A self-internalizing mitochondrial TSPO targeting imaging probe for fluorescence, MRI and EM
Bibliographic record
Abstract
Advances in probes for cellular imaging have driven discoveries in biology and medicine. Primarily, antibodies and small molecules have been made for contrast enhancement of specific proteins. The development of new dendrimer-based tools offers opportunities to tune cellular internalization and targeting, image multiple modalities in the same molecule and explore therapeutics. The translocator protein (TSPO) offers an ideal target to develop dendrimer tools because it is well characterized and implicated in a number of disease states. The TSPO-targeted dendrimers reported here, primarily ClPhIQ-PAMAM-Gd-Liss, are cell membrane permeable nanoparticles that enable labeling of TSPO and provide contrast in fluorescence, electron microscopy and magnetic resonance imaging. The molecular binding affinity for TSPO was found to be 0.51 μM, 3 times greater than the monomeric agents previously demonstrated in our laboratory. The relaxivity per Gd3+ of the ClPhIQ23-PAMAM-Gd18 dendrimer was 7.7 and 8.0 mM−1 s−1 for r1 and r2 respectively, approximately double that of the clinically used monomeric Gd3+ chelates. In vitro studies confirmed molecular selectively for labeling TSPO in the mitochondria of C6 rat glioma and MDA-MB-231 cell lines. Fluorescence co-registration with Mitotracker Green® and increased contrast of osmium-staining in electron microscopy confirmed mitochondrial labeling of these TSPO-targeted agents. Taken collectively these experiments demonstrate the versatility of conjugation of our PAMAM dendrimeric chemistry to allow multi-modality agents to be prepared. These agents target organelles and use complementary imaging modalities in vitro, potentially allowing disease mechanism studies with high sensitivity and high resolution techniques.
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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) | 0.001 | 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".