Imaging approaches for chromosome structures

Fukui, Kiichi; Kato, Seiji

Chromosome Research, 2021, 29, 5-17

Number of Access:318（2025-08-16 00:37 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/78960

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Title
Imaging approaches for chromosome structures
Creator
NRID
1000000311770 1000000311770
researchmap permalink
read0068600 read0068600
CreatorName
Fukui, Kiichi
CreatorName
Kato, Seiji
Subject
Quantitative chromosome map
Condensation pattern
CP (Condensation profile)
CHIAS 22 (Chromosome Image Analyzing System)
ImageJ
Machine learning
Description
This is a post-peer-review, pre-copyedit version of an article published in Chromosome Research. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10577-021-09648-3.
Abstract
This review describes image analyses for chromosome visible structures, focusing on the chromosome imaging system CHIAS (Chromosome Image Analyzing System). CHIAS is the first comprehensive imaging system for the analysis and characterization of plant chromosomes. A simulation method for human vision for capturing band positive regions was developed and used for the image analysis of large plant chromosomes with bands. Applying this method to C-banded Crepis chromosomes enabled recognition of band positive regions as seen by human vision. Furthermore, a new image parameter, condensation pattern was developed and successfully applied to identify small plant chromosomes such as rice and brassicas. Condensation profile (CP) derived from condensation pattern was also effective in developing quantitative chromosome maps. The result was quantitative chromosomal maps of several plants with small chromosomes, including Arabidopsis, diploid brassicas, rapeseed, rice, spinach, and sugarcane. In the final chapter, various applications of imaging techniques to the analysis of pachytene chromosomes, improved visibility of multicolor FISH images, 3D reconstruction of a human chromosome based on cross-section images obtained by a FIB/SEM, automatic extraction of chromosomal regions by machine learning, etc. are described.
Publisher
Springer Science and Business Media LLC
Source Title
Chromosome Research
Volume (Issue)
29
Page
5-17
Date of Issued
2021-02-15
Language
English
Handle URL
https://hdl.handle.net/11094/78960
PISSN
09673849
eISSN
15736849
NCID
AA10992704
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1007/s10577-021-09648-3
Access Rights
open access
oaire:version
Accepted Manuscript
Category
学術雑誌論文 Journal Article