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A multifunctional Wnt regulator underlies the evolution of rodent stripe patterns

Nature Ecology & Evolution volume 7, pages 2143–2159 (2023)Cite this article

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Abstract

Animal pigment patterns are excellent models to elucidate mechanisms of biological organization. Although theoretical simulations, such as Turing reaction–diffusion systems, recapitulate many animal patterns, they are insufficient to account for those showing a high degree of spatial organization and reproducibility. Here, we study the coat of the African striped mouse (Rhabdomys pumilio) to uncover how periodic stripes form. Combining transcriptomics, mathematical modelling and mouse transgenics, we show that the Wnt modulator Sfrp2 regulates the distribution of hair follicles and establishes an embryonic prepattern that foreshadows pigment stripes. Moreover, by developing in vivo gene editing in striped mice, we find that Sfrp2 knockout is sufficient to alter the stripe pattern. Strikingly, mutants exhibited changes in pigmentation, revealing that Sfrp2 also regulates hair colour. Lastly, through evolutionary analyses, we find that striped mice have evolved lineage-specific changes in regulatory elements surrounding Sfrp2, many of which may be implicated in modulating the expression of this gene. Altogether, our results show that a single factor controls coat pattern formation by acting both as an orienting signalling mechanism and a modulator of pigmentation. More broadly, our work provides insights into how spatial patterns are established in developing embryos and the mechanisms by which phenotypic novelty originates.

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Fig. 1: Development of hair placodes and pigment patterns in the African striped mouse.
Fig. 2: Relationship between Sfrp2 and Wnt signalling in embryonic skin.
Fig. 3: Modulator gradients control stripe patterning in a reaction–diffusion system.
Fig. 4: In vivo genome editing reveals that Sfrp2 regulates striped mouse coat patterns.
Fig. 5: Evolution of the Sfrp2 locus.

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Data availability

The bulk RNA-seq, scRNA-seq and ATAC-seq reads are submitted under an NCBI BioProject: PRJNA1004353. https://figshare.com/projects/Data_repository_for_A_multifunctional_Wnt_regulator_underlies_the_evolution_of_rodent_stripe_patterns_/175200. Source data are provided with this paper.

Code availability

Code used for scRNA-seq analysis, bulk RNA-seq analysis and comparative genomics is deposited at https://figshare.com/projects/Data_repository_for_A_multifunctional_Wnt_regulator_underlies_the_evolution_of_rodent_stripe_patterns_/175200.

References

  1. Mills, M. G. & Patterson, L. B. Not just black and white: pigment pattern development and evolution in vertebrates. Semin. Cell Dev. Biol. 20, 72–81 (2009).

    Article  CAS  PubMed  Google Scholar 

  2. Caro, T. & Mallarino, R. Coloration in mammals. Trends Ecol. Evol. 35, 357–366 (2020).

    Article  PubMed  Google Scholar 

  3. Cuthill, I. C. et al. The biology of color. Science 357, eaan0221 (2017).

    Article  PubMed  Google Scholar 

  4. Kratochwil, C. F. & Mallarino, R. Mechanisms underlying the formation and evolution of vertebrate color patterns. Annu. Rev. Genet. https://doi.org/10.1146/annurev-genet-031423-120918 (2023).

  5. Kondo, S. & Miura, T. Reaction–diffusion model as a framework for understanding biological pattern formation. Science 329, 1616–1620 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Kondo, S. An updated kernel-based Turing model for studying the mechanisms of biological pattern formation. J. Theor. Biol. 414, 120–127 (2017).

    Article  PubMed  Google Scholar 

  7. Turing, A. M. The chemical basis of morphogenesis. 1953. Bull. Math. Biol. 52, 153–197 (1990).

    Article  CAS  PubMed  Google Scholar 

  8. Vittadello, S. T., Leyshon, T., Schnoerr, D. & Stumpf, M. P. H. Turing pattern design principles and their robustness. Philos. Trans. A 379, 20200272 (2021).

    Article  Google Scholar 

  9. Patterson, L. B. & Parichy, D. M. Zebrafish pigment pattern formation: insights into the development and evolution of adult form. Annu. Rev. Genet. 53, 505–530 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Kaelin, C. B., McGowan, K. A. & Barsh, G. S. Developmental genetics of color pattern establishment in cats. Nat. Commun. 12, 5127 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mallarino, R. et al. Developmental mechanisms of stripe patterns in rodents. Nature 539, 518–523 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Haupaix, N. & Manceau, M. The embryonic origin of periodic color patterns. Dev. Biol. 460, 70–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Kaelin, C. B. et al. Specifying and sustaining pigmentation patterns in domestic and wild cats. Science 337, 1536–1541 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mallarino, R., Pillay, N., Hoekstra, H. E. & Schradin, C. African striped mice. Curr. Biol. 28, R299–R301 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Hardy, M. H. The secret life of the hair follicle. Trends Genet. 8, 55–61 (1992).

    Article  CAS  PubMed  Google Scholar 

  16. Millar, S. E. Molecular mechanisms regulating hair follicle development. J. Invest. Dermatol. 118, 216–225 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. Andl, T., Reddy, S. T., Gaddapara, T. & Millar, S. E. WNT signals are required for the initiation of hair follicle development. Dev. Cell 2, 643–653 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. van Loon, K., Huijbers, E. J. M. & Griffioen, A. W. Secreted frizzled-related protein 2: a key player in noncanonical Wnt signaling and tumor angiogenesis. Cancer Metastasis Rev. 40, 191–203 (2021).

    Article  PubMed  Google Scholar 

  19. Kim, M., Han, J. H., Kim, J.-H., Park, T. J. & Kang, H. Y. Secreted frizzled-related protein 2 (sFRP2) functions as a melanogenic stimulator; the role of sFRP2 in UV-induced hyperpigmentary disorders. J. Invest. Dermatol. 136, 236–244 (2016).

    Article  CAS  PubMed  Google Scholar 

  20. Liang, C.-J. et al. SFRPs are biphasic modulators of Wnt-signaling-elicited cancer stem cell properties beyond extracellular control. Cell Rep. 28, 1511–1525 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Lin, H. et al. sFRP2 activates Wnt/β-catenin signaling in cardiac fibroblasts: differential roles in cell growth, energy metabolism extracellular matrix remodeling. Am. J. Physiol. Cell Physiol. 311, C710–C719 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gupta, K. et al. Single-cell analysis reveals a hair follicle dermal niche molecular differentiation trajectory that begins prior to morphogenesis. Dev. Cell 48, 17–31 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Sennett, R. et al. An integrated transcriptome atlas of embryonic hair follicle progenitors, their niche, and the developing skin. Dev. Cell 34, 577–591 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rezza, A. et al. Signaling networks among stem cell precursors, transit-amplifying progenitors, and their niche in developing hair follicles. Cell Rep. 14, 3001–3018 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sulic, A.-M. et al. Transcriptomic landscape of early hair follicle and epidermal development. Cell Rep. 42, 112643 (2023).

    Article  CAS  PubMed  Google Scholar 

  26. Saxena, N., Mok, K.-W. & Rendl, M. An updated classification of hair follicle morphogenesis. Exp. Dermatol. 28, 332–344 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tsai, S.-Y. et al. Wnt/β-catenin signaling in dermal condensates is required for hair follicle formation. Dev. Biol. 385, 179–188 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Gat, U., DasGupta, R., Degenstein, L. & Fuchs, E. De Novo hair follicle morphogenesis and hair tumors in mice expressing a truncated beta-catenin in skin. Cell 95, 605–614 (1998).

    Article  CAS  PubMed  Google Scholar 

  29. Yu, K. et al. Conditional inactivation of FGF receptor 2 reveals an essential role for FGF signaling in the regulation of osteoblast function and bone growth. Development 130, 3063–3074 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Šošić, D., Richardson, J. A., Yu, K., Ornitz, D. M. & Olson, E. N. Twist regulates cytokine gene expression through a negative feedback loop that represses NF-κB activity. Cell 112, 169–180 (2003).

    Article  PubMed  Google Scholar 

  31. Hiscock, T. W. & Megason, S. G. Orientation of Turing-like patterns by morphogen gradients and tissue anisotropies. Cell Syst. 1, 408–416 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sick, S., Reinker, S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction–diffusion mechanism. Science 314, 1447–1450 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Van Gorder, R. A. Pattern formation from spatially heterogeneous reaction–diffusion systems. Philos. Trans. A 379, 20210001 (2021).

    Article  Google Scholar 

  34. Gierer, A. & Meinhardt, H. A theory of biological pattern formation. Kybernetik 12, 30–39 (1972).

    Article  CAS  PubMed  Google Scholar 

  35. Yochelis, A., Tintut, Y., Demer, L. L. & Garfinkel, A. The formation of labyrinths, spots and stripe patterns in a biochemical approach to cardiovascular calcification. New J. Phys. 10, 055002 (2008).

    Article  Google Scholar 

  36. McKay, R. & Kolokolnikov, T. Stability transitions and dynamics of mesa patterns near the shadow limit of reaction–diffusion systems in one space dimension. Discret. Contin. Dyn. Syst. B 17, 191–220 (2012).

    Google Scholar 

  37. Yoon, Y. et al. Streamlined ex vivo and in vivo genome editing in mouse embryos using recombinant adeno-associated viruses. Nat. Commun. 9, 412 (2018).

  38. Iozumi, K., Hoganson, G. E., Pennella, R., Everett, M. A. & Fuller, B. B. Role of tyrosinase as the determinant of pigmentation in cultured human melanocytes. J. Invest. Dermatol. 100, 806–811 (1993).

    Article  CAS  PubMed  Google Scholar 

  39. Edraki, A. et al. A compact, high-accuracy Cas9 with a dinucleotide PAM for in vivo genome editing. Mol. Cell 73, 714–726 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Enshell-Seijffers, D., Lindon, C., Wu, E., Taketo, M. M. & Morgan, B. A. β-Catenin activity in the dermal papilla of the hair follicle regulates pigment-type switching. Proc. Natl Acad. Sci. USA 107, 21564–21569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Morgan, B. A. The dermal papilla: an instructive niche for epithelial stem and progenitor cells in development and regeneration of the hair follicle. Cold Spring Harb. Perspect. Med. 4, a015180 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Steingrímsson, E., Copeland, N. G. & Jenkins, N. A. Melanocytes and the microphthalmia transcription factor network. Annu. Rev. Genet. 38, 365–411 (2004).

    Article  PubMed  Google Scholar 

  43. Jho, E.-H. et al. Wnt/beta-catenin/Tcf signaling induces the transcription of Axin2, a negative regulator of the signaling pathway. Mol. Cell. Biol. 22, 1172–1183 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Shtutman, M. et al. The cyclin D1 gene is a target of the beta-catenin/LEF-1 pathway. Proc. Natl Acad. Sci. USA 96, 5522–5527 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. He, T. C. et al. Identification of c-MYC as a target of the APC pathway. Science 281, 1509–1512 (1998).

    Article  CAS  PubMed  Google Scholar 

  46. Richardson, R. et al. The genomic basis of temporal niche evolution in a diurnal rodent. Curr. Biol. https://doi.org/10.1016/j.cub.2023.06.068 (2023).

  47. Gao, F. et al. EasyCodeML: a visual tool for analysis of selection using CodeML. Ecol. Evol. 9, 3891–3898 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kaelin, C. B. & Barsh, G. S. Genetics of pigmentation in dogs and cats. Annu Rev. Anim. Biosci. 1, 125–156 (2013).

    Article  PubMed  Google Scholar 

  49. Keller, S. H., Jena, S. G., Yamazaki, Y. & Lim, B. Regulation of spatiotemporal limits of developmental gene expression via enhancer grammar. Proc. Natl Acad. Sci. USA 117, 15096–15103 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kaufman, M. H. The Atlas of Mouse Development (Academic Press, 1992).

  51. Wu, J. & Wang, X. Whole-mount in situ hybridization of mouse embryos using DIG-labeled RNA probes. Methods Mol. Biol. 1922, 151–159 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Burns, K. J., Vasil, G. M., Oishi, J. S., Lecoanet, D. & Brown, B. P. Dedalus: a flexible framework for numerical simulations with spectral methods. Phys. Rev. Res. 2, 023068 (2020).

    Article  CAS  Google Scholar 

  55. Tuckerman, L. S. & Barkley, D. in Numerical Methods for Bifurcation Problems and Large-Scale Dynamical Systems (eds Doedel, E. & Tuckerman, L. S.) 453–466 (Springer, 2000).

  56. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

  58. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Joost, S. et al. The molecular anatomy of mouse skin during hair growth and rest. Cell Stem Cell 26, 441–457 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Beronja, S., Livshits, G., Williams, S. & Fuchs, E. Rapid functional dissection of genetic networks via tissue-specific transduction and RNAi in mouse embryos. Nat. Med. 16, 821–827 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Aasen, T. & Izpisúa Belmonte, J. C. Isolation and cultivation of human keratinocytes from skin or plucked hair for the generation of induced pluripotent stem cells. Nat. Protoc. 5, 371–382 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Hahn, W. C. et al. Enumeration of the Simian virus 40 early region elements necessary for human cell transformation. Mol. Cell. Biol. 22, 2111–2123 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kowalczyk, A. et al. RERconverge: an R package for associating evolutionary rates with convergent traits. Bioinformatics 35, 4815–4817 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yang, Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13, 555–556 (1997).

    CAS  PubMed  Google Scholar 

  68. Álvarez-Carretero, S., Kapli, P. & Yang, Z. Beginner’s guide on the use of PAML to detect positive selection. Mol. Biol. Evol. 40, msad041 (2023).

  69. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Shumate, A. & Salzberg, S. L. Liftoff: accurate mapping of gene annotations. Bioinformatics 37, 1639–1643 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. McLeay, R. C. & Bailey, T. L. Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinform. 11, 165 (2010).

    Article  Google Scholar 

  74. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Mallarino laboratory; Princeton LAR (C. Dmytrow, K. Gerhart, G. Barnett and J. McGuire) for help with striped mice husbandry; the LSI Genomics Core (W. Wang, J. M. Miller, J. Wiggins and J. Arley Volmar) for help with library preparation and sequencing; the Nikon Center of Excellence Confocal Microscopy Core (S. Wang and G. Laevsky); and members of the Rivera-Perez laboratory (Y. Yoon and J. Gallant) for help with in vivo genome editing experiments. We also thank E. F. Wieschaus, G. Deshpande and P. Holl for insights and discussion. This project was supported by an NIH grant to R.M. (R35GM133758). M.R.J. was supported by an NIH fellowship (F32 GM139253). S.L. was supported by a Presidential Postdoctoral Research fellowship (Princeton University). B.J.B. was supported by an NIH training grant (T32GM007388). C.Y.F. was supported by an NIH fellowship (F32 GM139240-01). C.F.G.-J. is partially supported by UC Irvine Chancellor’s ADVANCE Postdoctoral Fellowship Program. Q.N. was partially supported by an NSF grant DMS1763272 and a Simons Foundation grant (594598).

Author information

Author notes

  1. These authors contributed equally: Matthew R. Johnson, Sha Li.

Authors and Affiliations

  1. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Matthew R. Johnson, Sha Li, Benjamin J. Brack, Sarah A. Mereby, Jorge A. Moreno, Charles Y. Feigin, Jenna Gaska, Alexander Ploss, Stanislav Y. Shvartsman & Ricardo Mallarino

  2. Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Christian F. Guerrero-Juarez

  3. Department of Developmental and Cell Biology, University of California, Irvine, CA, USA

    Christian F. Guerrero-Juarez & Qing Nie

  4. Department of Mathematics, University of California, Irvine, CA, USA

    Christian F. Guerrero-Juarez & Qing Nie

  5. Center for Complex Biological Systems, University of California, Irvine, CA, USA

    Christian F. Guerrero-Juarez & Qing Nie

  6. NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, USA

    Christian F. Guerrero-Juarez & Qing Nie

  7. Center for Computational Biology, Flatiron Institute, New York, NY, USA

    Pearson Miller & Stanislav Y. Shvartsman

  8. Frederick National Laboratory for Cancer Research, Frederick, MA, USA

    Jaime A. Rivera-Perez

  9. The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Stanislav Y. Shvartsman

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  1. Matthew R. Johnson
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Contributions

M.R.J. and R.M. conceived the project and designed experiments. M.R.J. performed RNA-seq experiments and bulk RNA-seq analysis. S.L. performed the in vitro and in vivo genome editing in striped mice, with help from S.A.M. and J.A.R.-P. M.R.J. and S.L. performed all downstream processing and analysis of genome edited animals. P.M. and S.Y.S. did the mathematical modelling. C.F.G.-J. led the scRNA-seq analysis, with support from M.R.J. and Q.N. M.R.J., B.J.B. and R.M. performed in situ hybridizations. M.R.J., B.J.B., S.A.M. and R.M. performed the phenotypic characterization of striped mouse and laboratory mouse tissues, including immunofluorescence and histology. M.R.J. and S.A.M. performed the melanocyte cell culture experiments. J.A.M. did the evolutionary analysis. C.Y.F. generated the rhabdomyzed Mus genome and lift-over annotation. J.G. and A.P. generated the immortalized Rhabdomys fibroblasts. M.R.J. and R.M. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Ricardo Mallarino.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Patterns of hair placode formation in striped mice.

a, Side views of E13.5–E15.5 striped mouse embryos showing stages before the emergence of trunk hair placodes. Whole-mount in situ hybridization for placode markers Dkk4 and Ctnnb1 shows the presence of whisker placodes (arrows), which develop before trunk placodes. No expression is detected in dorsal skin. b, Side views of E16.5 striped mouse embryos displaying spatially restricted patterns of trunk hair placode formation, as visualized by whole-mount in situ hybridization for placode markers Wif1, Bmp4, Wnt10b Dkk1. c, Hematoxylin-Eosin staining on cross-sections of striped mouse E18.5 embryos reveals both mature placodes (arrows) and nascent placodes (asterisks); the latter are evidenced by thickening of the epidermis. d, Side views of E18.5 striped mouse embryos showing placode emergence in previously placode-barren regions, as visualized by whole-mount in situ hybridization for placode markers Dkk1 and Ctnnb1. e, Hematoxilin and Eosin (H&E) stains of longitudinal sections from different dorsal regions in striped mouse embryos. Placodes in Regions 1 (R1) and 3 (R3) emerge later than those in Region 2 (R2). Scale bars: 5 mm in (a and b); 200 µm (zoomed out) and 50 µm (inset) in (c); 5 mm in (d); 100 µm in (e). For a-e, three individuals per stage per gene were analysed.

Extended Data Fig. 2 Expression of selected Wnt modulators in E16.5 striped mouse embryos.

Fold expression changes of Wnt modulators in skin regions (R1, R2, R3, R4) dissected for bulk RNA-seq analysis. Shown are selected modulators that are expressed in a dorsoventral gradient. Fold expression changes were calculated from average FPKM values (n = 3 biologically independent samples.

Extended Data Fig. 3 Analysis of hair placode and dermal condensate markers.

a-b, Plots showing the subset of cells that express established hair placode (a) and dermal condensate (b) markers in the dorsal skin of E16.5 striped mice. c-d, Dot plots of hair placode25 (c) and dermal condensate26 (d) markers showing expression changes among the three different dorsal regions sampled. The size of the dot encodes the percentage of cells within a dorsal region, while the colour encodes the average expression level across all cells within a dorsal region (blue is high, red is low). Asterisks depict markers with high expression levels in Region 2 (R2), compared to Region 1 (R1) and Region 3 (R3). As described in the main text, R2 has visible hair follicles at this developmental stage, whereas R1 and R3 do not.

Extended Data Fig. 4 Expression of Sfrp2 in dermal fibroblasts.

a, Sfrp2 expressing fibroblasts are expressed primarily in the reticular (lower) dermis. Papillary (upper) and reticular (lower) dermis fibroblasts were defined based on previously established markers3; Papillary dermis: Ntn1, Pdpn, Ackr4, Lrig1, Apcdd1; Reticular dermis: Tgm2, Cnn1, Cdh2, Mgp, Dlk1. b, At E16.5, expression levels of Sfrp2 and the percentage of fibroblasts expressing Sfrp2 are highest in Region 1 (R1) and lowest in Region 3 (R3), in agreement with the dorsoventral gradient revealed by the bulk RNA-seq data. In b, n = 3 biologically independent samples. Left panel: bars represent average expression levels. Right panel: mean values (+/- SEM).

Extended Data Fig. 5 High expression of Sfrp2 in the reticular (lower) dermis coincides with low expression of LEF1.

a, In situ hybridization in striped mouse E16.5 embryos shows that Sfrp2 is primarily expressed in the reticular dermis. Right side image shows expression of Sfrp2 at subcellular resolution. b-c, LEF1 immunostaining in staged matched striped (b) and laboratory (c) mouse embryos. Red boxes denote zoomed-in regions. Scale bars: 200 µm (zoomed out) and 100 µm (zoomed in) in a; 200 µm (zoomed out) and 50 µm (zoomed in) in b and c. NT = neural tube. For a-c, three different individuals were analysed.

Extended Data Fig. 6 Dermo1 and Sfrp2 expressing fibroblasts.

A Dermo-Cre mouse was used to drive Cre expression in dermal fibroblasts. As illustrated above, a subset of Dermo1 expressing fibroblasts express Sfrp2. Thus, this mouse strain is adequate for driving expression of Cre in cells expressing Sfrp2.

Extended Data Fig. 7 Mathematical simulations.

a, Schematic showing the role of Sfrp2 as an inhibitor of Wnt signalling. b, Gradient steepness increases central stripe width independent of model. Each row depicts a schematic and equations governing a particular variant of our modulator-activator-inhibitor system (left) and the resulting simulations of stripe spacing for different gradient steepness values using these models (right). In all cases, gradient steepness affects stripe spacing. c, Predictions from an alternative model of positional information. Patterning based on positional information is inconsistent with our experimental results. We illustrate this by considering two standard paradigms for stripe patterning by positional information. Under a classic ‘French Flag’ model (left, top), each stripe (marked in grey) is assigned to a region of space in which a single morphogen gradient exists between two pathway-specific threshold concentrations (horizontal red lines). (top, left) Under such a paradigm, a substantial reduction in morphogen expression, in this case by 80 percent, makes it impossible for the gradient to reach certain thresholds entirely, leading to stripe loss. (bottom, left) Alternatively, stripes are frequently determined via an ‘opposing gradients’ motif via the interaction of multiple gradients. We depict one example, in which each stripe is determined by two opposite facing gradients, such that a stripe forms in the region where each gradient exceeds a morphogen-specific threshold. (right, bottom) Major reduction of a single morphogen eliminates one stripe while leaving the other unperturbed.

Extended Data Fig. 8 Generation of in vivo genome editing in striped mouse.

a, Schematic of the Sfrp2 locus (exons in red) showing the transcriptional start site (TSS), protospacer adjacent motif (PAM) short guide RNA (sgRNA) target/sequence. Four types of deletions were achieved: 2 bp, 13 bp, 466 bp 527 bp (white boxes). All mutations are predicted to cause frameshift mutations. b, Representative western blot of individuals carrying different combinations of wild-type and a 13 bp deleted allele (wild type: Sfrp2+/+; heterozygous: Sfrp2+/-; homozygous: (Sfrp-/-). Sfrp-/- have no detectable SFRP2 Protein (green). Bands ~30 kDa correspond to SFRP2 protein. b-TUBULIN (~50 kDa, red) was used as a loading control. In b, two different individuals from each genotype were analysed.

Extended Data Fig. 9 Phenotypic characterization of Sfrp2 mutants.

a and b, Whole-mount in situ hybridization for Dkk4 in wild-type and Sfrp2 knockout E16.5 embryos (a) and corresponding width measurements of dorsal regions 1 and 3 (that is, R1 and R3) (b). Note that Dkk4 expression diminishes in response to Sfrp2 knockout. c, Hair length measurements in postnatal day 3 wild-type and Sfrp2 knockout individuals. In b and c, n = 3 biologically independent samples for each Sfrp2 knockout and Sfrp2 wild-type individuals.

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Extended Data Fig. 10 Sfrp2 promotes melanogenesis by activating Wnt signalling.

In situ hybridization showing specific Sfrp2 expression in the dermal papilla of P4 striped mouse hair follicles. b, Melanocytes were stably transduced with either a control (LV-GFP) or an experimental (LV-Sfrp2GFP) lentivirus and expression of Wnt targets and melanogenesis genes in stably transduced control and experimental cells, as determined was determined via qPCR (P = 0.12026 (Axin); P = 0.001816 (C-myc); P = 0.006739 (CyclinD); P = 0.001040 (Mitf); P = 0.010712 (Tyr); ANOVA test; N = 4). c, Quantitative PCR (qPCR) showing Sfrp2 mRNA fold change levels along different dorsal skin regions in embryonic and postnatal stages (E16.5: P = 0.0283 (R1vsR2); P = 0.0062 (R1vsR3); P = 0.3959 (R2vsR3); E19.5: P = 0.8685 (R1vsR2); P = 0.6319 (R1vsR3); P = 0.9015 (R2vsR3); P0: P = 0.9724 (R1vsR2); P = 0.8207 (R1vsR3); P = 0.6971 (R2vsR3); P4: P = 0.0003 (R1vsR2); P = 0.0022 (R1vsR3); P = 0.0001 (R2vsR3); ANOVA test; N = 3 for E16.5, E19.5 P0, N = 4 for P4). Scale bars in a: 100 µm (left) and 25 µm (right). In a, three different individuals were analysed. In b and c, data are presented as mean values +/− SEM.

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Supplementary Data 1

Differentially expressed genes between regions 1 and 4 of E16.5 striped mice skin. Differentially expressed genes were determined using DESeq2. P value corrected for multiple testing (Padj < 0.05).

Supplementary Data 2

Differentially expressed genes between Sfrp2high and Sfrp2l°w fibroblast populations. Differentially expressed genes were determined using DESeq2. P value corrected for multiple testing (Padj < 0.05).

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Johnson, M.R., Li, S., Guerrero-Juarez, C.F. et al. A multifunctional Wnt regulator underlies the evolution of rodent stripe patterns. Nat Ecol Evol 7, 2143–2159 (2023). https://doi.org/10.1038/s41559-023-02213-7

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