- 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"
}- 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.
- 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"
}