sample preparation

样品制备
  • 文章类型: Journal Article
    Electron microscopy is a mainstay in the analysis of renal biopsies, where it is typically employed in a correlative fashion along with light and immunofluorescence microscopy. Despite the development of a growing armamentarium of molecular and biochemical analytic methods as well as new immunostains with a widening panel of immunoreactants, electron microscopy remains crucial to the diagnosis of a number of disorders involving the renal glomerulus, vasculature, and tubulointerstitial compartment. The number of renal biopsies continues to grow and the indications for these biopsies continue expanding together with our understanding of disease processes. Proper collection of biopsies and careful analysis of data emanating from diagnostic modalities, clinical information, imaging, gross and microscopic tissue analysis, including a wide range of ancillary studies, represent the essential paradigm for generating detailed diagnoses with clinical significance. This communication offers a guide to the pre-analytic and analytic process for renal biopsy examination, discusses diagnostic keys and pitfalls for an important category of renal diseases (immune complex disorders), and provides an introduction to a useful adjunct diagnostic method (ultrastructural immunolabeling). Renal pathologists should render expert diagnoses that guide patient management, provide prognostic information and lead to targeted new therapeutic interventions that are currently available.
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  • 文章类型: Journal Article
    对组织内代谢组的研究,有机体,有机体细胞或生物流体可以通过几种生物分析技术进行。其中,核磁共振(NMR)是主要的光谱方法之一。这是由于样品旋转技术,高分辨率魔角旋转(HR-MAS),其目标是分析体积样品质量为5至10mg的异质样品。最近,一种新的方法,高分辨率微魔角旋转(HR-μMAS),已经介绍了。它打开了,第一次,用核磁共振波谱研究微观标本(<500μg)的可能性,加强异质样本均匀采样的概念。和所有生物分析方法一样,清洁可靠的样品制备策略是设计代谢组学(或-组学,一般)研究。HR-μMAS的样品制备因μg级样品而变得复杂,尚未解决。本报告详细介绍了三种样本类型的策略:生物流体,流体基质和组织。它还为设计未来的显微标本的μMASNMR研究提供了基础。
    The study of the metabolome within tissues, organisms, cells or biofluids can be carried out by several bioanalytical techniques. Among them, nuclear magnetic resonance (NMR) is one of the principal spectroscopic methods. This is due to a sample rotation technique, high-resolution magic angle spinning (HR-MAS), which targets the analysis of heterogeneous specimens with a bulk sample mass from 5 to 10 mg. Recently, a new approach, high-resolution micro-magic angle spinning (HR-μMAS), has been introduced. It opens, for the first time, the possibility of investigating microscopic specimens (<500 μg) with NMR spectroscopy, strengthening the concept of homogeneous sampling in a heterogeneous specimen. As in all bioanalytical approaches, a clean and reliable sample preparation strategy is a significant component in designing metabolomics (or -omics, in general) studies. The sample preparation for HR-μMAS is consequentially complicated by the μg-scale specimen and has yet to be addressed. This report details the strategies for three specimen types: biofluids, fluid matrices and tissues. It also provides the basis for designing future μMAS NMR studies of microscopic specimens.
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  • 文章类型: Journal Article
    The minimum information required for a glycomics experiment (MIRAGE) project was established in 2011 to provide guidelines to aid in data reporting from all types of experiments in glycomics research including mass spectrometry (MS), liquid chromatography, glycan arrays, data handling and sample preparation. MIRAGE is a concerted effort of the wider glycomics community that considers the adaptation of reporting guidelines as an important step towards critical evaluation and dissemination of datasets as well as broadening of experimental techniques worldwide. The MIRAGE Commission published reporting guidelines for MS data and here we outline guidelines for sample preparation. The sample preparation guidelines include all aspects of sample generation, purification and modification from biological and/or synthetic carbohydrate material. The application of MIRAGE sample preparation guidelines will lead to improved recording of experimental protocols and reporting of understandable and reproducible glycomics datasets.
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