Lipidomics and metabolomics communities comprise various informatics tools; however, software programs handling multimodal mass spectrometry (MS) data with structural annotations guided by the Lipidomics Standards Initiative are limited. Here, we provide MS-DIAL 5 for in-depth lipidome structural elucidation through electron-activated dissociation (EAD)-based tandem MS and determining their molecular localization through MS imaging (MSI) data using a species/tissue-specific lipidome database containing the predicted collision-cross section values. With the optimized EAD settings using 14âeV kinetic energy, the program correctly delineated lipid structures for 96.4% of authentic standards, among which 78.0% had the sn-, OH-, and/or Câ=âC positions correctly assigned at concentrations exceeding 1âμM. We showcased our workflow by annotating the sn- and double-bond positions of eye-specific phosphatidylcholines containing very-long-chain polyunsaturated fatty acids (VLC-PUFAs), characterized as PC n-3-VLC-PUFA/FA. Using MSI data from the eye and n-3-VLC-PUFA-supplemented HeLa cells, we identified glycerol 3-phosphate acyltransferase as an enzyme candidate responsible for incorporating n-3 VLC-PUFAs into the sn1 position of phospholipids in mammalian cells, which was confirmed using EAD-MS/MS and recombinant proteins in a cell-free system. Therefore, the MS-DIAL 5 environment, combined with optimized MS data acquisition methods, facilitates a better understanding of lipid structures and their localization, offering insights into lipid biology.
MS-DIAL 5 multimodal mass spectrometry data mining unveils lipidome complexities.
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作者:Takeda Hiroaki, Matsuzawa Yuki, Takeuchi Manami, Takahashi Mikiko, Nishida Kozo, Harayama Takeshi, Todoroki Yoshimasa, Shimizu Kuniyoshi, Sakamoto Nami, Oka Takaki, Maekawa Masashi, Chung Mi Hwa, Kurizaki Yuto, Kiuchi Saki, Tokiyoshi Kanako, Buyantogtokh Bujinlkham, Kurata Misaki, KvasniÄka AleÅ¡, Takeda Ushio, Uchino Haruki, Hasegawa Mayu, Miyamoto Junki, Tanabe Kana, Takeda Shigenori, Mori Tetsuya, Kumakubo Ryota, Tanaka Tsuyoshi, Yoshino Tomoko, Okamoto Mami, Takahashi Hidenori, Arita Makoto, Tsugawa Hiroshi
| 期刊: | Nature Communications | 影响因子: | 15.700 |
| 时间: | 2024 | 起止号: | 2024 Nov 28; 15(1):9903 |
| doi: | 10.1038/s41467-024-54137-w | ||
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