Hydrogel platform facilitating astrocytic differentiation through cell mechanosensing and YAP-mediated transcription.

Astrocytes are multifunctional glial cells that are essential for brain functioning. Most existing methods to induce astrocytes from stem cells are inefficient, requiring couples of weeks. Here, we designed an alginate hydrogel-based method to realize high-efficiency astrocytic differentiation from human neural stem cells. Comparing to the conventional tissue culture materials, the hydrogel drastically promoted astrocytic differentiation within three days. We investigated the regulatory mechanism underlying the enhanced differentiation, and found that the stretch-activated ion channels and Yes-associated protein (YAP), a mechanosensitive transcription coactivator, were both indispensable. In particular, the Piezo1 Ca2+ channel, but not transient receptor potential vanilloid 4 (TRPV4) channel, was necessary for promoting the astrocytic differentiation. The stretch-activated channels regulated the nuclear localization of YAP, and inhibition of the channels down-regulated the expression of YAP as well as its target genes. When blocking the YAP/TEAD-mediated transcription, astrocytic differentiation on the hydrogel significantly declined. Interestingly, cells on the hydrogel showed a remarkable filamentous actin assembly together with YAP nuclear translocation during the differentiation, while a progressive gel rupture at the cell-hydrogel interface along with a change in the gel elasticity was detected. These findings suggest that spontaneous decrosslinking of the hydrogel alters its mechanical properties, delivering mechanical stimuli to the cells. These mechanical signals activate the Piezo1 Ca2+ channel, facilitate YAP nuclear transcription via actomyosin cytoskeleton, and eventually provoke the astrocytic differentiation. While offering an efficient approach to obtain astrocytes, our work provides novel insights into the mechanism of astrocytic development through mechanical regulation.

Unraveling the Mechanobiology Underlying Traumatic Brain Injury with Advanced Technologies and Biomaterials.

Traumatic brain injury (TBI) is a worldwide health and socioeconomic problem, associated with prolonged and complex neurological aftermaths, including a variety of functional deficits and neurodegenerative disorders. Research on the long-term effects has highlighted that TBI shall be regarded as a chronic health condition. The initiation and exacerbation of TBI involve a series of mechanical stimulations and perturbations, accompanied by mechanotransduction events within the brain tissues. Mechanobiology thus offers a unique perspective and likely promising approach to unravel the underlying molecular and biochemical mechanisms leading to neural cells dysfunction after TBI, which may contribute to the discovery of novel targets for future clinical treatment. This article investigates TBI and the subsequent brain dysfunction from a lens of neuromechanobiology. Following an introduction, the mechanobiological insights are examined into the molecular pathology of TBI, and then an overview is given of the latest research technologies to explore neuromechanobiology, with particular focus on microfluidics and biomaterials. Challenges and prospects in the current field are also discussed. Through this article, it is hoped that extensive technical innovation in biomedical devices and materials can be encouraged to advance the field of neuromechanobiology, paving potential ways for the research and rehabilitation of neurotrauma and neurological diseases.

High-Throughput, Living Single-Cell, Multiple Secreted Biomarker Profiling Using Microfluidic Chip and Machine Learning for Tumor Cell Classification.

Secreted proteins provide abundant functional information on living cells and can be used as important tumor diagnostic markers, of which profiling at the single-cell level is helpful for accurate tumor cell classification. Currently, achieving living single-cell multi-index, high-sensitivity, and quantitative secretion biomarker profiling remains a great challenge. Here, a high-throughput living single-cell multi-index secreted biomarker profiling platform is proposed, combined with machine learning, to achieve accurate tumor cell classification. A single-cell culture microfluidic chip with self-assembled graphene oxide quantum dots (GOQDs) enables high-activity single-cell culture, ensuring normal secretion of biomarkers and high-throughput single-cell separation, providing sufficient statistical data for machine learning. At the same time, the antibody barcode chip with self-assembled GOQDs performs multi-index, highly sensitive, and quantitative detection of secreted biomarkers, in which each cell culture chamber covers a whole barcode array. Importantly, by combining the K-means strategy with machine learning, thousands of single tumor cell secretion data are analyzed, enabling tumor cell classification with a recognition accuracy of 95.0%. In addition, further profiling of the grouping results reveals the unique secretion characteristics of subgroups. This work provides an intelligent platform for high-throughput living single-cell multiple secretion biomarker profiling, which has broad implications for cancer investigation and biomedical research.

Mechanical Stress Induces a Transient Suppression of Cytokine Secretion in Astrocytes Assessed at the Single-Cell Level with a High-Throughput Microfluidic Chip.

Brain cells are constantly subjected to mechanical signals. Astrocytes are the most abundant glial cells of the central nervous system (CNS), which display immunoreactivity and have been suggested as an emerging disease focus in the recent years. However, how mechanical signals regulate astrocyte immunoreactivity, and the cytokine release in particular, remains to be fully characterized. Here, human neural stem cells are used to induce astrocytes, from which the release of 15 types of cytokines are screened, and nine of them are detected using a protein microfluidic chip. When a gentle compressive force is applied, altered cell morphology and reinforced cytoskeleton are observed. The force induces a transient suppression of cytokine secretions including IL-6, MCP-1, and IL-8 in the early astrocytes. Further, using a multiplexed single-cell culture and protein detection microfluidic chip, the mechanical effects at a single-cell level are analyzed, which validates a concerted downregulation by force on IL-6 and MCP-1 secretions in the cells releasing both factors. This work demonstrates an original attempt of employing the protein detection microfluidic chips in the assessment of mechanical regulation on the brain cells at a single-cell resolution, offering novel approach and unique insights for the understanding of the CNS immune regulation.

Patient-specific Alzheimer-like pathology in trisomy 21 cerebral organoids reveals BACE2 as a gene dose-sensitive AD suppressor in human brain.

A population of more than six million people worldwide at high risk of Alzheimer's disease (AD) are those with Down Syndrome (DS, caused by trisomy 21 (T21)), 70% of whom develop dementia during lifetime, caused by an extra copy of ß-amyloid-(Aß)-precursor-protein gene. We report AD-like pathology in cerebral organoids grown in vitro from non-invasively sampled strands of hair from 71% of DS donors. The pathology consisted of extracellular diffuse and fibrillar Aß deposits, hyperphosphorylated/pathologically conformed Tau, and premature neuronal loss. Presence/absence of AD-like pathology was donor-specific (reproducible between individual organoids/iPSC lines/experiments). Pathology could be triggered in pathology-negative T21 organoids by CRISPR/Cas9-mediated elimination of the third copy of chromosome 21 gene BACE2, but prevented by combined chemical ß and γ-secretase inhibition. We found that T21 organoids secrete increased proportions of Aß-preventing (Aß1-19) and Aß-degradation products (Aß1-20 and Aß1-34). We show these profiles mirror in cerebrospinal fluid of people with DS. We demonstrate that this protective mechanism is mediated by BACE2-trisomy and cross-inhibited by clinically trialled BACE1 inhibitors. Combined, our data prove the physiological role of BACE2 as a dose-sensitive AD-suppressor gene, potentially explaining the dementia delay in ~30% of people with DS. We also show that DS cerebral organoids could be explored as pre-morbid AD-risk population detector and a system for hypothesis-free drug screens as well as identification of natural suppressor genes for neurodegenerative diseases.

Cellular uptake and transport characteristics of chitosan modified nanoparticles in Caco-2 cell monolayers.

The bioadhesive drug delivery systems, chitosan modified nanoparticles, can efficiently adhere to mucosal surface of gastrointestinal tract and prolong residence time but a deeper understanding about its cellular uptake and transport is still lack. Hence, the present study was designed to compare the process of uptake and transport between chitosan modified and unmodified PLGA nanoparticles. Chitosan modified nanoparticles were formulated by classic and chemical methods. The morphology and modification potency were characterized by zeta potential, FTIR, DSC, XPS and TEM. Coumarin-6 load chitosan modified nanoparticles were incubated with Caco-2 cells to study the process of uptake and transport. The kinetic of cellular uptake for chitosan modified and unmodified nanoparticles was time- and concentration-dependent endocytosis but the cellular uptake efficiency of modified nanoparticles was significantly higher than that of unmodified one within the high concentration range (25.0-100.0 µg/mL). The modified nanoparticles underwent clathrin-mediated endocytosis and macropinocytosis but unmodified nanoparticles occurred a clathrin-mediated endocytosis. Both types of nanoparticles can increase caulophine with low solubility and poor stability transmembrane transport and the modified nanoparticles exhibited a better transcellular permeability. Therefore, the chitosan modified nanoparticles showed significantly improved cellular uptake and transcellular transport and a potential improvement in the efficacy of oral drug delivery.

Pedestrian Trajectory Prediction in Extremely Crowded Scenarios.

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians' trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.

New version of continuous-discrete cubature Kalman filtering for nonlinear continuous-discrete systems.

For nonlinear continuous-discrete systems, this paper elaborates a new accurate implementation of continuous-discrete cubature Kalman filter (CD-CKF). As the main contribution of this work, the new Kalman prediction stage begins by integrating the nonlinear continuous model for all the cubature sample vectors; the prior estimate state and covariance prediction are based on the weighted statistics of these integrated cubature sample vectors and the Gauss-Legendre approximation scheme. The new square root form CD-CKF is also derived and accurately implemented by combining with the modified variable stepsize NIRK. As the advantages of proposed approach, the complicated and error-prone processes of solving covariance differential equation or calculating derivatives are avoided, while the positive semi-definiteness of prior error covariance are numerically guaranteed. Simulations of traffic control scenarios further confirm the new approach's superior filtering performance in both reliability and accuracy.

Village Building Identification Based on Ensemble Convolutional Neural Networks.

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.

Object Discovery: Soft Attributed Graph Mining.

We categorize this research in terms of its contribution to both graph theory and computer vision. From the theoretical perspective, this study can be considered as the first attempt to formulate the idea of mining maximal frequent subgraphs in the challenging domain of messy visual data, and as a conceptual extension to the unsupervised learning of graph matching. We define a soft attributed pattern (SAP) to represent the common subgraph pattern among a set of attributed relational graphs (ARGs), considering both their structure and attributes. Regarding the differences between ARGs with fuzzy attributes and conventional labeled graphs, we propose a new mining strategy that directly extracts the SAP with the maximal graph size without applying node enumeration. Given an initial graph template and a number of ARGs, we develop an unsupervised method to modify the graph template into the maximal-size SAP. From a practical perspective, this research develops a general platform for learning the category model (i.e., the SAP) from cluttered visual data (i.e., the ARGs) without labeling "what is where," thereby opening the possibility for a series of applications in the era of big visual data. Experiments demonstrate the superior performance of the proposed method on RGB/RGB-D images and videos.

Novel localization of formin mDia2: importin ß-mediated delivery to and retention at the cytoplasmic side of the nuclear envelope.

The formin family proteins are important regulators of actin polymerization that are involved in many cellular processes. However, little is known about their specific cellular localizations. Here, we show that Diaphanous-related formin-3 (mDia2) localizes to the cytoplasmic side of the nuclear envelope. This localization of mDia2 to the nuclear rim required the presence of a nuclear localization signal (NLS) sequence at the mDia2 N-terminal. Consistent with this result, super-resolution images demonstrated that at the nuclear rim, mDia2 co-localized with the nuclear pore complexes and a nuclear transport receptor, importin ß. Furthermore, an interaction between mDia2 and importin ß was detected by immunoprecipitation, and silencing of importin ß was shown to attenuate accumulation of mDia2 to the nuclear rim. We have shown previously that Ca(2+) entry leads to the assembly of perinuclear actin rim in an inverted formin 2 (INF2) dependent manner. mDia2, however, was not involved in this process since abolishing its localization at the nuclear rim by silencing of importin ß had no effect on actin assembly at the nuclear rim triggered by Ca(2+) stimulation.

Discovery of novel VEGFR-2 inhibitors. Part 5: Exploration of diverse hinge-binding fragments via core-refining approach.

Pathological angiogenesis plays a critical role in numerous diseases including malignancy. VEGFR-2 is the central regulators in angiogenesis and has become a promising target for anticancer drug design. We have identified a novel biphenyl-aryl urea incorporated with salicyladoxime (BPS-7) as potent VEGFR-2 inhibitor. As a continuation to our previous research, various aromatic-heterocyclic were introduced as hinge-binding fragment via a core-refining approach. Interestingly, many compounds exhibited comparable VEGFR-2 inhibition to Sorafenib. In particular, 12e and 12o displayed excellent VEGFR-2 inhibitory activity with IC50 values of 0.50 nM and 0.79 nM, respectively. Several title compounds showed considerable antiproliferative activity against A549 and SMMC-7721 cells. In addition, molecular docking was performed to rationalize the efficiency of the better compounds. These results will be instructive for further inhibitor design and optimization.

Mechanical stimulation induces formin-dependent assembly of a perinuclear actin rim.

Cells constantly sense and respond to mechanical signals by reorganizing their actin cytoskeleton. Although a number of studies have explored the effects of mechanical stimuli on actin dynamics, the immediate response of actin after force application has not been studied. We designed a method to monitor the spatiotemporal reorganization of actin after cell stimulation by local force application. We found that force could induce transient actin accumulation in the perinuclear region within ∼ 2 min. This actin reorganization was triggered by an intracellular Ca(2+) burst induced by force application. Treatment with the calcium ionophore A23187 recapitulated the force-induced perinuclear actin remodeling. Blocking of actin polymerization abolished this process. Overexpression of Klarsicht, ANC-1, Syne Homology (KASH) domain to displace nesprins from the nuclear envelope did not abolish Ca(2+)-dependent perinuclear actin assembly. However, the endoplasmic reticulum- and nuclear membrane-associated inverted formin-2 (INF2), a potent actin polymerization activator (mutations of which are associated with several genetic diseases), was found to be important for perinuclear actin assembly. The perinuclear actin rim structure colocalized with INF2 on stimulation, and INF2 depletion resulted in attenuation of the rim formation. Our study suggests that cells can respond rapidly to external force by remodeling perinuclear actin in a unique Ca(2+)- and INF2-dependent manner.