Collections / Spatial transcriptomic profiling of the aging mouse brain [spatial scRNA-seq]

Spatial transcriptomic profiling of the aging mouse brain [spatial scRNA-seq]

The mammalian brain can be divided into distinct structural and functional regions to perform a variety of diverse functions, but during normal aging, exactly how each region is affected, and the information interaction changes between different regions, remains largely unknown. To gain a better insight into these processes, here we generate a single-cell spatial transcriptomic (ST) atlas of young and old mice brains involving cerebrum, brain stem and fiber tracts regions. Based on the unbiased classification of spatial molecular atlas, 27 distinguished brain spatial domains were obtained, which are similar to known anatomical regions, but slightly different. Through differential expression analysis and gene set enrichment analysis (GSEA), we identified aging-related genes and pathways that vary in a coordinated or opposite manner across regions. Combined with single-cell transcriptomic data, we characterized the spatial distribution of cell types, identified an up-regulated gene Ifi27 across regions and cell types in VIS region. Through ligand-receptor interaction analysis, we identified all possible information interaction changes between regions with aging. In summary, we establish a brain spatial molecular atlas (accessible online at https:) to provide a rich resource of spatially differentially expressed genes and information interaction, which may help to understand aging and provide novel insights into the molecular mechanism of brain aging.

Datasets8
Size~3.2 GB
AssayVisium
Samplemouse · brain
KindTime course
PMID:38372175 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse193107")
coll.ordered_artifacts.df()                 # the 8 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()}
Open in Vitessce
Visium
Mouse brain
GSM5773455
mouse brain 393 MB 3,400 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size393 MB
Obs3,400
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773459
mouse brain 396 MB 3,269 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size396 MB
Obs3,269
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773453
mouse brain 409 MB 3,259 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size409 MB
Obs3,259
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773458
mouse brain 449 MB 3,543 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size449 MB
Obs3,543
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773460
mouse brain 380 MB 3,050 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size380 MB
Obs3,050
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773457
mouse brain 453 MB 3,681 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size453 MB
Obs3,681
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773454
mouse brain 400 MB 3,593 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size400 MB
Obs3,593
silver H&E Open in Vitessce
Open in Vitessce
Visium
Mouse brain
GSM5773456
mouse brain 383 MB 3,294 obs silver H&E
TechnologyVisium
Organismmouse
Tissuebrain
Size383 MB
Obs3,294
silver H&E Open in Vitessce

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