bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.09.26.754678

The functional neuronal lipotype is specified by activity and PS synthesis using dietary and lysosomal inputs

Abstract

Neurons maintain specialized membranes necessary for lifelong circuit function. While neuronal membranes thus contain unique lipidomes, how and why specific lipids become synaptically enriched remains mysterious. Here, we identify a conserved hallmark of mature neurons, phosphatidylserines containing omega-3 fatty acids (n-3 PS), which dynamically accumulate during synaptogenesis from Drosophila to humans. This neuronal lipotype depends on dietary n-3 and lysosomal catabolism mediated by Saposin, the phospholipase PLA2G15, and the lysophospholipid transporter spin/SPNS1. Reducing neuronal PS biosynthesis by targeting the PS synthase (Pss) caused lipidomic, synaptic, and behavioral deficits exacerbated by impaired glycerophospholipid catabolism. While dietary, lysosomal, and lipoprotein manipulations broadly altered n-3 levels, silencing developmental activity-templating neurons selectively reduced n-3 PS. Remarkably, pathogenic Pss variants lacking feedback inhibition similarly reduced n-3 PS and impaired circuit function. As n-3 PS is reduced during neurodegeneration but absent from iPSC-derived neurons, important relationships between lipotype and physiology may not be recapitulated in vitro.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lee, R. J., Rivas-Serna, I. M., Theisen, E. K., Bibo, A., Hardy, E. B., Cheng, W., Zhou, Z., Chavez-Rios, J. S., Forward, J. C., Pham, T. T., Thomas, C. I., Darehshouri, A., Mazurak, V., Clandinin, M. T., Vaughen, J.. 2026-10-01. The functional neuronal lipotype is specified by activity and PS synthesis using dietary and lysosomal inputs. https://doi.org/10.64898/2026.09.26.754678

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

KEEP EXPLORING

Related preprints

Chronic unpredictable stress induces negative cognitive bias in rats with sex-specific changes in neurogenesis and functional connectivity

Background: Females are more likely to be diagnosed with major depressive disorder (MDD) than males. Cognitive symptoms of MDD include negative cognitive bias, the perception of ambiguous situations as negative, which predicts MDD episode onset and severity. MDD is associated with changes to functional connectivity between regions involved in cognition, emotion, attention, and memory. Methods: Here we examined the impact of chronic unpredictable stress (CUS) on negative cognitive bias in male (n=18) and female (n=22) Sprague-Dawley rats. We assessed hippocampal neurogenesis and c-Fos expression in response to an ambiguous context across 15 regions of the frontal cortex, amygdala, nucleus accumbens, and hippocampus, including in newborn dentate gyrus (DG) neurons, to examine functional connectivity during cognitive bias testing. We conducted a graph-based network analysis and constructed linear models to predict freezing in the ambiguous context based on regional activation. Results: CUS-exposed rats froze more to the ambiguous context than non-CUS-exposed rats, regardless of sex. CUS decreased neurogenesis in the ventral DG of both sexes, and increased activation of new neurons in the dorsal DG of males only. In response to the ambiguous context, CUS males had an increase in c-Fos expression in the amygdala. CUS produced extensive, sex-specific network reorganization, decreasing global network efficiency in males but not in females. Freezing in the ambiguous context was predicted by activation of new neurons in combination with the ventral CA1 in opposing directions across sex. Conclusions: These findings suggest that sex-specific pathways drive the expression of CUS-induced negative cognitive bias, underscoring the necessity of sex-stratified preclinical and clinical research on negative cognitive bias in depression.

neuroscience↗

Fixel-wise Differences in White Matter Fiber-bundle Cross-section identified in Children who Stutter

Developmental stuttering has been repeatedly linked to decreased white matter integrity and structural connectivity differences in major white matter tracts. Prior diffusion-weighted MRI studies reporting such white matter structural differences involved voxel-averaged tensor measures (e.g., fractional anisotropy (FA)). A well-acknowledged limitation of this approach, however, has been that it cannot resolve distinct fiber populations within crossing-fiber regions. Fixel-based analysis addresses this limitation by quantifying individual fiber populations within a voxel and providing complementary measures of fiber density (FD), fiber-bundle cross-section (FC), and their combined measure (FDC).Here we applied Fixel-based analysis to diffusion MRI data from 171 children aged 3 - 13 years, including 84 children who stutter and 87 typically developing controls, to test whether white-matter differences associated with developmental stuttering were expressed as differences in FD, FC, and FDC and whether these measures show group-specific age-related patterns. We evaluated this at both the whole-brain level and within a priori white-matter tracts.FC, but not FD or FDC, showed significant Group and Group by Age effects, indicating that white-matter differences were primarily expressed in fiber-bundle macrostructure. Tract-of-interest analyses identified developmental differences in bilateral arcuate fasciculus FC that remained significant across tracts, with controls showing greater age-related increases in FC than children who stutter. Additional FC differences were observed across corticospinal and thalamocortical pathways, although these tract-wise effects were less robust to across-tract correction. These findings suggest that white-matter differences in developmental stuttering are prominently expressed in fiber-bundle macrostructure rather than fiber density and highlight divergent development of bilateral arcuate pathways. FBA provides fiber-specific information that complements conventional tensor-based diffusion measures and may improve characterization of white-matter development in childhood stuttering.

neuroscience↗

Bridging Sensory Extraction and Decision Dynamics: Face Pareidolia Emergent from Bottom-Up and Top-Down Interactions

Face pareidolia-the illusory perception of faces in everyday objects-provides a unique window into how the human brain resolves visual ambiguity. Whether pareidolia arises from bottom-up sensory processing or top-down, expectation-driven feedback, is currently debated. Here we recorded electroencephalography (EEG) while participants viewed rapid serial visual presentation (RSVP) streams of images, each of which was an object-to-face morph generated along a 21-image continuum via text-to-image diffusion models. Time-resolved representational similarity analyses revealed a clear spatiotemporal dissociation, in which early occipitotemporal areas tracked physical feature changes linearly, while later frontocentral channels collapsed this structural gradient into a sharp, sigmoidal profile reflecting discrete categorical decisions (face-like versus object-like). To characterise information transmission between these regions, we implemented a novel transformation-based representational connectivity analysis. These analyses uncovered an initial feedforward flow of information that transferred and transformed posterior linear traces into anterior sigmoidal representations. Crucially, partitioning the image continuum revealed that unambiguous stimuli (faces or objects) were predominantly resolved via feedforward information flow, whereas ambiguous pareidolic images recruited prolonged top-down feedback. Furthermore, perceptual decision sensitivity was biased by animacy priors and directly predicted individual behavioural reaction times during the task. Together, the findings help reconcile prior debate by establishing that face pareidolia is neither a feedforward process nor an isolated top-down projection, but an active, multi-stage recurrent process driven by feedforward feature extraction and top-down categorical resolution.

neuroscience↗