Version history

{
  "scope": "Adult mouse brain (9 regions, Drop-seq)",
  "claim_text": "A Drop-seq atlas of 690,000 single cells across nine adult mouse-brain regions, including cortex, defined 565 transcriptionally distinct cell groups and revealed glutamatergic-neuron specialization across cortical regions — providing the cell-population scaffolding on which mouse-cortex E→E circuit synthesis is built.",
  "raw_fields": {
    "n": 690000,
    "doi": "10.1016/j.cell.2018.07.028",
    "claim": "A Drop-seq atlas of 690,000 single cells across nine adult mouse-brain regions, including cortex, defined 565 transcriptionally distinct cell groups and revealed glutamatergic-neuron specialization across cortical regions — providing the cell-population scaffolding on which mouse-cortex E→E circuit synthesis is built.",
    "cite_key": "Saunders2018",
    "evidence": "690K-cell Drop-seq atlas; 565 cell groups; cross-region cortical glutamatergic specialization.",
    "effect_size": "690,000 cells profiled; 565 transcriptionally distinct groups; 9 brain regions",
    "text_access": "abstract_only",
    "study_system": "Adult mouse brain (9 regions, Drop-seq)",
    "argument_role": "supporting",
    "replication_status": "single-study",
    "claim_source_sentence": "To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": "we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells"
  },
  "section_id": "section_15",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_15_evidence_package.json",
  "effect_size": "690,000 cells profiled; 565 transcriptionally distinct groups; 9 brain regions",
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-15-methods-limits",
  "source_kind": "review_finding",
  "source_path": "evidence/section_15_evidence_package.json",
  "source_refs": [
    "paper:paper-a5fcb640f059"
  ],
  "source_span": "To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals.",
  "study_system": "Adult mouse brain (9 regions, Drop-seq)",
  "evidence_refs": [
    {
      "ref": "paper:paper-a5fcb640f059"
    }
  ],
  "section_title": "15. Methodological limits and emerging tools — what current mouse-cortex tools cannot yet measure about E→E recurrence (subthreshold network activity, fast plasticity in vivo, millimetre-scale dynamic connectomes), and what is on the near horizon",
  "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": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "690K-cell Drop-seq atlas; 565 cell groups; cross-region cortical glutamatergic specialization.",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single-study",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-15-methods-limits",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_15_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}