Memory engram synapse 3D molecular architecture visualized by cryoCLEM-guided cryoET
Bibliographic record
Abstract
Memory is incorporated into the brain as physicochemical changes to engram cells. These are neuronal populations that form complex neuroanatomical circuits, are modified by experiences to store information, and allow for memory recall. At the molecular level, learning modifies synaptic communication to rewire engram circuits, a mechanism known as synaptic plasticity. However, despite its functional role on memory formation, the 3D molecular architecture of synapses within engram circuits is unknown. Here, we demonstrate the use of engram labelling technology and cryogenic correlated light and electron microscopy (cryoCLEM)-guided cryogenic electron tomography (cryoET) to visualize the in-tissue 3D molecular architecture of engram synapses of a contextual fear memory within the CA1 region of the mouse hippocampus. Engram cells exhibited structural diversity of macromolecular constituents and organelles in both pre- and postsynaptic compartments and within the synaptic cleft, including in clusters of membrane proteins, synaptic vesicle occupancy, and F-actin copy number. This "engram to tomogram" approach, harnessing in vivo functional neuroscience and structural biology, provides a methodological framework for testing fundamental molecular plasticity mechanisms within engram circuits during memory encoding, storage and recall.
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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".