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Spatial Genomic Analysis: A Multiplexed Transcriptional Profiling Method that Reveals Subpopulations of Cells Within Intact Tissues

  • Antti Lignell
  • Laura KerosuoEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2002)

Abstract

Here, we present Spatial Genomic Analysis (SGA), a quantitative single-cell transcriptional profiling method that takes advantage of single-molecule imaging of individual transcripts for up to a hundred genes. SGA relies on a machine learning-based image analysis pipeline that performs cell segmentation and transcript counting in a robust way. SGA is suitable for various in situ applications and was originally developed to address heterogeneity in the neural crest, which is a transient embryonic stem cell population important for formation of various vertebrate body structures. After being specified as multipotent neural crest stem cells in the dorsal neural tube, they go through an epithelial to mesenchymal transition in order to migrate to different destinations around the body, and gradually turn from stem cells to progenitors prior to final commitment. The molecular details of this process remain largely unknown, and upon their emergence, the neural crest cells have been considered as a single homogeneous population. Technical limitations have restricted the possibility to parse the neural crest cell pool into subgroups according to multiplex gene expression properties. By using SGA, we were able to identify subgroups inside the neural crest niche in the dorsal neural tube. The high sensitivity of the method allows detection of low expression levels and we were able to determine factors not previously shown to be present in neural crest stem cells, such as pluripotency or lineage markers. Finally, SGA analysis also provides prediction of gene relationships within individual cells, and thus has broad utility for powerful transcriptome analyses in original biological contexts.

Keywords

Chicken embryo HCR Hybridization chain reaction In vivo single-cell analysis Neural crest stem cell niche Neural crest stem cells Pluripotency Single-molecule microscopy Quantitative single-molecule fluorescent in situ hybridization SGA smFISH Spatial genomic analysis Spatial genomics Spatial tissue transcriptome analysis 

Notes

Acknowledgments

This work was in part funded by the Division of Intramural Research of the National Institute of Dental and Craniofacial Research at the National Institutes of Health, Department of Health and Human Services, as well as grants from the Academy of Finland, Sigrid Juselius Foundation, Ella and George Ehrnrooth’s Foundation, Children’s Cancer Foundation Väre, and K. Albin Johansson Foundation to LK.

References

  1. 1.
    Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, Lao K, Surani MA (2010) RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 5(3):516–535.  https://doi.org/10.1038/nprot.2009.236 CrossRefPubMedGoogle Scholar
  2. 2.
    Raj A, van den Bogaard P, Rifkin SA, van Oudenaarden A, Tyagi S (2008) Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5(10):877–879.  https://doi.org/10.1038/nmeth.1253 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Lignell A, Kerosuo L, Streichan SJ, Cai L, Bronner ME (2017) Identification of a neural crest stem cell niche by spatial genomic analysis. Nat Commun 8(1):1830.  https://doi.org/10.1038/s41467-017-01561-w CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Shah S, Lubeck E, Schwarzkopf M, He TF, Greenbaum A, Sohn CH, Lignell A, Choi HM, Gradinaru V, Pierce NA, Cai L (2016) Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing. Development 143(15):2862–2867.  https://doi.org/10.1242/dev.138560 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Choi HM, Beck VA, Pierce NA (2014) Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano 8(5):4284–4294.  https://doi.org/10.1021/nn405717p CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L (2014) Single-cell in situ RNA profiling by sequential hybridization. Nat Methods 11(4):360–361.  https://doi.org/10.1038/nmeth.2892 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Sommer C, Straehle C, Kothe U, Hamprecht FA (2011) Ilastik: interactive learning and segmentation toolkit. IEEE international symposium on biomedical imaging. pp 230–233Google Scholar

Copyright information

© Springer Science+Business Media New York 2018

Authors and Affiliations

  1. 1.Single-Cell Quantitative Biology Lab, Department of Biochemistry and Developmental Biology, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
  2. 2.Neural Crest Development and Disease Unit, National Institute for Dental and Craniofacial Research, National Institutes of HealthBethesdaUSA

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