Metabolic profiling of human endothelial cells during autophagy assessed in a biomimetic microfluidic device model

Microfluidic systems in clinical diagnosis

The use of microfluidic devices is highly attractive in the field of biomedical and clinical assessments, as their portability and fast response time have become crucial in providing opportune therapeutic treatments to patients. The applications of microfluidics in clinical diagnosis and point-of-care devices are continuously growing. The present review article discusses three main fields where miniaturized devices are successfully employed in clinical applications. The quantification of ions, sugars, and small metabolites is examined considering the analysis of bodily fluids samples and the quantification of this type of analytes employing real-time wearable devices. The discussion covers the level of maturity that the devices have reached as well as cost-effectiveness. The analysis of proteins with clinical relevance is presented and organized by the function of the proteins. The last section covers devices that can perform single-cell metabolomic and proteomic assessments. Each section discusses several strategically selected recent reports on microfluidic devices successfully employed for clinical assessments, to provide the reader with a wide overview of the plethora of novel systems and microdevices developed in the last 5 years. In each section, the novel aspects and main contributions of each reviewed report are highlighted. Finally, the conclusions and future outlook section present a summary and speculate on the future direction of the field of miniaturized devices for clinical applications.

MROH7-TTC4 read-through lncRNA suppresses vascular endothelial cell apoptosis and is upregulated by inhibition of ANXA7 GTPase activity

Apoptosis of vascular endothelial cells (VEC) is the main form of vascular injury that is closely linked to numerous cardiovascular diseases. Therefore, it is important to find new factors that can suppress VEC apoptosis. By using long noncoding RNA (lncRNA) microarray analysis, we found a new read-through lncRNA, MROH7-TTC4, which acted as an apoptosis inhibitor in VECs. Furthermore, by using the inhibitor (ABO) of annexin A7 (ANXA7) GTPase, we discovered that ANXA7 translocated into nucleus and interacted with 5'→3' exoribonuclease (XRN2). The decreased XRN2 phosphorylation induced by ANXA7 GTPase activity inhibition, promoted MROH7-TTC4 expression. Moreover, T-cell intracellular antigen-1 (TIA1), a binding protein of MROH7-TTC4, processed it into MROH7 and TTC4 that could inhibit VEC apoptosis. Here, we conclude that inhibiting ANXA7 GTPase activity promotes the interaction of ANXA7 and XRN2 in nucleus, which regulates the read-through transcription of MROH7-TTC4, and TIA1 is responsible for the process of MROH7-TTC4 that inhibits apoptosis through MROH7 and TTC4.

Single-cell Analysis with Microfluidic Devices

A microfluidic device as a pivotal research tool in chemistry and life science is now widely recognized. Indeed, microfluidic techniques have made significant advancements in fundamental research, such as the inherent heterogeneity of single-cells studies in cell populations, which would be helpful in understanding cellular molecular mechanisms and clinical diagnosis of major diseases. Single-cell analyses on microdevices have shown great potential for precise fluid control, cell manipulation, and signal output with rapid and high throughput. Moreover, miniaturized devices also have open functions such as integrating with traditional detection methods, for example, optical, electrochemical or mass spectrometry for single-cell analysis. In this review, we summarized recent advances of single-cell analysis based on various microfluidic approaches from different dimensions, such as in vitro, ex vivo, and in vivo analysis of single cells.

LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates

In the process of anticancer drug development, research on the mechanism of action remains a major obstacle. In the present study, a cell metabolic profiling based discriminatory model was designed to give general direction on anticancer candidate mechanisms. Firstly, ultra-performance liquid chromatography in tandem with high-definition mass spectrometry was applied to obtain a comprehensive metabolic view of 12 human tumor cells. Secondly, multivariate data analysis was used to assess the metabolites' variations, and 42 metabolites were identified as the main contributors to the discrimination of different groups. Then a metabolite-based prediction model was constructed for the first time and verified by cross validation (R 2 = 0.909 and Q 2 = 0.869) and a permutation test (R 2 = 0.0871 and Q 2 = -0.4360). To validate if the model can be applied for mechanism prediction, 4 independent sample sets were used to train the model and the data dots of different drugs were located in different regions. Finally, the model was applied to predict the anticancer mechanism of two natural compounds and the results were consistent with several other studies. Overall, this is the first experimental evidence which reveals that a metabolic profiling based prediction model has good performance in anticancer mechanism research, and thus it may be a new method for rapid mechanism screening.