Label‐free quantitative proteomic analysis of extracellular vesicles released from fibroblasts derived from patients with spinal muscular atrophy
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive disorder that represents a significant cause of infant mortality. SMA is characterized by reduced levels of the Survival Motor Neuron protein leading to the loss of alpha motor neurons in the spinal cord and brain stem as well as defects in peripheral tissues such as skeletal muscle and liver. With progress in promising therapies such as antisense oligonucleotide and gene replacement, there remains a need to better understand disease subtypes and develop biomarkers for improved diagnostics and therapeutic monitoring. In this study, we have examined the utility of extracellular vesicles as a source of biomarker discovery in patient-derived fibroblast cells. Proteome examination utilizing data-independent acquisition and ion mobility mass spectrometry identified 684 protein groups present in all biological replicates tested. Label-free quantitative analysis identified 116 statistically significant protein alterations compared to control cells, including several known SMA biomarkers. Protein level differences were also observed in regulators of Wnt signaling and Cajal bodies. Finally, levels of insulin growth factor binding protein-3 were validated as being significantly higher in extracellular vesicles isolated from SMA cells. We conclude that extracellular vesicles represent a promising source for SMA biomarker discovery as well as a relevant constituent for advancing our understanding of SMA pathophysiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".