Embryo-scale, single-cell spatial transcriptomics

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, w...

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Vydáno v:Science (American Association for the Advancement of Science) Ročník 373; číslo 6550; s. 111
Hlavní autoři: Srivatsan, Sanjay R, Regier, Mary C, Barkan, Eliza, Franks, Jennifer M, Packer, Jonathan S, Grosjean, Parker, Duran, Madeleine, Saxton, Sarah, Ladd, Jon J, Spielmann, Malte, Lois, Carlos, Lampe, Paul D, Shendure, Jay, Stevens, Kelly R, Trapnell, Cole
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 02.07.2021
Témata:
Animals
Atlases as Topic
Body Patterning - genetics
Brain - embryology
Cell Movement
Embryo, Mammalian - embryology
Embryonic Development - genetics
Gene Expression Profiling - methods
Mice
Neurogenesis - genetics
Neurons - cytology
Single-Cell Analysis - methods
Transcriptome
ISSN:1095-9203, 1095-9203
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Abstract Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.
AbstractList Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.
Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.
Author Franks, Jennifer M
Duran, Madeleine
Shendure, Jay
Stevens, Kelly R
Barkan, Eliza
Packer, Jonathan S
Lampe, Paul D
Saxton, Sarah
Regier, Mary C
Ladd, Jon J
Spielmann, Malte
Srivatsan, Sanjay R
Grosjean, Parker
Lois, Carlos
Trapnell, Cole
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  year: 2021
  text: 2021-07-02
  day: 02
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PublicationTitle Science (American Association for the Advancement of Science)
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PublicationYear 2021
References 34750575 - Nat Cell Biol. 2021 Nov;23(11):1108. doi: 10.1038/s41556-021-00778-8
References_xml – reference: 34750575 - Nat Cell Biol. 2021 Nov;23(11):1108. doi: 10.1038/s41556-021-00778-8
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Snippet Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However,...
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StartPage 111
SubjectTerms Animals
Atlases as Topic
Body Patterning - genetics
Brain - embryology
Cell Movement
Embryo, Mammalian - embryology
Embryonic Development - genetics
Gene Expression Profiling - methods
Mice
Neurogenesis - genetics
Neurons - cytology
Single-Cell Analysis - methods
Transcriptome
Title Embryo-scale, single-cell spatial transcriptomics
URI https://www.ncbi.nlm.nih.gov/pubmed/34210887
https://www.proquest.com/docview/2548408601
Volume 373
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