels and CTC adhesion were established based on the confocal scanned 3D images. The modeling results are believed to provide insights into exploring tumor metastasis progression and inspire new ideas for anticancer therapy development.

From cell spheroids to vascularized cancer organoids: Microfluidic tumor-on-a-chip models for preclinical drug evaluations.

Chemotherapy is one of the most effective cancer treatments. Starting from the discovery of new molecular entities, it usually takes about 10 years and 2 billion U.S. dollars to bring an effective anti-cancer drug from the benchtop to patients. Due to the physiological differences between animal models and humans, more than 90% of drug candidates failed in phase I clinical trials. Thus, a more efficient drug screening system to identify feasible compounds and pre-exclude less promising drug candidates is strongly desired. For their capability to accurately construct in vitro tumor models derived from human cells to reproduce pathological and physiological processes, microfluidic tumor chips are reliable platforms for preclinical drug screening, personalized medicine, and fundamental oncology research. This review summarizes the recent progress of the microfluidic tumor chip and highlights tumor vascularization strategies. In addition, promising imaging modalities for enhancing data acquisition and machine learning-based image analysis methods to accurately quantify the dynamics of tumor spheroids are introduced. It is believed that the microfluidic tumor chip will serve as a high-throughput, biomimetic, and multi-sensor integrated system for efficient preclinical drug evaluation in the future.

Label-free detection of rare circulating tumor cells by image analysis and machine learning.

Detection and characterization of rare circulating tumor cells (CTCs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis.

Gut microbiota density influences host physiology and is shaped by host and microbial factors.

To identify factors that regulate gut microbiota density and the impact of varied microbiota density on health, we assayed this fundamental ecosystem property in fecal samples across mammals, human disease, and therapeutic interventions. Physiologic features of the host (carrying capacity) and the fitness of the gut microbiota shape microbiota density. Therapeutic manipulation of microbiota density in mice altered host metabolic and immune homeostasis. In humans, gut microbiota density was reduced in Crohn's disease, ulcerative colitis, and ileal pouch-anal anastomosis. The gut microbiota in recurrent Clostridium difficile infection had lower density and reduced fitness that were restored by fecal microbiota transplantation. Understanding the interplay between microbiota and disease in terms of microbiota density, host carrying capacity, and microbiota fitness provide new insights into microbiome structure and microbiome targeted therapeutics. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).