Revealing protein oligomerization and densities in situ using spatial intensity distribution analysis
Why this work is in the frame
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Bibliographic record
Abstract
Measuring protein interactions is key to understanding cell signaling mechanisms, but quantitative analysis of these interactions in situ has remained a major challenge. Here, we present spatial intensity distribution analysis (SpIDA), an analysis technique for image data obtained using standard fluorescence microscopy. SpIDA directly measures fluorescent macromolecule densities and oligomerization states sampled within single images. The method is based on fitting intensity histograms calculated from images to obtain density maps of fluorescent molecules and their quantal brightness. Because spatial distributions are acquired by imaging, SpIDA can be applied to the analysis of images of chemically fixed tissue as well as live cells. However, the technique does not rely on spatial correlations, freeing it from biases caused by subcellular compartmentalization and heterogeneity within tissue samples. Analysis of computer-based simulations and immunocytochemically stained GABA(B) receptors in spinal cord samples shows that the approach yields accurate measurements over a broader range of densities than established procedures. SpIDA is applicable to sampling within small areas (6 μm(2)) and reveals the presence of monomers and dimers with single-dye labeling. Finally, using GFP-tagged receptor subunits, we show that SpIDA can resolve dynamic changes in receptor oligomerization in live cells. The advantages and greater versatility of SpIDA over current techniques open the door to quantificative studies of protein interactions in native tissue using standard fluorescence microscopy.
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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 it