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Imaging the inner structure of chromosomes: contribution of focused ion beam/scanning electron microscopy to chromosome research.

Astari DwirantiFendi Sofyan ArifudinToshiyuki WakoKiichi Fukui
Published in: Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology (2021)
Visualization of the chromosome ultrastructure has revealed new insights into its structural and functional properties. The use of new methods for revealing not only the surface but also the inner structure of the chromosome has been emerged. Some methods have long been used, such as conventional transmission electron microscopy (TEM). Although it has indispensably contributed to the revelation of the ultrastructure of the various biological samples, including chromosomes, some challenges have also been encountered, such as laborious sample preparation, limited view areas, and loss of information on some parts due to ultramicrotome sectioning. Therefore, a more advanced method is needed. Scanning electron microscopy (SEM) is also advantageous in the surface visualization of chromosome samples. However, it is limited by accessibility to gain the inner structure information. Focused ion beam/scanning electron microscopy (FIB/SEM) provides a way to investigate the inner structure of the samples in a direct slice-and-view manner to observe the ultrastructure of the inner part of the sample continuously and further construct a three-dimensional image. This method has long been used in the material science field, and recently, it has also been applied to biological research, such as in showing the inner structure of chromosomes. This review article presents the contributions of this new method to chromosome research and its recent developments in the inner structure of chromosome and discusses its current and potential applications to the high-resolution imaging of chromosomes.
Keyphrases
  • electron microscopy
  • high resolution
  • copy number
  • public health
  • healthcare
  • magnetic resonance imaging
  • risk assessment
  • mass spectrometry
  • machine learning