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. 2021 Nov 20;101(5):542–550. doi: 10.1177/00220345211049785

Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes

Y Chiba 1,*, K Yoshizaki 2,*, T Tian 2, K Miyazaki 2, D Martin 3; Genomics and Computational Biology Core3,**, K Saito 4, A Yamada 4, S Fukumoto 1,4,✉
PMCID: PMC9052834  PMID: 34806461

Abstract

Organ development is dictated by the regulation of genes preferentially expressed in tissues or cell types. Gene expression profiling and identification of specific genes in organs can provide insights into organogenesis. Therefore, genome-wide analysis is a powerful tool for clarifying the mechanisms of development during organogenesis as well as tooth development. Single-cell RNA sequencing (scRNA-seq) is a suitable tool for unraveling the gene expression profile of dental cells. Using scRNA-seq, we can obtain a large pool of information on gene expression; however, identification of functional genes, which are key molecules for tooth development, via this approach remains challenging. In the present study, we performed cap analysis of gene expression sequence (CAGE-seq) using mouse tooth germ to identify the genes preferentially expressed in teeth. The CAGE-seq counts short reads at the 5′-end of transcripts; therefore, this method can quantify the amount of transcripts without bias related to the transcript length. We hypothesized that this CAGE data set would be of great help for further understanding a gene expression profile through scRNA-seq. We aimed to identify the important genes involved in tooth development via bioinformatics analyses, using a combination of scRNA-seq and CAGE-seq. We obtained the scRNA-seq data set of 12,212 cells from postnatal day 1 mouse molars and the CAGE-seq data set from postnatal day 1 molars. scRNA-seq analysis revealed the spatiotemporal expression of cell type–specific genes, and CAGE-seq helped determine whether these genes are preferentially expressed in tooth or ubiquitously. Furthermore, we identified candidate genes as novel tooth-enriched and dental cell type–specific markers. Our results show that the integration of scRNA-seq and CAGE-seq highlights the genes important for tooth development among numerous gene expression profiles. These findings should contribute to resolving the mechanism of tooth development and establishing the basis for tooth regeneration in the future.

Keywords: enamel, tooth development, gene expression, single cell sequencing, developmental biology, gene expression profiling

Introduction

The gene expression profile during organogenesis dictates the development and function of tissues and their constituent cell types. An understanding of the diversity of gene expression in distinct organs contributes to clarifying the mechanism of development, diagnosing diseases, exploring therapeutic methods, and developing regenerative medicine. Therefore, identification of tissue- or cell type–specific genes is a common approach to uncover the mechanism of organogenesis.

Various organs, such as teeth, hair, and salivary glands, originate from the ectoderm. Although these ectodermal organs play different roles as distinct organs, they have a common feature in that their development process is initiated from the formation of the ectodermal placode (Pispa and Thesleff 2003). Organ-specific gene regulation distinguishes the development processes in each ectodermal organ. The tooth germ is composed of diverse cells, and their development is dynamically regulated by spatiotemporal regulation of gene expression (Thesleff 2003). Dental epithelial stem cells give rise to various cell types, including ameloblasts and their progenitor inner enamel epithelium (IEE), outer enamel epithelium (OEE), stratum intermedium (SI), and stellate reticulum (SR). Dental mesenchymal stem cells differentiate into dental pulp, odontoblasts, subodontoblastic layers, and dental follicles. The coordination of dental cell types is essential for the development of functional teeth (Lacruz et al. 2017).

Recent studies have focused on resolving the complexity of dental cell types with single-cell RNA sequencing (scRNA-seq) approaches and have successfully established the atlas of gene expression profiles for all dental cell types in mouse and human teeth (Sharir et al. 2019; Chiba, Saito, et al. 2020; Krivanek et al. 2020; Wen et al. 2020; Pagella et al. 2021). These single-cell resolution analyses reveal differentially expressed genes (DEGs) among cell clusters and are thus useful for identifying marker genes for specific cell types. Currently, marker genes of dental cell types are well established; however, there have been few reports that provide clarity on the expression profile of these marker genes beyond organs. It is not clear whether these cell type–specific genes are tooth specific.

Cap analysis of gene expression sequence (CAGE-seq) is a cutting-edge method for bulk RNA-seq. The general RNA-seq method employs 100–base pair reads that cover the entire transcript, whereas CAGE-seq reads only the 5′-ends of transcripts with short reads (<27  base pairs). With a large-scale analysis of the 5′-ends of transcripts, CAGE-seq enables determination of the transcription start site (TSS) and accurate quantification of transcripts, excluding bias that comes from the transcript length (Murata et al. 2014). In the Functional Annotation of the Mammalian Genome 5 project (FANTOM5; https://fantom.gsc.riken.jp/5/), CAGE-seq was performed for 1,826 human and 1,026 mouse samples to obtain a comprehensive gene expression profile of the mammalian genome (Noguchi et al. 2017). These data sets were uploaded to the ZENBU genome viewer database (https://fantom.gsc.riken.jp/zenbu/), and the latest deposited data sets from various species, organs, and cell types can be explored (Severin et al. 2014). We recently performed CAGE-seq for developing teeth and identified tooth-specific TSSs (Funada et al. 2020). To our knowledge, this is the first CAGE-seq data set obtained from tooth germ; hence, this data set would be useful for identifying tooth-specific genes. Here, we hypothesized that the integration of the highlighted genes obtained from scRNA-seq and CAGE-seq could provide new insights into tooth development.

Materials and Methods

Detailed descriptions of the following are provided in the Appendix: CAGE-seq, secondary analysis of CAGE-seq, scRNA-seq library construction and sequencing, data processing and secondary analysis, reverse transcription quantitative polymerase chain reaction, immunofluorescence staining, and sequence alignment analysis.

Animals

All animal experiments were approved by the National Institute of Dental and Craniofacial Research Animal Care and Use Committee (ASP16-796) and the Ethics Committee of the Kyushu University Animal Experiment Center (A30-100-0). All the procedures were performed in accordance with relevant guidelines and regulations. The Tg(KRT14-RFP)#Efu (Krt14-RFP) mice were maintained as homozygous and housed in a facility approved by the American Association for the Accreditation of Laboratory Animal Care. Details on the housing environment of animals are provided in the Appendix, and all sections of this study followed the ARRIVE guidelines for reporting animal research and comply with the completed ARRIVE checklist (Kilkenny et al. 2010).

Results

ScRNA-seq Analysis Revealed the DEGs among Cell Clusters in Tooth Germ

Mouse molars or human teeth are not self-renewing organs. Thus, they can provide information on a particular developmental stage. In postnatal day 1 (P1) mouse molars, we can observe well-differentiated cell types, such as ameloblasts, and their progenitor IEE cells. In this stage, the dental cells begin to show unique roles as dental cells and express unique genes for tooth formation. First, we performed scRNA-seq to identify DEGs among the clusters with P1 molars. Second, we analyzed the expression of these genes in the CAGE-seq data set of tooth germ by comparison with data sets of the whole body from FANTOM5. Genes preferentially expressed in the tooth were named as “identified tooth-enriched genes” in this study and analyzed (Fig. 1).

Figure 1.

Figure 1.

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Schematic diagram of the procedure for identification of tooth-enriched genes. The top differentially expressed genes (DEGs) among clusters were identified from postnatal day (P1) mouse molars by single-cell RNA sequencing (scRNA-seq). Thereafter, DEGs were examined to determine if they were preferentially expressed in teeth via cap analysis of gene expression sequence (CAGE-seq). The genes that were highly expressed in teeth were defined as “identified tooth-enriched genes.”

scRNA-seq was performed with dissociated cells from P1 molars of Krt14-RFP mice to obtain single-cell resolution of gene expression profiles (Fig. 2). As demonstrated previously, Krt14-RFP marks the dental epithelium with fluorescence, enabling identification of the epithelial cluster (Chiba, Saito, et al. 2020). In P1 mouse molars, histologically, we could observe the following cell types; IEE, preameloblast, ameloblast, OEE, SI, and SR as dental epithelial cells and odontoblasts, subodontoblastic layers, dental pulp cells, and dental follicles as dental mesenchymal cells (Thesleff and Tummers 2008). We obtained 12,212 single-cell transcriptome data. The quality control statistics of the experiments are shown in Appendix Table 1. A total of 12 clusters were identified through graph-based principal component analysis of the transcriptomic signature. Krt14-RFP (Dsred) clearly indicated an epithelial component in this scRNA-seq data set (Fig. 2A, B). Differential gene expression analysis was performed to define cell types in the t-SNE plot (Fig. 2B; Appendix Fig. 1). The 12 clusters were classified into 5 epithelial cell types, 4 mesenchymal cell types, and 3 blood-derived cell types (Fig. 2A). Known dental cell markers that define clusters were observed in the top 20 DEGs among clusters (Appendix Tables 2 and 3). Based on these marker genes, clusters were annotated as ameloblast, preameloblast, quiescence-IEE, cycling-IEE, and nonameloblast in the epithelial population; odontoblasts, subodontoblastic layers, dental pulp, and dental follicles in the mesenchymal population; and erythrocytes, leukocytes, and endothelial cells in the blood-derived population (Fig. 2A). SI, SR, and OEE cells were classified into 1 cluster annotated “nonameloblast.” The epithelial cluster that highly expressed proliferation marker genes and IEE marker genes were annotated as “cycling-IEE.” The genes previously reported as cell markers in the top 5 DEGs are shown in bold text in Figure 2B and Appendix Figure 1. The other genes were not reported and might be novel candidates for cell type–specific markers. These results prove that scRNA-seq is a useful tool for identifying cell type–specific marker genes.

Figure 2.

Figure 2.

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Differentially expressed genes (DEGs) among cell types were identified via single-cell RNA sequencing (scRNA-seq) from postnatal day 1 (P1) mouse molars. (A) t-SNE plot of scRNA-seq data sets from mouse P1 molars. The left panel shows the distribution from the epithelium and mesenchyme. The right panel shows the results of cell population clustering based on known cell type marker genes. Twelve clusters were identified from 12,212 captured cells. The populations were classified per the gene list in Appendix Table 3. (B) Expression of DEGs among clusters was projected onto the t-SNE plot. The top 5 DEGs are listed on the left side of the t-SNE plot. Genes known as dental cell markers are in bold text. The dotted line indicates the cell population that highly expresses the indicated genes. Expression of DEGs of all clusters is shown in Appendix Figure 1. (C) Heat map analysis of the top 10 DEGs among clusters. The labeled genes are identified tooth-enriched genes. IEE, inner enamel epithelium.

CAGE-seq Analysis Identified Genes That Were Preferentially Expressed in Tooth Germ

We uploaded the CAGE-seq data set of mouse P1 molars to the ZENBU genome browser and compared it with the FANTOM5 database to identify genes preferentially expressed in teeth from among the DEGs in scRNA-seq. We selected all available developing mouse whole body CAGE data sets from FANTOM5—embryonic day 11 (E11), E12, E13, E14, E14.5, E16, E17, E17.5, E18, P0, P1, P6, and P10—and compared them with the data set of P1 molar. Genes that were highly expressed at >10-fold in the P1 molar data set versus the other data sets were defined as the “identified tooth-enriched genes” in this study (Fig. 1; Appendix Fig. 2) and are highlighted red in Appendix Table 2. Thereafter, we analyzed the distribution of the identified tooth-enriched genes in the scRNA-seq data set. Heat map analysis was performed on the top 10 DEGs among the clusters (Fig. 2C). The genes highlighted as the identified tooth-enriched genes were labeled in the generated heat map. We found that the highly expressed DEGs were tooth enriched in the ameloblast cluster; however, they were not tooth enriched in other cell clusters. These results suggest that ameloblasts might express unique genes in comparison with tissue and dental cell types.

Integration of CAGE-seq and scRNA-seq Provided a Deep Understanding of the Genes Preferentially Expressed in Teeth

To identify the candidate genes important for tooth development, we searched for tooth phenotypes related to these genes. The Phenotype/Alleles project in Mouse Genome Informatics (http://www.informatics.jax.org/phenotypes.shtml) was used to identify phenotypes associated with knockout mice. We selected all the identified tooth-enriched genes from Appendix Table 2 and examined their phenotypes in knockout mice. Genes related to tooth phenotype are annotated in bold in Appendix Table 4. Notably, approximately 50% of the identified tooth-enriched genes were associated with tooth phenotypes. This suggests that the identified tooth-enriched genes might play important roles in tooth development.

The distribution of the identified tooth-enriched genes indicated that ameloblasts show the most unique gene expression profile and may express key molecules for amelogenesis. Thereafter, we focused on the expression of ameloblast marker genes. CAGE reads from tooth germ showed a sharp peak near the TSS for all ameloblast marker genes (Fig. 3A). We subsequently examined the expression of these genes at a single-cell resolution (Fig. 3B). The results revealed that these ameloblast marker genes were highly and specifically expressed in the ameloblast cluster, as expected. Furthermore, we analyzed changes in the expression of ameloblast marker genes during tooth development. Reverse transcription quantitative polymerase chain reaction was performed with tooth germ RNA samples to clarify the expression of ameloblast marker genes in the developing tooth germ (Appendix Fig. 3). Most genes showed an expression peak at the secretory stage (P1 and P7), whereas Fxyd4 (FXYD domain-containing ion transport regulator 4) and dentin sialophosphoprotein (Dspp) maintained high expression levels until the maturation stage (P12), indicating that these 2 genes may play a role in the secretory and maturation stages of ameloblasts. Most of these ameloblast marker genes are canonical markers, whereas Fxyd4 and Acpp (acid phosphatase, prostate; Acp3) were identified as candidate genes for novel ameloblast markers. We validated the localization of Fxyd4 and Acpp in developing dental cells with immunofluorescence and found that these genes were expressed in the postnatal stage of ameloblasts (Fig. 4), which is consistent with the results of scRNA-seq analysis.

Figure 3.

Figure 3.

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Expression of ameloblast marker genes in cap analysis of gene expression sequence (CAGE-seq) and single-cell RNA sequencing data sets. (A) CAGE reads of ameloblast marker genes in the postnatal day (P1) molar data set and that of developing whole body from FANTOM5. The mapped counts were visualized through the ZENBU genome browser. Red arrowhead indicates CAGE peak. (B) Expression of ameloblast marker genes was projected onto the t-SNE plot (upper panel) and violin plot (lower panel). The dotted line indicates the cell population that highly expresses the indicated genes.

Figure 4.

Figure 4.

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Expression of FXYD4 and ACPP in developing tooth germ. Immunofluorescence of AMBN, FXYD4, and ACPP in embryonic day 16 (E16) and postnatal day (P1) molars. AMBN was used as a marker of secretory ameloblasts. Dotted line indicates the border of the dental epithelium. Dotted box indicates the area enlarged in the left panel. Scale bar: 200 µm. am, ameloblast; dm, dental mesenchyme; iee, inner enamel epithelium; M1, first molar; M2, second molar; od, odontoblast.

To understand the role of these novel marker genes in tooth development, we analyzed the expression of members of the FXYD protein family, including Fxyd4, and members of the acid phosphatase family, including Acpp, in scRNA-seq and CAGE-seq data sets. The FXYD protein family is composed of 7 members in mammals and shows specific expression depending on the tissues (Geering 2005). We examined the expression of these members in CAGE-seq data sets to analyze their distribution in the tooth germ (Appendix Fig. 4). Among FXYD family members, Fxyd3 (mammary tumor marker 8, Mat-8) and Fxyd4 were highly expressed in the tooth germ as compared with the whole body data sets. Thereafter, we analyzed the expression of these family members in scRNA-seq data sets by comparison with known ameloblast marker genes (Fig. 5A) and found that Fxyd3 was also highly expressed in ameloblasts (Fig. 5B). To determine the relationship between Fxyd3 and Fxyd4, we analyzed the sequence homology of these genes. Amino acid sequence alignment analysis revealed that amino acid sequences of FXYD3 and FXYD4 shared >50% sequence homology (Appendix Table 5). Phylogenic tree alignment located FXYD3 and FXYD4 closely among all FXYD protein family members (Fig. 5C), suggesting a close relationship between Fxyd3 and Fxyd4. Thereafter, we examined members of the acid phosphatase family. This family is composed of 7 members, and the expression of all family members was examined in CAGE-seq data sets. In the acid phosphatase family, Acpp and Acpt (acid phosphatase, testicular; Acp4) were preferentially expressed in the tooth germ. Notably, Acpt and Acpp were highly expressed in the ameloblast cluster (Fig. 5D). We analyzed the sequence similarity of the acid phosphatase family and found that ACPP, ACP2, and ACPT are closely located groups in the phylogenic tree alignment (Fig. 5E; Appendix Table 6). These analyses indicate that Fxyd3, Fxyd4, Acpp, and Acpt play roles in ameloblast differentiation and functions.

Figure 5.

Figure 5.

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Expression of FXYD family and acid phosphatase family in single-cell RNA sequencing (scRNA-seq) data sets. (A) Expression of known ameloblast lineage marker genes in scRNA-seq data set projected onto the t-SNE plot. Tbx1 as a marker of inner enamel epithelium (IEE), Shh as a marker of preameloblasts, and Ambn as a marker of ameloblasts. (B) Expression of the FXYD family members in scRNA-seq data set projected onto the t-SNE plot. The dotted line indicates the cell population that highly expresses the indicated genes. Red indicates the identified tooth-enriched genes. (C) Phylogenic tree of all the FXYD family members. (D) Expression of acid phosphate family members in the scRNA-seq data set projected onto the t-SNE plot. The dotted line indicates the cell population that highly expresses the indicated genes. Red indicates the identified tooth-enriched genes. (E) Phylogenic tree of all acid phosphate family members.

Discussion

In this study, we identified Fxyd4 and Acpp as candidate genes essential for tooth development. Fxyd4, also known as corticosteroid hormone-induced factor, belongs to the FXYD protein family (Béguin et al. 2001). FXYD protein acts as a tissue-specific modulator of the Na+/K+-ATPase ion transporter. Previous studies have reported that Fxyd4 is highly expressed in the kidneys (Geering 2005); however, we found that Fxyd4 and Fxyd3 were preferentially expressed in the tooth germ, and both were highly expressed in ameloblasts. These findings suggest that Fxyd4 and Fxyd3 may play a role in ameloblast differentiation and functions. Acid phosphatases are enzymes that hydrolyze esters of orthophosphoric acid in an acidic medium (Yousef et al. 2001; Muniyan et al. 2013). Acpp was originally identified as an acid phosphatase specific to the prostate gland and showed upregulated expression in prostate cancer (Muniyan et al. 2013). Later, it was found that Acpp is not specific to the prostate and that it is highly expressed in the columnar secretory epithelium of the salivary gland, kidney, spleen, and lung (Quintero et al. 2007). Transcriptome analysis revealed that Acpp and Acpt are highly expressed in teeth, especially in ameloblasts. Mutation of ACPT causes autosomal recessive hypoplastic amelogenesis imperfecta in humans (Seymen et al. 2016; Lacruz et al. 2017); however, the functional role of Acpt during ameloblast development remains unclear. Furthermore, based on protein similarity and structural features, ACPP, ACPT, and ACP2 have been suggested to be ancient paralogues (Yousef et al. 2001; Muniyan et al. 2013). These facts strongly suggest that Acpp and Acpt may act as important factors in ameloblast functions.

During tooth development, various genes are involved in the differentiation of dental cells and cell fate determination (Thesleff 2003; Yoshizaki et al. 2020). To date, numerous studies have sought to identify the genes involved in tooth development. To identify the specific factors in tooth development, we identified previously uncharacterized and preferentially expressed genes in teeth: enamel matrix protein ameloblastin (Ambn), the zinc-finger transcription factor epiprofin (Epfn/Sp6), the basic-helix-loop-helix transcription factor AmeloD, and G protein–coupled receptor Gpr115/Adgrf4 (Krebsbach et al. 1996; Nakamura et al. 2004; He et al. 2019). We demonstrated the importance of these genes in tooth development by analyzing tooth phenotypes in knockout mouse models (Fukumoto et al. 2004; Nakamura et al. 2008; Chiba et al. 2019; Chiba, Yoshizaki, et al. 2020). Furthermore, we identified several essential genes during tooth development with bulk RNA-seq of tooth germ (Miyazaki et al. 2016; Han et al. 2018); however, most of these genes are preferentially expressed in ameloblasts. The genes expressed in minor cell types, such as SI, SR, and OEE, were not easily picked up by bulk-based approaches. Therefore, a single-cell approach is necessary to resolve the complexity of dental cell types. Several groups recently reported the results of scRNA-seq analyses of the mouse and human tooth germ (Sharir et al. 2019; Chiba, Saito, et al. 2020; Krivanek et al. 2020; Wen et al. 2020; Fresia et al. 2021; Pagella et al. 2021). These studies provided marker genes even in minor cell types; however, the function of these genes during tooth development has not been reported. Therefore, it remains unclear whether these marker genes are functionally essential.

Currently, single-cell transcriptome analysis is well developed, and huge amounts of scRNA-seq data sets have been deposited in public databases (Papatheodorou et al. 2019; Lindeboom et al. 2021). scRNA-seq is a highly informative method that provides vast information about gene expression. However, because of its high sensitivity, it might be difficult to identify the truly functional and important genes involved in organogenesis. For example, catenin alpha 2 (Ctnna2) was cited as one of the ameloblast marker genes by Krivanek et al. (2020) and was highly expressed in the ameloblast cluster of our scRNA-seq data set. From these scRNA-seq data sets, Ctnna2 seems to be a specific marker gene of ameloblasts; however, CAGE-seq data sets showed that Ctnna2 is broadly expressed in various organs and therefore is not a tooth-specific gene (data not shown). Furthermore, although Ctnna2 knockout mice show abnormal neuron morphology and impaired cerebellum development (Park et al. 2002; Togashi et al. 2002), there has been no study on tooth abnormality in Ctnna2 knockout mice. These facts indicate that it is necessary to establish novel approaches to highlight tooth-specific and functional genes from scRNA-seq data sets. In this study, we combined scRNA-seq and CAGE-seq analyses as a novel approach to identify essential genes for tooth development. We first identified the candidates of cell type–specific genes with scRNA-seq analysis of teeth. Thereafter, we examined whether each gene was preferentially expressed in the tooth with CAGE-seq analysis. Thus, we selected several genes as candidate genes that are important for tooth development. Although we did not examine the function of candidate genes via experiments, most candidate genes have been reported to be involved in the development of tooth abnormalities in knockout mice. This indicates that these genes may play indispensable roles in tooth development and that integrating scRNA-seq and CAGE-seq is effective for identifying functional genes. Interestingly, the novel ameloblast markers Fxyd4 and Acpp were not identified in previous scRNA-seq studies (Sharir et al. 2019; Krivanek et al. 2020; Pagella et al. 2021). In addition, we demonstrated the importance of this approach by characterizing genes in the FXYD family and acid phosphatase family at the organ and cell levels. Therefore, integration of scRNA-seq and CAGE-seq can provide novel insight into dental cell markers.

To date, 3 groups have reported the atlas of molar tooth germ in mouse or human (Krivanek et al. 2020; Wen et al. 2020; Pagella et al. 2021). These data sets were obtained from adult mice or human third molars; therefore, we obtained data from developing molars in an earlier stage than the previous studies. These studies showed that the glia population is one of the independent clusters of the dental mesenchyme (Krivanek et al. 2020; Wen et al. 2020; Pagella et al. 2021). In the current data sets, the glia population was found in the dental follicle cluster (Appendix Fig. 5A, B), which is consistent with the results of histologic observation (Moe et al. 2012). Furthermore, Krivanek et al. (2020) indicated that in adult mouse molars, Fgf3+/Mki67+ progenitor pulp population was barely observed, which was one of the main populations of dental pulp in this data set (Appendix Fig. 6). These differences in the dental cell population may be related to the different developmental stages evaluated.

In summary, we identified canonical and novel tooth- and cell type–specific marker genes by combining scRNA-seq and CAGE-seq. These findings establish a novel strategy for identifying the key factors involved in tooth development.

Author Contributions

Y. Chiba, K. Yoshizaki, contributed to conception, design, and data analysis, drafted and critically revised the manuscript; T. Tian, K. Miyazaki, D. Martin, Genomics and Computational Biology Core, K. Saito, A. Yamada, contributed to data analysis, critically revised the manuscript; S. Fukumoto, contributed to conception and design, drafted and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Supplemental Material

sj-docx-1-jdr-10.1177_00220345211049785 – Supplemental material for Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes
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Supplemental material, sj-docx-1-jdr-10.1177_00220345211049785 for Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes by Y. Chiba, K. Yoshizaki, T. Tian, K. Miyazaki, D. Martin, K. Saito, A. Yamada and S. Fukumoto in Journal of Dental Research

Acknowledgments

NIDCD/NIDCR Genomics and Computational Biology Core authors: E.T. Boger (National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland, USA); B. Choudhury (National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland, USA); D. Martin (National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA); C. Zheng (National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA); Z. Wei (National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA); R.J. Morell (National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland, USA).

We thank DNAFORM for technical assistance in performing CAGE analysis. We also appreciate the technical assistance from the Research Support Center, Research Center for Human Disease Modeling, Kyushu University Graduate School of Medical Sciences. We thank Editage for English-language editing. This work utilized the computational resources of the National Institutes of Health HPC Biowulf cluster.

Footnotes

A supplemental appendix to this article is available online.

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant-in-aid from the Japan Society for the Promotion of Science (KAKENHI: JP17H01606 to S. Fukumoto, JP20K18747 to Y. Chiba, and JP18H03012 to K. Yoshizaki) and the Japan Science and Technology Agency FOREST Program (JPMJFR2013 to K. Yoshizaki). K. Yoshizaki was supported by the Takeda Science Foundation. T. Tian was a DC2 Research Fellow (Japan Society for the Promotion of Science) and supported by an Otsuka-Toshimi Scholarship. This work was also supported in part by the Intramural Research Program of the National Institute of Dental and Craniofacial Research, National Institutes of Health (1ZIADE000720-11); the National Institute of Dental and Craniofacial Research’s Gene Transfer Core Facility (ZIC DE000744-04), Veterinary Resources Core (ZIC DE000740-05), and Combined Technical Research Core Facility (ZIC DE000729-09); and the National Institute on Deafness and Other Communication Disorders’ Genomics and Computational Biology Core (ZIC DC000086).

Data Availability: The data sets analyzed in this study can be found in Gene Expression Omnibus, National Center for Biotechnology Information (GSE167989).

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sj-docx-1-jdr-10.1177_00220345211049785 – Supplemental material for Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes
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Supplemental material, sj-docx-1-jdr-10.1177_00220345211049785 for Integration of Single-Cell RNA- and CAGE-seq Reveals Tooth-Enriched Genes by Y. Chiba, K. Yoshizaki, T. Tian, K. Miyazaki, D. Martin, K. Saito, A. Yamada and S. Fukumoto in Journal of Dental Research

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