Version history

{
  "kind": "infographic",
  "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.",
  "provider": "other",
  "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"
  },
  "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",
  "source_refs": [
    "paper:paper-45d28ae23ca2",
    "paper:paper-c57fcb1aef2a"
  ],
  "section_title": "Molecular Identity and Transcriptomic Taxonomy",
  "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"
  },
  "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"
}