Sci-Space E14 Mouse Embryo Data(Dataset ID: STDS0000080)

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474
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153
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Genes: 52,636
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PMID: 34210887
Summary
Visualization
Data
Analysis results
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Dataset information
Summary:
Spatial patterns of gene expression span many scales, and are shaped by both local (e.g. cell-cell interactions) and global (e.g. tissue, organ) context. However, most in situ methods for profiling gene expression either average local contexts or are restricted to limited fields of view. Here we introduce sci-Space, a scale-flexible method that retains single cell resolution while resolving spatial heterogeneity in gene expression at larger scales. As a proof-of-concept, we apply sci-Space to the developing mouse embryo (E14), capturing the approximate spatial coordinates of profiled cells from whole embryo serial sections.
Overall design:
Combinatorially indexed single cell TNA sequencing libraries. Each read contains a combination of PCR indices and a 3-prime barcode that uniquely identify the reads from a single cell. Sequenced molecules consist of both barcoded cDNA and barcoded synthetic hash-oligo molecules used to mark the spatial position from which a cell is derrived.
Technology:
sci-Space
Platform:
GPL21626
Species:
Mus musculus(mm10)
Tissues:
Embryo
Submission date: 2021-02-12Update date: 2021-07-01
DOI: To be continue
Contributors
Sanjay R Srivatsan,Mary Regier
Contact: sanjays@uw.edu, sanjayrsrivatsan@gmail.com
Accessions
GEO Series Accessions: GSE166692
How to cite
  • Cite database of STOmicsDB:
    [1] Xu, Zhicheng et al. "STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization." Nucleic acids research vol. 52,D1 (2024): D1053-D1061. doi: 10.1093/nar/gkad933'
  • Cite visualization dataset:
    [2] Sanjay R Srivatsan,Mary Regier. Sci-Space E14 Mouse Embryo Data[DS/OL]. STOmicsDB, 2021[2021-02-12]. https://db.cngb.org/stomics/datasets/STDS0000080/. doi: xxxxxx
    #Format: {contributors}. {title}[DS/OL]. STOmicsDB, {the year of submission data}[{submission data}]. {dataset link}. doi: {doi ID}
  • Cite original data article:
    Citation: Srivatsan, Sanjay R et al. “Embryo-scale, single-cell spatial transcriptomics.” Science (New York, N.Y.) vol. 373,6550 (2021): 111-117. doi:10.1126/science.abb9536