Description
MiLiMA (Mouse Integrated Lipidomics and Metabolomics Atlas) is an interactive resource designed to investigate how diet and microbiome-modulating interventions shape metabolism across multiple mouse tissues. The atlas integrates untargeted liquid chromatography–mass spectrometry (LC–MS)-based metabolomics and lipidomics with 16S rRNA microbiome profiling to provide a comprehensive overview of metabolic alterations induced by nutritional and microbial perturbations.
Male C57BL/6J mice were maintained on either a standard chow diet or a high-fat diet (HFD). Each dietary group was further divided into four intervention groups: control (CTRL), antibiotic treatment (ATB), probiotic treatment (PBIO), and combined antibiotic and probiotic treatment (ATB+PBIO) (n = 8 per group). Plasma, liver, epididymal white adipose tissue (eWAT), feces, and cecum content were collected for metabolomics and lipidomics analyses, while feces and cecum content were additionally analyzed by 16S rRNA sequencing.
Methodology
Samples were extracted using the LIMeX biphasic extraction workflow, generating separate fractions for complex lipids and polar metabolites. Untargeted metabolomics and lipidomics were performed using complementary LC–MS platforms based on reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), covering both positive and negative electrospray ionization modes. In addition, targeted LC–MS analysis of derivatized short-chain fatty acids (SCFAs) was performed. Microbiome composition was characterized using 16S rRNA gene sequencing.
Data acquisition
The MiLiMA atlas comprises 320 biological samples collected from five matrices together with pooled quality control (QC) samples, superior QC (SQC) samples, method blanks, and reference materials. Untargeted analyses were performed using six complementary LC–MS workflows covering complex lipids, polar metabolites, and medium-polarity metabolites, together with a targeted assay for SCFAs. Raw data were acquired in data-dependent acquisition (DDA) mode on high-resolution mass spectrometer (Q Exactive Plus).
Data processing
Raw LC–MS data were processed using MS-DIAL, including peak detection, alignment, deconvolution, and metabolite annotation based on accurate mass, retention time, and MS/MS spectra. Signal drift was corrected using LOESS based on pooled superior quality control (SQC) samples, followed by normalization according to sample amount or volume. Metabolites and lipids were manually reviewed prior to inclusion in the atlas. Statistical analyses available through the web application include univariate tests, ANOVA, PCA, and PLS-DA, providing an interactive framework for exploring metabolic responses to diet and microbiome-modulating interventions.