SUMMARY
Cell fate transitions are essential for specialization of stem cells and their niches, but the precise timing and sequence of molecular events during embryonic development are largely unknown. Here, we show that dermal condensates (DC), signaling niches for epithelial progenitors in hair placodes, are specified before niche formation and function. With 3D/4D microscopy we identify unclustered DC precursors. With population-based and single-cell transcriptomics we define a molecular time-lapse of dynamic niche signatures and the developmental trajectory as the DC lineage emerges from fibroblasts. Co-expression of downregulated fibroblast and upregulated DC genes in niche precursors reveals a transitory molecular state following a proliferation shutdown. Waves of transcription factor and signaling molecule expression then consolidate DC niche formation. Finally, ablation of epidermal Wnt signaling and placode-derived FGF20 demonstrates their requirement for DC-precursor specification. These findings uncover a progenitor-dependent niche precursor fate and the transitory molecular events controlling niche formation and function.
HIGHLIGHTS
Precursors of the hair follicle niche are specified before niche cluster formation
Bulk/single cell RNA-seq defines early niche fate at molecular transitional state
Successive waves of transcription factor/signaling genes mark niche fate acquisition
Niche fate acquisition is not “pre-programmed” and requires FGF20 from progenitors
INTRODUCTION
Many adult tissues are maintained during homeostasis or repaired after injury by resident stem cells as a resource of reserve cells with multilineage potential. Stem cell functions are either intrinsically controlled or are typically regulated by a large variety of neuronal, hormonal and other inputs from the microenvironment or niche (Chacón-Martínez et al., 2018; Ge and Fuchs, 2018; Heitman et al., 2018; Morrison and Spradling, 2008; Rezza et al., 2014) In many cases, signals are derived from specialized niche cells that control stem cell quiescence for long periods of time, regulate stem cell self-renewal, or guide their differentiation into one or more lineages of the respective tissue. Although neighboring niche cell types and their signals have been identified in several tissues such as bone marrow (Morrison et al., 2014; Tamma and Ribatti, 2017; Wei and Frenette, 2018), intestine (Shoshkes-Carmel et al., 2018), brain (Katsimpardi and Lledo, 2018), lung (Lee et al., 2017) and skin (Gonzales and Fuchs, 2017; Hsu et al., 2014b; Rompolas and Greco, 2014; Sennett and Rendl, 2012), our understanding of the molecular controls of niche fate specification remains limited. Equally unclear is the precise timing with which niche cells emerge during embryonic development relative to stem/progenitor cell fates and their mutual impact on each other.
In mice, hematopoietic stem cells first emerge from the dorsal aorta, then home to the placenta, spleen, and liver during successive developmental stages before finally relocating to the bone marrow (Dzierzak and Bigas, 2018). There periarteriolar stromal cells (Kunisaki et al., 2013) and osteoblasts (Calvi et al., 2003; Zhang et al., 2003) provide niche signals for quiescent hematopoietic stem cells and multipotent progenitors (Morrison et al., 2014; Wei and Frenette, 2018) for continued blood production, but when and how the niche is specified remains unknown. Similarly, specialized glial cells in the neurogenic niche (Falk and Götz, 2017), perialveolar fibroblasts close to lung progenitors (Nabhan et al., 2018), and interstitial fibroblasts surrounding the crypt stem cells in the intestine (Stzepourginski et al., 2017) have been identified as rich sources of niche signals to regulate nearby stem and progenitor cell functions in their respective systems. How these specialized niche cells acquire their stem cell supporting fate and how this relates to stem cell specification during development is largely unexplored.
In hair follicles, rapidly dividing progenitors in the proximal bulb region receive signals from the centrally-located dermal papilla (DP) niche, which controls hair follicle progenitor proliferation, migration and differentiation into several lineages of the hair shaft and its channel during continuous hair growth (Avigad Laron et al., 2018; Clavel et al., 2012; Morgan, 2015; Sennett and Rendl, 2012; Yang et al., 2017). During the hair cycle after bulb degeneration and rest, the DP niche sends activating signals to the stem cells in the hair germ to regenerate a new hair bulb (Hsu et al., 2014a). Many studies have investigated the developmental processes and morphogenetic events during embryonic hair follicle formation, including the emergence of stem cell and niche compartments (Chen et al., 2012; Millar, 2002; Ouspenskaia et al., 2016; Sennett and Rendl, 2012; Zhang et al., 2008, 2009). Around embryonic day (E) 12.5, secreted epidermal Wnts activate broad dermal Wnt/β-catenin signaling (Chen et al., 2012; Zhang et al., 2009). This is upstream of still unidentified dermal signals that at ∼E13.5 induce the fate specification of hair follicle progenitors in patterned pre-placodes (Figure 1A, hair follicle stage 0) (Paus et al., 1999) that can be molecularly identified by Dkk4 and Edar expression (Sick et al., 2006; Zhang et al., 2009). Progenitor cell rearrangement, compaction, and migration then form the physically identifiable placode (Pc), likely due to continued dermal signals (Figure 1A, stage 1) (Ahtiainen et al., 2014). During this process, Pc progenitors signal back to the dermis for formation of dermal condensates (DC) (Figure 1A, stage 1, DC1), fibroblast-derived specialized cell clusters that act as signaling niches for placode progenitors to regulate continued hair follicle development (Millar, 2002; Sennett and Rendl, 2012; Sennett et al., 2015). Pc progenitors also give rise to suprabasal Sox9+ precursors of the future adult hair follicle stem cells in the bulge after completion of hair follicle morphogenesis (Ouspenskaia et al., 2016; Xu et al., 2015). Continued signal exchange between the DC and Pc leads to progenitor proliferation and downgrowth of the polarized hair germ (Figure 1A, stage 2, DC2), with the DC at the leading edge that, after an elongated hair peg stage, becomes engulfed by the progenitors to form the DP niche of the hair-shaft producing bulb (Grisanti et al., 2013; Sennett and Rendl, 2012). Several key signaling pathways have been identified for progenitor and niche formation (Biggs and Mikkola, 2014; Millar, 2002; Sennett and Rendl, 2012). Wnt/β-catenin signaling is essential and the most upstream for placode (Huelsken et al., 2001; Zhang et al., 2009) and condensate (Tsai et al., 2014) formation. Downstream Eda/Edar signaling is required for maintaining Wnt signaling and placode stabilization (Schmidt-Ullrich, 2006; Zhang et al., 2009). FGF20, another Wnt target, is a key placode-derived signal required for DC niche formation (Huh et al., 2013) in a process involving cell aggregation (Biggs et al., 2018).
The morphological detection of the DC after placode initiation (Chen et al., 2012), combined with discovery of DC markers (Botchkarev et al., 1999; Chen et al., 2012; Clavel et al., 2012; Huh et al., 2013) and the definition of a DC gene expression signature (Sennett et al., 2015) has led to the current model of niche fate specification in response to progenitor signal(s) that coincides with condensate formation. However, the identification of unclustered dermal cells with high and localized Wnt activity beneath presumptive placodes (Zhang et al., 2009), and low-level expression of DC signature genes in immunophenotypically non-DC dermal cells (Sennett et al., 2015) raise the possibility of DC niche fate acquisition prior to cluster formation. Contextualizing these early events in hair follicle niche formation is further complicated by the rapidity with which morphogenesis occurs; Wnt signaling activity appears virtually simultaneously in Pc and DC (Tsai et al., 2014; Zhang et al., 2008), rendering parsing out the precise order and timing difficult. Recent work also demonstrated direct mesenchymal self-organization ability in the absence of an epidermal pre-pattern (Glover et al., 2017) further calling into question the origin and timing of niche fate specification (Figure 1A): 1) is the niche fate specified before niche cluster formation and signaling function and 2) how is the timing of niche fate acquisition related to progenitor fate and signaling?
Here, by combining 3D and 4D time-lapse live imaging with fluorescence-activated cell sorting, bulk and single-cell transcriptomics, proliferation kinetics, and mutant mouse models, we identify fated precursors of the clustered DC niche prior to signaling center formation. Based on a molecular time-lapse of dynamically changing niche gene signatures and ordering the cell fates in developmental pseudotime we define the cell fate trajectory from fibroblasts through DC niche precursors that are fated to become the clustered niche. In this process, DC precursors (pre-DC) undergo a transitional molecular state that is consolidated towards the DC niche biological fate through waves of transcription factor and signaling molecule upregulation. Finally, with epidermal Wnt signaling ablation that prevents placode formation, and with deletion of placode-derived FGF20 that precludes DC aggregation we demonstrate dependence of niche cell precursor acquisition on placode progenitor fate.
RESULTS
Identification of Unclustered Precursors of Dermal Condensates
Previous studies have demonstrated cell aggregation is the cellular mechanism inciting DC formation (Biggs et al., 2018; Glover et al., 2017), but when and how the DC niche fate is acquired remains unknown. Here, to identify potential DC-fated precursors before cluster formation and niche establishment, we followed up on our previous observation of a mixed population of Schwann and DC-like cells isolated from embryonic Sox2GFP reporter skin (Figure S1A) (Sennett et al., 2015), that expresses low levels of GFP along with a variety of DC and Schwann cell genes (Figures S1A, S1B and S1C). GFP (Figures 1B and S1A) and Sox2 (Figure S1B) are highly expressed in early DC (Clavel et al., 2012; Driskell et al., 2009; Sennett et al., 2015), where condensed cells are visible underneath flat and barely invaginated EDAR+ hair placodes of stage 1 hair follicles (Figure 1B, open arrow) (Paus et al., 1999; Pispa et al., 2008; Sennett et al., 2015). GFP levels remain high after initial placode downgrowth and polarization in stage 2 hair follicles (Figure 1B, arrow). We named these two DC stages with high Sox2 expression DC1 and DC2 accordingly, and surmised that cells with low Sox2GFP expression could represent a precursor state. However, difficulty of detecting low GFP levels, even in Schwann cells, in sagittal skin sections precluded their convincing discovery (Figure 1B). To detect GFP low cells with high sensitivity we performed multicolor immunofluorescence in whole mount skin of E15.0 Sox2GFP embryos (Figure 1C). As predicted from sagittal sections, in the top view GFP was strong in DC1 and DC2, but with anti-GFP staining, low GFP levels were now detectable also in Schwann cells, highlighted by ITGA6, forming a GFP low/ITGA6+network within the developing skin (Figure 1C, asterisks). Importantly, we found few loosely arranged GFP low/ITGA6--cells in the dermis in a location fitting of a hair follicle pattern (Figure 1C, arrowhead). Co-labeling with the previously identified DC marker FOXD1 (Figure S1C) (Sennett et al., 2015) labeled DC1, DC2 and these GFP low cells, suggesting that they might be DC precursors (pre-DC) before DC niche formation. High magnification images suggested that these cells are still unclustered, unlike the clearly visible cell aggregations of DC1 stage hair follicles (Figure 1D).
To demonstrate that potential pre-DC cells are associated with hair follicles we next performed whole mount immunofluorescence for SOX2 and EDAR on E15.0 PdgfraH2BGFP back skin. Pdgfra is highly expressed in fibroblast-type mesenchymal cells (Figure S1C) (Collins et al., 2011; Hamilton et al., 2003), which are marked in this reporter line by nuclear GFP. In optical slices of 3D confocal scans, SOX2+ pre-DC cells at the dermal level were underneath EDAR+ placodes at the epidermal level and appeared to be unclustered compared to aggregated DC1 and DC2 (Figures 1E and S1D). Overlap of SOX2 with nuclear H2BGFP demonstrated the fibroblast-type of potential pre-DC cells. 3D reconstructions further illustrated the unclustered state of pre-DC cells (Figure S1E, Supplemental Movies 1-3). Z-projections demonstrated the close proximity of unclustered pre-DC to the overlying placode (Figure 1F). Finally, to confirm that pre-DC cells are truly unclustered we measured, in 3D, the distance of each nucleus to its five nearest neighbors for pre-DC, DC1 and DC2 (Figure 1G). Cells in DC2 clusters were significantly closer to each other compared to all others. Pre-DC cells, however, were significantly further apart compared to those of DC1 and DC2, confirming their loose initial arrangement. As other H2BGFP+ fibroblasts were interspersed between pre-DC cells (Figure 1E), it suggests that early fate acquisition does not have to be synchronized among direct neighbors. In summary, our data thus far identified the existence of uncondensed potential DC precursor cells suggesting that the DC fate is specified prior to DC formation and function.
DC Precursor Cells Are in a Transitional Transcriptional State Towards a DC Niche Fate
After identifying potential DC precursors in stage 0 hair follicles with SOX2 protein, GFP reporter and the DC signature gene FOXD1, we next sought to isolate this population for exploring the molecular controls of DC fate acquisition and its niche functions. The simultaneous presence of pre-DC, DC1 and DC2 from stage 0, stage 1 and 2 hair follicles, respectively, in E15.0 back skin provided us with an excellent opportunity to compare multiple parallel developmental stages in isolated cells. For this, we modified our previous DC isolation method (Figure S1A) (Sennett et al., 2015) to separately purify Sox2GFP high DC1 and DC2 by differential DC marker CXCR4 expression (Sennett et al., 2014) in DC2 (arrow) compared to DC1 (open arrow) (Figure 2A and 2B). Simultaneously we stained for mesenchymal marker PDGFRA to isolate dermal fibroblasts (Fb), for ITGA6 to obtain Schwann (Sch) cells and separately isolate pre-DC as the Sox2GFP low/PDGFRA+/CXCR4-population. Additionally, PDGFRA-/Sox2GFP-cells were sorted as a negative (Neg) population containing all other remaining skin cells (Figure 2B). To confirm enrichment of Fb and DC we performed qRT-PCR of known marker genes (Figure 2C). As expected, Lum (Lumican) was highly enriched in fibroblasts, while Cxcr4 was absent in pre-DC and detected at increasing levels in DC1 and DC2, and Sox2 expression was low in both pre-DC and Schwann cells and high in DC1 and DC2. Importantly, Schwann cell marker Fabp7 showed strong enrichment in Schwann cells and not in isolated pre-DC (Figure S2A), and all other Fb/DC markers were also absent (Figure 2C). Finally, Foxd1 was highly expressed at all three DC stages, and not in Fb or Schwann cells, confirming successful isolation of pre-DC cells in agreement with our 3D imaging analysis.
We then proceeded with population-based transcriptome analysis that provided high sensitivity of gene signature discovery, as previously described (Rezza et al., 2016; Sennett et al., 2015). Bulk RNA-sequencing (RNA-seq) was performed with low RNA amounts obtained from all isolated populations. We first globally compared the transcriptomes by principal component analysis of the top variably expressed genes (Figure S2B) and hierarchical clustering (Figure S2C), confirming the similarities between DC1 and DC2 and establishing multiple co-regulated gene groups within all six populations. Interestingly, pre-DC was positioned between Fb and DC1 suggesting pre-DC dually share characteristics of both dermal fibroblasts and clustered DC. We next calculated significantly differentially expressed genes (DEGs) in each population by ANOVA (false discovery rate < 0.05) and compared the resulting total of 6,454 genes. We then established gene signatures using significant DEGs and selecting for genes with FPKM > 1 and an expression fold change > 2, which resulted in 1,890 signature genes for all six cell types. To focus on the potential pre-DC cells and their relationship to fibroblasts and the condensate niche, we analyzed gene expression relationships in a four-way Venn diagram analysis all significantly increased genes in Fb, pre-DC, DC1 and DC2 compared to Sch and Neg populations (Figure 2D and Table S1). A total of 1,157 genes were enriched in the four mesenchymal populations, including 692 population-unique signature genes. However, we also found several interesting population overlaps. As the DP and its embryonic DC predecessor are fibroblast-derived mesenchymal cells (Driskell et al., 2013; Jinno et al., 2010), it was revealing to note that the unique pre-DC overlap with Fb (28 genes) was larger than both the unique overlaps of Fb and DC1 (1 gene) and Fb and DC2 (5 genes) (Figure 2D, blue), suggesting that pre-DC shares transcriptional similarity with fibroblasts fitting with a Fb-to-DC trajectory. Indeed, exploring the expression trajectory of genes shared between Fb and pre-DC demonstrated a transition of high-to-low expression from Fb to DC with intermediate levels in pre-DC (Figure 2E). Several genes included extracellular matrix (ECM)-components such as Ccbe1 and Eln, and ECM-modifiers such as Sulf1 (Figure 2F). Conversely, differential expression analysis confirmed a high overlap of 119 genes between pre-DC, DC1, and DC2 (Figure 2D, red), which included 58 previously described DC signature genes (Sennett et al., 2015). Also here, pre-DC expressed many of these shared DC signature genes at intermediate levels between fibroblasts and DC1/DC2, suggestive of its developmental precursor status to morphologically identifiable DC (Figures 2E and 2G).
We next complemented population-based bulk RNA-sequencing with single cell transcriptome analyses that allow for exploration of minute cell-to-cell transcriptional changes that define cell heterogeneity and differentiation (Grün et al., 2015; Haber et al., 2017; Joost et al., 2016; Yang et al., 2017). Importantly, it also enables a high-resolution definition of lineage relationships along a developmental trajectory (Artegiani et al., 2017; Herman et al., 2018), with which we can corroborate the developmental link of pre-DC between fibroblasts and DC1/DC2. For this, we used Sorting and Robot-Assisted Transcriptome Sequencing (SORT-Seq) (Muraro et al., 2016) with our established DC cell isolation strategy. SORT-seq is a powerful approach combining live cell isolation of identified, known single cells with their transcriptome information through an index, connecting sorted cells with single cell RNA-seq wells (Figure 2H). We isolated, index sorted, and sequenced 1,371 fibroblasts, 149 pre-DC, 152 DC1, and 151 DC2. Of those, 1,076 fibroblasts, 107 pre-DC, 118 DC1, and 82 DC2 were used for analysis based on a minimum requirement of 6,000 transcripts/cell and established quality controls to exclude poorly sequenced cells. Based on population frequencies observed by flow cytometry it would require ∼15,000 total unenriched cells to obtain similar transcriptome information of our sorted pre-DC cells (150x enrichment). We then used the Rare Cell Type Identification 3 (RaceID3) clustering algorithm that was previously used in various iterations to identify rare intestinal cell types and lineage relationships between stem cells and differentiated cells (Grün et al., 2015, 2016; Herman et al., 2018). RaceID3 uses k-medoid clustering of the transcriptome-based correlation matrix that is visualized in two dimensions using t-distributed stochastic neighbor embedding (tSNE) (Van Der Maaten and Hinton, 2008) to show cell-cell gene expression similarities. Superimposing the Fb and DC cell identities onto the RaceID3-generated tSNE revealed clear distinctions between all isolated populations confirming our cell isolation strategy (Figure 2I). At the same time, it highlighted similarities between all DC stages and placed pre-DC between fibroblasts and DC1/DC2, similarly suggesting a lineage trajectory from Fb to DC through an intermediate pre-DC fate transition. As expected, all sorted DC populations expressed established DC signature genes, including Fgf10 (Mailleux et al., 2002; Suzuki et al., 2000), Sox2, Foxd1, and Tbx18 (Grisanti et al., 2013) (Figure 2J), and sorted fibroblasts expressed established fibroblast markers, including Col1a1, Lrig1 (Driskell et al., 2013), Dcn (Ferdous et al., 2010; Philippeos et al., 2018), and Lum (Philippeos et al., 2018) (Figure 2K). Also here, cursory exploration of expression patterns revealed gradual downregulation of Fb genes and upregulation of DC genes (Figure 2L). Altogether, these bulk and single cell transcriptome data from purified embryonic skin cells indicate that the unclustered pre-DC precursors are at a transcriptionally transitional state from fibroblasts towards a permanent DC niche fate.
DC Precursors are Developmental Intermediates Between Fibroblasts and The Mature DC Niche
As the identified pre-DC cells express genes characteristic of both fibroblasts and DC, we hypothesized that they are precursors of the mature DC niche at an intermediate developmental state between fibroblasts and DC. To explore this lineage trajectory, we first utilized RaceID3 to identify distinct clusters of single cells with similar transcriptome as a basis for calculating lineage relationships between related cell types, and visualized them in t-SNE (Figure 3A). Clusters 6, 7 and 8 were highly similar in shape and distribution to our sorted pre-DC, DC1 and DC2 cells, respectively (Figures 2H and 3A), again validating our sorting strategy. Calculation of the overlap percentages confirmed that clusters 6, 7, and 8 were mostly comprised of pre-DC, DC1 and DC2, respectively (Figure S3A). We then applied the Stem Cell Identification (StemID2) algorithm (Grün et al., 2016; Herman et al., 2018) to order the RaceID3-identified clusters by populating the links between the clusters’ medoids with cells from the linked clusters based on degree of transcriptional similarity (Figure 3B). StemID2 enables postulating differentiation trajectories and stem cell identity from single cell RNA-sequencing data (Grün et al., 2016; Herman et al., 2018). Amongst DC clusters (clusters 6, 7, 8), the pre-DC-enriched cluster 6 was closer to the fibroblast clusters. Amongst the sorted fibroblasts, fibroblast cluster 5 was the sole calculated link to pre-DC cluster 6, suggesting that pre-DC cells emerged from those closely related fibroblasts (Figure 3B). It also demonstrated that pre-DC are the link for transition into DC1/DC2, which were connected by StemID2 to pre-DC with equal confidence. To independently confirm this developmental trajectory, we turned to an alternative approach, using the Scanpy toolkit (Wolf et al., 2018) for Louvain clustering and diffusion maps. Louvain clustering, a method to extract communities from large networks (Blondel et al., 2008), with the same 1,383 cells as used for RaceID3 clustering, also showed clear distinctions between all sorted populations, placing again pre-DC between fibroblasts and DC1 (Figures 3C and S3B). Importantly, applying diffusion pseudotime, a method for ordering cells along lineage trajectories toward different cell fates (Haghverdi et al., 2016), clearly demonstrated the lineage path of niche specification from nearest fibroblasts (Louvain cluster 5) through pre-DC towards DC1 and DC2 (Figures 3D, S3C).
Having established by single cell RNA-sequencing analyses the differentiation trajectory from Fb to DC2, we next re-examined the bulk RNA-sequencing for gene expression trends. For this we performed hierarchical clustering on all genes that had a >2 fold difference between maximum and minimum FPKM of any 2 populations. We noted four major trends: genes with highest expression in fibroblasts (down A and B), and genes with highest expression in pre-DC (up C), DC1 (up D) and DC2 (up E) (Figure 3E and Table S2). 2,687 genes were downregulated from fibroblasts through DC2 (Figure 3F). Closer examination of these genes revealed major downregulation of many Fb transcription factors, adhesion/ECM molecules, and signaling factors (Figures 3G and 3H) suggesting their involvement in the rapid and sustained loss of fibroblast function as the DC fate is acquired and throughout DC differentiation.
Finally, we utilized our single cell transcriptome data to precisely define the pseudotemporal ordering of fibroblasts and DC populations along the differentiation trajectory and explore patterns of gene regulation during niche fate acquisition. For this, we devised a self-organizing map using the fibroblast clusters closest to DC and the DC-enriched clusters (Figure 3I). We excluded Dlk1 expressing fibroblast clusters 1-3 that likely represented reticular fibroblasts of the lower dermis unrelated to DC and hair formation (Figure S3D) (Driskell et al., 2013), and included fibroblast clusters 4 and 5 as both were intermingled in Louvain cluster 5 and closest to pre-DC-enriched cluster 6 (Figure S3E). Pseudotime gene expression analysis revealed cognate but more refined trends to those observed in the bulk RNA-sequencing (Figure 3I and Table S3): genes with their highest expression in fibroblasts (down clusters 1, 2), and genes with their highest expression in pre-DC (up clusters 3, 4), DC1 (up cluster 6), and DC2 (up clusters 5, 7). Amongst the 2,687 genes downregulated from fibroblasts through DC2 in population-based RNA-sequencing (Figure 3F) and the 1,342 genes identified in the single cell transcriptomes (Figure 3J), 951 genes were in common (35% of bulk-identified targets, 70% of single cell-identified targets). Also here, these included several transcription factors, adhesion and ECM components, and signaling molecules necessary for fibroblast function (Figure 3K). Creb3l1 is a secreted factor essential for collagen metabolism (Keller et al., 2018), while Irx1 and Twist1 are transcription factors that control expression of matrix-stiffening components (García-Palmero et al., 2016), and Zeb1 is known for promoting expression of vimentin (Liu et al., 2008). Additionally, expression of numerous collagens such as Col1a1, Col1a2, and Col3a1, as well as other ECM components such as Dcn and Bgn, and P4hb, an enzyme essential for procollagen synthesis (Winter and Page, 2000), was decreased between fibroblasts and DC populations. Finally, we identified signaling molecules that are uniquely expressed in fibroblasts, such as Efna5, Cthrc1, Angptl4, Cntn4, Fbln1, and Wnt4 that are all downregulated on the path to DC lineage differentiation. Altogether these data suggest that fibroblasts quickly halt transcription of fibroblast-associated genes as they transition through the pre-DC intermediate into the definitive DC niche fate.
Loss of Proliferation is Concomitant with Acquisition of DC Fate
It has been well established that niche cells in mature dermal papillae are quiescent and very rarely divide (Moffat, 1968; Tobin et al., 2003). Also the aggregated DC, as the embryonic precursor of the DP (Grisanti et al., 2013), was recently shown to be largely non-proliferative (Biggs et al., 2018). Our discovery of unclustered pre-DC cells then provided an opportunity to fine-map the timing of cell-cycle exit with respect to niche fate acquisition. As predicted, many genes involved in the molecular control of cell proliferation, including major cell cycle components, were downregulated from fibroblasts to DC2 (Figure 4A). Interestingly, gene set enrichment analysis revealed a significant correlation of proliferation-associated genes in fibroblasts compared to pre-DC, suggesting that the exit of proliferation could be as early as during pre-DC fate acquisition (Figure 4B). Indeed, in pseudotime-ordered cells a rapid declining expression of pro-proliferative genes such as Rbbp7, Mki67, and Pcna coincided with the sharp upregulation of cell-cycle inhibitors, such as Cdkn1a and Btg1 (Zhu et al., 2013). Importantly, both events occurred prior to the emergence of pre-DC and remained sustained through DC2 (Figures 4C and 4D), suggesting that cell cycle exit is a feature of pre-DC fate acquisition.
To independently test when proliferation exit occurs relative to DC fate acquisition, we performed EdU proliferation assays to mark proliferating cells during the S-phase of the cell cycle (Figures 4E and S4A). EdU injection 6 hours prior to E15.0 analysis showed that 33% of fibroblasts were proliferating (Figure 4F). By contrast, less than 3% of DC1 and DC2 were going through S-phase suggesting that the vast majority of maturing DC cells had exited the cell cycle. Notably, only 3% of pre-DC precursors incorporated EdU suggesting that already during this early step of DC fate acquisition cells shut down proliferation. Analyzing 12 and 24 hour chases showed increased EdU incorporation in pre-DC and DC1 fated cells, likely reflecting their developmental history coming from actively proliferating fibroblasts at the time of EdU injection. Interestingly, with the 12 hour EdU chase the percentage of proliferating cells in DC2 was significantly lower than in pre-DC and DC1 and as low as 6h levels. Given that DC1 is progressing to DC2 during this 12 hour chase, the results herein suggest that progression to DC2 from DC1 does not require cell proliferation.
Finally, to confirm that exit of proliferation is already occurring during DC niche fate establishment before pre-DC precursor detection, we tracked with 4D time-lapse live imaging the cellular processes up to pre-DC fate specification (Figure 4G). For this we utilized live imaging of embryonic ex vivo back skins from Tbx18H2BGFP mice that express high levels of nuclear GFP in the DC (Grisanti et al., 2013). Image analysis of DC1 and DC2 confirmed high GFP expression as expected (Figure S4B). Importantly, pre-DC cells also already express high nuclear GFP, while fibroblasts exhibit low GFP expression levels (Figure 4G). Tracking cell divisions during a 6 hour period prior to pre-DC fate acquisition detected actively-proliferating interfollicular fibroblasts as predicted (Figure 4G and 4H). By contrast, no pre-DC cells were dividing during the same time span, indicating that proliferation exit is occurring during DC fate acquisition in the fibroblasts-to-pre-DC transition.
Three Distinct Waves of Transcription Factor and Signaling Molecule Upregulation Occur Along the DC Niche Developmental Trajectory
Besides shutdown of proliferation and gradual loss of the fibroblast gene expression signature, a gradual upregulation of the DC niche signature occurs during DC niche fate acquisition (Figures 2E, 2G, 2L, 3E and 3I). We next explored the precise timing of the molecular fate acquisition and consolidation from fibroblasts to pre-DC, through DC1 and DC2. We discovered three distinct waves of DC gene upregulation for pre-DC cell fate acquisition, DC1 clustering, and hair germ downgrowth stages at DC2, in both bulk (Figure 5A) and single cell (Figure 5C and Table S3) transcriptional analyses. The first wave was evident in gene clusters whose expression peaked early at pre-DC and then dropped, which likely includes factors necessary for fate acquisition and other early functions. Two additional waves of co-regulated gene clusters had sustained expression through DC2, but differed only in when their expression peaked: mid genes reached maximal expression in DC1, and late genes reached maximal expression in DC2. All three waves contained many transcription factors that could represent potential master regulators for DC niche specification and development (Figure 5B-5G and Table S4). Transcription factors expressed in the early wave included known key fate regulators such as Twist2 (Dermo1) (Li et al., 1995), the Wnt signaling transcription factor Lef1, and the TGF-β effector Smad3 (Figures 5B, 5D, 5F and S5A). Twist2 knock-out animals have sparse hair and thin skin, among many other abnormalities (Šošić et al., 2003). Similarly, Lef1 knock-out results in abrogation of whiskers and back skin hair follicles, and of other ectodermal appendages such as mammary glands and teeth (van Genderen et al., 1994). We confirmed Twist2 expression in pre-DC cells by in-situ hybridization, which is shut down at the DC1 stage (Figure 5D). Many more transcription factors were in the mid gene cluster (Figures 5E, 5F and S5B) Mid-expressing transcription factors include Trps1, Tshz1, Foxd1, Prdm1, and Sox18. Mutations in Trps1 are associated with Trichorhinophalangeal syndrome, a disorder characterized by hair follicle defects among others (Giedion et al., 1973), and Ambras Syndrome, a hypertrichosis disorder (Fantauzzo et al., 2008). Prdm1 is a well-characterized marker for early papillary dermal development (Driskell et al., 2013; Horsley et al., 2006; Robertson et al., 2007). Mutations in Sox18 have been shown to impact differentiation of adult dermal papillae (Villani et al., 2017). Finally, we resolved many transcription factors whose expression peaked in DC2, including Notch target genes Hey1 and Hes5, as well as Glis2, Foxp1, and Alx4 (Figures 5E, 5F and S5C). Foxp1 has noted functions in maintaining the quiescence of adult hair follicle stem cells (Leishman et al., 2013). Human mutations in Alx4 have been associated with noted defects in hair follicle differentiation (Kayserili et al., 2009). In total, we discovered 11 early-wave, 16 mid-wave and 24 late-wave transcription factors (Figure 5G) that followed these temporally-restricted upregulation patterns along pseudotime, suggesting that a coordinated array of molecular changes is required for pre-DC to mature through DC1 and DC2.
As the dermal papilla acts as a major signaling niche for epithelial progenitor cells during postnatal hair growth and for stem cells in cycling adult hair follicles, we finally explored the expression and upregulation of signaling molecules during DC fate specification and niche formation. Also here, with our bulk and single cell RNA sequencing analyses we discovered a distinct pattern of signaling waves (Figures 5H-5K and Table S4). Signaling factors expressed in the short window of time around pre-DC include the Wnt inhibitor Dkk1, Cyr61, and Fst (Figures 5I and 5J and S5D). Cyr61, known to induce senescence in fibroblasts, is also dynamically expressed in wounding healing to reduce fibrosis, likely through cell-cell adhesion interactions with integrins and heparin sulfate proteoglycans (Jun and Lau, 2010). Fst knock-out is associated with abnormal whisker growth (Jhaveri et al., 1998) and abrogated hair follicle development (Nakamura et al., 2003). The mid-acting signaling molecules include well known regulators Fgf10, the TGF-β modulator Ltbp1, and Sema6a (Figures 5I and 5J and S5E); late-acting signaling molecules include key ligands such as Igfbp4, Inhba, Rspo3, Dll1, and Bmp3 (Figure 5I and 5J and S5F) – many of which continue to be expressed in the adult dermal papilla (Rezza et al., 2016). The global display as cumulative percent of total corroborates a resolution of 8 early, 11 mid, and 24 late upregulated signaling factors (Figure 5K). Overall these data suggest that development of DC, from fate acquisition in a subset of fibroblasts through aggregated DC1 and DC2, is accompanied by three distinct waves of expression of transcription factors and signaling molecules that in concert are likely regulating the specification of the DC niche fate and initiation of DC niche function.
DC Cell Fate Specification Requires Signals from Pre-existing Placodes
Our data suggest that the transition of fibroblasts to pre-DC for DC fate acquisition depends on the early wave of transcription factor and signaling molecule upregulation. However, it is unclear how the upregulation of these early DC genes is triggered, i.e. whether it is a pre-programmed cell-autonomous process or whether it relies on intercompartmental signals from epithelial placode progenitors. Understanding this developmental hierarchy essentially addresses the order of progenitor and niche fate specification. Previous studies have demonstrated that Wnt/β-catenin signaling in the epidermis is essential for placode formation and hair follicle morphogenesis including the formation of aggregated DCs (Huelsken et al., 2001; Zhang et al., 2008). In light of our identification of unclustered pre-DC cells, we asked whether an early DC fate is established in the absence of placode progenitors. To this end, we blocked placode formation by conditionally ablating Wnt/β-catenin signaling in the embryonic epidermis of K14-Cre;β-cateninflfl mice. In E14.5 control skin we observed, as described above, multiple developmental stages of SOX2+ ITGA6- DCs underneath EDAR+ placodes, including pre-DC (Figures 6A and S6A). Additional double immunofluorescence with FOXD1 confirmed pre-DC cell identity (Figure S6B). As predicted, in the β-catenin-ablated conditional knockout skin, EDAR+ placodes and established DC niches, aggregated SOX2+ ITGA6- DC1 and DC2, were absent (Figures 6B and S6A). Intriguingly, we neither detected groups of unclustered SOX2+ ITGA6- (Figure 6B) or SOX2+ FOXD1+ ITGA6- (Figure S6B) pre-DC cells in the absence of placodes, indicating that DC cell fate acquisition was also blocked. These data demonstrate that DC cell fate acquisition from fibroblasts requires the presence of placode progenitor cells that likely provided essential placode-derived signals.
Placode Progenitor-Derived FGF20 Is Required For DC Cell Fate Specification
In searching for potential placode-derived signals essential for DC fate acquisition, we turned to Fgf20, a well-known downstream target of Wnt/β-catenin signaling that is required for DC formation (Huh et al., 2013). Recent studies have further demonstrated that FGF signaling is required for cell migration to form morphologically distinguishable DC clusters (Biggs et al., 2018; Glover et al., 2017). Having discovered pre-DC cells prior to DC formation, we asked whether Fgf20 is required for DC niche fate specification before cell aggregation. To this end, we generated Fgf20-ablated embryos by crossing fertile and viable Fgf20Cre-GFP/Cre-GFP knockout mice with Fgf20LacZ/+ mice; in both strains the first exon of the Fgf20 allele is replaced with a Cre-GFP fusion or LacZ gene (Huh et al., 2012, 2015). In E14.5 heterozygous Fgf20Cre-GFP/+ control skins, normal hair follicle development was observed with multiple DC stages underneath GFP+ placodes (Figures 6C and S6C). On the other hand, in Fgf20 knockout skins of Fgf20Cre-GFP/LacZ embryos aggregated mature DCs were absent underneath GFP+ placodes, as described previously (Figures 6D and S6C) (Huh et al., 2013). Intriguingly, here like in the Wnt/β-catenin signaling ablation above, SOX2+/ITGA6- or SOX2+/FOXD1+ pre-DC cells were also absent (Figures 6C, S6C and S6D), suggesting that FGF20 is a key placode-derived signal essential for DC fate specification, prior to cell aggregation.
DISCUSSION
The timing of fibroblast-to-DC niche transformation has been traditionally described by the emergence of a characteristic tightly clustered DC beneath a thickened epidermal placode (Millar, 2002). It has been noted that at the earliest identified stage of bona fide HF morphogenesis, a distinct placode exists before the morphological DC cluster forms. Recently, two studies have demonstrated that the structured DC forms through directed migration of dermal fibroblasts (Biggs et al., 2018; Glover et al., 2017), but the precise timing and specific molecular drivers of DC fate-acquisition in relation to the physical aggregation process and to the timing of placode progenitor specification remained unexplored.
Here we demonstrate that de novo expression of the definitive (clustered) DC markers, Sox2 (Clavel et al., 2012; Driskell et al., 2009; Sennett et al., 2015) and Foxd1 (Sennett et al., 2015), first appears before any appreciable observed DC clustering. Taking advantage of Sox2 and other DC molecular markers, both pre- and post-clustering, we were now able to define the molecular landscape of the earliest fate-acquired DC precursors and their most closely related fibroblasts, further refining our previous molecular characterization of the established DC niche (Sennett et al., 2015). Reinforcing our novel sorting strategy for population-based RNA sequencing analyses of distinct unclustered DC precursors and clustered DC stages, unbiased lineage prediction from single-cell transcriptomes not only reconstructed the DC differentiation path of the bulk approach but also provided a highly resolved account of the molecular transitional states and developmental trajectory from non-committed fibroblasts through DC2.
Complementing the model of DC niche formation through directed migration from ex vivo live imaging (Biggs et al., 2018; Glover et al., 2017), we found that while the fibroblasts surrounding the DC can have robust proliferative activity, the earliest fate-acquired, Sox2+ DC precursors have significantly diminished proliferation indicative that the mature DC is a product of continual fibroblast recruitment (Figure 7A). We interestingly observed an early steady decline in the expression of proliferation-related genes in fibroblasts most closely-related to pre-DC in pseudotime. This preemptive suppression of proliferation occurs prior to upregulation of early DC-fate genes and is particularly intriguing since the establishment of asymmetrically positioned DC are required in maintaining cell-fate asymmetry in the epithelium giving rise to both hair follicle stem cells and progenitors during early down growth of developing follicles (Cetera et al., 2018; Ouspenskaia et al., 2016; Xu et al., 2015). Therefore, by extension, it can be posited that tight regulation of DC size by proliferation suppression is vital to maintain its asymmetric positioning for proper morphogenesis and hair follicle stem cell establishment. The potential necessity of cell-cycle exit for acquisition of DC fate, the cell intrinsic and/or extrinsic mechanisms to achieve cell cycle arrest, and the precise molecular controls that prevent over-recruitment through migration remain to be studied in more detail.
From both the bulk and single cell transcriptome analyses, we identified transcription factors enriched in pre-DC including Twist2, Lef1 and Smad3 indicating an immediate shift in the gene regulatory network during early niche fate acquisition potentially acting as trailblazers for the downstream upregulation of mid- and late-expressed transcription factors and genes for DC formation and function such as those encoding cell-adhesion and signaling proteins (Figure 7B). The reconstructed lineage order in pseudotime also revealed a subset of fibroblasts closely related to pre-DC raising the possibility of a yet earlier precursor stage to the DC niche fate. While pinpointing their precise spatial location poses a considerable challenge because of the paucity of distinctly expressed genes to be used as molecular markers, clustering analyses place these cells broadly among upper dermal fibroblasts which harbor multipotent dermal fibroblast progenitors (Driskell et al., 2013).
Epithelial-mesenchymal interdependence during early HF morphogenesis has been shown by multiple compartment-specific disruptions affecting the development of its counterpart (Andl et al., 2002; Chen et al., 2012; Tsai et al., 2014; Zhang et al., 2009). By preventing placode formation through epidermal β-catenin ablation (Huelsken et al., 2001), we determined from the absence of pre-DC, early DC fate acquisition is not an autonomous pre-program of the upper dermis, but rather requires local directive signals from spatially patterned placodes. Genetic ablation of Fgf20 has previously demonstrated its requirement for DC formation by hair follicle stage 1 through migration (Biggs et al., 2018; Huh et al., 2013), and furthermore here we show that FGF20 directly promotes the early DC niche fate. Similarly, recent identification of an Fgf20-expressing olfactory epithelial stem cell niche revealed a Wnt-regulated FGF20 requirement for formation of the underlying mesenchymal condensations that form nasal turbinates (Yang et al., 2018). Taken together these data indicate that the progenitors in nascent hair follicle pre-placodes signal to underlying fibroblasts at least in part through FGF20 to induce early DC fate prior to cluster-forming migration, and unequivocally demonstrate that progenitor fate precedes establishment of its supportive niche.
We now propose an update to the model of progenitor and niche specification during early hair follicle morphogenesis (Figure 7A and 7B): Wnt-dependent placode progenitors signal to the underlying dermal fibroblasts via the FGF/FGFR signaling axis initiating a cascade of dynamic transcriptional waves that regulate DC niche fate specification. Through multifaceted signaling interactions between placode, pre-DC and unspecified interfollicular dermal fibroblasts, fate specified DC precursor cells centrally migrate to form the clustered DC (DC1). Continued pre-DC fate acquisition and migration incorporates additional cells to the DC cluster, while in the earliest established DC the new transcriptional regulatory network – set up by the first transcriptional wave – begins to upregulate expression of the molecular machinery crucial for signaling functions from the DC niche to the progenitors above.
METHODS
Mice
Specific strain and genotype details are provided in the Key Resources Table. Mice were housed in facilities operated by the Center for Comparative Medicine and Surgery (CCMS) at the Icahn School of Medicine at Mount Sinai (ISMMS) and all animal experiments were performed in accordance with the guidelines and approval of the Institutional Animal Care and Use Committee at ISMMS. Embryo ages were determined by designating the morning of the appearance of a vaginal plug as E0.5.
EdU incorporation
For EdU-uptake proliferation assays, pregnant females mated with Sox2GFP males were injected intraperitoneally with a single dose of EdU/PBS (500 μg/g body weight) 6, 12 or 24 hours prior to sacrifice. EdU detection was performed with the Click-It EdU AlexaFluor 647 Imaging Kit (Life Technologies) according to manufacturer’s instructions.
Immunofluorescence staining
Tissue sections were embedded in OCT (Tissue Tek) and cut at a thickness of 10 μm with a cryostat (Leica). Whole-mounted embryo skins or sections from E15.0 embryos were fixed in 4% PFA and washed with PBS. Whole-mount samples were additionally permeabilized for 2 hr in 0.3% Triton X-100 in PBS. Tissues were blocked in PBS-Triton with BSA/NDS for 2 hour at room temperature. Primary antibody labelling against EDAR (goat, 1:100, R&D), SOX2 (Goat, 1:100, Stemgent), FOXD1 (Goat, 1:100, Santa Cruz), GFP (chicken, 1:800, Abcam), and CXCR4 (Rat, 1:100, BD Pharmagen) were carried out overnight at 4ºC. After PBS washes, samples were incubated with Rhodamine Red-X-, AlexaFluor 488-, AlexaFluor 555-, or AlexaFluor 647-conjugated donkey anti-goat, rabbit, chicken or rat secondary antibodies (Jackson Immunoresearch, Invitrogen). Samples were again PBS washed, counterstained with 4’6’-diamidino-2-phenilindole (DAPI) and mounted onto slides with antifade mounting medium.
Image acquisition and processing
Whole mount immunofluorescence and stained tissue sections were imaged using Leica SP5 DMI confocal and Leica DM5500 widefield microscopes, respectively, both equipped with Leica LASAF software. For 3D whole mount imaging, z-stacks of up to 60 planes with 1 μm vertical intervals were acquired with Leica 40X, 63X or 100X oil-immersion lenses. 3D stack images from confocal microscopy were processed and analyzed with ImageJ/FIJI (NIH) or Imaris 3/4D image visualization and analysis software (Bitplane).
RNA in situ hybridization
Skins were harvested from E15.0 embryos and were fixed in 4% PFA, embedded into OCT, and cut into 20μm-thick sections. Slides were dehydrated through a graded ethanol (EtOH) series (50%, 70% and 100% EtOH/H2O). Slides were stored at −20°C overnight in 100% EtOH. The following day, slides were air dried and fixed in 4% PFA for 15 minutes at room temperature. In situ hybridization for Twist2 was performed with RNAscope ISH, according to the manufacturer’s instructions (RNAscope 2.5 HD Reagent Kit-RED, Advanced Cell Diagnostics). Following the RNAscope protocol, slides were processed for co-immunofluorescence and imaged as described above.
E15.0 cell isolation and sorting
To generate single cell suspensions from E15.0 Sox2GFP dorsal skins, tissues were collected and processed as previously described (Sennett et al., 2015). Briefly, back skins were harvested by microdissection and were pooled for FACS for bulk RNA-sequencing while skin from a single embryo was taken for single cell sorting prior to single cell RNA-sequencing. Skins were then digested in a dispase (Invitrogen)/ collagenase (0.03%, Worthington) solution with 20 U/µl of DNase (Roche) at 4º C overnight followed the next day by 30 minutes incubation at 37º C. Cells were then filtered through 40µm-pore nylon cell strainers, followed by centrifugation at 350xg for 10 minutes. Resuspended cell pellets were stained with primary antibodies against EpCAM (BrV605-conjugated rat Ab, 1:100, Biolegend), CXCR4 (PE-conjugated rat Ab, 1:100, Biolegend), ITAG6 (BrV421-conjugated rat Ab, 1:100, Biolegend), PDGFRA (biotinylated rat Ab, 1:50, Affymetrix). This was followed by secondary labeling with Streptavidin-APC (1:200, Biolegend). Dead cells were identified by DAPI uptake. Cells were sorted on a FACSAria II fluorescence cytometry sorter (BD Biosciences) for both bulk and single cell RNA-sequencing. For single cell RNA-sequencing, live fibroblasts, pre-DC, DC1, and DC2 were index sorted into 384-well hard shell plates (Biorad) pre-loaded with 5 μl of vapor-lock (QIAGEN) containing a 100-200 nl mix of RT primers, dNTPs, and synthetic mRNA spike-ins. After sorting, plates were immediately quick spun and frozen to ࢤ80°C.
Real-Time qRT-PCR
Total RNA from the FACS-sorted cells was purified with the Absolutely RNA Nanoprep Kit (Agilent) and quantified with the NanoDrop spectrophotometer (Thermo Scientific). Reverse transcription was performed with the SuperScript III First-Strand Synthesis System using oligo(dT) primers (Invitrogen). Real-time qRT-PCR was performed with a LightCycler 480 (Roche) instrument using LightCycler DNA Master SYBR Green I reagents (Roche). Fold changes between samples were calculated based on the 2-ΔΔCt method and normalized to Gapdh.
cDNA library preparation for bulk RNA-sequencing
Total RNA from the FACS-sorted cells was purified with the Absolutely RNA Nanoprep Kit (Agilent). RNA concentration and quality were measured by the Agilent Bio-analyzer. Samples with RIN (RNA integrity number) scores of 9.6 or higher were further processed. 5 µl of RNA from each sample was used for reverse transcription, followed by amplification using the RNA Ovation RNA-Seq System V2 (NuGEN). Using the Ovation Ultralow DR Library System (NuGEN), cDNA libraries were generated from 100 ng amplified cDNA with a unique barcoded adaptor for each sample. Library concentration and quality were quantified by Qbit (Invitrogen) and the Agilent Bioanalyzer. Samples were then sequenced on the Illumina HiSeq 200 platform using a 50-nt single-read setting.
cDNA library reparation for single cell RNA-sequencing
Single cell RNA-sequencing was done following the SORT-seq protocol (Muraro et al., 2016), which improves on the CEL-Seq2 protocol (Hashimshony et al., 2016) with automation. After sorting, cells were lysed at 65°C for 5 minutes. RT and second strand mixes were dispensed by the Nanodrop II liquid handling platform for cDNA library preparation (GC Biotech). For each 384 well plate, 384 primers (1 library of 384 cells) was used. Single cell double-stranded cDNAs were pooled and in-vitro transcribed for linear amplification. Illumina sequencing libraries were prepared using TruSeq small RNA primers (Illumina) and sequenced paired-end twice at a read length of 75 bp using the Illumina NextSeq.
4D time-lapse live imaging
Tbx18H2BGFP E14.0 embryonic dorsal skin was harvested by microdissection in ice-cold PBS, sandwiched between a 8 µm Nuclepore filter (Whatman) and the bottom of a 35 mm Lumox culture dish (Greiner) such that the epidermal side of the skin was in contact with the Lumox membrane, i.e. the bottom of the dish. The skin-membrane sandwich was stabilized with Matrigel (Corning). Explants were cultured in DMEM (phenol red free, Invitrogen) containing 10% fetal bovine serum, 1% HEPES, and 1% penicillin-streptomycin. After overnight culturing, 3D Z-stack images of up to 20 planes with 2 μm vertical intervals were acquired at 10 or 25 minute intervals during a total of 16-20 hours using a Zeiss LSM880 equipped with a 20X Plan Apo 0.8 NA air objective or a Zeiss Axio Observer Z1 Yokogawa spinning disk equipped with a 20x Plan Apo 0.4 NA air objective. During imaging sessions, explants were maintained in a live-cell chamber at 37°C with 5% CO2 and humidity control.
Quantification of DC clustering state
3D reconstructed images for nearest neighbor measurements of pre-DC, DC1, DC1 and interfollicular dermal fibroblasts were generated in Imaris. Automated segmentation of each cell was then performed by placing a point sphere at the geometric center of each nucleus from which each point’s discrete coordinates were used to quantify the absolute distances between nuclei. Multiple hypothesis testing of the data from the average distance between 5 nearest neighbors among cells from pre-DC, DC1, DC2 and interfollicular fibroblasts were performed using Anova: single factor.
Quantification of proliferation by EdU-uptake
Percent EdU+ among pre-DC, DC1, DC2 and interfollicular dermal cells was manually counted from acquired 3D confocal scans in ImageJ/FIJI. Sox2GFP+/ITGA6- cells were considered as DC cells whereas GFP-/ITGA6- cells were counted as interfollicular dermal cells. For each injection time point skins from 2 embryos were analyzed, from which 3 DCs/regions of each morphological stage were quantified (i.e. 3 follicles x 4 stages per embryo per time point). Statistical analysis was performed using one-tailed student’s t-test.
Quantification of proliferation by cell division during time-lapse live imaging
To measure proliferation with time-lapse live imaging, 4D images acquired from explants were processed and analyzed with Imaris. 15625um2 areas (350×350pixels) containing pre-DC or interfollicular dermis were obtained by cropping from larger 20X fields. Areas containing pre-DC were identified by locating DCs at the final time point and back tracing in time through the clustering process over a 6-hour period whereas the areas containing interfollicular dermis were defined as not containing any DC over the entire imaging session. Total cell numbers and mitotic events were quantified manually. Hypothesis testing was performed using student’s t-test.
Bulk RNA-sequencing data analysiss
All raw RNA sequencing reads were mapped to the mouse genome (mm10) with TopHat v2.0.3 (Trapnell et al., 2009) coupled with the Bowtie2 (Langmead and Salzberg, 2012) and aligned with default parameters. Transcriptomes were assembled and fragments per kilobase per million reads (FPKM) for each gene were computed with Cufflinks v2.1.1 (Trapnell et al., 2010) with default parameters. Differentially expressed genes (DEGs) were identified using Cuffdiff (with default parameters except for the library normalization method was upper quartile normalization, where FPKMs were scaled via the ratio of the 75 quartile fragment counts to the average 75 quartile value across all libraries) and ANOVA with the Benjamini-Hochberg correction for multiple hypothesis testing with significance cut off FDR <0.05. The Fisher exact test was used for enrichment analysis with the same multiple hypotheses testing correction procedure and cut off. Principle component analyses (PCA) were performed for samples using BioJupies (Torre et al., 2018). Population signature genes were defined by DEGs with a FPKM≥1, and a fold enrichment≥2 compared to all other populations. Signature genes of multiple population overlaps featured in the Venn diagram analysis were defined similarly as individual-population signature genes by which each overlapping population must individually meet minimal expression level and fold-enrichment constraints.
Selection of genes used in developmental trajectory analyses were obtained by excluding genes with too low expression across all stages (FPKMany population < 1) and genes with similar expression levels across each developmental stage (FPKMmax < 2*FPKMmin). The remaining genes were log2 transformed and z-score standardized to obtain 0-mean and unit standard deviation. Gene clustering was performed with Morpheus (Broad Institute: https://software.braodinstitute.org/morpheus) using Spearman rank correlation with average linkage. Gene ontology and KEGG pathway enrichment analyses were carried out using Enrichr (Chen et al., 2013). Gene Set Enrichment Analysis (Subramanian et al., 2005) was performed using GenePattern (Reich et al., 2006).
Single cell RNA-sequencing data analysis
Analysis of single-cell RNA-seq data was performed independently in an R environment (R 3.5.1, RStudio 1.1.453) and a Python environment (Python 3.6.5, Jupyter 1.0.0). Only samples with more than 6000 transcripts/cell were included in the analysis. Additionally, mitochondrial genes, ERCC spike-ins, and non-expressed genes were excluded from downstream analysis. The remaining 1383 cells and 18483 genes were analyzed independently using the RaceID3 package (R-based) (Grün et al., 2015, 2016; Herman et al., 2018) or the Scanpy package (Python-based) (Wolf et al., 2018).
For RaceID3 analyses, normalization was performed by simple rescaling, which is done by dividing the transcript counts per cell by the total number of transcripts, and multiplying this by the minimum total number of transcripts across cells. Principal components with overrepresentation of genes related to GO annotations “cell proliferation” and “cell cycle” were removed using the CCcorrect function. Evaluation of transcriptome similarities by 1-Pearson’s correlation coefficient followed by k-medoids clustering was used to cluster cells and t-distributed stochastic neighbor embedding (tSNE) (Van Der Maaten and Hinton, 2008) was used for visualization.
Using the Scanpy module, highly variable genes were extracted, data was log-normalized, and principal components were computed. Principal components showing significant overrepresentation of genes linked to the GO annotations “cell proliferation” and “cell cycle” in the top or bottom 1% quantile of loadings were removed in a fashion similar to the CCcorrect function in RaceID3. Cells were clustered using the Louvain algorithm (Blondel et al., 2008), and visualized using UMAP (McInnes and Healy, 2018).
Pseudotime analysis was performed on a subset of cells: clusters 4, 5, 6, 7, and 8 in RaceID3 analysis and clusters 1, 2, 3, and 5 in Scanpy analysis which comprise dermal condensate cells and fibroblast precursors of the latter. Cell order along pseudotime was inferred using StemID2 (Grün et al., 2016; Herman et al., 2018) or diffusion pseudotime (Haghverdi et al., 2016).
For easier reproducibility, the complete code is available online (see Key Resources Table and Data Resources).
Cumulative percent was calculated as the running sum of FPKM values divided by the total FPKM sum. Cumulative percent plots for early, mid, and late expressed genes were generated by calculating the mean cumulative percent values for each position (cell) in the SOM order.
Software
RaceID3 and StemID2 were run on RStudio version 1.1.453. Scanpy was run in Python version 3.6.5 in a Jupyter Notebook version 1.0.0. The scripts run are available at https://github.com/rendllab and https://github.com/kasperlab.
Data Resources
The accession number for the sequencing data reported in this paper is NCBI GEO:XXXX (bulk RNA-sequencing) and NCBI GEO:XXXX (single cell RNA-sequencing.)
AUTHOR CONTRIBUTIONS
K.W.M., R.S., and M.R. conceived the overall project design. K.W.M., N.S., N.H., L.G., D.S., Y.S., and L.M.Y. performed experiments. K.W.M., N.S., N.H., and Z.W. analyzed bulk RNA-seq data. N.S., M.M., and T.J. performed bioinformatics analysis of single cell RNA-seq data. All authors discussed results and participated in the preparation and editing of the manuscript. K.W.M, N.S., N.H, and M.R. wrote the manuscript. M.R. supervised the study.
DECLARATION OF INTERESTS
The authors declare no competing interests
Supplemental Information Inventory
Supplemental Figures
Figure S1, Related to Figure 1 - Identification of Unclustered Dermal Condensate Precursors
Figure S2, Related to Figure 2 - Validation of Sorting Strategy and Population-Level Analysis of RNA-seq Data from Sorted DC Niche and Comparable Skin Populations
Figure S3, Related to Figure 3 - Resolving Developmental Trajectory with Louvain and RaceID3 Clustering
Figure S4, Related to Figure 4 - Fate-Acquired DC Precursors Demonstrate Rapid and Sustained Suppression of Proliferation
Figure S5, Related to Figure 5 - DC Fate Acquisition and Development is Marked by Three Discrete Waves of Transcription Factor and Signaling Molecule Upregulation
Figure S6, Related to Figure 6 - DC Cell Fate Specification Requires Pre-existing Placodes and Progenitor-Derived FGF20
Supplemental Tables
Table S1, Related to Figure 2 – Gene Identities in Venn Diagram of Gene Expression Relationships Between Fb, pre-DC, DC1 and DC2
Table S2, Related to Figure 3 – Results of Hierarchical Clustering for Gene Trend Analysis
Table S3, Related to Figure 3 – Gene Identities in Self Organizing Map of Co-Regulated Gene Clusters Across Pseudotime
Table S4, Related to Figure 5 - Transcription Factors and Signaling Components Expressed During Early, Mid, and Late DC Gene Expression Waves
ACKNOWLEDGEMENTS
We thank Elena Ezhkova (ISMMS) for sharing mice and reagents. Many thanks to the personnel at the Flow Cytometry and Microscopy CoRE at ISMMS and to Venu Pothula for technical assistance during single cell sorting. Thanks also go to the personnel at the Genome Technology Center at NYU, and to Judith Vivié for expert assistance with single cell RNA sequencing at the Hubrecht Institute (Netherlands). Additionally, we thank Marja Mikkola (U. Helsinki) and Peggy Myung (Yale)) for helpful discussions and valuable comments. K.W.M was supported by The Science Appearance Career Development Award fellowship from the Dermatology Foundation. N.H. was supported by grants T32GM007280 from NIH/NIGMS and F30AR070639 from NIH/NIAMS. A.M. was supported by NIH grants U54-HL127624 (LINCS-DCIC) and U24-CA224260 (IDG-KMC). D.M.O. was supported by NIH grant HL111190 and R21DC017042. M.R. was supported by grants from NIH/NIAMS (R01AR071047; R01AR063151) and New York State Department of Health (NYSTEM-C029574), and a fellowship from the Irma T. Hirschl Trust.