In vivo single-cell gene editing using RNA electroporation reveals sequential adaptation of cortical neurons to excitatory-inhibitory imbalance
Bibliographic record
Abstract
Abstract The balance between excitatory and inhibitory neurotransmission is fundamental for normal brain function, yet the adaptation of individual neurons to disrupted excitatory-inhibitory balance is not well understood. We developed highly efficient, in vivo RNA electroporation-based single-cell gene editing to investigate neuronal responses to loss of fast inhibition. Using CRISPR-Cas9 components delivered as RNA, we knocked out GABA-A receptor β subunits in individual layer 2/3 cortical neurons in mouse visual cortex, eliminating fast inhibition. In vivo patch-clamp recordings revealed that cortical neurons adapted to inhibition loss through two sequential mechanisms: a transient reduction of excitatory synaptic input, followed by intrinsic membrane property changes that decreased input resistance. This sequential adaptation program ultimately prevented target neurons from contributing spikes to the cortical network. Our RNA-based single-cell gene editing approach enables investigation of cellular responses independent of network effects, providing new insights into neuronal homeostasis and gene function in individual cells in vivo.
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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 | 0.000 | 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) | 0.001 | 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".