Identification of glial-cell-line-derived neurotrophic factor-regulated proteins of striatum in mouse model of Parkinson disease

Glial-cell-line-derived neurotrophic factor (GDNF) is a potent survival factor for dopaminergic neurons, and hence serves as a therapeutic candidate for the treatment of Parkinson's disease. However, despite the potential clinical and physiological importance of GDNF, its mechanism of action is unclear. Therefore, we employed a state-of-the-art proteomic technique, DIGE, along with MS and a bioinformatics tool called Database for Annotation, Visualization and Integrated Discovery (DAVID), to profile proteome changes in the parkinsonian mouse striatum after GDNF challenge. Forty-six unique differentially expressed proteins were successfully identified, which were found either up-regulated and/or down-regulated at the two time points 4 and 72 h compared with the control. Proteins involved in cell differentiation and system development formed the largest part of the proteins regulated under GDNF. Furthermore, the aberrant expression of HSPs and mitochondria-associated proteins were noticeable. Moreover, mitochondrial stress 70 protein and heat shock cognate 71 kDa protein, whose relative levels increased significantly in GDNF-treated striatum, were further evaluated with Western blot and RT-PCR, demonstrating a good agreement with quantitative proteomic data. These data will provide some clues for understanding the mechanisms by which GDNF promotes the survival of dopaminergic neurons.     

Neuroproteomics and systems biology-based discovery of protein biomarkers for traumatic brain injury and clinical validation

The rapidly growing field of neuroproteomics has expanded to track global proteomic changes underlying various neurological conditions such as traumatic brain injury (TBI), stroke, and Alzheimer's disease. TBI remains a major health problem with approximately 2?million incidents occurring annually in the United States, yet no affective treatment is available despite several clinical trials. The absence of brain injury diagnostic biomarkers was identified as a significant road-block to therapeutic development for brain injury. Recently, the field of neuroproteomics has undertaken major advances in the area of neurotrauma research, where several candidate markers have been identified and are being evaluated for their efficacy as biological biomarkers in the field of TBI. One scope of this review is to evaluate the current status of TBI biomarker discovery using neuroproteomics techniques, and at what stage we are at in their clinical validation. In addition, we will discuss the need for strengthening the role of systems biology and its application to the field of neuroproteomics due to its integral role in establishing a comprehensive understanding of specific brain disorder and brain function in general. Finally, to achieve true clinical input of these neuroproteomic findings, these putative biomarkers should be validated using preclinical and clinical samples and linked to clinical diagnostic assays including ELISA or other high-throughput assays.     

Understanding dendritic cell biology and its role in immunological disorders through proteomic profiling

Dendritic cells (DC) have always been present on the bright spot of immune research. They have been extensively studied for the last 35 years, and much is known about their different phenotypes, stimulatory capacity, and role in the immune system. During the last 15 years, great attention has been given to studies on global gene and protein expression profiles during the differentiation and maturation processes of these cells. It is well understood that studying the proteome, together with information on the role of protein post-translational modifications (PTM), will reveal the real dynamics of a living cell. The rapid increase of proteomic studies during the last decade describing the differentiation and maturation process in DCs, as well as modifications brought by the use of different compounds that either increase or decrease their immunogenicity, reflects the importance of understanding the molecular processes behind the functional properties of these cells. In the present review, we will give an overview of proteomic studies focusing on DCs. Thereby we will concentrate on the importance of these studies in understanding DC behavior from a molecular point of view and how these findings have aided in understanding the differences in functional properties of these cells.     

超声波在干细胞治疗中的应用进展

超声波是指频率在 20 kHz 以上的机械波,具有机械效应、热效应和空化效应。不同强度的超声波作用于生物体,会对其器官、组织和细胞等产生不同程度的生物学效应。随着工程技术的发展,超声波已在医学显像、实体肿瘤治疗等生物医学领域具有广泛应用,低强度超声波也成为辅助干细胞治疗各类疾病的重要工程手段。深入了解超声波在干细胞治疗中所起的作用及机制,将促进广大研究者对超声波技术的了解,从而重视并加大超声波在该领域的研究力度及广泛应用。该文综述了超声波在干细胞治疗中的研究进展,对超声促进干细胞增殖、分化和迁移、细胞示踪显影及靶向药物传递等前沿研究进展进行了全面介绍,并对超声波在干细胞治疗领域的应用前景提出了设想。     

Integration of a hematopoietic progenitor cell program using the ACT/DB database system

Infusion of hematopoietic progenitor cells following high-dose chemotherapy is frequently used to treat patients with hematological malignancies and solid tumors. We have developed a comprehensive software system to monitor these patients once they are entered into an experimental protocol. The captured data encompasses all phases of progenitor cell therapy including progenitor cell mobilization and collection, stem cell processing, as well as cell infusion and engraftment kinetics. Particular attention was paid to the quality assurance and quality control functionality of the software during development of data entry forms and reports. The system was developed using the ACT/DB client-server database, which utilizes Microsoft Access as a front-end and accesses either an Oracle or SQL Server database. ACT/DB has been modified for deployment on the Internet in order to take advantage of Web-based technology. Information technology can help to integrate the diverse data requirements of complex therapeutic trials.     

Performance evaluation of 3D polystyrene 96-well plates with human neural stem cells in a calcium assay

In this study, we have generated a high-throughput screening (HTS)-compatible 3D cell culture platform by chemically "welding" polystyrene scaffolds into standard 2D polystyrene 96-well plates. The variability of scaffolds was minimized by introducing automation into the fabrication process. The fabricated 3D cell culture plates were compared with several commercially available 3D cell culture platforms with light and scanning electron microscopy. Voltage-gated calcium channel functionality was used to access the Z' factors of all plates, including a 2D standard plate control. It was found that with the No-Wash Fluo-4 calcium assay and neural progenitor cells, all plates display acceptable Z' factors for use in HTS. The plates with "welded" polystyrene scaffolds have several advantages, such as being versatile and economical, and are ready to use off the shelf. These characteristics are especially desired in HTS preclinical drug discovery applications.     

Global robust stability of delayed neural networks with a class of general activation functions

In this paper, the global robust stability is discussed for delayed neural networks with a class of general activation functions. By constructing new Lyapunov functionals, several novel conditions are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of neural networks with time delays. These conditions do not require the activation functions to be differentiable, bounded or monotonically nondecreasing. The results obtained here are generalizations of some earlier results reported in the literature for neural networks with time delays. In addition, two examples are given to illustrate our proposed results.     

Spiral-shaped inertial stem cell device for high-throughput enrichment of iPSC-derived neural stem cells

Stem cell enrichment plays a critical role in both research and clinical applications. The typical method for stem cell enrichment may use invasive processes and takes a long period of time. Spiral-shaped microfluidic devices, which combine lift and Dean drag forces to direct cells of different sizes into separate trajectories, can be used to noninvasively process samples at a rate of milliliters per minute. This paper presents a simple 2-loop spiral-shaped inertial microfluidic devices with the aid of sheath flow to enrich neural stem cells (NSCs), derived from induced pluripotent stem cells. NSCs and spontaneously differentiated non-neural cells were mixed and flowed through the spiral-shaped devices. Samples collected at the outlets were analyzed for purity and recovery. It was found that the device focused the NSCs into a narrow trajectory, which could then be collected in two out of the eight outlets. The device was tested at different flow rates and found that the most highly enriched fractions (2.1×) with NSCs recovery 93% were achieved at the flow rate (3 ml/min). Next, we extended our investigation from 2-loop design to 10-loop design to eliminate the use of sheath flow. NSCs were enriched to 2.5×, but only 38% of the NSCs were recovered from the most enriched fractions. Spiral-shaped microfluidic devices are capable of rapid, label-free enrichment of target stem cells, and have great potential in point-of-care tissue preparation.     

The proteome of the human breast cancer cell line MDA-MB-231: Analysis by LTQ-Orbitrap mass spectrometry

We have used a combination of SDS-PAGE and LTQ-Orbitrap MS to explore the proteome of the highly invasive MDA-MB-231 breast cancer cell line. Based on about 520?000 MS/MS spectra, a total of 3481 proteins were identified and subsequently classified according to their cellular distribution and molecular function. Interestingly, a large proportion of proteins (38%) were from cellular membranes and we were able to characterize numerous proteins involved in cancer initiation and progression such as the tumor suppressor p53 and the epidermal growth factor receptor. Together, this study represents the largest proteome database of breast cancer cells realized to date and demonstrates the value of using Orbitrap MS for deeper proteome analysis.     

2-D DIGE identification of differentially expressed heterogeneous nuclear ribonucleoproteins and transcription factors during neural differentiation of human embryonic stem cells

Neural stem cells (NSC) are progenitors that can give rise to all neural lineages. They are found in specific niches of fetal and adult brains and grow in vitro as non-adherent colonies, the neurospheres. These cells express the intermediate filament nestin, commonly considered an NSC marker. NSC can be derived as neurospheres from human embryonic stem cells (hESC). The mechanisms of cellular programming that hESC undergo during differentiation remain obscure. To investigate the commitment process of hESC during directed neural differentiation, we compared the nuclear proteomes of hESC and hESC-derived neurospheres. We used 2-D DIGE to conduct a quantitative comparison of hESC and NSC nuclear proteins and detected 1521 protein spots matched across three gels. Statistical analysis (ANOVA n = 3 with false discovery correction) revealed that only 2.1% of the densitometric signal was significantly changed. The ranges of average ratios varied from 1.2- to 11-fold at a statistically significant p-value <0.05. MS/MS identified 15 regulated proteins previously shown to be involved in chromatin remodeling, mRNA processing and gene expression regulation. Notably, three members of the heterogeneous nuclear ribonucleoprotein family (AUF-1, and FBP-1 and FBP-2) register a 54, 70 and 99% increased expression, highlighting them as potential markers for NSC in vitro derivation. By contrast, Cpsf-6 virtually disappears with differentiation with an 11-fold drop in NSC, highlighting this protein as a novel marker for undifferentiated ESC.     

Proteomic analysis as a means to approach limbal stem cell biology in a search for stem cell markers

The cornea consists of three main layers: an outer surface epithelium, the stroma, and the endothelium. A clear cornea is necessary for optimal vision and is maintained and repaired from limbal epithelial stem cells located in the limbus between the cornea and the sclera. Diseases and injury may result in deficiency of the stem cells impairing their ability to renew the corneal epithelium. Patients with limbal stem cell deficiency experience chronic pain and ultimately blindness. Attempts to treat the disease are based on replacement of the stem cells by transplantation or by culturing the stem cells. We here review the proteomic techniques that so far have been used to approach characterization of limbal stem cells and markers to identify them. It is apparent that the field is in a rather inchoate state due to the scarcity and relative inaccessibility of the stem cells. However, the importance of revealing limbal stem cell biology and identifying stem cell biomarkers calls for greater use of emerging methodology. Strategies for future studies are discussed.     

Existence and global asymptotic stability of periodic solution for discrete and distributed time-varying delayed neural networks with discontinuous activations

In this paper, we study a general class of neural networks with discrete and distributed time-varying delays, whose neuron activations are discontinuous and may be unbounded or nonmonotonic. By using the Leray-Schauder alternative theorem in multivalued analysis, matrix theory and generalized Lyapunov-like approach, we obtain some sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the periodic solution. Moreover, when all the variable coefficients and time delays are real constants, we discuss the global convergence in finite time of the neural network dynamical system. Our results extend previous works not only on discrete and distributed time-varying delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete and distributed time-varying delayed neural networks with discontinuous activations. Two numerical examples are given to illustrate the effectiveness of our main results.     

Mining the low molecular weight proteome of blood

Serum and plasma are composed of highly complex protein/peptide mixtures resulting from the systemic monitoring of every biological process in a living organism. Some of these sentinel changes are extremely short-lived while others produce more stable by-products. In addition, since biological events occur simultaneously and with overlapping physiological demands, separating the desired ones from "background" changes is an exceptional challenge. In this review, we outline a definition of the "low molecular weight proteome" as a valuable subcomponent of the blood proteome. We make a case that this derivative proteome is as information rich and equally complex as the parent proteome. We discuss some of the technical challenges in the analysis of the low molecular weight proteome with an emphasis on MS-based analytical approaches. With specific example of several reported methodologies we attempt to frame the current state-of-the-art in study design as a guide to future efforts.     

电子组织:一种具有自适应能力的可重构仿生硬件结构

具有自适应能力的仿生硬件是容错领域一个新兴的研究方向.同类细胞替换、成体干细胞分化和异类细胞转化等生物机制是人体血液组织健壮性的重要来源.受这些生物机制的启发,提出了一种名为电子组织的自适应可重构多细胞阵列结构.该结构采用了基于标记与识别的数据处理方式,解除了传统多细胞阵列结构中操作与细胞单元间的严格绑定的数据处理方式,使得电子组织具备了更为灵活的细胞单元替换能力,并在此基础上实现了同类细胞替换、成体干细胞分化和异类细胞转化3种仿生机制.这3种机制使电子组织具备了层次化的自我修复能力;成体干细胞分化和异类细胞转化机制又赋予了电子组织自我进化的能力.在FPGA上实现了原型系统,通过故障注入实验验证了原型系统的自我修复能力和自我进化能力;并通过与电子DNA结构的比较,对电子组织的自适应能力进行了分析与讨论.     

Constructing hysteretic memory in neural networks

Hysteresis is a unique type of dynamic, which contains an important property, rate-independent memory. In addition to other memory-related studies such as time delay neural networks, recurrent networks, and reinforcement learning, rate-independent memory deserves further attention owing to its potential applications. In this paper, we attempt to define hysteretic memory (rate independent memory) and examine whether or not it could be modeled in neural networks. Our analysis results demonstrate that other memory-related mechanisms are not hysteresis systems. A novel neural cell, referred to herein as the propulsive neural unit, is then proposed. The proposed cell is based on a notion related the submemory pool, which accumulates the stimulus and ultimately assists neural networks to achieve model hysteresis. In addition to training by backpropagation, a combination of such cells can simulate given hysteresis trajectories.     

Dissipativity analysis of neural networks with time-varying delays

A new definition of dissipativity for neural networks is presented in this paper. By constructing proper Lyapunov functionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.     

Projective synchronization for fractional-order memristor-based neural networks with time delays

In this paper, the global projective synchronization for fractional-order memristor-based neural networks with multiple time delays is investigated via combining open loop control with the time-delayed feedback control. A comparison theorem for a class of fractional-order systems with multiple time delays is proposed. Based on the given comparison theorem and Lyapunov method, the synchronization conditions are derived under the framework of Filippov solution and differential inclusion theory. Several feedback control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization for the fractional-order memristor-based neural networks with time delays. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

     

Global asymptotic stability of high-order Hopfield type neural networks with time delays

This paper studies the problem of global asymptotic stability of a class of high-order Hopfield type neural networks with time delays. By utilizing Lyapunov functionals, we obtain some sufficient conditions for the global asymptotic stability of the equilibrium point of such neural networks in terms of linear matrix inequality (LMI). Numerical examples are given to illustrate the advantages of our approach.