The authors present a multimodal imaging workflow that combines gold-standard immunohistochemistry with gel-assisted mass spectrometry imaging to perform spatial lipidomics at single-cell resolution. The method is designed to preserve compatibility with antibody-based cell-type labeling while enabling in situ mass spectrometric measurement of native lipid compositions and distributions across tissue architecture.
The work addresses a recognized trade-off in spatial lipidomics between spatial resolution, sensitivity, and molecular coverage by integrating immunohistochemical identification of cell types with a gel-assisted platform for mass spectrometry imaging compatible with modern instrumentation.
The approach explicitly supports standard immunohistochemical labeling workflows and couples those labels to subsequent gel-assisted mass spectrometry imaging. This compatibility enables identification of specific cell types in intact tissue prior to or concurrent with acquisition of lipidomic data, permitting assignment of measured lipid signals to defined cellular populations at single-cell scale.
The authors state the method is intended for seamless integration with established antibody-based histology techniques and mass spectrometry imaging hardware, although the abstract does not provide experimental parameters or stepwise protocol details.
Intact brain tissue, and specifically the cerebellum, served as the testbed for method demonstration. The cerebellum provides distinct, well-characterized neuronal populations—such as Purkinje cells—and subregions that enable evaluation of cell-type- and subregion-specific lipid distributions. The authors used this tissue context to probe the method’s ability to map lipids across timepoints and anatomical compartments within a diseased brain.
The authors applied the multimodal workflow to tissue within the pathological context of a neurodegenerative lysosomal storage disorder. Using single-cell lipidomic profiling, they report the capacity to measure lipid accumulation within individual cells in situ and to distinguish lipid profiles of Purkinje cells from non-Purkinje cells.
Specifically, the study identified spatiotemporally distinct accumulation of particular glycosphingolipids and phospholipids within cell populations in the diseased cerebellum. The abstract summarizes differential accumulation by cell type and by disease timepoint, indicating that the technique can resolve changes linked to disease progression.
Beyond single-cell differences, the authors mapped lipid distributions across cerebellar subregions and across timepoints in disease progression. These spatial maps highlighted heterogeneous patterns of lipid accumulation that varied with both anatomical subregion and stage of disease, supporting the method’s use for localized molecular phenotyping within complex tissue architectures.
The abstract does not list specific lipid species beyond the general classes of glycosphingolipids and phospholipids, nor does it provide quantitative performance metrics, limits of detection, or coverage breadth; those details are expected to be in the full manuscript or supplementary materials.
Using unsupervised clustering of single-cell lipidomic profiles, the authors observed molecular divergence linked to both disease progression and cerebellar subregion. Clustering delineated groups of cells whose lipidomes separated healthy and neurodegenerative states, suggesting the approach can reveal emergent molecular phenotypes at single-cell resolution in situ.
This analytic layer indicates the method’s potential for discovery of cell-type- and region-specific lipid alterations that track with pathology, although the abstract does not provide algorithmic details, cluster metrics, or validation statistics.
This manuscript is posted as a preprint on bioRxiv and has not undergone peer review. The corresponding DOI is provided in the source. A competing interest is disclosed: R.G. is a co-inventor on multiple patents related to expansion microscopy; the other authors declare no competing interests.
Reported funders include the U.S. National Institutes of Health (several grant identifiers listed in the source abstract), the U.S. National Science Foundation (CAREER award), the McKnight Foundation, the Kinship Foundation (Searle Scholars Program), and startup/institutional support from the University of Illinois Chicago.
The abstract and metadata include the posting date and citation for the preprint; full experimental methods, data, and supplementary details are available from the preprint source but are not contained within this abstract summary.