Evidence map›Paper›PMID 38238073›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2024

Divergent Learning-Related Transcriptional States of Cortical Glutamatergic Neurons.

Katie L Dunton, Nathan G Hedrick, Saber Meamardoost, Chi Ren, James R Howe, Jing Wang, Cory M Root, Rudiyanto Gunawan, Takaki Komiyama, Ying Zhang and 1 more

Open access · greenAbstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 50% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Katie L DuntonDepartment of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston 02881, Rhode Island.
Nathan G HedrickDepartment of Neurobiology, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla 92093, California.
Saber MeamardoostDepartment of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo 14260, New York.
Chi RenDepartment of Neurobiology, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla 92093, California.ORCID 0000-0002-0947-8516
James R HoweDepartment of Neurobiology, School of Biological Sciences, University of California San Diego, La Jolla 92093, California.
Jing WangDepartment of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston 02881, Rhode Island.
Cory M RootDepartment of Neurobiology, School of Biological Sciences, University of California San Diego, La Jolla 92093, California.ORCID 0000-0003-0193-8183
Rudiyanto GunawanDepartment of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo 14260, New York.ORCID 0000-0002-6480-7976
Takaki KomiyamaDepartment of Neurobiology, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla 92093, California eunjung.hwang@rosalindfranklin.edu yingzhang@uri.edu tkomiyama@ucsd.edu.ORCID 0000-0001-9609-4600
Ying ZhangDepartment of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston 02881, Rhode Island eunjung.hwang@rosalindfranklin.edu yingzhang@uri.edu tkomiyama@ucsd.edu.ORCID 0000-0001-8759-0194
Eun Jung HwangDepartment of Neurobiology, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla 92093, California eunjung.hwang@rosalindfranklin.edu yingzhang@uri.edu tkomiyama@ucsd.edu.
University of California San Diego · USUniversity of Rhode Island · USUniversity at Buffalo, State University of New York · USRosalind Franklin University of Medicine and Science · US

Funding

Cortical Control of Motor LearningR01NS091010 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KOMIYAMA, TAKAKI · 2015 to 2024
$3.7M
Deconstructing Functional Circuits of Motor Cortex During Motor Learning - Microscope Repair Administrative SupplementR01NS125298 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Takaki Komiyama · 2022 to 2026
$2.8M
Correlated light and ultrastructural imaging of learning-related synaptic plasticityR21NS112750 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KOMIYAMA, TAKAKI · 2020 to 2020
$434k
Articulating the presynaptic input structure to functionally defined, learning-related synaptic clustersK99NS114175 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HEDRICK, NATHAN G · 2020 to 2021
$236k
NINDS NIH HHS K99 NS114175NINDS NIH HHS R01 NS091010NINDS NIH HHS R01 NS125298NINDS NIH HHS R21 NS112750
6 · The paper itself

Abstract

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. "Train" and "control" neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

Indexed as

LearningNeuronal PlasticityAnimalsMaleMiceMotor SkillsNeuronsWaterWaterL2/3 glutamatergic neuronmotor engrammouse primary motor cortexplasticitysingle-nucleus RNA sequencingwater deprivation

Identifiers

PMID38238073
PMCPMC10919205
OpenAlexW4390967696

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.