LipidIN: a comprehensive repository for flash platform-independent annotation and reverse lipidomics

Hao Xu, Tianhang Jiang, Yuxiang Lin, Lei Zhang, Huan Yang, Xiaoyun Huang, Ridong Mao, Zhu Yang, Changchun Zeng, Shuang Zhao, Lijun Di, Wenbin Zhang, Jun Zeng (), Zongwei Cai () and Shu-Hai Lin ()
Additional contact information
Hao Xu: Xiamen University
Tianhang Jiang: Xiamen University
Yuxiang Lin: Fujian Medical University Union Hospital
Lei Zhang: Xiamen University
Huan Yang: Xiamen University
Xiaoyun Huang: Jimei University
Ridong Mao: Xiamen University
Zhu Yang: Hong Kong Baptist University
Changchun Zeng: Shenzhen Longhua District Central Hospital
Shuang Zhao: Xiamen Meliomics Co., Ltd.
Lijun Di: University of Macau
Wenbin Zhang: Fourth Military Medical University
Jun Zeng: Jimei University
Zongwei Cai: Hong Kong Baptist University
Shu-Hai Lin: Xiamen University

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Improving annotation accuracy, coverage, speed and depth of lipid profiles remains a significant challenge in traditional lipid annotation. We introduce LipidIN, an advanced framework designed for flash platform-independent annotation. LipidIN features a 168.5-million lipid fragmentation hierarchical library that encompasses all potential chain compositions and carbon-carbon double bond locations. The expeditious querying module achieves speeds exceeding one hundred billion queries per second across all mass spectral libraries. The lipid categories intelligence model is developed using three relative retention time rules, reducing false positive annotations and predicting unannotated lipids with a 5.7% estimated false discovery rate, covering 8923 lipids cross various species. More importantly, LipidIN integrates a Wide-spectrum Modeling Yield network for regenerating lipid fragment fingerprints to further improve accuracy and coverage with a 20% estimated recall boosting. We further demonstrate the utility of LipidIN in multiple tasks for lipid annotation and biomarker discovery in clinical cohorts.

Date: 2025
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DOI: 10.1038/s41467-025-59683-5

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