Identification of a neural crest stem cell niche by Spatial Genomic Analysis
Abstract
The neural crest is an embryonic population of multipotent stem cells that form numerous defining features of vertebrates. Due to lack of reliable techniques to perform transcriptional profiling in intact tissues, it remains controversial whether the neural crest is a heterogeneous or homogeneous population. By coupling multiplex single molecule fluorescence in situ hybridization with machine learning algorithm based cell segmentation, we examine expression of 35 genes at single cell resolution in vivo. Unbiased hierarchical clustering reveals five spatially distinct subpopulations within the chick dorsal neural tube. Here we identify a neural crest stem cell niche that centers around the dorsal midline with high expression of neural crest genes, pluripotency factors, and lineage markers. Interestingly, neural and neural crest stem cells express distinct pluripotency signatures. This Spatial Genomic Analysis toolkit provides a straightforward approach to study quantitative multiplex gene expression in numerous biological systems, while offering insights into gene regulatory networks via synexpression analysis.
Additional Information
© 2017 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received: 11 July 2017; Accepted: 27 September 2017; Published online: 28 November 2017. This work was supported by Grants HD037105 to M.E.B. and HD075605, 1DP2OD008530, and 5R21DA038468 to L.C., as well as fellowships from Jane and Aatos Erkko Foundation, Ella and Georg Ehrnrooth Foundation, and Väre Foundation to L.K. Author Contributions: L.K. and A.L. conceived the project. A.L. and L.K. designed the experiments, and L.C. and M.E.B. supervised the research. L.K. and A.L. analyzed the data. SGA was developed by A.L. and L.C. together with L.K. Sample preparation was performed by L.K., single molecule fluorescent in situ hybridization protocol and imaging was performed by A.L., and the data analysis pipeline was developed by A.L. and S.J.S. The paper was written by L.K., A.L., M.E.B. and L.C. The authors declare no competing financial interests.Attached Files
Published - s41467-017-01561-w.pdf
Supplemental Material - 41467_2017_1561_MOESM1_ESM.pdf
Supplemental Material - 41467_2017_1561_MOESM2_ESM.docx
Supplemental Material - 41467_2017_1561_MOESM3_ESM.mp4
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Additional details
- PMCID
- PMC5705662
- Eprint ID
- 83495
- Resolver ID
- CaltechAUTHORS:20171128-101613048
- HD037105
- NIH
- HD075605
- NIH
- 1DP2OD008530
- NIH
- 5R21DA038468
- NIH
- Jane and Aatos Erkko Foundation
- Ella and Georg Ehrnrooth Foundation
- Väre Foundation
- Created
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2017-11-28Created from EPrint's datestamp field
- Updated
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2022-03-21Created from EPrint's last_modified field