Objective:
Methods such as differential scanning calorimetry (DSC) and complementary structural assays are widely used to evaluate protein and peptide-based haircare actives. Still, they are applied late in new ingredient development and require significant resources. A predictive, high-throughput strategy for early-stage screening of candidate materials for selective hair keratin binding remains needed. Additionally, identifying the specific keratin regions targeted by these actives can strengthen mechanistic understanding and guide rational product design.

Methods:
We present a high-throughput screening platform for assessing selective binding of proteins, peptides, and hydrolysates to human hair keratins. Fluorescently labelled materials are incubated in 96-well plates coated with defined keratin peptide fragments representing key structural domains. After controlled wash steps, designed to mimic pre-damaged, post-damaged or healthy hair, retained fluorescence is quantified using a plate reader to determine relative binding affinity and selectivity. Differential wash conditions are applied to explore potential interaction mechanisms, including covalent and electrostatic contributions.

Results:
Distinct binding profiles were observed across the tested materials, including selective affinity for regions of Keratin 32 and Keratin 82, which are abundant in the hair cuticle. Wash-dependent differences suggested multiple interaction modes, including disulphide-mediated and charge-driven associations.

Conclusion:
This scalable, cost-effective platform enables rapid, function-led prioritisation of protein and peptide actives, supporting precision-driven haircare development and accelerating innovation from discovery to formulation.

Understanding the cellular mechanisms underlying variation in human hair fibre traits remains a key challenge in cosmetic science and trichology. Large-scale genetic and sequencing studies have identified numerous loci associated with traits such as hair pigmentation, fibre shape, and thickness; however, these approaches do not directly reveal which hair follicle cell types mediate these effects. To address this, we developed Hair Score, a computational pipeline that integrates diverse genetic dataset such as human genome-wide association studies (GWAS) and other relevant sequencing data from other species with transcriptomic profiles of hair follicle cell populations.

The Hair Score pipeline maps trait-associated genes onto transcriptomic datasets representing distinct follicular cell types. Gene expression within each population is quantified and aggregated to generate a cumulative score reflecting the relative contribution of each cell type to the biology of a given hair fibre trait. This enables systematic identification of follicular compartments most likely to influence fibre properties.

Application of Hair Score to datasets relating to hair pigmentation, strand shape, and thickness revealed distinct cell-type associations across traits. For pigmentation, melanocytes emerged as the highest-scoring contributors when using cheetah RNAseq data, consistent with their central role in pigment production. In addition, signals from adjacent follicular cell populations were observed, highlighting intercellular communication within the follicle. EDN3 was prioritised as a secreted protein, supporting a role for signalling between follicular cells that regulate melanocyte activity and ultimately hair colour.

For hair shape, the inner root sheath was the strongest-associated cell population, consistent with its role in guiding the emerging fibre.

Together, these findings demonstrate that integrating genetic and transcriptomic data can reveal the cellular context underlying human hair fibre traits. The Hair Score provides a scalable framework for linking genetic discoveries to specific follicular cell types and signalling pathways, the identification of novel targets for cosmetic science.

Objective:
Research on textured hair is accelerating as the understanding of curl behaviour and targeted care needs increasingly come into focus. Tensile testing of textured hair provides valuable information about elasticity, tensile strength and extensibility. However, as curl levels increase, so does the inter-fibre variability, complicating the analysis and the interpretation of influences of curl type on fibre behaviour. In particular, the ‘toe in’ effect makes it difficult to consistently determine tensile parameters across different curl patterns, as the elastic region deviates from a straight line. Given the importance of the modulus of elasticity (E Modulus), we investigate whether the first derivative of the stress-strain curve is an alternative and consistent method for modulus determination.

Methods:
Human tresses were classified by professional stylists using the Walker Scale (Types 1-4C). Hair fibres were taken from the tresses and measured for curl count and stretched length. The curl values for fibres provide the foundation for a detailed comparison across curl categories. This metric is the basis to investigate how fibre curl patterns impact tensile properties. All tensile tests were carried out in wet conditions.

Results:
Using the 1st derivative approach, the influence of fibre ‘toe-in’, machine bias and human error on the modulus was minimised, improving the accuracy of measurements at high curl levels. In particular, it could be confirmed and quantified that as curl value increases, the E modulus drops.

Conclusion:
This study examines critical challenges in textured-hair research and introduces methodologies that substantially enhance the precision and interpretability of tensile testing for curly hair. These advancements streamline scientific communication and offer valuable implications for both the cosmetic industry and for salon professionals.