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Record W1861860658 · doi:10.1016/j.febslet.2015.10.022

Molecular heterogeneity of midbrain dopaminergic neurons – Moving toward single cell resolution

2015· review· en· W1861860658 on OpenAlexfundno aff
Angela Anderegg, Jean‐François Poulin, Rajeshwar Awatramani

Bibliographic record

VenueFEBS Letters · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Alliance for Research on Schizophrenia and DepressionNational Institute of Neurological Disorders and StrokeIsrael National Road Safety AuthorityFonds de Recherche du Québec - SantéMichael J. Fox Foundation for Parkinson's Research
KeywordsMidbrainNeuroscienceNeuronBiologyDopaminergicDopamineHomogeneousCellular neuroscienceCell typeStriatumCellCentral nervous systemGenetics

Abstract

fetched live from OpenAlex

Since their discovery, midbrain dopamine (DA) neurons have been researched extensively, in part because of their diverse functions and involvement in various neuropsychiatric disorders. Over the last few decades, reports have emerged that midbrain DA neurons were not a homogeneous group, but that DA neurons located in distinct anatomical locations within the midbrain had distinctive properties in terms of physiology, function, and vulnerability. Accordingly, several studies focused on identifying heterogeneous gene expression across DA neuron clusters. Here we review the importance of understanding DA neuron heterogeneity at the molecular level, previous studies detailing heterogeneous gene expression in DA neurons, and finally recent work which brings together previous heterogeneous gene expression profiles in a coordinated manner, at single cell resolution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.041
GPT teacher head0.278
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations87
Published2015
Admission routes1
Has abstractyes

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