ArticleData in brief2025
Targeted lipidomics dataset of central nervous system and plasma from mice with experimental autoimmune encephalomyelitis.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This dataset was generated from a preclinical study that used the relapsing-remitting experimental autoimmune encephalomyelitis (EAE) model in SJL/J mice to examine lipid signalling in neuroinflammation. The study examined how the reference compounds FTY720 (fingolimod, 0.5 mg/kg/day) affected the autotaxin/lysophosphatidic acid (ATX/LPA) axis and related lipid mediators. The mice were divided into three groups: control (no EAE), EAE, and EAE plus fingolimod. Tissue samples were collected from the plasma, cerebellum, hippocampus, and prefrontal cortex, resulting in 26 biological samples. Targeted lipidomics was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify 62 lipid species, including lysophosphatidic acids, ceramides, sphingoid bases, and endocannabinoids. The dataset is provided in both raw and imputed formats, along with comprehensive sample-level metadata. The data are organized into three comma-separated values (CSV) files: (1) the original quantitative lipidomics data matrix with missing values, (2) a log₁₀-transformed imputed dataset with missing values addressed using random forest imputation, and (3) a metadata file detailing the characteristics of the samples, group assignments, and tissue type. Standardized variable naming and detailed metadata facilitate cross-referencing and integration with other datasets. This resource enables comparative analyses of lipid profiles across tissues and treatment groups. It supports statistical and machine learning applications and enables the evaluation of data augmentation strategies, including statistical and generative AI approaches. This dataset can be reused in studies of neuroinflammation, lipid signalling, and biomarker discovery, as well as in the development of methods for computational biology and omics data analysis.
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