A multi-scale model for hair follicles reveals heterogeneous domains driving rapid spatiotemporal hair growth patterning

  1. Qixuan Wang
  2. Ji Won Oh
  3. Hye-Lim Lee
  4. Anukriti Dhar
  5. Tao Peng
  6. Raul Ramos
  7. Christian Fernando Guerrero-Juarez
  8. Xiaojie Wang
  9. Ran Zhao
  10. Xiaoling Cao
  11. Jonathan Le
  12. Melisa A Fuentes
  13. Shelby C Jocoy
  14. Antoni R Rossi
  15. Brian Vu
  16. Kim Pham
  17. Xiaoyang Wang
  18. Nanda Maya Mali
  19. Jung Min Park
  20. June-Hyug Choi
  21. Hyunsu Lee
  22. Julien Legrand
  23. Eve Kandyba
  24. Jung Chul Kim
  25. Moonkyu Kim
  26. John Foley
  27. Zhengquan Yu
  28. Krzysztof Kobielak
  29. Bogi Andersen
  30. Kiarash Khosrotehrani
  31. Qing Nie  Is a corresponding author
  32. Maksim V Plikus  Is a corresponding author
  1. University of California, Irvine, United States
  2. School of Medicine, Kyungpook National University, Republic of Korea
  3. School of Medicine, Kyungpook National University, Korea (South), Republic of
  4. University of Queensland Diamantina Institute, Australia
  5. University of Southern California, United States
  6. Kyungpook National University Hospital, Republic of Korea
  7. Indiana University School of Medicine, United States
  8. College of Biological Sciences, China Agricultural University, China

Abstract

The control principles behind robust cyclic regeneration of hair follicles (HFs) remain unclear. Using multi-scale modeling we show that coupling inhibitors and activators with physical growth of HFs is sufficient to drive periodicity and excitability of hair regeneration. Model simulations and experimental data reveal that mouse skin behaves as a heterogeneous regenerative field, composed of anatomical domains where HFs have distinct cycling dynamics. Interactions between fast-cycling chin and ventral HFs and slow-cycling dorsal HFs produce bilaterally symmetric patterns. Ear skin behaves as a hyper-refractory domain with HFs in extended rest phase. Such hyper-refractivity relates to high levels of BMP ligands and WNT antagonists, in part expressed by ear-specific cartilage and muscle. Hair growth stops at the boundaries with hyper-refractory ears and anatomically discontinuous eyelids, generating wave-breaking effects. We posit that similar mechanisms for coupled regeneration with dominant activator, hyper-refractory, and wave-breaker regions can operate in other actively renewing organs.

Data availability

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Qixuan Wang

    Department of Mathematics, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Ji Won Oh

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5742-5120
  3. Hye-Lim Lee

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Anukriti Dhar

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Tao Peng

    Department of Mathematics, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Raul Ramos

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Christian Fernando Guerrero-Juarez

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Xiaojie Wang

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Ran Zhao

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Xiaoling Cao

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Jonathan Le

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Melisa A Fuentes

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Shelby C Jocoy

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Antoni R Rossi

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Brian Vu

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Kim Pham

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Xiaoyang Wang

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. Nanda Maya Mali

    Department of Anatomy, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  19. Jung Min Park

    Department of Anatomy, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  20. June-Hyug Choi

    Department of Anatomy, School of Medicine, Kyungpook National University, Daegu, Korea (South), Republic of
    Competing interests
    The authors declare that no competing interests exist.
  21. Hyunsu Lee

    Department of Anatomy, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  22. Julien Legrand

    Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  23. Eve Kandyba

    Department of Pathology, University of Southern California, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9219-5284
  24. Jung Chul Kim

    Hair Transplantation Center, Kyungpook National University Hospital, Daegu, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  25. Moonkyu Kim

    Hair Transplantation Center, Kyungpook National University Hospital, Daegu, Republic of Korea
    Competing interests
    The authors declare that no competing interests exist.
  26. John Foley

    Department of Dermatology, Medical Sciences Program, Indiana University School of Medicine, Bloomington, United States
    Competing interests
    The authors declare that no competing interests exist.
  27. Zhengquan Yu

    State Key Laboratories for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8696-2013
  28. Krzysztof Kobielak

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
  29. Bogi Andersen

    Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7181-2768
  30. Kiarash Khosrotehrani

    Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  31. Qing Nie

    Department of Mathematics, University of California, Irvine, Irvine, United States
    For correspondence
    qnie@math.uci.edu
    Competing interests
    The authors declare that no competing interests exist.
  32. Maksim V Plikus

    Department of Developmental and Cell Biology, University of California, Irvine, Irvine, United States
    For correspondence
    plikus@uci.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8845-2559

Funding

National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01-AR067273)

  • Maksim V Plikus

National Research Foundation of Korea (2016R1C1B1015211)

  • Ji Won Oh

National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01-AR061552)

  • Krzysztof Kobielak

National Health and Medical Research Council (1023371)

  • Kiarash Khosrotehrani

National Science Foundation (DGE-1321846)

  • Christian Fernando Guerrero-Juarez

National Institute of General Medical Sciences (GM055246)

  • Christian Fernando Guerrero-Juarez

Pew Charitable Trusts (29641)

  • Maksim V Plikus

National Science Foundation (DMS 1161621)

  • Qing Nie

National Cancer Institute (T32-CA009054)

  • Hye-Lim Lee

National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01-AR056439)

  • Bogi Andersen

National Science Foundation (DMS 1562176)

  • Qing Nie

National Institute of General Medical Sciences (P50-GM076516)

  • Qing Nie

National Institute of General Medical Sciences (R01-GM107264)

  • Qing Nie

National Institute of Neurological Disorders and Stroke (R01-NS095355)

  • Qing Nie

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Valerie Horsley, Yale University, United States

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#2012-3054 and #2013-3081) of the University of California, Irvine.

Version history

  1. Received: October 28, 2016
  2. Accepted: June 29, 2017
  3. Accepted Manuscript published: July 11, 2017 (version 1)
  4. Version of Record published: September 22, 2017 (version 2)

Copyright

© 2017, Wang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 7,696
    views
  • 1,285
    downloads
  • 46
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Qixuan Wang
  2. Ji Won Oh
  3. Hye-Lim Lee
  4. Anukriti Dhar
  5. Tao Peng
  6. Raul Ramos
  7. Christian Fernando Guerrero-Juarez
  8. Xiaojie Wang
  9. Ran Zhao
  10. Xiaoling Cao
  11. Jonathan Le
  12. Melisa A Fuentes
  13. Shelby C Jocoy
  14. Antoni R Rossi
  15. Brian Vu
  16. Kim Pham
  17. Xiaoyang Wang
  18. Nanda Maya Mali
  19. Jung Min Park
  20. June-Hyug Choi
  21. Hyunsu Lee
  22. Julien Legrand
  23. Eve Kandyba
  24. Jung Chul Kim
  25. Moonkyu Kim
  26. John Foley
  27. Zhengquan Yu
  28. Krzysztof Kobielak
  29. Bogi Andersen
  30. Kiarash Khosrotehrani
  31. Qing Nie
  32. Maksim V Plikus
(2017)
A multi-scale model for hair follicles reveals heterogeneous domains driving rapid spatiotemporal hair growth patterning
eLife 6:e22772.
https://doi.org/10.7554/eLife.22772

Share this article

https://doi.org/10.7554/eLife.22772

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Chananchida Sang-aram, Robin Browaeys ... Yvan Saeys
    Research Article

    Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

    1. Computational and Systems Biology
    Maksim Kleverov, Daria Zenkova ... Alexey A Sergushichev
    Research Article

    Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provides a streamlined access to more than 96000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT license.