SOGO-SOFI, light-modulated super-resolution optical fluctuation imaging using only 20 raw frames for high-fidelity reconstruction
Bibliographic record
Abstract
Taking advantage of the stochastic photoswitching of genetically encodable reversibly photoswitchable fluorescent proteins (RSFPs), super-resolution optical fluctuation imaging (SOFI) and its variant photochromic stochastic optical fluctuation imaging (pcSOFI) are valuable tools for wide field super-resolution (SR) imaging. Live-cell (pc)SOFI, which requires a small number of original frames to reconstruct an SR image, is prone to structural discontinuity artifacts and low spatial resolution. Herein, we developed a repeated synchronized on- and gradually off-switching SOFI (SOGO-SOFI) that maximized the photoswitching frequency of RSFPs by light modulation and required only 20 frames for high-quality reconstruction. Live-cell SOGO-SOFI imaging of the endoplasmic reticulum (ER) exhibited 10 times higher temporal resolution (100 fps) and fewer artifacts than pcSOFI. Moreover, a combination of SOGO-SOFI with Airyscan further increased the image contrast and the resolution of Airyscan by a factor of 1.5 from 140 nm to 91 nm. The capabilities of SOGO-SOFI were further demonstrated by dual-color imaging of nucleolar proteins in mammalian cells and deep imaging of ER structures in thick brain slices (20.6 µm).
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".