Antibody-Based PET Imaging of Misfolded Superoxide Dismutase 1 in an Amyotrophic Lateral Sclerosis Mouse Model
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease characterized by motor neuron loss in the motor cortex, brain stem, and spinal cord. Mutations in the superoxide dismutase 1 (SOD1) gene, resulting in misfolding of its protein product, are a common cause of ALS. Currently, there is no approved ALS diagnostic tool. Here, we present the development of a PET radiotracer, [89Zr]Zr-desferoxamine (DFO)-α-miSOD1, targeting selectively misfolded SOD1 (misSOD1). Methods: DFO-α-miSOD1 was prepared by conjugating α-miSOD1 antibody with DFO and labeled with 89Zr. A longitudinal imaging study was performed to identify the optimal mouse age and time after administration of [89Zr]Zr-DFO-α-miSOD1 for the detection of misSOD1 aggregation in transgenic mice overexpressing misSOD1 and in wild-type mice. Subsets of mice were either coinjected with an excess of α-miSOD1 or imaged with deglycosylated [89Zr]Zr-DFO-α-miSOD1 to assess target specificity. The internal radiation dose for [89Zr]Zr-DFO-α-miSOD1 was estimated by extrapolating data from mouse biodistribution experiments. Results: Imaging with [89Zr]Zr-DFO-α-miSOD1 was optimal in 136-d-old transgenic mice on day 10 after administration. Significant accumulation of [89Zr]Zr-DFO-α-miSOD1 was detected in the spinal cord and cartilage of ALS transgenic mice compared with the wild-type mice (P = 0.01). The radiotracer accumulation is selective and blockable with an excess of α-miSOD1. Deglycosylated [89Zr]Zr-DFO-α-miSOD1 results in high-contrast detection of misSOD1 but is prone to aggregation. The dosimetry for [89Zr]Zr-DFO-α-miSOD1 is comparable to that for other 89Zr-based tracers currently used in humans. Conclusion: This work thus establishes that [89Zr]Zr-DFO-α-miSOD1 PET can detect misSOD1 in transgenic mice, paving the way for application in early diagnosis of ALS and therapeutic monitoring.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".