Details

kind
infographic
provider
other
section_id
section_02_evidence_package
source_url
https://github.com/AllenNeuralDynamics/ComputationalReviewSST/blob/89b7e9787cd90e942b0adb531d549af3ddad30f1/evidence/section_02_evidence_package.json
target_ref
wiki_page:computationalreviewsst-02
review_repo
ComputationalReviewSST
section_ref
wiki_page:computationalreviewsst-02
source_path
evidence/section_02_evidence_package.json
section_title
Molecular Identity and Transcriptomic Taxonomy
generation_status
complete
review_bundle_ref
analysis_bundle:ab-8466d095488a
origin_url
https://github.com/AllenNeuralDynamics/ComputationalReviewSST/blob/89b7e9787cd90e942b0adb531d549af3ddad30f1/evidence/section_02_evidence_package.json
commit_sha
89b7e9787cd90e942b0adb531d549af3ddad30f1
created_by
persona-jerome-lecoq-gbo-neuroscience
repository_url
https://github.com/AllenNeuralDynamics/ComputationalReviewSST
Raw fields (4)
prompt
Layer 5 SST interneurons divide into T-shaped Martinotti, fanning-out Martinotti, and non-Martinotti subtypes with distinct proportions and target layers, establishing that SST interneurons have fundamentally different roles depending on their morphological subtype.
raw_fields
{
  "papers": [
    {
      "doi": "10.1523/jneurosci.2415-17.2017",
      "value": "~10%",
      "method": "electrophysiology + morphological reconstruction",
      "metric": "T-shaped Martinotti proportion",
      "cite_key": "Nigro2018",
      "condition": "barrel cortex L5",
      "study_system": "mouse",
      "value_source_sentence": "T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively."
    },
    {
      "doi": "10.1523/jneurosci.2415-17.2017",
      "value": "~50%",
      "method": "electrophysiology + morphological reconstruction",
      "metric": "Fanning-out Martinotti proportion",
      "cite_key": "Nigro2018",
      "condition": "barrel cortex L5",
      "study_system": "mouse",
      "value_source_sentence": "T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively."
    },
    {
      "doi": "10.1523/jneurosci.2415-17.2017",
      "value": "~40%",
      "method": "electrophysiology + morphological reconstruction",
      "metric": "Non-Martinotti proportion",
      "cite_key": "Nigro2018",
      "condition": "barrel cortex L5",
      "study_system": "mouse",
      "value_source_sentence": "T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively."
    },
    {
      "doi": "10.1523/jneurosci.0661-06.2006",
      "value": "nearly perfect segregation",
      "method": "transgenic GFP lines + electrophysiology",
      "metric": "X94 (non-Martinotti-like) vs X98 (Martinotti-like) segregation",
      "cite_key": "Ma2006",
      "condition": "barrel cortex",
      "study_system": "mouse",
      "value_source_sentence": "By all criteria, there was nearly perfect segregation of X94 and X98 GFP+ neurons, whereas GIN GFP+ neurons exhibited intermediate properties."
    }
  ],
  "comparison_id": "sst-l5-subtype-proportions",
  "comparison_name": "Proportions of Layer 5 SST Interneuron Morphological Subtypes",
  "comparison_type": "convergent evidence",
  "what_it_reveals": "Layer 5 SST interneurons divide into T-shaped Martinotti, fanning-out Martinotti, and non-Martinotti subtypes with distinct proportions and target layers, establishing that SST interneurons have fundamentally different roles depending on their morphological subtype.",
  "homogeneity_check": {
    "caveats": [
      "Both studies from mouse barrel cortex, enhancing comparability",
      "Ma 2006 used transgenic lines while Munoz-Manchado 2018 used SST-Cre + morphological classification",
      "GIN line intermediate properties suggest a continuum that morphological classification may oversimplify"
    ],
    "comparable": true
  },
  "suggested_plot_type": "grouped bar"
}
source_refs
[
  "paper:paper-45d28ae23ca2",
  "paper:paper-c57fcb1aef2a"
]
source_policy
{
  "mode": "public_source_pointer_with_short_context",
  "notes": [
    "Local review repositories are read-only inputs.",
    "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
  ],
  "source_commit_sha": "89b7e9787cd90e942b0adb531d549af3ddad30f1",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST"
}

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