{
"papers": [
{
"n": 0,
"doi": "10.1038/s41467-019-14198-8",
"value": "5",
"method": "scRNA-seq + RNA in situ validation",
"metric": "astrocyte subtypes identified by scRNA-seq",
"n_analyzed": "~5,600 astrocytes",
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "astrocytes profiled by 10x scRNA-seq",
"scope_region": "mouse cortex + hippocampus",
"study_system": "adult mouse cortex and hippocampus (forebrain)",
"taxonomic_level": "subtype",
"scope_population": "FACS-enriched astrocytes",
"value_source_sentence": "Our analysis identifies five transcriptomically distinct astrocyte subtypes in adult mouse cortex and hippocampus.",
"experimental_conditions": "10x Genomics scRNA-seq of FACS-sorted astrocytes"
},
{
"n": 0,
"doi": "10.1016/j.cell.2018.06.021",
"value": "7",
"method": "scRNA-seq, hierarchical taxonomy",
"metric": "astrocyte types identified",
"n_analyzed": "~500,000 cells total (whole nervous system)",
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "cells sequenced (across nervous system, not astrocyte-only count)",
"scope_region": "whole mouse nervous system (brain + spinal cord)",
"study_system": "whole adult mouse nervous system",
"taxonomic_level": "type",
"scope_population": "all cells captured",
"value_source_sentence": "We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters.",
"experimental_conditions": "10x Genomics scRNA-seq"
},
{
"n": 0,
"doi": "10.1038/s41593-020-0602-1",
"value": "3",
"method": "LaST spatial transcriptomics + scRNA-seq",
"metric": "cortical astrocyte spatial layers",
"n_analyzed": "46 candidate genes screened across cortical columns",
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "marker genes profiled, astrocytes imaged across cortex",
"scope_region": "mouse cerebral cortex",
"study_system": "mouse cortex (LaST spatial transcriptomics)",
"taxonomic_level": "layer/gradient",
"scope_population": "cortical astrocytes",
"value_source_sentence": "Screening 46 candidate genes for astrocyte diversity across the mouse cortex, we identified superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from those of neurons.",
"experimental_conditions": "large-area spatial transcriptomic map of cortex"
},
{
"n": 10,
"doi": "10.1038/s41467-018-03940-3",
"value": "layer II/III n=27 cells; layer VI n=16 cells (from 5 brains each); significant layer differences in orientation/arborization (P<0.001)",
"method": "3D morphological reconstruction + molecular markers",
"metric": "cortical astrocyte morphological classes compared by layer (II/III vs VI)",
"n_analyzed": "43 astrocytes total from 10 brains",
"ci_or_error": "*** P < 0.001",
"text_access": "fulltext",
"n_definition": "astrocytes reconstructed / brains (mice)",
"scope_region": "mouse somatosensory cortex, layers I-VI",
"study_system": "mouse somatosensory cortex, P60",
"taxonomic_level": "layer (II/III vs VI)",
"scope_population": "sparse-labeled individual astrocytes",
"value_source_sentence": "The data are means (layer II/III astrocytes, n = 27 cells from five brains; layer VI astrocytes, n = 16 cells from five brains). * P < 0.05, ** P < 0.01, *** P < 0.001 versus corresponding values for layer VI astrocytes (two-way ANOVA followed by Bonferroni’s test) We next quantified such morphological differences among astrocytes in all cortical layers including layer I in which astrocytes were previously shown to differ from those in other layers in terms of Ca 2+ signaling activity and gene expression , but their morphological differences have remained uncharacterized.",
"experimental_conditions": "sparse labeling (Glast-EMTB-GFP; Glast-CreERT2; Rosa-tdTomato)"
},
{
"n": 0,
"doi": "10.1016/j.cell.2018.07.028",
"value": "565",
"method": "Drop-seq scRNA-seq",
"metric": "transcriptionally distinct cell groups across 9 mouse brain regions",
"n_analyzed": "690,000 individual cells",
"ci_or_error": null,
"text_access": "abstract_only",
"n_definition": "cells profiled (all cell types, not astrocyte-only)",
"scope_region": "9 mouse brain regions",
"study_system": "adult mouse brain, 9 regions",
"taxonomic_level": "cluster (all cells, not astrocyte-only)",
"scope_population": "all cells (astrocytes a subset)",
"value_source_sentence": "We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals.",
"experimental_conditions": "Drop-seq of dissociated brain regions"
}
],
"comparison_id": "regional-astrocyte-subtype-counts",
"comparison_name": "Reported number of astrocyte molecular/spatial subtypes across regions and atlases",
"comparison_type": "cross-study conflict",
"what_it_reveals": "The number of reported astrocyte subtypes varies strongly by platform, region sampled, and clustering granularity (3 layers [Bayraktar 2020] — 5 subtypes [Batiuk 2020] — 7 region-restricted types [Zeisel 2018] — 565 overall clusters across 9 regions [Saunders 2018]). This illustrates that regional functional heterogeneity is real but taxonomic level must be specified when reporting counts.",
"homogeneity_check": {
"caveats": [
"Saunders 2018's 565 count is ALL cells across 9 regions, not astrocyte-only — not comparable to the others at face value.",
"Batiuk (5) restricted to forebrain; Zeisel (7) is whole nervous system (brain + spinal cord).",
"Bayraktar (3 layers) uses spatial gradient identities in cortex only; not directly comparable to scRNA-seq subtype counts.",
"Lanjakornsiripan compares two specific layers (II/III vs VI), not a global subtype count."
],
"n_definition_uniform": "false",
"scope_region_uniform": "false",
"taxonomic_level_uniform": "false",
"scope_population_uniform": "false"
},
"suggested_plot_type": "bar chart (by scope_region and taxonomic_level) with legend for taxonomic_level"
}