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Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization

Zhu, Qian and Shah, Sheel and Dries, Ruben and Cai, Long and Yuan, Guo-Cheng (2018) Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization. Nature Biotechnology, 36 (12). pp. 1183-1190. ISSN 1087-0156. PMCID PMC6488461.

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How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images.

Item Type:Article
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URLURL TypeDescription ReadCube access CentralArticle
Cai, Long0000-0002-7154-5361
Yuan, Guo-Cheng0000-0002-2283-4714
Additional Information:© 2018 Springer Nature Limited. Received 21 December 2017; accepted 27 August 2018; published online 29 October 2018. Code availability: Code has been deposited at Data availability: Expression data, spatial coordinates, SVM predictions, HMRF domains, expression box plots categorized by domains and cell types, and interactive visualizations are available at The scRNA-seq dataset referenced in this study is GSE71585. This research was supported by a Claudia Barr Award, a Chan Zuckerberg Initiative Award, and NIH grant R01HL119099 to G.-C.Y., and by grants from the Paul G. Allen Foundation Discovery Center, NIH HD075605 and TR01 OD024686 to L.C. Author Contributions: G.-C.Y. and L.C. conceived and supervised the project. Q.Z. and G.-C.Y. conceived the HMRF and SVM models. Q.Z. and G.-C.Y. conducted and supervised the computational analyses. S.S. and L.C. conducted and supervised the seqFISH experiments. Q.Z., S.S., R.D., G.-C.Y. and L.C. wrote the manuscript. All of the authors contributed ideas for this work. All of the authors reviewed and approved the manuscript. The authors declare no competing financial interests.
Funding AgencyGrant Number
Claudia Barr AwardUNSPECIFIED
Chan Zuckerberg InitiativeUNSPECIFIED
Paul G. Allen Family FoundationUNSPECIFIED
NIHTR01 OD024686
Issue or Number:12
PubMed Central ID:PMC6488461
Record Number:CaltechAUTHORS:20180821-132916530
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88987
Deposited By: George Porter
Deposited On:29 Oct 2018 16:27
Last Modified:03 Oct 2019 20:12

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