A Simple and Efficient Method for Visualizing Individual Cells in vivo by Cre-Mediated Single-Cell Labeling by Electroporation (CREMSCLE)
Bibliographic record
Abstract
Efficient methods for visualizing cell morphology in the intact animal are of great benefit to the study of structural development in the nervous system. Quantitative analysis of the complex arborization patterns of brain cells informs cell-type classification, dissection of neuronal circuit wiring, and the elucidation of growth and plasticity mechanisms. Time-lapse single-cell morphological analysis requires labelling and imaging of single cells in situ without contamination from the ramified processes of other nearby cells. Here, using the Xenopus laevis optic tectum as a model system, we describe CRE-Mediated Single-Cell Labeling by Electroporation (CREMSCLE), a technique we developed based on bulk co-electroporation of Cre-dependent inducible expression vectors, together with very low concentrations of plasmid encoding Cre recombinase. This method offers efficient, sparse labeling in any brain area where bulk electroporation is possible. Unlike juxtacellular single-cell electroporation methods, CREMSCLE relies exclusively on the bulk electroporation technique, circumventing the need to precisely position a micropipette next to the target cell. Compared with viral transduction methods, it is fast and safe, generating high levels of expression within 24 h of introducing non-infectious plasmid DNA. In addition to increased efficiency of single-cell labelling, we confirm that CREMSCLE also allows for efficient co-expression of multiple gene products in the same cell. Furthermore, we demonstrate that this method is particularly well-suited for labeling immature neurons to follow their maturation over time. This approach therefore lends itself well to time-lapse morphological studies, particularly in the context of early neuronal development and under conditions that prevent more difficult visualized juxtacellular electroporation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".