4.5 Article

Spatial distribution of organelles in leaf cells and soybean root nodules revealed by focused ion beam-scanning electron microscopy

期刊

FUNCTIONAL PLANT BIOLOGY
卷 45, 期 1-2, 页码 180-191

出版社

CSIRO PUBLISHING
DOI: 10.1071/FP16347

关键词

FIB-SEM; HPF-FS; membrane continuity; plasmodesmata; root nodules; tomogram

资金

  1. National Science Foundation [1456761]
  2. Division Of Integrative Organismal Systems
  3. Direct For Biological Sciences [1456761] Funding Source: National Science Foundation

向作者/读者索取更多资源

Analysis of cellular ultrastructure has been dominated by transmission electron microscopy (TEM), so images collected by this technique have shaped our current understanding of cellular structure. More recently, three-dimensional (3D) analysis of organelle structures has typically been conducted using TEM tomography. However, TEM tomography application is limited by sample thickness. Focused ion beam-scanning electron microscopy (FIB-SEM) uses a dual beam system to perform serial sectioning and imaging of a sample. Thus FIB-SEM is an excellent alternative to TEM tomography and serial section TEM tomography. Animal tissue samples have been more intensively investigated by this technique than plant tissues. Here, we show that FIB-SEM can be used to study the 3D ultrastructure of plant tissues in samples previously prepared for TEM via commonly used fixation and embedding protocols. Reconstruction of FIB-SEM sections revealed ultra-structural details of the plant tissues examined. We observed that organelles packed tightly together in Nicotiana benthamiana Domin leaf cells may form membrane contacts. 3D models of soybean nodule cells suggest that the bacteroids in infected cells are contained within one large membrane-bound structure and not the many individual symbiosomes that TEM thin-sections suggest. We consider the implications of these organelle arrangements for intercellular signalling.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据