Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

Journal: The Journal of investigative dermatology
PMID: 39236901

Abstract

Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learning-based computer vision approach for the high-throughput quantification of individual hair fibers at a high resolution. Our innovative computer vision tool can distinguish and extract overlapping fibers for quantification of multivariate features, including length, width, and color, to generate single-hair phenomes of diverse conditions across the lifespan of mice. Using our tool, we explored the effects of hormone signaling, genetic modifications, and aging on hair follicle output. Our analyses revealed hair phenotypes resultant of endocrinological, developmental, and aging-related alterations in the fur coats of mice. These results demonstrate the efficacy of our deep hair phenomics tool for characterizing factors that modulate the hair follicle and developing, to our knowledge, previously unreported diagnostic methods for detecting disease through the hair fiber. Finally, we have generated a searchable, interactive web tool for the exploration of our hair fiber data at skinregeneration.org.

Authors

  • Jasson Makkar
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Jorge Flores
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Mason Matich
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Tommy T Duong
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Sean M Thompson
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Yiqing Du
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Isabelle Busch
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Quan M Phan
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Qing Wang
    School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. qwang@163.com.
  • Kristen Delevich
    Department of Integrative Physiology and Neuroscience, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA; Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
  • Liam Broughton-Neiswanger
    Washington Animal Disease Diagnostic Laboratory, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
  • Iwona M Driskell
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
  • Ryan R Driskell
    School of Molecular Biosciences, Washington State University, Pullman, Washington, USA; Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA. Electronic address: ryan.driskell@wsu.edu.

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