| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-10-02 |
| タイトル |
|
|
タイトル |
Changes of mass spectra patterns on a brain tissue section revealed by deep learning with imaging mass spectrometry data |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
Yamada, Hidemoto
Xu, Lili
Eto, Fumihiro
Takeichi, Rei
Islam, Ariful
Mamun, Md. AI
Zhang, Chi
Yao, Ikuko
Sakamoto, Takumi
Aramaki, Shuhei
Kikushima, Kenji
Sato, Tomohito
Takahashi, Yutaka
Machida, Manabu
Kahyo, Tomoaki
Setou, Mitsutoshi
|
| 書誌情報 |
en : Journal of the American Society for Mass Spectrometry
巻 33,
号 9,
p. 1607-1614,
発行日 2022-09-07
|
| 出版者 |
|
|
出版者 |
American Chemical Society |
|
言語 |
en |
| 権利 |
|
|
権利情報 |
This document is the Accepted Manuscript version of a Published Work that appeared in final form in "Journal of the American Chemical Society", copyright 2022 American Society for Mass Spectrometry after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/jasms.2c00080. |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents’ brain sections. CNN produced classification models with high accuracy and low loss rate in any test datasets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS, and mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. Based on the extracted mass spectra pattern features, the histo-logically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging gener-ated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no sig-nificant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment. |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1044-0305 |
| EISSN |
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収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1879-1123 |
| NII書誌ID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA10823809 |
| PubMed番号 |
|
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関連タイプ |
isVersionOf |
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|
識別子タイプ |
PMID |
|
|
関連識別子 |
35881989 |
| 出版社DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1021/jasms.2c00080 |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
JASMS-33-1607.pdf (1.3 MB)
