Reaction-diffusion may underlie human fingerprint diversity

Summary by Katherine Rogers: Glover, J.D., Sudderick, Z.R., Shih, B.B., Batho-Samblas, C., Charlton, L., Krause, A.L., Anderson, C., Riddell, J., Balic, A., Li, J., Klika, V., Woolley, T.E., Gaffney, E.A., Corsinotti, A., Anderson, R.A., Johnston, L.J., Brown, S.J., Wang, S., Chen, Y., Crichton, M.L., Headon, D.J. (2023). The developmental basis of fingerprint pattern formation and variation. Cell 186, 940-956 e920. 

Image credit: Midjourney

Did you use a fingerprint ID to unlock your MacBook and read this? If so, you took advantage of your unique fingerprints, which developed before birth between the ages of 10-17 weeks (Wertheim and Maceo, 2002). Work from the Headon lab (Glover et al., 2023) addresses the question of how human fingerprints form—what genes direct their formation, what mechanical forces are involved, and what processes give rise to the extreme diversity of fingerprint patterns used by Apple, the DMV, and law enforcement?

The “volar” skin on human palms and soles features raised ridges called “dermatoglyphs” that comprise our grip-enhancing fingerprints. Non-volar skin lacks dermatoglyphs but contains hair follicles. To determine the genetic programs underlying volar vs. non-volar skin development, the Headon lab performed histological analyses and single-nucleus RNAseq in 14-week human samples. This revealed that hair follicles and dermatoglyphs initially share a developmental program that diverges at later stages. Both tissue type precursors initially experience high WNT, EDAR, and FGF20 activity. However, hair follicles subsequently activate SHH, recruiting a group of underlying mesenchymal cells that contribute to follicle formation. Dermatoglyph precursors do not activate SHH and fail to recruit support cells, instead activating TGFA. Dermatoglyph formation was therefore proposed to result from the truncated deployment of a hair follicle program.

What mechanical forces are involved in fingerprint formation? The authors assessed whether dermatoglyph morphology could result from tissue buckling caused by cell crowding and subsequent tissue deformation (Kücken and Newell, 2005). However, cell shape quantification did not indicate the presence of forces expected at the site of dermatoglyph formation, nor did activity of the stress-sensing protein YAP prefigure formation. Instead, they found greater cell proliferation at the base of developing dermatoglyphs, as well as differences in softness of the involved tissue layers. Together, this suggests a “banded proliferation” mechanism in which a symmetry-breaking change in gene expression creates bands of low and high proliferation that underly dermatoglyph morphology. Alterations to these tissue mechanics may explain the missing fingerprints observed in some people with keratin mutations.   

But how do these mechanisms—shared by all typically developing humans—create diverse fingerprints? This diversity could spring from a Turing reaction-diffusion system (Turing, 1952). In a reaction-diffusion system, a diffusing activator activates itself and an inhibitor. This can create patterns including zebra-like stripes (Kondo and Miura, 2010) or gradients (Müller et al., 2012). Experiments and simulations showed that a reaction-diffusion system with WNT/EDAR as activator and BMP as inhibitor could underly fingerprint patterning. The authors propose that fingerprint diversity is caused by activation of the system at initiation sites unique to each individual, possibly influenced by RSPO2 activity. The resulting activity waves stemming from initiation sites may be further influenced by skin crease “boundaries” resulting from finger movement beginning around week 12. Known fingerprint patterns including arches, loops, and whorls were reproduced in simulations by changing the timing, location, and angle of initiation sites. The stunning diversity of human fingerprint patterns may therefore arise by initiation of a WNT/EDAR/BMP-based reaction-diffusion system at different locations in developing volar skin.

Glover, J. D., Sudderick, Z. R., Shih, B. B., Batho-Samblas, C., Charlton, L., Krause, A. L., Anderson, C., Riddell, J., Balic, A., Li, J., et al. (2023). The developmental basis of fingerprint pattern formation and variation. Cell 186, 940-956 e920.

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

Kücken, M. and Newell, A. C. (2005). Fingerprint formation. J Theor Biol 235, 71-83.

Müller, P., Rogers, K. W., Jordan, B. M., Lee, J. S., Robson, D., Ramanathan, S. and Schier, A. F. (2012). Differential diffusivity of Nodal and Lefty underlies a reaction-diffusion patterning system. Science 336, 721-724.

Turing, A. M. (1952). The chemical basis of morphogenesis. Philos T Roy Soc B 237, 37-72.

Wertheim, K. and Maceo, A. (2002). The Critical Stage of Friction Ridge and Pattern Formation. Journal of Forensic Identification.