Lipid organization and turnover in the plasma membrane of human differentiating neural progenitor cells revealed by time-of-flight secondary ion mass spectrometry imaging
Bibliographic record
Abstract
Membrane lipids have been known to influence multiple signalling and cellular processes. Dysregulation of lipids at the neuronal membrane is connected to a significant alteration of the brain function and morphology, leading to brain diseases and neurodegeneration. Understanding the lipid composition and turnover of neuronal membrane will provide a significant insight into the molecular events underlying the regulatory effects of these biomolecules in a neuronal system. In this study, we aimed to characterize the composition and turnover of the plasma membrane lipids in human neural progenitor cells (NPCs) at an early differentiation stage into midbrain neurons using ToF-SIMS imaging. Lipid composition of the native plasma membrane was explored, followed by an examination of the lipid turnover using different isotopically labelled lipid precursors, including 13C-choline, 13C-lauric acid, 15N-linoleic, and 13C-stearic. Our results showed that differentiating NPCs contain a high abundance of ceramides, glycerophosphoserines, neutral glycosphingolipids, diradylglycerols, and glycerophosphocholines at the plasma membrane. In addition, different precursors were found to incorporate into different membrane lipids which are specific for the short- or long-carbon chains, and the unsaturation or saturation stage of the precursors. The lipid structure of neuronal membrane reflects the differentiation status of NPCs, and it can be altered significantly using a particular lipid precursor. Our study illustrates a potential of ToF-SIMS imaging to study native plasma membrane lipids and elucidate complex cellular processes by providing molecular -rich information at a single cell level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".