Transcriptional Dissection of Human Limbal Niche Compartments by Massive Parallel Sequencing

Corneal epithelium is maintained throughout life by well-orchestrated proliferation of limbal epithelial stem cells (LESCs), followed by migration and maturation centripetally towards the ocular surface. Disturbance of LESCs can potentially lead to a blinding condition, which can be reversed by reconstitution of a functional LESC pool. The current clinical procedures are effective to some degree, however, deeper knowledge of the molecular interplay within the limbal niche is necessary to achieve a fully satisfactory patient outcome. The present study was thus undertaken to carry out a comprehensive transcriptome analysis of four distinct human limbal compartments, including basal limbal crypts (BLCs), superficial limbal crypts (SLCs), cornea, and the supporting stroma, with the aid of laser capture microdissection and deep RNA sequencing. The tissue harvest pipeline was rigorously optimized so that the exposure to cold ischemia would be less than five minutes. The global gene ontology analysis confirmed existence of primitive cells in BLCs, migratory and activated cells in SLCs, and differentiated cells in cornea. Interestingly, many significantly upregulated genes in SLCs mapped to processes involved in regulation of vasculature, such as sFLT1. In contrast, BLCs exhibited many genes mapping to neurogenic processes and processes related to cell development. The primitive nature of BLCs was, furthermore, confirmed by the KEGG pathway analysis, and some potential regulators of LESCs were revealed, such as Lrig1 and SOX9. The analysis also yielded comprehensive lists of uniquely expressed genes in both BLCs and cornea, which may be useful to identify possible biomarkers. In conclusion, the current investigation provides new insight into the relationship between distinct cell populations within the limbal niche, identifies candidates to be verified for novel biological functions, and yields a wealth of information for prospective data mining.