Living Cell Microarrays: An Overview of Concepts

Living cell microarrays are a highly efficient cellular screening system. Due to the low number of cells required per spot, cell microarrays enable the use of primary and stem cells and provide resolution close to the single-cell level. Apart from a variety of conventional static designs, microfluidic microarray systems have also been established. An alternative format is a microarray consisting of three-dimensional cell constructs ranging from cell spheroids to cells encapsulated in hydrogel. These systems provide an in vivo-like microenvironment and are preferably used for the investigation of cellular physiology, cytotoxicity, and drug screening. Thus, many different high-tech microarray platforms are currently available. Disadvantages of many systems include their high cost, the requirement of specialized equipment for their manufacture, and the poor comparability of results between different platforms. In this article, we provide an overview of static, microfluidic, and 3D cell microarrays. In addition, we describe a simple method for the printing of living cell microarrays on modified microscope glass slides using standard DNA microarray equipment available in most laboratories. Applications in research and diagnostics are discussed, e.g., the selective and sensitive detection of biomarkers. Finally, we highlight current limitations and the future prospects of living cell microarrays.Niedersächsische Krebsgesellschaft e.V.BIOFABRICATION FOR NIFE InitiativeLower Saxony ministry of Science and CultureVolkswagen Stiftun

Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast- growing technological advances in high throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA) are integrated, while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. It was possible to find a set of highly correlated features distinguishing glioblastoma from low- grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients were identified, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets.O glioma é atualmente um dos tipos mais prevalentes de cancro cerebral primário. Dado o seu elevado nível de heterogeneidade e dada a complexidade dos seus marcadores moleculares biológicos, muitos esforços têm sido realizados para classificar com precisão o tipo de glioma em cada paciente, o que, por sua vez, é fundamental para melhorar o diagnóstico precoce e aumentar a sobrevivência. No entanto, como resultado dos avanços tecnológicos em rápido crescimento na sequenciação de dados e na evolução da com- preensão molecular da biologia do glioma, a sua classificação foi recentemente sujeita a alterações significativas. Neste estudo, múltiplas modalidades ómicas de glioma (in- cluindo mRNA, metilação de DNA e miRNA) provenientes do The Cancer Genome Atlas (TCGA) são integradas, juntamente com a utilização das classes revistas e reclassificadas de glioma, com um método supervisionado baseado em análise de correlação canónica esparsa (DIABLO) para discriminar entre os tipos de glioma. Foi possível encontrar um conjunto de características altamente correlacionadas que distinguem o glioblastoma dos gliomas de baixo grau (LGG) que estavam principalmente associadas à ruptura das vias de sinalização dos receptores de tirosina quinases e à organização e remodelação da matriz extracelular. Por outro lado, a discriminação dos tipos LGG foi caracterizada principalmente por variáveis envolvidas nos processos de ubiquitinação e transcrição de DNA. Além disso, foram identificados vários novos biomarcadores de glioma potencial- mente úteis tanto no diagnóstico quanto no prognóstico dos pacientes, incluindo os genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A e HEPN1. No geral, este método de classificação permitiu discriminar com desempenho muito elevado os diferentes pacientes com glioma, simultaneamente procurando informações comuns entre os vários tipos de dados, permitindo, em última análise, a compreensão de mecanis- mos essenciais que impulsionam a heterogeneidade em glioma e revelam potenciais alvos terapêuticos

Behavioral Impairment in Aquatic Organisms Exposed to Neurotoxic Pollutants

Neuroactive chemicals are the largest group of micropollutants present in European rivers. There is increasing concern about the behavioral effects of these neuroactive chemicals on aquatic wildlife, potentially resulting in detrimental effects on individual, population, and community levels of ecological organization. This Special Issue, titled “Behavioral Impairment in Aquatic Organisms Exposed to Neurotoxic Pollutants”, presents original research and review articles addressing behavioral impairment induced by different aquatic invertebrate and vertebrate species to neuroactive chemicals. The selected studies include different methodological approaches, such as multi-compartment, automated plug and play, and homemade setups systems. We believe that this collection provides essential information regarding research and challenges on the behavioral ecotoxicity of invertebrate and vertebrate aquatic organisms, as well as the molecular mechanisms behind these effects

Advancing human health risk assessment

Acknowledgements: The European Food Safety Authority (EFSA) and authors wish to thank the participants of the break‐out session ‘Advancing risk assessment science – Human health’ at EFSA's third Scientific Conference ‘Science, Food and Society’ (Parma, Italy, 18–21 September 2018) for their active and valuable contributions to the discussion. We also thank Hans Verhagen for carefully proofreading it.Peer reviewedPublisher PD

Evolutionary and deep mining models for effective biomarker discovery

With the advent of high-throughput biology, large amounts of molecular data are available for purposeful analysis and evaluation. Extracting relevant knowledge from high-throughput biomedical datasets has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, the datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. This is evidenced by the limited success these methods have had in detecting robust and reliable biomarkers for cancers and other complicated diseases. This could also explain the lack of finding generic biomarkers among the identified published genes for identical diseases or clinical conditions. This thesis proposes and evaluates the efficacy of two novel feature mining models established on the basis of the evolutionary computation and deep learning paradigms to position and solve biomarker discovery as an optimisation problem. Deep learning methods lack the transparency and interpretability found in the evolutionary paradigm. To overcome the inherent issue of poor explanatory power associated with the deep learning, this research also introduces a novel deep mining model that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations to aid feature selection. As a result, salient biomarkers for breast cancer and the positivity of the Estrogen and Progesterone receptors are discovered robustly and validated reliably across a wide range of independently generated breast cancer data samples

Present and future of surface-enhanced Raman scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article

NetCore: a network propagation approach using node coreness

We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules

Antioxidant and DPPH-Scavenging Activities of Compounds and Ethanolic Extract of the Leaf and Twigs of Caesalpinia bonduc L. Roxb.

Antioxidant effects of ethanolic extract of Caesalpinia bonduc and its isolated bioactive compounds were evaluated in vitro. The compounds included two new cassanediterpenes, 1α,7α-diacetoxy-5α,6β-dihydroxyl-cass-14(15)-epoxy-16,12-olide (1)and 12α-ethoxyl-1α,14β-diacetoxy-2α,5α-dihydroxyl cass-13(15)-en-16,12-olide(2); and others, bonducellin (3), 7,4’-dihydroxy-3,11-dehydrohomoisoflavanone (4), daucosterol (5), luteolin (6), quercetin-3-methyl ether (7) and kaempferol-3-O-α-L-rhamnopyranosyl-(1Ç2)-β-D-xylopyranoside (8). The antioxidant properties of the extract and compounds were assessed by the measurement of the total phenolic content, ascorbic acid content, total antioxidant capacity and 1-1-diphenyl-2-picryl hydrazyl (DPPH) and hydrogen peroxide radicals scavenging activities.Compounds 3, 6, 7 and ethanolic extract had DPPH scavenging activities with IC50 values of 186, 75, 17 and 102 μg/ml respectively when compared to vitamin C with 15 μg/ml. On the other hand, no significant results were obtained for hydrogen peroxide radical. In addition, compound 7 has the highest phenolic content of 0.81±0.01 mg/ml of gallic acid equivalent while compound 8 showed the highest total antioxidant capacity with 254.31±3.54 and 199.82±2.78 μg/ml gallic and ascorbic acid equivalent respectively. Compound 4 and ethanolic extract showed a high ascorbic acid content of 2.26±0.01 and 6.78±0.03 mg/ml respectively.The results obtained showed the antioxidant activity of the ethanolic extract of C. bonduc and deduced that this activity was mediated by its isolated bioactive compounds

Gene Expression Biomarkers of the Response To Sleep Loss With and Without Modafinil

Sleep disruption presents a substantial risk to health and safety, particularly due to the risks of performance degradation in safety-critical operations that can result in catastrophic injuries or mortality. Federal regulations exist to minimize the risks of fatigue with limitations on hours worked and requirements for fatigue risk management plans. Yet, even with workload controls and scheduled opportunities for rest, fatigue may be caused by factors such as personal and lifestyle choices, illness, and circadian disruption from travel across multiple time zones. Complicating risk mitigation is the challenge of identifying and measuring fatigue. Here, we report on gene expression biomarkers (biological indicators) for cognitive impairment during sleep loss. We observe hundreds of genes whose expression is associated with attention changes during one night of sleep loss. Several genes are identified that we previously associated with attention impairment in a separate study of sleep loss. The reproducibility of findings may indicate the robustness of these candidate fatigue impairment biomarkers. However, some biomarker genes only associate with certain tests of impairment (e.g., attention lapses but not self-reported fatigue), suggesting that different biomarker panels may be developed to assess the particular cognitive domains that need monitoring for a given safety critical operation. We also find that using a drug countermeasure (modafinil) not only helps mitigate impairment on tests of attention lapses, but also disrupts gene expression associations with attention lapses. Further research is needed to confirm whether this represents a unique effect of modafinil administration, or emphasizes the need to ensure biomarker validation occurs both in the presence and absence of countermeasures