Abstract
Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used. Here, we present a high-throughput sample preparation protocol for the imaging of both longitudinal (curvature) and cross-sectional scalp hair morphology. Additionally, we describe and validate a new Python package designed to process longitudinal and cross-sectional hair images, segment them, and provide measurements of interest. Lastly, we apply our methods to an admixed African-European sample (n = 140), demonstrating the benefit of quantifying hair morphology over classification, and providing evidence that the relationship between cross-sectional morphology and curvature may be an artefact of population stratification rather than a causal link.
Highlights
Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used
Two objectively quantifiable aspects of hair morphology can be delineated: its longitudinal curvature and its cross-sectional geometry. Work in this field has been plagued by a lack of standardization in methods and issues of replicability, in part due to inadequate detailing of methods used and subjectivity in their application (Supplementary Information). In light of these challenges, we have developed sample preparation and image analysis methods that allow for the high-throughput phenotyping of hair fiber cross-sectional geometry and curvature
We found that a low melt point plastic such as polycaprolactone allowed us to lay multiple hairs of any morphology in parallel lines
Summary
Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used. Two objectively quantifiable aspects of hair morphology can be delineated: its longitudinal curvature and its cross-sectional geometry (see Fig. 1) Work in this field has been plagued by a lack of standardization in methods and issues of replicability, in part due to inadequate detailing of methods used and subjectivity in their application (Supplementary Information). In light of these challenges, we have developed sample preparation and image analysis methods that allow for the high-throughput phenotyping of hair fiber cross-sectional geometry and curvature. We present a comprehensive description of the protocols used for sample preparation and a novel computational tool for the analysis of images created with those protocols
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