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. Author manuscript; available in PMC: 2024 Nov 1.
Published in final edited form as: Pigment Cell Melanoma Res. 2023 Jul 18;36(6):531–541. doi: 10.1111/pcmr.13112

Dynamic regulation of chromatin accessibility during melanocyte stem cell activation

Seoyeon Lee 1,*, Luye An 2,*, Paul D Soloway 1,2, Andrew C White 2
PMCID: PMC10794558  NIHMSID: NIHMS1926036  PMID: 37462349

Summary

Melanocyte stem cells (McSCs) of the hair follicle are necessary for hair pigmentation and can serve as melanoma cells of origin when harboring cancer-driving mutations. McSCs can be released from quiescence, activated, and undergo differentiation into pigment-producing melanocytes during the hair cycle or due to environmental stimuli, such as ultraviolet-B (UVB) exposure. However, our current understanding of the mechanisms regulating McSC stemness, activation, and differentiation remains limited. Here, to capture the differing possible states in which murine McSCs can exist, we sorted melanocyte nuclei from quiescent (telogen) skin, skin actively producing hair shafts (anagen), and skin exposed to UVB. With these sorted nuclei, we then utilized single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing (snATAC-seq) and characterized three melanocyte lineages: quiescent McSCs (qMcSCs), activated McSCs (aMcSCs), and differentiated melanocytes (dMCs) that co-exist in all three skin conditions. Furthermore, we successfully identified differentially accessible genes and enriched transcription factor binding motifs for each melanocyte lineage. Our findings reveal potential gene regulators that determine these melanocyte cell states and provide new insights into how aMcSC chromatin states are regulated differently under divergent intrinsic and extrinsic cues. We also provide a publicly available online tool with a user-friendly interface to explore this comprehensive dataset, which will provide a resource for further studies on McSC regulation upon natural or UVB-mediated stem cell activation.

Introduction

Cutaneous melanocytes are neural crest-derived cells that provide pigmentation to the hair and the skin epidermis by the production and transfer of melanin (Slominski and Paus, 1993). In the adult murine dorsal skin, melanocyte stem cells (McSCs) are located within the hair follicles in close association with hair follicle stem cells. McSC activity is tightly regulated by the hair follicle stem cell niche and McSC activation and differentiation can be controlled by the hair cycle (Chou et al., 2013; Nishimura et al., 2002; Slominski and Paus, 1993) and/or environmental stimuli. Additionally, McSCs are important contributors to the epidermal re-pigmentation process for vitiligo patients undergoing phototherapy (Lee and Fisher, 2014; Mull et al., 2015). Therefore, defining McSC regulatory mechanisms is critical for both understanding the mechanistic basis for hair pigmentation during homeostasis and determining manipulation strategies to improve therapy for vitiligo patients.

McSC activation is synchronized with the three phases of the hair cycle: telogen (the quiescent phase), anagen (the activation phase) (Fuchs et al., 2001), and catagen (the regression phase) (Tobin, 1998; Tobin et al., 1998). During telogen, McSCs remain quiescent within the hair follicle stem cell niche (the bulge), while active proliferation and differentiation are triggered upon transition to the anagen phase. The hair bulb, formed during anagen, contains the differentiated melanocytes that deposit pigment during hair growth (Nishimura et al., 2002). Independent from the transition to anagen, McSCs can be activated by environmental cues such as ultraviolet-B (UVB) radiation, a component of sunlight with a wavelength between 280m-315m (Chou et al., 2013; Moon et al., 2017). Under UVB-irradiation, activated McSCs (aMcSCs) located in the hair bulge proliferate and migrate upwards into the epidermis and repopulate epidermal melanocytes for skin pigmentation (Nishimura, 2011). These McSC properties have gained attention as a potential strategic target for skin re-pigmentation therapy (Peterson and King, 2021). Despite the importance of McSCs as a source for epidermal melanocytes during vitiligo re-pigmentation, the mechanism(s) of McSC activation and migration due to UVB stimulation still remain largely unclear.

Over several decades, many genes and signaling pathways involved in melanocyte function, including WNT and BMP signaling, have been identified (Botchkareva et al., 2003; Infarinato et al., 2020; Levy et al., 2006). Studies have also shown that the Microphthalmia-Associated Transcription Factor (MITF) and the Transcription Factor AP2 (TFAP2) family are critical for promoting McSC differentiation and melanin deposition from melanocytes (Levy et al., 2006). Yet, there are no reports, to our knowledge, that identify the transcription factors that regulate McSC activation and differentiation in response to UVB irradiation, despite the importance of this process for phototherapy. Moreover, McSCs only exist in a very small proportion (about 1%) within the skin cell milleu (Chung et al., 2011; Harris et al., 2018; Joshi et al., 2019; Michalak-Mićka et al., 2022), thus some previous single cell studies using whole skin were unable to distinguish McSCs from differentiated melanocytes (Joost et al., 2020).

Chromatin accessibility assays have successfully identified different cell types and profiled their unique chromatin states (Cusanovich et al., 2018). Here, we use Dct-rtTA; Tre-H2B-GFP (DG) transgenic mice, where McSC nuclei are labeled by H2B-GFP, for McSC nuclei enrichment using fluorescence-activated nuclei sorting (FANS) (Zaidi et al., 2011). By isolating McSCs from the dorsal skin of DG mice at telogen, anagen, and after UVB irradiation, and performing single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq), we identified three melanocyte lineages: quiescent McSCs (qMcSCs), activated McSCs (aMcSCs), and differentiated melanocytes (dMCs). Moreover, we characterized the enriched genes and transcription factor binding motifs in each melanocyte lineage. Our data elucidate how chromatin accessibilities are regulated by intrinsic cues of the hair follicle cycle and by extrinsic environmental stimuli such as UVB. For an interactive exploration of our snATAC-seq data, we provide a Shiny-based web tool (Ouyang et al., 2021) for use in future studies aimed at investigating potential regulators that control McSC stemness, activation and differentiation (URL: http://www.andrewwhitelab.com/tools).

Results

Acquisition of melanocyte nuclei from a melanocyte lineage tracing mouse model

UVB-irradiated skin contains quiescent, activated, and even differentiated melanocytes. However, distinguishing quiescent, activated, and differentiated melanocytes in UVB-irradiated skin can be challenging due to limited known markers. To address this, we utilized our current knowledge of melanocyte populations in different phases of the murine hair cycle, where quiescent McSCs are predominant in telogen skin, and differentiated melanocytes are more abundant in late anagen skin (Fig. 1a). We employed DG mice, where all melanocyte lineages are labeled with GFP under doxycycline water treatment (Tumbar et al., 2004; Zaidi et al., 2011), to identify melanocyte lineages. Dorsal skin from DG mice during telogen, anagen, and after three UVB irradiations in telogen skin were collected (Fig. 1b). We then digested the skin to a single-cell suspension using collagenase and extracted nuclei. GFP+ nuclei were sorted by FANS (Fig. S1a). Since melanocytes are very rare populations in the whole skin, and to minimize the loss of melanocytes, we gated on GFPlow nuclei following snATAC-seq library preparation and sequencing. To confirm the distribution of melanocyte lineages in each sample, we imaged sectioned dorsal skin collected at the time points shown in Fig. 1b. As expected, GFP+ melanocytes were present in the hair bulge during telogen, in the hair bulb during anagen, and in the infundibulum and interfollicular epidermis after UVB exposure (Fig. 1c).

Figure 1:

Figure 1:

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Melanocyte lineages throughout the hair cycle and after UVB irradiation.

a,b, Experimental approach to enrich melanocytes in differing states at particular stages of the hair follicle cycle (telogen and anagen) and after UVB irradiation of the skin. a, Schematic of melanocyte cell states in the telogen and anagen phase of the hair follicle cycle and after UVB exposure. b, Experimental timeline for collecting telogen, anagen, and UVB-irradiated skin. c, GFP lineage tracing and DAPI staining in telogen, anagen, and after UVB exposure. Location of melanocyte lineages are shown by expression of Dct-GFP (Dct-rtTA; Tre-H2B-GFP) at the hair bulge (telogen), hair bulb (anagen), and infundibulum (UVB irradiated). Scale bar: 100μm.

After applying quality control cutoffs (Fig. S1b,c), we obtained chromatin accessibility profiles from a total of 14,674 cells (2,585 cells in telogen, 5,474 cells in anagen, and 6,615 cells in UVB-exposed skin). By subjecting the snATAC data to unsupervised clustering after dimensional reduction, we identified eight distinct clusters (Fig. 2a). To determine the cell types corresponding to each cluster, we performed gene ontology (GO) enrichment analysis for the top 100 differentially accessible genes in each cluster and used this information to predict biological processes associated with each cluster (Fig. S2a). We also validated the cell type assignments by examining the accessibility of known marker genes for each cell type, including melanocytes, fibroblasts, and keratinocytes (Fig. S2b). Despite FANS enrichment for melanocytes, we observed nuclei from other skin cell types in the melanocyte-enriched population. Among the eight clusters, three clusters exhibited robust accessibility to the gene body and promoter of Dct, a melanocyte lineage-specific gene, which was not observed in other clusters (Fig. 2b,c). Therefore, we designated these three clusters as melanocyte1–3.

Figure 2:

Figure 2:

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Single-nucleus chromatin accessibility of melanocyte lineage cells.

a, UMAP plot of 14,674 cells (2,585 telogen cells, 5,474 anagen cells, 6,615 UVB cells) that passed quality control. Eight different clusters were identified by unbiased clustering and colors represent the five major cell types. b, UMAP plot of Dct gene accessibility. c, Genome tracks showing aggregated snATAC-seq profiles near the Dct promoter in each cluster. d, UMAP plot of 5,695 cells from the three melanocyte clusters. Colors represent three unique melanocyte lineage cell clusters. e, Normalized gene accessibility of stemness and differentiation genes. Positive accessibility indicates that the gene is more accessible than the mean, and negative accessibility indicates that the gene is less accessible than the mean across all samples. f, Bar plots showing the proportion of melanocyte subtypes in telogen, anagen, and telogen after UVB exposure. g,h, Differential cell abundance analysis using Milo (Dann et al., 2022) of cell neighborhood changes in the anagen phase (g) or after UVB exposure (h) compared to telogen phase. The size of circles (Nhood size) represents the number of cells in a neighborhood; the thickness of lines (overlap size) indicates the number of cells shared between neighborhoods. The circles are colored by the log-fold differences (logFC) between groups. Purple-colored points indicate that more cells in that neighborhood were found in the anagen phase (g) or after UVB exposure (h). White circles indicate that the neighborhoods are not differentially abundant in one group (FDR 10%).

The cell type annotation was found to be consistent with the cell identities predicted by publicly available mouse skin scRNA-seq (Joost et al., 2020) (Fig. S2c). However, we observed that only one (melanocyte 3) of the three clusters we identified as melanocytes was predicted as such, based on the Joost et al. scRNA-seq dataset (Joost et al., 2020). We hypothesized that this may be due to the limited number of melanocytes present in this whole skin scRNA-seq dataset (5 cells and 120 cells in telogen and anagen, respectively), while our study involved active enrichment of this low-abundance cell population, which allowed for the identification of more melanocyte subtypes.

Characterization of three unique melanocyte lineages

The initial analysis resulted in 8 cell clusters, representing 5 major cell types, including 3 melanocyte clusters. However, including non-melanocytes in the analysis reduced our ability to differentiate the three melanocyte clusters. Therefore, we re-clustered the three Dct positive melanocyte populations comprising 5,695 cells (Fig. 2d) to further characterize the melanocyte populations. By comparing the accessibility of “stemness” and “differentiation” genes identified from another previously published scRNA-seq dataset (Infarinato et al., 2020), we designated three melanocyte populations: quiescent melanocyte stem cells (qMcSCs), activated McSC (aMcSCs) and differentiated melanocytes (dMCs) (Fig. 2d). qMcSCs showed high accessibility on genes including Sbno2, Jun, and Fos, whereas dMCs exhibited higher accessibility on genes involved in melanocyte differentiation and melanin synthesis such as Mitf, Kit, Tyrp1, Tyr, Mlana, Mc1r, and Oca2 (Fig. 2e). Interestingly, aMcSCs, found between qMcSCs and dMCs, exhibited an intermediate gene accessibility profile for both stemness and differentiation genes (Fig. 2e), suggesting a transitional stage in the melanocyte activation process. However, it is worth noting that the aMcSCs in our dataset are activated by UVB, which is distinct from anagen-induced aMcSCs identified in the Infarinato et al. dataset (Infarinato et al., 2020).

To further validate the cell type assignment from our snATAC-seq profile, we compared our three cell clusters with the melanocytes sorted from telogen (qMcSC), anagen I-II (aMcSC), and anagen VI (McSC progeny) reported in the Infarinato et. al dataset (Infarinato et al., 2020). The analysis revealed an overlap between our qMcSC and aMcSCs with their sorted telogen and anagen I-II melanocytes, respectively. Even though the McSC progenies were distributed across all three clusters, most of the dMCs in our snATAC-seq profile were predicted as McSC progeny (Fig. S2d).

Furthermore, we examined our snATAC-seq profiles for the percentage of each cell type in telogen, anagen, and UVB-exposed skin. Consistent with anagen hair follicle histology (Fig. 1c), we found that about 96% of dMC cells were from mice in the anagen phase (Fig. 2f). Only 7% of all cells from mice in anagen were qMcSCs (Fig. 2f). aMcSCs contained more than 85% of cells from mice exposed to UVB (Fig. 2f). To further confirm whether the cells predicted as aMcSCs have a heightened proliferation state, we analyzed their gene accessibility for cell cycle genes and conducted pseudotime analysis. We found that the cell cycle genes were relatively more accessible in the aMcSC cluster, suggesting that they have a higher potential to proliferate (Fig. S2e). Additionally, pseudotime representing a cell’s relative status on the developmental trajectory suggested that the cells in the aMcSC cluster are in a transitional state between quiescent and differentiated melanocytes (Fig. S2f). These results provide additional rationale for the aMcSC designation for this cluster. Interestingly, we observed that more than half of telogen skin melanocytes were aMcSCs. Although many cells from both the telogen phase and UVB-exposed skin were present in the aMcSC cluster, heterogeneity is found within this group. Specifically, we observed that the cells from the telogen phase were closely located to qMcSCs, which have an earlier pseudotime, while the aMcSCs from UVB-exposed skin were densely located near the differentiated cells (Fig. S2g). This observation may suggest heterogeneity within the aMcSC population and is potentially indicative of a “primed yet quiescent” McSC population (Joshi et al., 2019), and/or a dynamic cell state toggling between transit-amplifying and stemness (Sun et al., 2023).

To further explore the cell proportions in different phases and experimental conditions, we used Milo (Dann et al., 2022) to test differential cell abundance by comparing cell neighborhoods of each cell to its nearest neighbors in k-nearest neighbor graphs. This approach allows for the identification of cells that are differentially abundant without depending on discrete cell clusters. We found that, in comparison to cells from mice in the telogen phase, more cells tended to be in a differentiated state during anagen phase (Fig. 2g). Additionally, we found that more cells were in an activated state after UVB exposure (Fig. 2h). In summary, we successfully identified three melanocyte lineages defined as quiescent, activated, and differentiated melanocytes from McSCs isolated from telogen, anagen and UVB-irradiated skin.

Characterizing the molecular signature of each melanocyte population

To gain a deeper understanding of the molecular heterogeneity among the three melanocyte lineages, we compared the accessibility of gene bodies and 2kb upstream regions between each cell population. We found that Lamb1, Col27a1, Mbp, Kcna1, Kcna5, Kcnj10, and Kcnh8 were relatively more enriched in qMcSCs, whereas Trpm1, Oca2, Pde10a, Slc24a4, Slc7a8, and Ago2 were more accessible in dMCs (Fig. 3a,b). The genes that were more accessible in dMCs were highly expressed in melanocytes compared to other skin cell types (Joost et al., 2020) and McSC progeny compared to McSCs based on publicly available scRNA-seq data (Infarinato et al., 2020) (Fig. S3a,b). Additionally, we found that aMcSCs had higher accessibility at Ldb2, Dnm3, Cubn, Leprel1, and Sema3a (Fig. 3c). Sema3a, in particular, was relatively more accessible in aMcSCs from UVB-exposed skin (Fig. S3c).

Figure 3:

Figure 3:

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Differentially accessible genes and transcription factor motifs in three melanocyte lineages.

a-c, Differentially accessible gene analysis of each melanocyte lineage cell cluster compared to other melanocyte lineage cells. Positive log2-fold change indicates increased accessibility in qMcSC (a), dMC (b), and aMcSC (c). d,e, Immunostaining of Lamb1 (d) and Ago2 (e) in telogen, anagen and after UVB exposure (left), and quantification (right). Melanocyte lineages are shown by Dct-GFP labeling in the hair bulge and hair bulb. DAPI (grey), Dct-GFP (green), and Lamb1 (red). n=4 for each group. f, Gene ontology (GO) enrichment for biological processes for the top 50 significantly more accessible genes. ****, ***, and ** indicate p-value < 0.0001, 0.001, and 0.01, respectively. Statistical test: one-way ANOVA. Scale bar: 100μm (d,e). g, Heatmap showing the enrichment of transcription factor binding motifs in each melanocyte lineage.

To assess if accessibility differences in melanocyte sub-populations were associated with corresponding differences in protein production, we conducted immunofluorescence staining. Among the genes that showed a log2 fold change greater than 0.5 in accessibility, we performed staining for Laminb1 (Lamb1) and Argonaute2 (Ago2) and quantified the number of GFP+ melanocytes that co-expressed Lamb1 and Ago2 in sectioned DG skin (Fig. 3d,e). Consistent with our differential gene accessibility analysis results, a higher proportion of qMcSCs overlapped with Lamb1 in the telogen hair bulge and a higher proportion of dMCs overlapped with Ago2 in the anagen hair bulb.

To profile differences among the three melanocyte lineages into functional categories, we performed GO analysis of the top 50 differentially accessible genes from each cluster (Fig. 3f). We found that the genes with higher accessibility in qMcSCs were associated with axonogenesis and gliogenesis, consistent with their neural crest origin and their identity as a melanocyte progenitor. Furthermore, cell-substrate adhesion and extracellular matrix (ECM) organization signature were highlighted in qMcSCs but not aMcSCs or dMCs, suggesting that cell mobility is acquired during McSC activation and differentiation. The genes more accessible in dMCs were involved in pigmentation and melanin biosynthetic processes, consistent with their more mature and differentiated state compared to the other two clusters. The genes that were relatively more accessible in aMcSCs were involved in regionalization and stem cell differentiation, consistent with the process of transitioning from progenitors to melanin-producing melanocytes (Fig. 3f).

Specific chromatin accessibility landscapes determine melanocyte lineages

While earlier analyses focused on genes with differential accessibility, we aimed to identify transcription factor motifs that could be important for the gene regulation machinery driving melanocyte lineage transitions. To achieve this, we expanded the analysis to any differentially accessible genomic interval. Transcription factor motifs enriched at sites of differential accessibility were compared among the three melanocyte lineages (Fig. 3g).

We found that Nuclear factor I (Nfi), Specificity protein (Sp), and Krüppel-like factor (Klf) family motifs were enriched in differentially accessible sites that are more accessible in qMcSCs relative to the other two melanocyte populations. Nfi factors have been shown to regulate hair follicle stemness genes by governing chromatin accessibility of master transcription factors (Adam et al., 2020), while Klf family members regulate pluripotency in mouse embryonic stem cells (Jiang et al., 2008). Klf2, Klf4, and Klf5 have been shown to regulate the expression of key pluripotency factors such as Oct4, Sox2, and Nanog (Jiang et al., 2008). Our data are consistent with findings that the Nfi, Sp, and Klf chromatin modulator families play key roles in maintaining the stemness of HFSCs and highlight the potential role of Sp family members in maintaining McSCs stemness. Furthermore, our findings demonstrate that the emergence of dMCs is accompanied by lost accessibility at these motifs, indicating a potential role in the differentiation of melanocytes.

We observed that binding motifs for Mitf, Tcf7, and Lef1 were enriched in dMCs (Fig. 3g), which is consistent with recent ATAC-seq data generated by FACS-isolated McSC progeny from the anagen hair bulb (Infarinato et al., 2020). Mitf is a critical transcription factor for melanocyte differentiation and regulates the expression of pigmentation genes (Kawakami and Fisher, 2017). Lef1/Tcf(s) mediate the activity of nuclear β-catenin and canonical Wnt signaling, which can activate Mitf and promote melanocyte proliferation and McSC differentiation (Behrens et al., 1996; Guo et al., 2016).

To investigate the potential regulatory mechanisms underlying McSC activation, we analyzed the transcription factor motifs enriched in aMcSCs. We found that after UVB exposure, the binding sites of transcription factor AP2 (TFAP2) were relatively enriched in aMcSCs (Fig. 3g). The TFAP2 family was found to be the most enriched binding motif within differentially accessible regions in aMcSCs (Fig. S4a). Notably, the TFAP2 family was relatively enriched in aMcSCs from UVB-exposed skin compared to the aMcSCs from the two other samples (Fig. S4b). Mutations in the TFAP2A gene can cause premature hair graying in humans and defects in melanocyte differentiation in mice and zebrafish (Praetorius et al., 2013; Seberg et al., 2017), and tfap2b is necessary for McSC-dependent melanocyte regeneration in zebrafish (Brombin et al., 2022). To validate the relevance of TFAP2 in McSC activation, we performed immunofluorescence staining for TFAP2A and TFAP2B on telogen, anagen, and UVB-irradiated DG skin sections and determined that the nuclei co-localized with melanocytes in each sample (Fig. 4a, S4c). Consistent with our motif enrichment results, we observed that around 20% of bulge McSCs in telogen samples showed nuclei co-localization with TFAP2A, which is the lowest among all three stages. To better demonstrate the dynamics of TFAP2A/B chromatin accessibility along McSC activation and differentiation, we specifically quantified the TFAFP2A/B nuclei co-localization with migrating McSCs and epidermal migrated MCs in UVB-exposed skin, which are considered aMcSCs and dMCs respectively. Very interestingly, we found TFAP2A/B nuclei co-localization more frequently along the McSC progression toward differentiation. dMCs in the anagen bulb showed the highest level of TFAPA/B nuclei co-localization, which was inconsistent to our motif enrichment data shown in Fig. 3g. Our interpretation of this observation is that even though more genes involved in McSC differentiation regulatory network may be activated by TFAP2A/B in aMcSCs, higher local transcription factor concentrations might be required in fully differentiated melanocytes to maintain the terminal differentiation cell state.

Figure 4:

Figure 4:

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Enrichment of TFAP2A binding motifs in aMcSCs.

a, Representative images of TFAP2A immunofluorescent staining on telogen, anagen, and UVB-irradiated DG dorsal skin sections (left), and quantification (right) n=4 per group. **,***,**** indicates pvalue < 0.01, 0.001,0.0001 respectively. Statistics test: one-way ANOVA. Scale bar: 100um. b, Number of TFAP2A binding sites from publicly available mouse immortalized melanocyte ChIP-seq data (Seberg et al., 2017) compared to peaks enriched in aMcSC versus qMcSC (left) and dMC (right). Gene ontology (GO) enrichment terms and FDR for biological process using the nearest genes (<100kb) to the peaks enriched in aMcSC and having TFAP2A motifs.

To further investigate the potential functions of TFAP2A in aMcSCs, we identified overlapping peaks that were more accessible in aMcSCs compared to qMcSCs or dMCs. We then used TFAP2A binding motifs obtained from TFAP2A ChIP-seq data from mouse Melan-A cells as the reference to identify its potential functions in aMcSCs. We found that almost 50% of aMcSC-specific peaks compared to qMcSC had binding motifs for TFAP2A, and the nearest genes to these peaks were associated with melanocyte differentiation and neural crest cell migration (Fig. 4b). Similarly, over 50% of aMcSC-specific peaks compared to dMC overlapped with TFAP2A binding sites, and the nearest genes to these peaks were involved in vasculature development and epithelial migration (Fig. 4b). Moreover, the significantly enriched peaks in aMcSCs within the Sema3a gene was the binding motif for TFAP2A (Fig. S4d). Finally, TFAP2 paralogs can function as pioneer factors for Mitf (Kenny et al., 2022). Our data suggest that under environmental stimuli such as UVB irradiation, TFAP2A might play an important role in McSC activation to promote migration and differentiation, and as a pioneer factor that alters the chromatin accessibility required to transition to a dMC state.

Discussion

We present a detailed analysis of chromatin accessibility in melanocytes at different stages of the hair cycle and after UVB irradiation in mice, at single-nucleus resolution. Our dataset, along with an intuitive search tool, provides a valuable resource for investigating chromatin accessibility of any gene in murine melanocytes. Our results highlight changes in cell abundance, gene accessibility, and motif enrichment across the three melanocyte lineages (qMcSCs, aMcSCs, and dMCs) driven by intrinsic cues of the hair follicle cycle (telogen and anagen) and extrinsic environmental stimuli (UVB). These changes may contribute to a gene regulatory network that governs McSC maintenance, activation, and differentiation.

Our analysis identified Lamb1 and Ago2 as more accessible genes in qMcSCs and dMCs, respectively (Fig. 3a,b). Our immunofluorescence staining showed that Lamb1 is present at higher levels in the hair bulge where qMcSCs are located and Ago2 at higher levels in the hair bulb where dMCs are found (Fig. 3d,e). Future studies are needed to define the precise roles of Lamb1 and Ago2 in melanocyte function. Laminins are extracellular matrix glycoproteins (Lee et al., 2021) and may help preserve the quiescent state and tissue localization of qMcSCs under normal conditions. Argonaute proteins are known for their roles in RNA-mediated gene silencing (Hutvagner and Simard, 2008), raising the possibility that changes in microRNA (miRNA) regulation may be important for the emergence or function of dMCs. Thus, it will be interesting to characterize miRNA expression and identify miRNA target genes in dMCs found in the hair bulb.

We also found that Sema3a was more accessible in aMcSCs, and that the TFAP2A binding motif within gene body of Sema3a was highly enriched in aMcSCs, suggesting a regulatory role of TFAP2 and Sema3a in McSC activation. However, further evaluation is required to determine the extent to which Sema3a regulates the activation and migration of aMcSCs after UVB irradiation.

Our findings highlight potential gene markers for melanocyte cell lineages and transcriptional regulators for McSC stemness, activation, and differentiation, and suggest a potential mechanism of UVB-induced McSC activation through the transcription factor TFAP2 family regulatory network. Future studies will illuminate whether this is a key mechanism for McSC activation and migration induced by UVB irradiation.

Materials and Methods

Animals

All animal experiments were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC) at Cornell University (protocol #2014–0096). 7-week-old Dct-rtTA; Tre-H2B-GFP mice were used in this study (Tumbar et al., 2004; Zaidi et al., 2011). Telogen samples were collected from mice that received drinking water with doxycycline (20 mg dissolved in 100 ml sterilized water) continuously for five days. Anagen samples were collected after chemical depilation with Nair cream, which was applied to the shaved dorsal skin of mice briefly anesthetized with isoflurane. The skin was then wiped gently with wet tissues to prevent irritation by the cream. Following anagen initiation, mice were given drinking water containing doxycycline for five consecutive days. UVB irradiation was performed on the shaved dorsal skin of mice, which were given drinking water with doxycycline for five days and exposed to UVB (2.2mJ/cm2) three times over one week, with one day of rest between exposures.

Tissue immunostaining

The dorsal skin was collected and fixed overnight at 4°C in 10% neutral buffered formalin. The fixed tissues were embedded in OCT compound (Tissue-Tek) and snap-frozen on dry ice. Frozen tissues were then fixed in 10% neutral buffered formalin for 10 minutes, followed by sectioning at 7μm. The sections were rehydrated with PBS twice for 10 minutes, and nuclei were stained using Fluoroshield mounting medium with DAPI (Abcam, #ab104139). For antibody immunostaining, the sections were blocked with 10% normal donkey serum in PBST and then incubated with primary antibodies: anti-Lamb1 (1:400, Abcam, #ab256380), anti-Ago2 (1:400, Abcam, #ab186733), anti-TFAP2A (1:200, Cell Signaling, #3215S), and anti-TFAP2B (1:200, Cell Signaling, #2509S) in blocking buffer overnight at 4°C. After washing with PBST, the sections were incubated with fluorochrome-conjugated secondary antibodies: Alexafluor 488- or 594-conjugated donkey anti-rabbit or anti-rat IgGs (Thermo Fisher, #A21207, A21208, A21209, and Abcam #ab150156) for 60 minutes at room temperature. Images were captured using a Leica DM2500 upright microscope with a DFC7000T camera.

Nuclei isolation from mouse dorsal skin

To prepare mouse skin cells for analysis, hair on the dorsal skin was clipped and the adipose layer under the dermis was removed with a blunt scalpel. The skin was then incubated in 4 mL of pre-warmed Collagenase I (20 mg/mL in DMEM/F-12) at 37°C for 1 hour. After adding 3 mL of keratinocyte serum-free medium (Life Technologies, #17005042), the cell suspension was filtered using a 100 μM cell strainer. The tissue chunks were then incubated in 2 mL of 0.25% Trypsin/EDTA solution for 10 min and filtered again using a 40 μM cell strainer. The cell suspension was centrifuged, washed with PBS, and centrifuged again. Finally, the cell pellet was resuspended in 1 mL of PBS and centrifuged.

After cell isolation, nuclei were isolated using the ATAC-seq method described by Corces et al. (Corces et al., 2017). The cell pellets were resuspended in 100 μL of cold ATAC-seq resuspension buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, and 3 mM MgCl2 in water) containing 0.1% NP40, 0.1% Tween-20, and 0.01% digitonin. The mixture was centrifuged for 10 min at 500g in a pre-chilled (4°C) fixed-angle centrifuge. After removing the supernatant, nuclei were resuspended in 1 mL PBS buffer and filtered through a 40 μM cell strainer. GFP+ nuclei were collected using a BD FACS Aria Fusion.

snATAC-seq with combinatorial indexing

Based on the collected volume from FANS, 5X Tagmentation buffer (50 mM Tris pH 7.5, 25 mM MgCl2, 50% DMF, 0.5% Tween 20, and 0.05% Digitonin) was added. The nuclei concentration was adjusted to approximately 1,000 nuclei per well based on FANS-estimated numbers of nuclei. Next, 8 μL of nuclei were added to each well of a 96-well plate containing 1 μL of either ME-A or ME-B carrying barcoded transposome per well at a concentration of around 1.5 μM. Tagmentation was carried out at 50°C for 30 min. After tagmentation, 10 μL of 40 μM EDTA was added and the plate was incubated at 37°C for 15 min to stop the Tn5 reaction. Next, 10 μL of sort buffer (2% BSA and 2 mM EDTA in PBS) was added, and all wells were pooled and centrifuged for 5 min at 500g. Nuclei were resuspended in 1 mL of sort buffer, filtered through a 35 μm mesh cell strainer (Corning, #352235), and stained with 3 μM Draq7 (Abcam, #ab109202). Then, 20 Draq7+ nuclei were sorted into each well of a 96-well plate containing 16.5 μL of elution buffer (2% BSA and 10 mM Tris pH 8.0) using a BD FASC Melody. After sorting, the plates were frozen at −80°C.

Upon thawing, 2 μL of 0.2% SDS was added to each well, and the plate was incubated for 7 min at 55°C. Next, 2.5 μL of 10% Triton X-100 was added. For PCR amplification, 1.5 μL of 25 μM Primer i5 and 1.5 μL of 25 μM Primer i7 were added, and the plate was gently vortexed and spun down briefly. Then, 25 μL of PCR mix (Q5 DNA polymerase (NEB, #M0491), 2 mM dNTP, Q5 buffer, and 1X GC Enhancer) were added. PCR was performed using the following protocol: 72°C for 5 min, 98°C for 30 sec, 17 cycles of [98°C for 10 sec, 63°C for 30 sec, 72°C for 30 sec], 72°C for 5 min, and held at 4°C. Amplified DNA libraries were pooled and purified using MinElute columns (Qiagen, #28004) with a vacuum apparatus (Qiagen, #19413). Size selection was performed using Ampure XP Beads (Beckman Coulter, #A63880). Finally, the libraries were sequenced on a Nextseq500 sequencer (Illumina) with a custom recipe as follows: Read 1: [36 imaged cycles], Index 1: [8 imaged cycles, 27 dark cycles, 8 imaged cycles], Index 2: [8 imaged cycles, 21 dark cycles, 8 imaged cycles], Read 2: [36 imaged cycles], all with a mid-lane output PE 150bp.

snATAC-seq data pre-processing

The fastq files were initially demultiplexed by appending cell barcodes to each read. Subsequently, the demultiplexed reads were mapped to the mm10 reference genome using Bowtie2 and sorted by read name using samtools. Fragments that were improperly paired, duplicated, or had a mapping quality below 30 were removed from the paired-end reads. Cells that had less than 1,000 reads and a transcription start site (TSS) enrichment score less than 5 were also filtered out. Using the cell-by-bin (window) matrix, initial clusters were identified, and peak calling was performed using MACS2 callpeak. Gene activity was predicted by counting the reads on the gene coordinates extended to include 2kb upstream regions.

Identification of cell clusters and cell type annotation

We initially obtained 14,674 cells from 8 anagen mice (3 female (F), 5 male (M)), 12 telogen mice (8F, 2M), and 24 UV-irradiated mice (14F, 10M) that passed quality control (reads per cell > 1,000 and TSS enrichment score > 5). To perform clustering analysis, we used a cell-by-peak matrix that was normalized by a TF-IDF transformation and applied shared neighbor network (SNN) graph-based clustering with the top 30 principal components, except for the first principal component (Hao et al., 2021; Stuart et al., 2019). The resulting cell clusters were visualized using uniform manifold approximation and projection (UMAP), which identified 8 major clusters.

To identify cell cluster identities, we conducted differential accessibility analysis using the ‘FindAllMarker’ or ‘FindMarker’ function in Signac (Stuart et al., 2019) to identify genes or peaks enriched in individual cell clusters compared to all other cell clusters. Enriched gene sets (adjusted p-value or FDR < 0.05 and absolute log2 fold change > 1.2) were subjected to GO term enrichment analysis using ClusterProfiler (Yu et al., 2012). We then confirmed the cell type annotation based on snATAC-seq data using publicly available scRNA-seq data of mouse skin and scRNA-seq data of FACS-purified melanocytes. To transfer cell labels from the reference scRNA-seq and snRNA-seq data, we integrated scRNA-seq and snRNA-seq datasets with our snATAC-seq by using the ‘FindTransferAnchors’ function in Seurat with 2:30 dimensions and CCA reduction method after Log normalization of the two datasets (Hao et al., 2021; Stuart et al., 2019).

Cell abundance test with Milo

We conducted a differential cell abundance analysis on samples collected from hair follicles in two phases and after UVB exposure using Milo (Dann et al., 2022). Milo utilizes a k-nearest neighbor graph to identify cell neighborhoods and assigns cells to these neighborhoods. We counted the number of cells in each neighborhood and calculated the log-fold changes between the two experimental conditions.

Transcription factor motif analysis with chromVAR

Motif activity scores were computed using chromVAR (Schep et al., 2017). We then used differential motif enrichment analysis to identify potential regulatory sequences for each cell type. Specifically, we computed average differences in z-score for each motif between pairs of cell types, and identified motifs that were differentially enriched across cell types.

Pseudotime trajectory analysis with Monocle3

We performed pseudotime analysis using Monocle3 (Trapnell et al., 2014) using our snATAC-seq data. The cells were ordered by rooting qMcSCs.

Co-accessibility with Cicero

To infer cis-regulatory networks, we utilized the cicero R package, which evaluates co-accessibility between pairs of regulatory elements (Pliner et al., 2018). This approach enables the linking of potential distal enhancers to promoters by identifying co-accessible pairs of DNA elements across groups of cells.

Data availability

The snATAC-seq data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE232908. The processed snATAC-seq dataset used in our analysis is publicly available for interactive exploration at the following URL: http://www.andrewwhitelab.com/tools. The mouse skin scRNA-seq data used in this study were obtained from GEO accession number GSE129218 (Joost et al., 2020), while the scRNA-seq data from sorted melanocytes were obtained from GEO accession number GSE147299 (Infarinato et al., 2020). The mouse TFAP2A ChIP-seq data used in this study were obtained from GEO accession number GSE72953 (Seberg et al., 2017).

Supplementary Material

Supplementary Figures

Acknowledgments

We thank William Zhuang for animal genotyping and Dr. Hyeoungsun Moon for initiating the project. We thank the funding from a Cornell VERGE scholar fellowship (SL and LA) and NIAMS NIH 5R01AR075755 (ACW). We thank the Cornell Biotechnology Resource Center for flow cytometry (RRID: SCR_021740) and sequencing services (RRID: SCR_021727).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figures
NIHMS1926036-supplement-Supplementary_Figures.docx (2.3MB, docx)

Data Availability Statement

The snATAC-seq data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE232908. The processed snATAC-seq dataset used in our analysis is publicly available for interactive exploration at the following URL: http://www.andrewwhitelab.com/tools. The mouse skin scRNA-seq data used in this study were obtained from GEO accession number GSE129218 (Joost et al., 2020), while the scRNA-seq data from sorted melanocytes were obtained from GEO accession number GSE147299 (Infarinato et al., 2020). The mouse TFAP2A ChIP-seq data used in this study were obtained from GEO accession number GSE72953 (Seberg et al., 2017).

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