High-Throughput最新文献

Pre-transfusion bedside compatibility test is mandatory to check that the donor and the recipient present compatible groups before any transfusion is performed. Although blood typing devices are present on the market, they still suffer from various drawbacks, like results that are based on naked-eye observation or difficulties in blood handling and process automation. In this study, we addressed the development of a red blood cells (RBC) agglutination assay for point-of-care blood typing. An injection molded microfluidic chip that is designed to enhance capillary flow contained anti-A or anti-B dried reagents inside its microchannel. The only blood handling step in the assay protocol consisted in the deposit of a blood drop at the tip of the biochip, and imaging was then achieved. The embedded reagents were able to trigger RBC agglutination in situ, allowing for us to monitor in real time the whole process. An image processing algorithm was developed on diluted bloods to compute real-time agglutination indicator and was further validated on undiluted blood. Through this proof of concept, we achieved efficient, automated, real time, and quantitative measurement of agglutination inside a passive biochip for blood typing which could be further generalized to blood biomarker detection and quantification.

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.

Over the last three decades, novel "omics" platform technologies for the sequencing of DNA and complementary DNA (cDNA) (RNA-Seq), as well as for the analysis of proteins and metabolites by mass spectrometry, have become more and more available and increasingly found their way into general laboratory life. With this, the ability to generate highly multivariate datasets on the biological systems of choice has increased tremendously. However, the processing and, perhaps even more importantly, the integration of "omics" datasets still remains a bottleneck, although considerable computational and algorithmic advances have been made in recent years. In this mini-review, we use a number of recent "multi-omics" approaches realized in our laboratories as a common theme to discuss possible pitfalls of applying "omics" approaches and to highlight some useful tools for data integration and visualization in the form of an exemplified case study. In the selected example, we used a combination of transcriptomics and metabolomics alongside phenotypic analyses to functionally characterize a small number of Cycling Dof Transcription Factors (CDFs). It has to be remarked that, even though this approach is broadly used, the given workflow is only one of plenty possible ways to characterize target proteins.

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.

Among applicable high-throughput techniques in cardiovascular biology, whole-transcriptome sequencing is of particular use. By utilizing RNA that is isolated from virtually all cells and tissues, the entire transcriptome can be evaluated. In comparison with other high-throughput approaches, RNA sequencing is characterized by a relatively low-cost and large data output, which permits a comprehensive analysis of spatiotemporal variation in the gene expression profile. Both shear stress and cyclic strain exert hemodynamic force upon the arterial endothelium and are considered to be crucial determinants of endothelial physiology. Laminar blood flow results in a high shear stress that promotes atheroresistant endothelial phenotype, while a turbulent, oscillatory flow yields a pathologically low shear stress that disturbs endothelial homeostasis, making respective arterial segments prone to atherosclerosis. Severe atherosclerosis significantly impairs blood supply to the organs and frequently requires bypass surgery or an arterial replacement surgery that requires tissue-engineered vascular grafts. To provide insight into patterns of gene expression in endothelial cells in native or bioartificial arteries under different biomechanical conditions, this article discusses applications of whole-transcriptome sequencing in endothelial mechanobiology and vascular tissue engineering.

Drug discovery encompasses processes ranging from target selection and validation to the selection of a development candidate. While comprehensive drug discovery work flows are implemented predominantly in the big pharma domain, early discovery focus in academia serves to identify probe molecules that can serve as tools to study targets or pathways. Despite differences in the ultimate goals of the private and academic sectors, the same basic principles define the best practices in early discovery research. A successful early discovery program is built on strong target definition and validation using a diverse set of biochemical and cell-based assays with functional relevance to the biological system being studied. The chemicals identified as hits undergo extensive scaffold optimization and are characterized for their target specificity and off-target effects in in vitro and in animal models. While the active compounds from screening campaigns pass through highly stringent chemical and Absorption, Distribution, Metabolism, and Excretion (ADME) filters for lead identification, the probe discovery involves limited medicinal chemistry optimization. The goal of probe discovery is identification of a compound with sub-µM activity and reasonable selectivity in the context of the target being studied. The compounds identified from probe discovery can also serve as starting scaffolds for lead optimization studies.

Very often the clinical features of rare neurodegenerative disorders overlap with those of other, more common clinical disturbances. As a consequence, not only the true incidence of these disorders is underestimated, but many patients also experience a significant delay before a definitive diagnosis. Under this scenario, it appears clear that any accurate tool producing information about the pathological mechanisms of these disorders would offer a novel context for their precise identification by strongly enhancing the interpretation of symptoms. With the advent of proteomics, detection and identification of proteins in different organs/tissues, aimed at understanding whether they represent an attractive tool for monitoring alterations in these districts, has become an area of increasing interest. The aim of this report is to provide an overview of the most recent applications of proteomics as a new strategy for identifying biomarkers with a clinical utility for the investigation of rare neurodegenerative disorders.

Although a number of combinatorial/high-throughput approaches have been developed for biomaterial hydrogel optimization, a gradient sample approach is particularly well suited to identify hydrogel property thresholds that alter cellular behavior in response to interacting with the hydrogel due to reduced variation in material preparation and the ability to screen biological response over a range instead of discrete samples each containing only one condition. This review highlights recent work on cell-hydrogel interactions using a gradient material sample approach. Fabrication strategies for composition, material and mechanical property, and bioactive signaling gradient hydrogels that can be used to examine cell-hydrogel interactions will be discussed. The effects of gradients in hydrogel samples on cellular adhesion, migration, proliferation, and differentiation will then be examined, providing an assessment of the current state of the field and the potential of wider use of the gradient sample approach to accelerate our understanding of matrices on cellular behavior.