Identification of Vulnerable Interneuron Subtypes in 15q13.3 Microdeletion Syndrome Using Single-Cell Transcriptomics
Bibliographic record
Abstract
Background A number of rare copy number variants (CNVs) have been linked to neurodevelopmental disorders. However, because CNVs encompass many genes, it is often difficult to identify the mechanisms that lead to developmental perturbations. Methods We used 15q13.3 microdeletion to propose and validate a novel strategy to predict the impact of CNV genes on brain development that could further guide functional studies. We analyzed single-cell transcriptomics datasets containing cortical interneurons to identify their developmental vulnerability to 15q13.3 microdeletion, which was validated in mouse models. Results We found that Klf13 —but not other 15q13.3 genes—is expressed by precursors and neuroblasts in the medial and caudal ganglionic eminences during development, with a peak of expression at embryonic day (E)13.5 and E18.5, respectively. In contrast, in the adult mouse brain, Klf13 expression is negligible. Using Df(h15q13.3)/+ and Klf13 +/− embryos, we observed a precursor subtype-specific impairment in proliferation in the medial ganglionic eminence and caudal ganglionic eminence at E13.5 and E17.5, respectively, corresponding to vulnerability predicted by Klf13 expression patterns. Finally, Klf13 +/− mice showed a layer-specific decrease in parvalbumin and somatostatin cortical interneurons accompanied by changes in locomotor and anxiety-related behavior. Conclusions We show that the impact of 15q13.3 microdeletion on precursor proliferation is grounded in a reduction in Klf13 expression. The lack of Klf13 in Df(h15q13.3)/+ cortex might be the major reason for perturbed density of cortical interneurons. Thus, the behavioral defects seen in 15q13.3 microdeletion could stem from a developmental perturbation owing to selective vulnerability of cortical interneurons during sensitive stages of their development.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".