Adaptation of Magnified Analysis of the Proteome for Excitatory Synaptic Proteins in Varied Samples and Evaluation of Cell Type-Specific Distributions
Bibliographic record
Abstract
Growing evidence suggests a remarkable diversity and complexity in the molecular composition of synapses, forming the basis for the brain to execute complex behaviors. Hence, there is considerable interest in visualizing the spatial distribution of such molecular diversity at individual synapses within intact brain circuits. Yet this task presents significant technical challenges. Expansion microscopy approaches have revolutionized our view of molecular anatomy. However, their use to study synapse-related questions outside of the labs developing them has been limited. Here we independently adapted a version of Magnified Analysis of the Proteome (MAP) and present a step-by-step protocol for visualizing over 40 synaptic proteins in brain circuits. Surprisingly, our findings show that the advantage of MAP over conventional immunolabeling was primarily due to improved antigen recognition and secondarily physical expansion. Furthermore, we demonstrated the versatile use of MAP in brains perfused with paraformaldehyde or fresh-fixed with formalin and in formalin-fixed paraffin-embedded tissue. These tests expand the potential applications of MAP to combinations with slice electrophysiology or clinical pathology specimens. Using male and female mice expressing YFP-ChR2 exclusively in interneurons, we revealed a distinct composition of AMPA and NMDA receptors and Shank family members at synapses on hippocampal interneurons versus on pyramidal neurons. Quantitative single synapse analyses yielded comprehensive cell type distributions of synaptic proteins and their relationships. These findings exemplify the value of the versatile adapted MAP procedure presented here as an accessible tool for the broad neuroscience community to unravel the complexity of the "synaptome" across brain circuits and disease states.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".