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用于多重荧光生物图像分析的稳健归一化协议。

Robust normalization protocols for multiplexed fluorescence bioimage analysis.

作者信息

Ahmed Raza Shan E, Langenkämper Daniel, Sirinukunwattana Korsuk, Epstein David, Nattkemper Tim W, Rajpoot Nasir M

机构信息

Department of Computer Science, University of Warwick, Coventry, CV4 7AL UK.

Biodata Mining Group, Bielefeld University, Bielefeld, Germany.

出版信息

BioData Min. 2016 Mar 5;9:11. doi: 10.1186/s13040-016-0088-2. eCollection 2016.

Abstract

study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270-8, 2006; Proc Natl Acad Sci 110:11982-7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples.

摘要

在亚细胞水平研究共定位蛋白的图谱绘制及相互作用对于理解复杂的生物学现象至关重要。绘制共定位蛋白的最新技术之一是循环使用标准免疫荧光显微镜(《自然生物技术》24:1270 - 8, 2006;《美国国家科学院院刊》110:11982 - 7, 2013)。不幸的是,这些技术存在问题,即在一次运行以及不同运行之间,蛋白质标记信号的强度和定位存在变异性。因此,有必要在对来自多次运行的多重生物成像(MBI)数据进行任何进一步分析之前,将其预处理协议标准化到可比的规模。在本文中,我们比较了各种归一化协议,并根据所得结果提出了一种强大的归一化技术,该技术对使用拓扑成像系统(TIS)从不同运行中收集的MBI数据产生一致的结果。两位病理学家和两位生物学家对所提出的方法在一组结直肠癌患者的TIS样本数据集上产生的归一化结果给予了高度评价。我们表明,所提出的方法在结直肠癌和组织学正常样本的细胞表型分布上产生了更高的类间库尔贝克 - 莱布勒(KL)散度和更低的类内KL散度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d7eb/4779207/d23695ebc87a/13040_2016_88_Fig2_HTML.jpg

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