Collections / Spatial epigenome-transcriptome co-profiling of mammalian tissues

Spatial epigenome-transcriptome co-profiling of mammalian tissues

We present spatially resolved high-spatial-resolution genome-wide co-mapping of epigenome and transcriptome by simultaneously profiling of chromatin accessibility and gene expression (spatial-ATAC-RNA-seq), as well as histone modification and gene expression (spatial-CUT&Tag-RNA-seq) on the same tissue section at cellular level by combining the microfluidic deterministic barcoding strategy in DBiT-seq and the chemistry used in ATAC-seq/CUT&Tag.

Datasets6
Size~273 MB
Assaysspatial ATAC-seq · spatial CUT&Tag
Samplemouse · brain
KindTime course
Flag a data issue
10.1038/s41586-023-05795-1 ↗
Download the whole collection
Python · sddb client 
from sddb import Catalog

cohort = Catalog().collection("geo-gse218593")
cohort.members        # the 6 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/")
Install (Python 3.12+) Docs →
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-gse218593")
coll.ordered_artifacts.to_dataframe()   # the 6 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]
Install (Python 3.12+) Docs →
Shell 
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse218593 --dest data/
Install (Python 3.12+) Docs →

Licenses: 6 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 · 6
Open in Vitessce
spatial ATAC-seq
Mouse brain
GSM6753041
mouse brain 16.7 MB 2,498 obs bronze H&E NCBI GEO policy
Technologyspatial ATAC-seq
Organismmouse
Tissuebrain
Size16.7 MB
Obs2,498
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
spatial CUT&Tag
Mouse brain
GSM6753042
mouse brain 9.0 MB 2,499 obs bronze H&E NCBI GEO policy
Technologyspatial CUT&Tag
Organismmouse
Tissuebrain
Size9.0 MB
Obs2,499
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
spatial ATAC-seq
Mouse brain
GSM6753043
mouse brain 44.3 MB 9,215 obs bronze H&E NCBI GEO policy
Technologyspatial ATAC-seq
Organismmouse
Tissuebrain
Size44.3 MB
Obs9,215
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
spatial CUT&Tag
Mouse brain
GSM6753044
mouse brain 85.1 MB 9,752 obs bronze H&E NCBI GEO policy
Technologyspatial CUT&Tag
Organismmouse
Tissuebrain
Size85.1 MB
Obs9,752
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
spatial CUT&Tag
Mouse brain
GSM6753045
mouse brain 62.2 MB 9,370 obs bronze H&E NCBI GEO policy
Technologyspatial CUT&Tag
Organismmouse
Tissuebrain
Size62.2 MB
Obs9,370
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
spatial CUT&Tag
Mouse brain
GSM6753046
mouse brain 55.8 MB 9,548 obs bronze H&E NCBI GEO policy
Technologyspatial CUT&Tag
Organismmouse
Tissuebrain
Size55.8 MB
Obs9,548
bronze H&E NCBI GEO policy Open in Vitessce

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}
}