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Peer-reviewedCC BY-NC 4.0Nucleic Acids Research

scSpatial: an atlas for single-cell spatial omics

  • Huaicheng Sun1
  • Yun Bei1
  • Zijie Zhu1
  • Jiannan Deng1,2
  • Yuan Zhou1
  • Minjie Mou1,2
  • Taofeng Wei2
  • Hengbin Cao1
  • Qingxia Yang3
  • Chong Yao1
  • Feng Zhu1,2
  1. 1School of Pharmacy, Affiliated Huzhou Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University , Hangzhou 310058 ,
  2. 2The Second Affiliated Hospital, Zhejiang University School of Medicine, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University , Hangzhou 310018 ,
  3. 3Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine , Hangzhou 310058 ,
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Abstract

Single-cell spatial omics (SCSO) has reshaped spatial profiling by enabling simultaneous molecular characterization and spatial mapping at single-cell resolution, providing insights into cellular identities and tissue architecture within native biological contexts. By integrating spatial coordinates with molecular profiles and cell identities, SCSO datasets have been widely used to construct spatially resolved cell atlases, characterize disease-associated tissue remodeling, and map disease-relevant cell populations. However, these SCSO datasets remain fragmented across individual publications, and comprehensive SCSO resources are still lacking. Here, scSpatial was therefore developed as an atlas for SCSO with expert-curated cell annotations and high-resolution spatial mappings. The current release of scSpatial curates a large-scale collection of SCSO data, comprising over 1800 tissue sections from 630 projects generated using 16 spatial technologies; provides >20 million spatially resolved cells annotated into 173 standardized Cell Ontology types; and offers SCSO-tailored analyses and interactive visualizations, including spatial gene expression atlases, cell type distribution maps, spatially variable genes, gene expression correlations, and pathway-level spatial activity profiles. scSpatial is freely accessible to all users without requiring login at https://idrblab.org/scspatial/.

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Cite

BibTeXRIS
BibTeX
@article{sun2026scspatial,
  title = {{scSpatial: an atlas for single-cell spatial omics}},
  author = {Huaicheng Sun and Yun Bei and Zijie Zhu and Jiannan Deng and Yuan Zhou and Minjie Mou and Taofeng Wei and Hengbin Cao and Qingxia Yang and Chong Yao and Feng Zhu},
  journal = {Nucleic Acids Research},
  year = {2026},
  month = oct,
  doi = {10.1093/nar/gkag993},
  url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag993/8986455},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - scSpatial: an atlas for single-cell spatial omics
AU  - Huaicheng Sun
AU  - Yun Bei
AU  - Zijie Zhu
AU  - Jiannan Deng
AU  - Yuan Zhou
AU  - Minjie Mou
AU  - Taofeng Wei
AU  - Hengbin Cao
AU  - Qingxia Yang
AU  - Chong Yao
AU  - Feng Zhu
PY  - 2026
DA  - 2026/10/23
JO  - Nucleic Acids Research
DO  - 10.1093/nar/gkag993
UR  - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag993/8986455
AB  - Single-cell spatial omics (SCSO) has reshaped spatial profiling by enabling simultaneous molecular characterization and spatial mapping at single-cell resolution, providing insights into cellular identities and tissue architecture within native biological contexts. By integrating spatial coordinates with molecular profiles and cell identities, SCSO datasets have been widely used to construct spatially resolved cell atlases, characterize disease-associated tissue remodeling, and map disease-relevant cell populations. However, these SCSO datasets remain fragmented across individual publications, and comprehensive SCSO resources are still lacking. Here, scSpatial was therefore developed as an atlas for SCSO with expert-curated cell annotations and high-resolution spatial mappings. The current release of scSpatial curates a large-scale collection of SCSO data, comprising over 1800 tissue sections from 630 projects generated using 16 spatial technologies; provides >20 million spatially resolved cells annotated into 173 standardized Cell Ontology types; and offers SCSO-tailored analyses and interactive visualizations, including spatial gene expression atlases, cell type distribution maps, spatially variable genes, gene expression correlations, and pathway-level spatial activity profiles. scSpatial is freely accessible to all users without requiring login at https://idrblab.org/scspatial/.
SN  - 0305-1048
SN  - 1362-4962
ER  -