Imaging of translocator protein upregulation is selective for pro‐inflammatory polarized astrocytes and microglia
Bibliographic record
Abstract
Abstract Translocator protein (TSPO) expression is increased in activated glia, and has been used as a marker of neuroinflammation in PET imaging. However, the extent to which TSPO upregulation reflects a pro‐ or anti‐inflammatory phenotype remains unclear. Our aim was to determine whether TSPO upregulation in astrocytes and microglia/macrophages is limited to a specific inflammatory phenotype. TSPO upregulation was assessed by flow cytometry in cultured astrocytes, microglia, and macrophages stimulated with lipopolysaccharide (LPS), tumor necrosis factor (TNF), or interleukin‐4 (Il‐4). Subsequently, mice were injected intracerebrally with either a TNF‐inducing adenovirus (AdTNF) or IL‐4. Glial expression of TSPO and pro‐/anti‐inflammatory markers was assessed by immunohistochemistry/fluorescence and flow cytometry. Finally, AdTNF or IL‐4 injected mice underwent PET imaging with injection of the TSPO radioligand 18 F‐DPA‐713, followed by ex vivo autoradiography. TSPO expression was significantly increased in pro‐inflammatory microglia/macrophages and astrocytes both in vitro, and in vivo after AdTNF injection ( p < .001 vs. control hemisphere), determined both histologically and by FACS. Both PET imaging and autoradiography revealed a significant ( p < .001) increase in 18 F‐DPA‐713 binding in the ipsilateral hemisphere of AdTNF‐injected mice. In contrast, no increase in either TSPO expression assessed histologically and by FACS, or ligand binding by PET/autoradiography was observed after IL‐4 injection. Taken together, these results suggest that TSPO imaging specifically reveals the pro‐inflammatory population of activated glial cells in the brain in response to inflammatory stimuli. Since the inflammatory phenotype of glial cells is critical to their role in neurological disease, these findings may enhance the utility and application of TSPO imaging.
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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".