Localising elements in single-particle reconstructions by REEL-EM: Reconstructed Electron Energy-Loss - Elemental Mapping
Bibliographic record
Abstract
Abstract For structures determined by single particle cryo-EM, no technique currently exists for mapping elements to defined locations, leading to errors in the assignment of metals and other ions, cofactors, substrates, inhibitors, and lipids that play essential roles in activity and regulation. Elemental mapping in the electron microscope is well established for dose-tolerant samples but is challenging for biological samples, especially in a cryo-preserved state. Here, we combine electron energy-loss spectroscopy (EELS) with single-particle image processing to allow elemental mapping in cryo-preserved macromolecular complexes. Proof-of-principle data show that our method, REEL-EM, allows 3D reconstruction of EELS data, such that a high total electron dose is accumulated across many copies of a complex. Working with two test samples, we demonstrate that we can reliably localise abundant elements. We discuss the current limitations of the method and potential future developments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".