A Multifunctional Anchor for Multimodal Expansion Microscopy
Bibliographic record
Abstract
Abstract In situ imaging of biomolecular location with nanoscale resolution enables mapping of the building blocks of life throughout biological systems in normal and disease states. Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to ExM is the equipping of different kinds of molecule, with different kinds of anchoring moiety, so they can all be pulled apart by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables multiple kinds of molecules ( e.g., proteins and RNAs) to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and greatly reduces cost (by 2-10 fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this unified ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructure, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues, and can support the visualization of other kinds of biomolecular species as well. Thus, uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".