A new technique for quantitative analysis of hair loss in mice using grayscale analysis

Tulasi Ponnapakkam, Ranjitha Katikaneni, Rohan Gulati, Robert Gensure

Research output: Contribution to journalArticle

Abstract

Alopecia is a common form of hair loss which can occur in many different conditions, including male-pattern hair loss, polycystic ovarian syndrome, and alopecia areata. Alopecia can also occur as a side effect of chemotherapy in cancer patients. In this study, our goal was to develop a consistent and reliable method to quantify hair loss in mice, which will allow investigators to accurately assess and compare new therapeutic approaches for these various forms of alopecia. The method utilizes a standard gel imager to obtain and process images of mice, measuring the light absorption, which occurs in rough proportion to the amount of black (or gray) hair on the mouse. Data that has been quantified in this fashion can then be analyzed using standard statistical techniques (i.e., ANOVA, T-test) This methodology was tested in mouse models of chemotherapy-induced alopecia, alopecia areata and alopecia from waxing. In this report, the detailed protocol is presented for performing these measurements, including validation data from C57BL/6 and C3H/HeJ strains of mice. This new technique offers a number of advantages, including relative simplicity of application, reliance on equipment which is readily available in most research laboratories, and applying an objective, quantitative assessment which is more robust than subjective evaluations. Improvements in quantification of hair growth in mice will improve study of alopecia models and facilitate evaluation of promising new therapies in preclinical studies.

Original languageEnglish (US)
Article numbere52185
JournalJournal of Visualized Experiments
Volume2015
Issue number97
DOIs
StatePublished - Mar 9 2015

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Keywords

  • Alopecia
  • Alopecia areata
  • Chemotherapy-induced alopecia
  • Grayscale
  • Hair
  • Issue 97
  • Mice
  • Structural biology

ASJC Scopus subject areas

  • Neuroscience(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

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