Detecting somatic variants in purified brain <scp>DNA</scp> obtained from surgically implanted depth electrodes in epilepsy
Bibliographic record
Abstract
OBJECTIVE: Somatic variants causing epilepsy are challenging to detect, as they are only present in a subset of brain cells (e.g., mosaic), resulting in low variant allele frequencies. Traditional methods relying on surgically resected brain tissue are limited to patients undergoing brain surgery. We developed an improved protocol to detect somatic variants using DNA from stereoelectroencephalographic (SEEG) depth electrodes, enabling access to a larger patient cohort and diverse brain regions. This protocol mitigates issues of contamination and low yields by purifying neuronal nuclei using fluorescence-activated nuclei sorting (FANS). METHODS: SEEG depth electrodes were collected upon extraction from 41 brain regions across 17 patients undergoing SEEG. Nuclei were isolated separately from depth electrodes in the affected brain regions (seizure onset zone) and the unaffected brain regions. Neuronal nuclei were isolated using FANS, and DNA was amplified using primary template amplification. Short tandem repeat (STR) analysis and postsequencing allelic imbalance assessment were used to evaluate sample integrity. High-quality amplified DNA samples from affected brain regions, patient-matched unaffected brain regions, and genomic DNA were subjected to whole exome sequencing (WES). A bioinformatic workflow was developed to reduce false positives and to accurately detect somatic variants in the affected brain region. RESULTS: Based on DNA yield and STR analysis, 14 SEEG-derived neuronal DNA samples (seven affected and seven unaffected) across seven patients underwent WES. From the variants prioritized using our bioinformatic workflow, we chose four candidate variants in MTOR, CSDE1, KLLN, and NLE1 across four patients based on pathogenicity scores and association with phenotype. All four variants were validated using digital droplet polymerase chain reaction. SIGNIFICANCE: Our approach enhances the reliability and applicability of SEEG-derived DNA for epilepsy, offering insights into its molecular basis, facilitating epileptogenic zone identification, and advancing precision medicine.
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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 | 0.002 |
| 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 | 0.000 | 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".