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bioRxiv · 10.64898/2026.01.18.696654

Array-CNCC: precise aggregation and arrayed plating facilitate quantitative phenotyping of human cranial neural crest cells and craniofacial disease modelling

Abstract

Facial development is highly sensitive to genetic and environmental perturbation, with craniofacial malformation associated with over one-third of congenital birth defects. The face arises during an early and largely inaccessible window of embryonic development, with a large contribution from transient and multipotent cranial neural crest cells (CNCCs). Assessment of the molecular and cellular mechanisms driving normal and disordered human facial development therefore relies greatly on the use of in vitro cellular models. Here, we adapted a neurosphere-based CNCC differentiation protocol to facilitate robust quantification of early specification and migration events. Introduction of single-cell aggregation with arrayed plating enabled standardisation of neurosphere size, growth and patterning. Inclusion of fibronectin coating enhanced the efficiency of neurosphere attachment and synchronicity of CNCC migration timing. To demonstrate application of the Array-CNCC method, we developed a strategy for mosaic co-culture, which can facilitate differentiation of wildtype untreated cells directly alongside cells exposed to distinct drug treatments or genetic alterations. Finally, we present a screening approach which we use to test the impact of distinct extracellular matrix components on neurosphere morphology, CNCC migration and gene expression. Together, the Array-CNCC method is highly amenable to quantitative phenotyping and screening approaches, enabling enhanced craniofacial disease modelling with both cellular and molecular readouts.

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Ozga, E., Milto, K. M., Demurtas, M., Bates, L. E., Grimes, G., Azami, T., Su, J., De Angelis, C., Trizzino, M., Nichols, J., Long, H. K.. 2026-01-18. Array-CNCC: precise aggregation and arrayed plating facilitate quantitative phenotyping of human cranial neural crest cells and craniofacial disease modelling. https://doi.org/10.64898/2026.01.18.696654

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