Cellular Modeling of CLN6 with IPSC-derived Neurons and Glia
Bibliographic record
Abstract
ABSTRACT Neuronal ceroid lipofuscinosis (NCL), type 6 (CLN6) is a neurodegenerative disorder associated with progressive neurodegeneration leading to dementia, seizures, and retinopathy. CLN6 encodes a resident-ER protein involved in trafficking lysosomal proteins to the Golgi. CLN6p deficiency results in lysosomal dysfunction and deposition of storage material comprised of Nile Red + lipids/proteolipids that include subunit C of the mitochondrial ATP synthase (SUBC). White matter involvement has been recently noted in several CLN6 animal models and several CLN6 subjects had neuroimaging was consistent with leukodystrophy. CLN6 patient-derived induced pluripotent stem cells (IPSCs) were generated from several of these subjects. IPSCs were differentiated into oligodendroglia or neurons using well-established small-molecule protocols. A doxycycline-inducible transgenic system expressing neurogenin-2 (the I3N-system) was also used to generate clonal IPSC-lines (I3N-IPSCs) that could be rapidly differentiated into neurons (I3N-neurons). All CLN6 IPSC-derived neural cell lines developed significant storage material, CLN6-I3N-neuron lines revealed significant Nile Red + and SUBC + storage within three and seven days of neuronal induction, respectively. CLN6-I3N-neurons had decreased tripeptidyl peptidase-1 activity, increased Golgi area, along with increased LAMP1 + in cell bodies and neurites. SUBC + signal co-localized with LAMP1 + signal. Bulk-transcriptomic evaluation of control- and CLN6-I3N-neurons identified >1300 differentially-expressed genes (DEGs) with Gene Ontogeny (GO) Enrichment and Canonical Pathway Analyses having significant changes in lysosomal, axonal, synaptic, and neuronal-apoptotic gene pathways. These findings indicate that CLN6-IPSCs and CLN6-I3N-IPSCs are appropriate cellular models for this disorder. These I3N-neuron models may be particularly valuable for developing therapeutic interventions with high-throughput drug screening assays and/or gene therapy.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".