High-throughput phenotyping methods for quantifying hair fiber morphology

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 preparati...

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Autores principales:	Tina Lasisi, Arslan A. Zaidi, Timothy H. Webster, Nicholas B. Stephens, Kendall Routch, Nina G. Jablonski, Mark D. Shriver
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2280919b896d45f8aa3f1e3ffc7092b3
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spelling	oai:doaj.org-article:2280919b896d45f8aa3f1e3ffc7092b32021-12-02T17:51:13ZHigh-throughput phenotyping methods for quantifying hair fiber morphology10.1038/s41598-021-90409-x2045-2322https://doaj.org/article/2280919b896d45f8aa3f1e3ffc7092b32021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90409-xhttps://doaj.org/toc/2045-2322Abstract 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.Tina LasisiArslan A. ZaidiTimothy H. WebsterNicholas B. StephensKendall RoutchNina G. JablonskiMark D. ShriverNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tina Lasisi Arslan A. Zaidi Timothy H. Webster Nicholas B. Stephens Kendall Routch Nina G. Jablonski Mark D. Shriver High-throughput phenotyping methods for quantifying hair fiber morphology
description	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.
format	article
author	Tina Lasisi Arslan A. Zaidi Timothy H. Webster Nicholas B. Stephens Kendall Routch Nina G. Jablonski Mark D. Shriver
author_facet	Tina Lasisi Arslan A. Zaidi Timothy H. Webster Nicholas B. Stephens Kendall Routch Nina G. Jablonski Mark D. Shriver
author_sort	Tina Lasisi
title	High-throughput phenotyping methods for quantifying hair fiber morphology
title_short	High-throughput phenotyping methods for quantifying hair fiber morphology
title_full	High-throughput phenotyping methods for quantifying hair fiber morphology
title_fullStr	High-throughput phenotyping methods for quantifying hair fiber morphology
title_full_unstemmed	High-throughput phenotyping methods for quantifying hair fiber morphology
title_sort	high-throughput phenotyping methods for quantifying hair fiber morphology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2280919b896d45f8aa3f1e3ffc7092b3
work_keys_str_mv	AT tinalasisi highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT arslanazaidi highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT timothyhwebster highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT nicholasbstephens highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT kendallroutch highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT ninagjablonski highthroughputphenotypingmethodsforquantifyinghairfibermorphology AT markdshriver highthroughputphenotypingmethodsforquantifyinghairfibermorphology
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High-throughput phenotyping methods for quantifying hair fiber morphology

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