bioRxivCell Biology
Stem cell heterogeneity and reiteration of developmental signaling underlie melanocyte regeneration in zebrafish
W. T. Frantz, S. Iyengar, J. Neiswender, A. Cousineau, R. Maehr, C. J. Ceol
Abstract
Tissue-resident stem cells are present in many adult organs, where they are important for organ homeostasis and repair in response to injury. However, the signals that activate these cells and the mechanisms governing how these cells self-renew or differentiate are highly context-dependent and incompletely understood, particularly in non-hematopoietic tissues. In the skin, melanocyte stem cells (McSCs) are responsible for replenishing mature pigmented melanocytes. In mammals, these cells reside in the hair follicle bulge and bulb niches where they are activated during homeostatic hair follicle turnover and following melanocyte destruction, as occurs in vitiligo and other skin hypopigmentation disorders. Recently, we identified adult McSCs in the zebrafish. To elucidate mechanisms governing McSC self-renewal and differentiation we analyzed individual transcriptomes from thousands of melanocyte lineage cells during the regeneration process. We identified transcriptional signatures for McSCs, deciphered transcriptional changes and intermediate cell states during regeneration, and analyzed cell-cell signaling changes to discover mechanisms governing melanocyte regeneration. We identified KIT signaling via the RAS/MAPK pathway as a regulator of McSC direct differentiation. Analysis of the scRNAseq dataset also revealed a population of mitfa/aox5 co-expressing cells that divides following melanocyte destruction, likely corresponding to cells that undergo self-renewal. Our findings show how activation of different subpopulations of mitfa-positive cells underlies self-renewal and differentiation to properly reconstitute the melanocyte pigmentary system following injury.
- organism: zebrafish
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: deep learning
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