The lipidomic architecture of the mouse brain
Posted on: 9 February 2026
Preprint posted on 14 October 2025
Lipids get the space they deserve: Fusar Bassini et al. pioneer a MALDI-MS imaging and computational analysis, bring membrane lipids and their spatial organization to the spotlight and provide evidence of their, previously underestimated importance.
Selected by CRM UoE Journal Club, Garyfallia Gouna, Rana Fetit
Categories: bioinformatics, cell biology, neuroscience
Graphical abstract created by Rana Fetit.
Background
The brain is the second most lipid-enriched organ in the human body after adipose tissue and is constituted of several classes of lipids, such as phospholipids, sphingomyelin and cholesterol.
Lipids play major roles in brain homeostasis, spanning from integral cell membrane components to energy storage and signaling, both intra- and intercellularly. It is becoming increasingly
apparent that, in both health and disease, the brain lipidome is altered, with several studies describing the lipid signature of anatomical brain regions and cell types. However, our understanding of lipid distribution at the subcellular level, their implications in cell function and overall brain connectivity are very limited. Fusar Bassini et al. addressed this gap using a state-of-the-art spatial lipidomic approach. They map the mouse brain lipidome to functional anatomy, exploring cell type specificity, and provide novel insight into the connectome.
Key Findings
1. A 3D lipidomic atlas of the mouse brain
Using state-of-the-art MALDI-MS imaging and a suite of bioinformatic analysis tools, Fusar Bassini et al. mapped the spatial organization of 172 nonredundant, high confidence membrane lipid
species across coronal sections from the whole mouse adult brain, with a raster interval of 25μm. Both sexes were included. This spatial lipid information was subsequently mapped onto the Allen Brain Common Coordinate Framework, creating the Lipid-Brain-Atlas, aligned to anatomical, quasi-cellular and metabolic information.
2. Lipizones reveal a new axis of brain architecture
The authors identified 539 lipid clusters, termed “lipizones”. Lipizones were hierarchically clustered in gray and white matter/mixed-rich territories and defined anatomical brain structures,
in contrast to individual lipids. While lipizones often represented distinct brain niches, some lipizones spanned across anatomical boundaries. Interestingly, lipid input alone could predict
specific brain regions, highlighting that lipizones carry biological meaning.
3. Brain lipids are irreducible to the transcriptome
By integrating online datasets, spatial atlases and bioinformatic tools, Fusar Bassini et al. concluded that individual enzyme-gene transcription cannot determine the lipidome in a 1:enzyme-to-1:lipid fashion. However, when applying a nonlinear regression strategy, they showed that the full transcriptome, strongly representing cell types, could partially predict lipid signature (56% of lipid variance). These results suggest that, although the brain lipidome is not determined per se by transcript levels, the full transcriptome can partially predict spatial lipid distribution, coupled with cell-specific gene signatures.
4. Lipizones capture the bodies and terminals of cell types
Building on the transcriptome-to-lipidome observations, the authors discovered that 76% of the identified lipizones significantly mark cell type regions. Utilizing the Allen cell locations and
connectivity streams, they showed that lipizones match cell type specific bodies and terminals and map separated brain regions with established functional connectivity. Therefore, the spatial
lipidomic profile can capture single-cell type distant projections along with cell bodies, a characteristic that spatial transcriptomic lacks. Strikingly, lipizones uncovered new spatial organization within cortical areas, suggesting structural and functional connectivity absent from Allen cell locations.
5. Structured diversity of myelinated regions
The new spatial organization revealed by lipizones prompted the authors to investigate highly myelinated brain regions, identifying regionally structured lipid diversity in white matter. Their
findings suggest that the lipid composition of myelin varies across areas, with this regional heterogeneity driven by the lipidome of oligodendrocytes and variable degrees of myelination rather than axonal lipid composition.
6.The ventricular system is metabolically zonated
Next, Fusar Bassini et al. explored another highly metabolic region, the brain ventricles, further clustering the identified lipizones across ventricular/periventricular regions. This additional
clustering revealed distinct lipid groups lining ventricular walls and two specific lipid clusters lining the choroid plexus, providing evidence for matched cell-type heterogeneity with functionality and anatomical similarity across the rostrocaudal axis. Surprisingly, several sphingomyelin species were highly enriched in the ventricles, while 6 lipids preferentially line the ventricular walls, correlating with adult neurogenesis. Altogether, these data suggest that distinct lipid profiles can drive ventricular system metabolism.
7.Pregnancy re-organizes the brain lipidome
Lastly, the authors investigated how sex and physiological challenges, such as pregnancy, shape brain lipidome. Differences in sex had no major effects on the lipidome, although a ventricular
supertype with enrichment of 151 lipids and moderate increase of phosphatidylcholines was apparent in females. Pregnancy, on the other hand, resulted in major lipid remodeling, particularly in the outer cortex and myelin-rich regions, showing increased levels of specific myelin-associated lipids (HexCer 42:2 and sphingolipids) as well as region-specific remodeling which extended to the ventricles, hindbrain, cerebellum and hypothalamus. Collectively, their findings highlight how the lipidome is dynamic, responds to regional regulation, and can adapt to physiological states.
Why do we like this research?
This impressive work yields a significant resource for the lipid community, and highlights the importance of lipids to other research fields. The Mouse Lipid-Brain-Atlas is an easily accessible
website for researchers to explore lipid patterns, their spatial clusters and relevant metabolic and functional organization. The integration with publicly available spatial transcriptomic and cell
lipidomic databases, along with the proof that sole lipid input can predict brain anatomy, underscores the biological importance of brain lipids. The authors employed cutting-edge computational analysis and prediction tools, showing that lipid input can be used as a label-free approach from anatomical connectivity to brain functionality. The inclusion of both sexes, along
with pregnancy as a physiological challenge, further gives weight to the key findings of this study, showcasing a holistic, inclusive approach in identifying the brain’s lipid architecture and its biological relevance. Next steps can include the identification of lipizones in preclinical disease models to compare with the “physiological” lipizones signature.
Questions to the authors
1. Although this work revealed a connection between transcriptionally defined cell types and lipizones, their findings show that lipid distributions cannot be reliably inferred by gene expression. What is the regulatory level which determines the lipid signature of a brain region and can we define it?
2. Why were positive ion measurements preferred compared to negative ion measurements for the MALDI-MS imaging experimental acquisition?
Abbreviations
MALDI-MS: matrix-assisted laser desorption/ionization mass spectrometry
References
1. Yoon, J.H, Seo, Y. et al. Brain lipidomics: From functional landscape to clinical significance. Sci. adv. 8, eadc9317 (2022). https://doi.org/10.1126/sciadv.adc9317
2. Harayama, T., Riezman, H. Understanding the diversity of membrane lipid composition. Nat Rev Mol Cell Biol 19, 281–296 (2018). https://doi.org/10.1038/nrm.2017.138
3. Osetrova, M., Tkachev, A., Mair, W. et al. Lipidome atlas of the adult human brain. Nat Commun 15, 4455 (2024). https://doi.org/10.1038/s41467-024-48734-y
4. Fitzner, D. et al. Cell-type- and brain-region-resolved mouse brain lipidome. Cell Rep. 32, 108132 (2020) https://doi.org/10.1016/j.celrep.2020.108132
5. Feringa, F.M., Koppes-den Hertog, S.J., Wang, L.Y. et al. The Neurolipid Atlas: a lipidomics resource for neurodegenerative diseases. Nat Metab 7, 2142–2164 (2025). https://doi.org/10.1038/s42255-025-01365-z
6. Wang, Q., Ding, SL., Li, Y. et al. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell 181, 936–953 (2020). https://doi.org/10.1016/j.cell.2020.04.007
doi: https://doi.org/10.1242/prelights.42883
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