bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.09.10.675421

Lysosomal multi-omics reveals altered sphingolipid catabolism as driver of lysosomal dysfunction in the aging brain.

Abstract

Recent data indicate that lipid composition has profound influence on the brain function and that changes in lipid homeostasis affect brain aging and predisposition to neurodegenerative diseases. Lipids dynamically reside in multiple intracellular locations and their organellar distribution is important for specific interactions and biological function. During brain aging lipid changes have been specifically noted in lysosomes, but the identity of the accumulated lipids, their interactions with other biomolecules such as proteins, and their functional relevance have not been characterized. We used mass spectrometry (MS) to assess longitudinal changes in the lipidome and proteome of lysosomes isolated from the mouse cortex, from the age of 3- to 24-months. Our statistical and machine learning analyses identified two factors demonstrating predictive power for age and differences in both lipids and proteins. Of these, factor 1 was the best predictor of sample age. Factor 1 lipids with the highest feature importance included multiple species of hexosylceramides (HexCer) and their sulfonated derivatives, sulfatides (SHexCer), all of which increased with age. Increased factor 1 proteins included myelin proteins, select sphingolipid catabolism enzymes and proteins associated with lysosomal storage diseases. Our analyses suggested that mechanisms underlying factor 1 encompass the combination of an age-dependent increase in lysosomal delivery of myelin components and alterations in lysosomal sphingolipid catabolism favoring degradation of sphingomyelin over HexCer. The overall age-related lysosomal changes resembled those observed in lysosomal storage diseases, particularly Gaucher disease, where accumulation of HexCer species is associated with lysosomal dysfunction. To corroborate factor 1 predictions, we employed a combination of biochemical, imaging and flow cytometry approaches, which confirmed alterations in sphingolipid catabolism and lysosomal accumulation of myelin components. These changes were associated with age-related alteration in lysosomal morphology, lysosomal dysfunction and inhibition of autophagy in both neurons and microglia. Our findings indicate that factors contributing to lysosomal aging resemble those observed in lysosomal storage diseases and underscore the significance of organelle-specific analyses for dissecting mechanisms contributing to brain aging.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sarkar, C., Chen, Y., Nguyen, D. P., Weldemariam, M. M., Morel, Y., Tabari, A. A. M., Gorny, N., Pettyjohn-Robin, O., Hegdekar, N., Thapa, S., Kachi, S. A., Bustos, S., Zalesak-Kravec, S., Williams, C., Leahy, N., Chou, R. T., Kumar, S. D., McCracken, C., Blanpied, T. A., Karbowski, M. A., Jones, J. W., Kane, M. A., Cummings, M. A., Lipinski, M. M.. 2025-09-13. Lysosomal multi-omics reveals altered sphingolipid catabolism as driver of lysosomal dysfunction in the aging brain.. https://doi.org/10.1101/2025.09.10.675421

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗