Cryo-SOFI enabling low-dose super-resolution correlative light and electron cryo-microscopy
Why this work is in the frame
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Bibliographic record
Abstract
Correlative light and electron cryo-microscopy (cryo-CLEM) combines information from the specific labeling of fluorescence cryo-microscopy (cryo-FM) with the high resolution in environmental context of electron cryo-microscopy (cryo-EM). Exploiting super-resolution methods for cryo-FM is advantageous, as it enables the identification of rare events within the environmental background of cryo-EM at a sensitivity and resolution beyond that of conventional methods. However, due to the need for relatively high laser intensities, current super-resolution cryo-CLEM methods require cryo-protectants or support films which can severely reduce image quality in cryo-EM and are not compatible with many samples, such as mammalian cells. Here, we introduce cryogenic super-resolution optical fluctuation imaging (cryo-SOFI), a low-dose super-resolution imaging scheme based on the SOFI principle. As cryo-SOFI does not require special sample preparation, it is fully compatible with conventional cryo-EM specimens, and importantly, it does not affect the quality of cryo-EM imaging. By applying cryo-SOFI to a variety of biological application examples, we demonstrate resolutions up to ∼135 nm, an improvement of up to three times compared with conventional cryo-FM, while maintaining the specimen in a vitrified state for subsequent cryo-EM. Cryo-SOFI presents a general solution to the problem of specimen devitrification in super-resolution cryo-CLEM. It does not require a complex optical setup and can easily be implemented in any existing cryo-FM system.
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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