Faculty Opinions recommendation of Deep sequencing of somatosensory neurons reveals molecular determinants of intrinsic physiological properties.
Bibliographic record
Abstract
Dorsal root ganglion (DRG) sensory neuron subtypes defined by their in vivo properties display distinct intrinsic electrical properties. We used bulk RNA sequencing of genetically labeled neurons and electrophysiological analyses to define ion channel contributions to the intrinsic electrical properties of DRG neuron subtypes. The transcriptome profiles of eight DRG neuron subtypes revealed differentially expressed and functionally relevant genes, including voltage-gated ion channels. Guided by these data, electrophysiological analyses using pharmacological and genetic manipulations as well as computational modeling of DRG neuron subtypes were undertaken to assess the functions of select voltage-gated potassium channels (Kv1, Kv2, Kv3, and Kv4) in shaping action potential (AP) waveforms and firing patterns. Our findings show that the transcriptome profiles have predictive value for defining ion channel contributions to sensory neuron subtype-specific intrinsic physiological properties. The distinct ensembles of voltage-gated ion channels predicted to underlie the unique intrinsic physiological properties of eight DRG neuron subtypes are presented.Copyright © 2019 Elsevier Inc. All rights reserved. PMID: 31248728 Funding information This work was supported by: NINDS NIH HHS, United States Grant ID: R37 NS036855 NINDS NIH HHS, United States Grant ID: R35 NS097344 NINDS NIH HHS, United States Grant ID: R01 NS036855 Howard Hughes Medical Institute, United States
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".