.png){kind=icon}
返回
A Bayesian cluster analysis method for single-molecule localization microscopy data
.png){kind=icon}
PRE
AI
DOI：10.1038/nprot.2016.149.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is similar to 18 h; user input takes similar to 1 h.
Keyword:
OPTICAL RECONSTRUCTION MICROSCOPY
SUPERRESOLUTION MICROSCOPY
PLASMA-MEMBRANE
PROTEIN HETEROGENEITY
LIPID RAFTS
ORGANIZATION
PALM
SOFTWARE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
16
论文数:
4.0K
被引数:
5.6W
机构
引用论文
Steady-state cross-correlations for live two-colour super-resolution localization data sets.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Bayesian cluster identification in single-molecule Localization microscopy dataNATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Functional Nanoscale Organization of Signaling Molecules Downstream of the T Cell Antigen ReceptorIMMUNITY
OAAI
IMMUNITYIF26.3
OAAI分享收藏Superresolution Microscopy Reveals Nanometer-Scale Reorganization of Inhibitory Natural Killer Cell Receptors upon Activation of NKG2DSCIENCE SIGNALING
SCIENCE SIGNALINGIF6.6
PREAI
分享收藏Cooperative Clustering Digitizes Biochemical Signaling and Enhances its FidelityBIOPHYSICAL JOURNALOAAI
BIOPHYSICAL JOURNALIF3.1
OAAI分享收藏Quantifying spatial organization in point-localization superresolution images using pair correlation analysisNATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAI分享收藏Imaging biological structures with fluorescence photoactivation localization microscopyNATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAI分享收藏Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)通过随机光学重建显微镜 (STORM) 进行亚衍射极限成像NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容