spatial ATAC-RNA-seq
Mouse brain
GSM6204623
mouse brain 171 MB 2,373 pixels bronze H&E NCBI GEO policy
Technologyspatial ATAC-RNA-seq
Organismmouse
Tissuebrain
Pixels2,373
bronze H&E NCBI GEO policy
Collections / geo-gse205052
geo-gse205052
Datasets3
Size~1.2 GB
Assayspatial ATAC-RNA-seq
Samplemouse · multiple
KindPublication
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse205052")
cohort.members # the 3 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/") Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse205052")
coll.ordered_artifacts.to_dataframe() # the 3 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()] Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse205052 --dest data/Licenses: 3 NCBI GEO policy
About the study
Spatial epigenome-transcriptome co-profiling of mammalian tissues.
Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context<sup>1-5</sup>. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.
Member datasets · 3
spatial ATAC-RNA-seq
Mouse embryo
GSM6204624
mouse embryo 811 MB 8,886 pixels bronze H&E NCBI GEO policy
Technologyspatial ATAC-RNA-seq
Organismmouse
Tissueembryo
Pixels8,886
bronze H&E NCBI GEO policy
spatial ATAC-RNA-seq
Mouse embryo
GSM6801813
mouse embryo 230 MB 2,187 pixels bronze H&E NCBI GEO policy
Technologyspatial ATAC-RNA-seq
Organismmouse
Tissueembryo
Pixels2,187
bronze H&E NCBI GEO policy
Cite this collection
doi.org/10.1038/s41586-023-05795-1 ↗ BibTeX
@article{Zhang_2023,
title = {Spatial epigenome–transcriptome co-profiling of mammalian tissues},
volume = {616},
ISSN = {1476-4687},
url = {http://dx.doi.org/10.1038/s41586-023-05795-1},
DOI = {10.1038/s41586-023-05795-1},
number = {7955},
journal = {Nature},
publisher = {Springer Science and Business Media LLC},
author = {Zhang, Di and Deng, Yanxiang and Kukanja, Petra and Agirre, Eneritz and Bartosovic, Marek and Dong, Mingze and Ma, Cong and Ma, Sai and Su, Graham and Bao, Shuozhen and Liu, Yang and Xiao, Yang and Rosoklija, Gorazd B. and Dwork, Andrew J. and Mann, J. John and Leong, Kam W. and Boldrini, Maura and Wang, Liya and Haeussler, Maximilian and Raphael, Benjamin J. and Kluger, Yuval and Castelo-Branco, Gonçalo and Fan, Rong},
year = {2023},
month = Mar,
pages = {113–122}
}