Two-Color Spatial Cumulant Analysis Detects Heteromeric Interactions between Membrane Proteins
Bibliographic record
Abstract
Abstract Fluorescence fluctuation spectroscopy (FFS) can be used to measure the aggregation of fluorescently labeled molecules and is typically performed using time series data. Spatial intensity distribution analysis (SpIDA) and fluorescence moment image analysis (FMIA) are established tools for measuring molecular brightnesses from single-color images collected with laser scanning microscopes. We have extended these tools for analysis of two-color images to resolve heteromeric interactions between molecules labeled with spectrally distinct chromophores. We call these new methods two-color SpIDA (2c-SpIDA) and two-color spatial cumulant analysis (2c-SpCA). To implement these techniques on a hyperspectral imaging system, we developed a spectral shift filtering (SSF) technique to remove artifacts due to intrinsic crosstalk between detector bins. We determined that 2c-SpCA provides better resolution from samples containing multiple fluorescent species, hence this technique was carried forward to study images of living cells. We used fluorescent heterodimers labeled with EGFP and mApple to quantify the effects of resonance energy transfer and incomplete maturation of mApple on brightness measurements. We show that 2c-SpCA can detect the interaction between two components of trimeric G-protein complexes. Thus 2c-SpCA presents a robust and computationally expedient means of measuring heteromeric interactions in cellular environments. Statement of Significance Fluorescence fluctuation spectroscopy (FFS) techniques determine biophysical parameters from samples containing fluorescently labeled biomolecules by considering the statistical nature of fluorescent signals measured with photodetectors. The present study introduces two-color spatial cumulant analysis (2c-SpCA) to the canon of FFS techniques. 2c-SpCA analyzes pixel-value data of two-color images collected with laser scanning fluorescence microscopes. We show that 2c-SpCA can determine several biophysical parameters in living cells including Forster resonance energy transfer efficiency, the dark state fraction of fluorescent proteins, and heteromerization between distinctly labeled proteins. In comparison to existing techniques, 2c-SpCA requires very few image frames for analysis, minimal computations, and can be applied to images of fixed tissue samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".