Nanoscale Resolution Imaging of Whole Mouse Embryos Using Expansion Microscopy
Bibliographic record
Abstract
Nanoscale imaging of whole vertebrates is essential for the systematic understanding of human diseases, yet this goal has not yet been achieved. Expansion microscopy (ExM) is an attractive option for accomplishing this aim; however, the expansion of even mouse embryos at mid- and late-developmental stages, which have fewer calcified body parts than adult mice, is yet to be demonstrated due to the challenges of expanding calcified tissues. Here, we introduce a state-of-the-art ExM technique, termed whole-body ExM, that utilizes cyclic digestion. This technique allows for the super-resolution, volumetric imaging of anatomical structures, proteins, and endogenous fluorescent proteins (FPs) within embryonic and neonatal mice by expanding them 4-fold. The key feature of whole-body ExM is the alternating application of two enzyme compositions repeated multiple times. Through the simple repetition of this digestion process with an increasing number of cycles, mouse embryos of various stages up to E18.5, and even neonatal mice, which display a dramatic difference in the content of calcified tissues compared to embryos, are expanded without further laborious optimization. Furthermore, the whole-body ExM's ability to retain FP signals allows the visualization of various neuronal structures in transgenic mice. Whole-body ExM could facilitate studies of molecular changes in various vertebrates.
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) | 0.000 | 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 | 0.001 |
| 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".