Thyroid hormone regulates distinct paths to maturation in pigment cell lineages

Abstract

Thyroid hormone (TH) regulates diverse developmental events and can drive disparate cellular outcomes. In zebrafish, TH has opposite effects on neural crest derived pigment cells of the adult stripe pattern, limiting melanophore population expansion, yet increasing yellow/orange xanthophore numbers. To learn how TH elicits seemingly opposite responses in cells having a common embryological origin, we analyzed individual transcriptomes from thousands of neural crest-derived cells, reconstructed developmental trajectories, identified pigment cell-lineage specific responses to TH, and assessed roles for TH receptors. We show that TH promotes maturation of both cell types but in distinct ways. In melanophores, TH drives terminal differentiation, limiting final cell numbers. In xanthophores, TH promotes accumulation of orange carotenoids, making the cells visible. TH receptors act primarily to repress these programs when TH is limiting. Our findings show how a single endocrine factor integrates very different cellular activities during the generation of adult form.

In situdifferentiation of iridophore crystallotypes underlies zebrafish stripe patterning

“…To test this model, we examined iridophore behaviors by time-lapse imaging of membrane targeted mCherry driven by regulatory elements of pnp4a 22,25 . If morphogenetic respecification accounts for variation in iridophore morphology and patterning, then events resembling EMT or MET should be observable: densely packed iridophores of the completed interstripe should delaminate to populate the stripe, whereas loosely arranged iridophores of completed stripes should aggregate to initiate new interstripes.…”

“…We further found that intrinsic differences in iridophore optical properties could generate a strong contrast in color between stripes and interstripes independent of pigments in other cell types. This was manifested in fish that lack both melanin in melanophores and carotenoids in xanthophores, owing to mutations in both tyrosinase and scarb1 , respectively 25 . Here, differences in color between stripes and interstripes (i.e., blue versus silvery-yellow) persisted even in the absence of other pigments ( Fig.…”

“…Center panel is oblique illumination revealing surface features and non-iridescent colors of iridophores. Right panel is membrane-targeted mCherry (mem-mCherry) driven at high levels in iridophores by regulatory elements of purine nucleoside phosphorylase 4a ( pnp4a ) (Eom et al, 2015 45 ; Saunders et al, 2019 25 ; Spiewak et al, 2018 22 ) to reveal cell boundaries and arrangements. Pixel values are inverted for easier comparison to brightfield images.…”

FGFR3 is a positive regulator of osteoblast expansion and differentiation during zebrafish skull vault development

“…Using a combination of known markers (Figure 6B,C,Figure 6 supplement 1 B-E and data 2 figure 6), we classified the 20 clusters into six categories: (i) cells expressing osteogenic markers (OCs; clusters 0,1,3,4,9,12, and 13), (ii) cells expressing chondrogenic markers (CHs; cluster 18), (iii) cells expressing immune markers (ICs; clusters 2, 8, 11, 16, and 19), (iv) cells expressing epidermal markers (clusters 5, 6, 10, 14, and 15), (v) cells expressing endothelial markers (cluster 17) and (vi) unannotated cells (cluster7). Despite the lack of annotation, we noticed that nearly 10% of the cells in cluster 7 were probably pigment cells, which are numerous in the zebrafish model (Figure 6supplement 1F)(Saunders et al, 2019).The low observed proportion (<1%) of cells expressing classical CH marker genes (such as col2a1, sox9, sox5 and sox6 ) was consistent with reports whereby cartilage is involved in cranial vault formation (Figure 6E)(Kanther et al, 2019).Osteogenic cells involved in membranous ossification accounted for the largest group of cells (65%)(Figure 6 B, D and E). Cranial vault formation requires a wellorchestrated set of genes, which controls the differentiation of mesenchymal cells into mature osteoblasts; this enabled us to identify several clusters(Figure 6D, E).…”

Dimensionality reduction by UMAP to visualize physical and genetic interactions

“…5 A more recent method, uniform manifold approximation (UMAP), estimates a topology of the high dimensional data and uses this information to construct a low-dimensional representation that preserves relationships present in the data. 6 UMAP has been particularly useful to precisely define cell types in mixed populations based on data from single-cell RNA-seq experiments [7][8][9][10][11][12][13] ; it also performs well on other goldstandard datasets. 6,14 Because UMAP is better able to preserve elements of the data structure from high dimensional space than similar outputs from t-SNE, it captures local relationships within distinct clusters in addition to global relationships between clusters.…”