October 2019
Volume 60, Issue 13
Open Access
Retina  |   October 2019
Transcriptome and DNA Methylome Signatures Associated With Retinal Müller Glia Development, Injury Response, and Aging
Author Affiliations & Notes
  • Siyuan Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
  • Jingyi Guo
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
  • Shuyi Chen
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
  • Correspondence: Shuyi Chen, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China; chenshy23@mail.sysu.edu.cn
Investigative Ophthalmology & Visual Science October 2019, Vol.60, 4436-4450. doi:https://doi.org/10.1167/iovs.19-27361
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Siyuan Lin, Jingyi Guo, Shuyi Chen; Transcriptome and DNA Methylome Signatures Associated With Retinal Müller Glia Development, Injury Response, and Aging. Invest. Ophthalmol. Vis. Sci. 2019;60(13):4436-4450. doi: https://doi.org/10.1167/iovs.19-27361.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: The purpose of this study was to systematically characterize and correlate the transcriptome and DNA methylome signatures of mouse Müller cells that may underlie the development, physiological functions, and regeneration capacity of these cells.

Methods: Mouse Müller cells under normal, injury, and aging conditions were sorted from Müller cell–specific green fluorescent protein (GFP)-expressing mice. RNA sequencing was used to sequence transcriptomes, and reduced representation bisulfite sequencing was used to sequence DNA methylomes. Various bioinformatics tools were used to compare and correlate the transcriptomes and DNA methylomes.

Results: Müller cells express a distinct transcriptome that is in line with their retinal supporting roles and dormant retinogenic status. Injury changes the Müller cell transcriptome dramatically but fails to stimulate the cell cycle machinery and retinogenic factors to the states observed in early retinal progenitor cells (RPCs). Müller cells exhibit a less methylated genome than that of early RPCs, but most regulatory elements for Müller cell– and RPC-specific genes are similarly hypomethylated in both Müller cells and RPCs, except for a subset of Müller cell–specific functional genes. Aging only subtly affects the transcriptome and DNA methylome of Müller cells.

Conclusions: Failure to reactivate the cell cycle machinery and retinogenic factors to necessary levels might be key barriers blocking Müller cells from entering an RPC-like regeneration state. DNA methylation might regulate the expression of a subset of Müller cell–specific functional genes during development but is likely not involved in restricting the regeneration activity of Müller cells.

Müller cells are the primary glia of the retina and play essential supporting roles in maintaining the structural and physiological homeostasis of the retina. Müller cell nuclei are located in the midstratum of the inner nuclear layer of the retina, but their cell bodies extend across the entire thickness of the retina and project numerous short lateral branches to ensheath all nearby retinal neurons. This special radial structure of Müller cells allows them to intimately interact with retinal neurons in both healthy and diseased conditions. The well-established functions of Müller cells include maintaining the ion and water homeostasis of the retinal microenvironment, providing nutrition for retinal neurons, recycling neurotransmitters and photopigments, protecting retinal neurons from oxidative stress, regulating retinal blood flow, and contributing to the blood–retinal barrier.1 Consistent with the essential functions of Müller cells, depletion or malfunction of Müller cells causes severe disruption of retinal structure and visual function, eventually leading to retinal degeneration.24 
Upon assaults to the retina (physical, chemical, or pathological), Müller cells respond with a series of gliotic reactions, including reduced potassium conductance and membrane depolarization, cellular hypertrophy, and upregulation of intermediate filaments, such as GFAP, nestin, and vimentin1 More interestingly, upon injury, Müller cells exhibit a tendency to proliferate by downregulating the cell cycle inhibitor p27Kip1.5 This proliferation tendency of Müller cells is exploited most effectively in lower vertebrates, such as zebrafish; Müller cells in these species react to injuries with dedifferentiation, extensive proliferation, and differentiation to all types of retinal neurons to repair the retina.6,7 However, the regeneration activity of Müller cells decreases to a negligible level in mammals.8 Nonetheless, the dramatic regeneration ability of Müller cells in lower vertebrates has inspired researchers to explore ways to use endogenous Müller cells to regenerate retinal neurons in situ in mammals, with the ultimate goal of developing regeneration methods for treating human retinal degeneration diseases. Encouragingly, by overexpressing transcription factors (TFs) coupled with epigenetic manipulations or signaling pathway stimulation, some retinal interneurons and photoreceptors have been successfully regenerated from reprogrammed Müller cells in situ in mice.911 However, thus far, only limited types of retinal neurons can be regenerated, and retinal ganglion cells, the major cells damaged in glaucoma, which is the most prevalent retinal degeneration disease, seem resistant to regeneration from Müller cells. Therefore, efforts are needed to investigate the molecular barriers blocking the regeneration capacity of Müller cells in mammals and to develop more efficient protocols to regenerate various types of retinal neurons in situ. 
From a developmental perspective, Müller cells share the same progenitor as retinal neurons. Müller cells and retinal neurons are all generated by retinal progenitor cells (RPCs) in a highly ordered sequential differentiation process. Müller cells are the last cell type generated by RPCs toward the end of retinogenesis.12,13 A number of gene expression analyses have noted interesting overlaps of gene expression patterns between Müller cells and RPCs, which partially explains the regeneration potential of this mature retinal cell type.14,15 RPCs change their competence to generate retinal cells during retinogenesis such that early RPCs generate early-born retinal neurons, including retinal ganglion cells, horizontal cells, amacrine cells, and cone photoreceptors, and they gradually transform to late RPCs to generate later-stage retinal cells, including bipolar cells, rod photoreceptors, and Müller cells.16,17 As the last cell type differentiated from late RPCs, Müller cells are closer to late RPCs than to early RPCs molecularly,14 which may explain why Müller cells are more prone to be reprogrammed to late-stage retinal neurons such as rods and bipolar cells.9,10 However, early RPCs proliferate more actively and possess the full potential to generate all types of retinal neurons. Exactly how Müller cells are different from early-stage RPCs at the genome-wide level awaits further investigation. 
In this study, we systematically measured, compared, and correlated the transcriptomes and DNA methylomes of Müller cells and early RPCs, as well as the transcriptomes and DNA methylomes of Müller cells under injury and aging conditions, to explore the possible molecular mechanisms governing the development, physiological functions, and regeneration capacity of Müller cells. 
Methods
Animals
All animal studies were performed in compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Institutional Animal Care and Use Committee of Zhongshan Ophthalmic Center. Rlbp1-GFP mice were kindly provided by Edward M. Levine from the University of Utah and maintained on a C57BL/6J background. This mouse strain specifically and persistently expresses green fluorescent protein (GFP) in Müller cells (Supplementary Fig. S1A).18 For retinal injury, 2-month-old Rlbp1-GFP mice were anesthetized with ketamine (130 mg/kg) and xylazine (9 mg/kg); then, left eyes received intravitreal injection of 2 μL 0.1 M N-methyl-D-aspartic acid (NMDA) (Sigma-Aldrich Corp., St. Louis, MO, USA) using injection syringes with 32-gauge needles (Hamilton, Reno, NV, USA). 
RNA Sequencing (RNA-Seq)
Müller cells were obtained through fluorescence-activated cell sorting (FACS) of GFP+ cells from dissociated retinal cells of 2-month-old or 1.5-year-old Rlbp1-GFP mice (Supplementary Fig. S1B). Sorted Müller cells from four to six eyes were pooled for one RNA-Seq experiment. RPCs were collected by papain dissociation of neural retinas of embryonic day 12.5 embryos derived from mating between Rlbp1-GFP mice. RPCs from five or six embryos of the same litter were pooled for one RNA-Seq experiment. At least two replicates were performed for each type of cell. Samples were collected and stored in TRIzol (Thermo Fisher Scientific, Waltham, MA, USA) in a −80°C freezer until submitted to Novel Bioinformatics Co., Ltd. (Shanghai, China) for RNA extraction, library preparation, sequencing, and data analyses. The cDNA libraries were constructed using the TruSeq Stranded Total RNA with Ribo-Zero Gold kit (Illumina, San Diego, CA, USA), and sequenced by HiSeq XTen (Illumina) on a 150 base pair (bp) paired-end run. A total of 6 to 8 × 107 reads were generated for each sample, and on average, 96% reads were mapped to the mouse genome. 
RNA-Seq Data Processing
Before read mapping, clean reads were obtained from the raw reads by removing the adaptor sequences and low-quality reads. The clean reads were then aligned to mouse genome (GRCm38/mm10) using HISAT2.19 HTseq20 was used to get gene counts, and the fragments per kilobase per million mapped fragments (FPKM) method as used to normalize the gene expression. We applied the limma algorithm21 on the value of Log2(FPKM+0.5) to filter differentially expressed genes (DEGs) under the following criteria: (1) fold change > 2 or < 0.5; (2) P value < 0.05, false discovery rate (FDR) < 0.05. For long noncoding RNA (lncRNA) cis prediction, we identified genomic localization of lncRNAs and paired mRNAs that are less than 10 kb upstream or downstream away from the lncRNA. Gene Ontology (GO) term analysis was performed using DAVID bioinformatics resources.22 For gene functional association network analysis, the gene coexpression network modeling algorithm was used23 based on the normalized expression values of genes.24 We focused on cell type–specific TFs and genes in enriched GO terms in each cell type. For each pair of genes, we calculated the Pearson correlation and chose the significant correlation pairs (FDR < 0.05) to construct the network. Zebrafish high-throughput sequencing data were downloaded from Gene Expression Omnibus (GEO): zebrafish Müller cells (SRR4241537, SRR4241538, SRR4241539), 36hpf zebrafish eyes (SRR5398205-SRR5398212), zebrafish retinal neuroepithelial cells (SRR8417667, SRR8417669, SRR8417671). 
Reduced Representation Bisulfite Sequencing (RRBS)
Müller cells and RPCs were collected as for RNA-Seq, and two to four eyes were pooled for one RRBS library preparation. Genomic DNA was extracted with a Genomic DNA Purification Kit (Promega, Madison, WI, USA) and stored at −80°C. RRBS was performed following a published protocol with minor modifications. Briefly, genomic DNA was digested using MspI (NEB, Ipswich, MA, USA), followed by end-repair, A-tailing, adapter ligation, bisulfite conversion, and PCR library preparation using a NEXTflex Bisulfite-Seq kit (Bioo Scientific, Austin TX, USA) and EZ DNA methylation-gold kit (Zymo Research, Irvine, CA, USA) following the manufacturer's instructions. Libraries were sent to Novel Bioinformatics Co., Ltd. for sequencing and data analyses. The libraries were quality controlled with Agilent 2000 (Agilent, Santa Clara, CA, USA) and then sequenced by HiSeq XTen on a 150-bp paired-end run. A total of 2 to 5 × 107 reads were generated for each sample, and on average, 65% reads were mapped to the mouse genome. 
Bisulfite-PCR Sequencing
Genomic DNA was extracted as for RRBS; then, DNAs were bisulfite-converted using EZ DNA methylation-gold kit (Zymo Research) and amplified using EpiMark Hot start Taq DNA polymerase (NEB). Amplicons were excised from the agarose gel and cloned into pGEM-T easy vector (Promega) for sequencing. Data were analyzed using QUMA.25 
RRBS Data Processing
Raw sequence reads were quality-trimmed using TrimGalore (Babraham Institute, Cambridge, UK) to remove the adaptor sequences and low-quality reads. Then, the clean data were aligned to mouse genome (GRCm38/mm10) using Bismark.26 Differences in methylation between groups were measured using the CpG sites (regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide) with read coverage more than 10 by a logistic regression model built in methylKit,27 allowing us to identify differentially methylated regions (DMRs, 200 bp) with at least a 25% difference in methylation levels and a q value less than 0.01. Finally, the related genes and genome features were assigned to DMRs using ChIPseeker package.28 
Promoter and TF-Binding Site Methylation Level Calculation
Promoters were defined as 2000-bp sequence flanking transcription start site (TSS). High-density CpG promoter (HCP), intermediate-density promoter (ICP), and low-density CpG promoter (LC) were annotated as previously published.29 TF-binding sites were downloaded from the ChIP-Atlas database (http://chip-atlas.org/; in the public domain); then, the binding sites were converted to mm10 reference build with the UCSC LiftOver tool (https://genome.ucsc.edu/cgi-bin/hgLiftOver; in the public domain). For each genomic region, the DNA methylation level was calculated as the average DNA methylation levels of all CpG sites with read coverage more than 10 within the region. 
Data Deposit
All sequencing data of this study have been deposited at GEO (GSE124532). 
Results
The Transcriptome of Müller Cells Is Distinct From That of Early RPCs
RNA sequencing was performed on FACS-sorted 2- to 3-month-old mouse Müller cells, as well as on early RPCs from embryonic day 12.5 embryos. Gene expression comparison revealed that Müller cells and early RPCs expressed dramatically different transcriptomes (Fig. 1A). Compared to early RPCs, Müller cells had 2450 significantly upregulated protein-coding genes and 3717 significantly downregulated protein-coding genes (Fig. 1A; Supplementary Table S1) (fold change > 2, FDR < 0.05). By comparing these 2450 Müller cell–enriched genes with the published Müller cell–specific and –enriched genes revealed by single cell microarray analysis,30 20 out of 32 “Müller genes” revealed by Roesch et al.30 were also enriched in the Müller cell transcriptome of this study (Supplementary Fig. S2A; Supplementary Table S1). Among these genes are many well-known Müller cell–specific genes, such as Abca8a, Rlbp1, Clu, Aqp4, and Kcnj10, which were also verified individually by RT-quantitative (q)PCR (Supplementary Fig. S3). We also compared our list of Müller cell–enriched genes with the list of genes enriched in postnatal day 1 (p1) and p4 Hes1-GFP+ retinal cells31 but found that only a small portion of the lists overlapped, which was likely caused by the mixed population of differentiating Müller cells and uncommitted mitotic RPCs in early postnatal day Hes1-expressing retinal cells analyzed by Ueno et al.31 (as illustrated by the abundant expression of cell cycle genes and nervous system developmental genes in these cells) (Supplementary Fig S2B; Supplementary Table S1). 
Figure 1
 
Müller glia and early RPCs express distinct transcriptomes. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells versus early RPCs (from embryonic day 12.5 mouse embryos). The transcripts significantly enriched in RPCs (FC < 0.5, FDR < 0.05) and Müller cells (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated in Müller cells. (C) GO terms related to biological processes enriched in the genes upregulated in RPCs. (D) Functional association gene network in Müller cells. (E) Functional association gene network in RPCs. TFs with the highest degree of gene–gene interactions are highlighted in (D, E) by darker texts and lines. The color scale represents Log2FPKM.
Figure 1
 
Müller glia and early RPCs express distinct transcriptomes. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells versus early RPCs (from embryonic day 12.5 mouse embryos). The transcripts significantly enriched in RPCs (FC < 0.5, FDR < 0.05) and Müller cells (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated in Müller cells. (C) GO terms related to biological processes enriched in the genes upregulated in RPCs. (D) Functional association gene network in Müller cells. (E) Functional association gene network in RPCs. TFs with the highest degree of gene–gene interactions are highlighted in (D, E) by darker texts and lines. The color scale represents Log2FPKM.
Gene Ontology term enrichment analysis was performed to examine the biological processes that are enriched or depleted in Müller cells. The Müller cell transcriptome was significantly enriched for GO terms that are in line with their essential supporting roles in maintaining retinal structural and physiological homeostasis (Fig. 1B). For example, Müller cells abundantly express a variety of binding proteins, transporters, and recycling enzymes for photopigments and neurotransmitters to support the visual perception function of retinal neurons (e.g., Rlbp1, Rbp3, Glul, Slc17a7, and Cyp2d22) (Supplementary Figs. S4A, S4C). Müller cells also express large numbers of ion, organics, and water channel proteins to maintain the ever-changing microenvironment challenged by active retinal neuron activity (e.g., Atp1a1, Lrrc8b, Ttyh1, Aqp4, and Kcnj10) (Supplementary Fig. S4B). Moreover, Müller cells express large groups of adhesion molecules and extracellular matrix proteins that differ from those of RPCs, which reflects the intimate direct interaction and structural supporting relationship of Müller cells with other cells in the retina (e.g., Spon1, Vtn, Itgb5, Vcam1, and Itga9) (Supplementary Fig. S4D). Additionally, Müller cells express high levels of the angiogenic growth factor Vegfa, as well as two key receptors for VEGF ligands, Kdr and Flt1 (Supplementary Fig. S4E), suggesting that Müller cells are not only important sources of VEGF growth factors for the retinal vasculature but are also regulated by the signaling pathway. Interestingly, Müller cells also express several key regulators in the circadian rhythm regulatory pathway, including Cry2, Per3, Per1, and Per2, suggesting that Müller cells are involved in circadian rhythm regulation (Supplementary Fig. S4F). 
In contrast to the wide distribution of GO terms in Müller cell–upregulated genes, genes depleted in Müller cells compared to those in early RPCs were mostly related to cell cycle regulation, involving genes participating in cell division, mitotic nuclear division, and DNA replication (Fig. 1C). A large number of genes involved in various aspects of cell cycle regulation, including cell cycle drivers-cyclins (Ccna2, Ccnb1, Ccnb2, Ccnd1, Ccnd2, Ccne1, and Ccne2) and cyclin-dependent kinases (Cdk1, Cdk2, and Cdk4), DNA replication factors (Mcm2, Mcm3, Mcm4, Mcm5, Mcm6, and Mcm7), mitosis progression (Ndc80, Plk1, Cdca2, and Bub1b), and other key regulators, such as Mki67, Aurka, and E2f1, were all significantly downregulated in mature Müller cells compared to those in RPCs (Supplementary Fig. S5A). The drastically different expression patterns of cell cycle regulators in Müller cells and RPCs are in line with the dramatically different proliferation capacities of the two cell types and may represent the fundamental molecular barrier Müller cells need to conquer when they reenter the proliferative progenitor/stem cell-like state. In addition to cell cycle regulators, a large number of nervous system development regulatory genes were dramatically downregulated in Müller cells compared to those in early RPCs, reflecting the much more potent neurogenic capacity of early RPCs compared with Müller cells (Fig. 1C; Supplementary Fig. S5B). The dramatically downregulated neurogenic factors in Müller cells include those TFs essential for the differentiation of early retinal neurons, such as Atoh7, Sox11, Foxn4, Pou4f1, Neurog2, Ascl1, Sox4, and Isl13234 (Supplementary Figs. S5B, S5D). On the other hand, several important RPC proliferation and multipotency regulatory TFs, including Rax, Pax6, Vsx2, Sox2, and Six3,35 were abundantly and almost equally expressed in Müller cells and RPCs, while the late RPC marker and Müller cell fate regulator Sox936 was more abundant in Müller cells than in early RPCs (Supplementary Fig. S5D), which is consistent with published reports15,37 and supports the idea that Müller cells maintain a certain level of RPC properties. Finally, GO term analysis showed that “DNA methylation on the cytosine” process was more enriched in RPCs than in Müller cells (Fig. 1C). Indeed, two key DNA methyltransferases, Dnmt3b and Dnmt1, were highly expressed in RPCs, while they were dramatically downregulated in Müller cells (Supplementary Fig. S5C), suggesting that DNA methylation is more active in RPCs than in Müller cells. 
TFs are key regulators for gene expression. Müller cells and RPCs each express a unique group of TFs. We used a coexpression functional association gene network modeling strategy23 to predict potential regulatory relationships between cell type–specific TFs and genes in enriched pathways in the cells. The analyses predicted hundreds of functional association relationships between TFs and key pathway components in Müller cells or RPCs (Figs. 1D, 1E). For example, Mafk and Klf4 were the top two TFs that showed the highest degree of functional association with enriched pathway components in Müller cells (Fig. 1D). Mafk is a member of the small Maf TF family that has been shown to regulate the oxidative stress response.38 The gene association network analysis predicted that Mafk is functionally associated with a number of ion, water, and neural transmitter transporters as well as genes supporting visual perception in Müller cells, indicating that Mafk might play important roles in regulating the visual supporting functions of Müller cells. Similarly, Klf4, a well-known pluripotency promoter TF,39 might also be an important TF for the expression of genes essential for visual supporting functions of Müller cells (Fig. 1D). In RPCs, Barhl2, Tbx20, and Ebf3 were the three TFs that showed the highest degree of functional association with enriched pathway genes. These three TFs have been shown to play important roles in regulating cell fate determination, including neurons.4042 Our gene association network analysis indicates that these TFs not only regulate the expression of retina developmental genes but also regulate a large number of cell cycle genes, suggesting their additional roles in cell proliferation regulation (Fig. 1E). In addition, Mybl2 and Hmga2, two DNA binding transcription regulators that have been shown to regulate cell proliferation in stem cells and cancers,4347 showed a high degree of functional association with cell cycle genes, suggesting that these two genes are important regulators for controlling proliferation in RPCs (Fig. 1E). The functional importance of these TF–gene associations in Müller cells and RPCs is worth further experimental testing. 
Gene Expression Changes in Müller Cells During Injury and Aging
To examine how Müller cells respond to injury and aging at the transcriptome level, we performed RNA sequencing on Müller cells sorted from retinas 2 days after NMDA treatment and from the retinas of 1.5-year-old mice. NMDA treatment kills retinal ganglion cells and amacrine cells, while Müller cells respond to NMDA injury with a gliosis reaction as in other pathological conditions but only occasionally reenter the cell cycle in mice (Supplementary Fig. S6). Gene expression comparison showed that a large number of genes exhibited dramatic changes in expression in Müller cells after NMDA injury, and more genes were upregulated than downregulated (fold change > 2, FDR < 0.05) (Fig. 2A; Supplementary Table S2). GO term enrichment analysis showed that many genes participating in the translation process were dramatically upregulated in Müller cells under the injury condition (Fig. 2B), suggesting elevated translation activity of Müller cells upon injury. Genes upregulated in Müller cells under the injury condition were also enriched for cytoskeleton organization, wound response, cell death, and immunity (Fig. 2B), consistent with the known reaction of Müller cells to retinal injury.48 Downregulated genes in Müller cells upon NMDA treatment were enriched for cell adhesion molecules (Fig. 2B), suggesting that Müller cells change their ways of interacting with surrounding retinal cells in response to retinal injury. In addition, the Wnt signaling pathway in Müller cells seemed to be affected by injury (Fig. 2B), which will be examined further in the next section. 
Figure 2
 
Transcriptome changes in Müller cells during injury and aging. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells 2 days after NMDA intravitreal injection. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated and downregulated in Müller cells after NMDA injury. (C) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells from aged versus young mice. (D) The Venn diagram shows the overlap of the genes upregulated or downregulated in Müller cells from aged mice with the genes upregulated or downregulated in Müller cells after NMDA injury.
Figure 2
 
Transcriptome changes in Müller cells during injury and aging. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells 2 days after NMDA intravitreal injection. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated and downregulated in Müller cells after NMDA injury. (C) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells from aged versus young mice. (D) The Venn diagram shows the overlap of the genes upregulated or downregulated in Müller cells from aged mice with the genes upregulated or downregulated in Müller cells after NMDA injury.
Comparing gene expression levels in Müller cells in young and old mice, there were 488 genes upregulated in aged Müller cells (fold change > 2, FDR < 0.05), and over 80% of them were upregulated more than 5-fold (Fig. 2C; Supplementary Table S3). For example, Msi, Trpm3, Gnas, Fgfr1, and Add1 were the top five most upregulated genes that were expressed 100 times more in aged Müller cells than in young Müller cells (Supplementary Table S3). Genes upregulated in aged Müller cells are involved in a variety of biological processes, but no GO terms were enriched. On the other hand, there were only 34 genes downregulated in aged Müller cells, and LOC102639800, Snrpn, Sap25, Ubl4a, and Rbm4 were the top five most downregulated genes that showed over 45 times lower expression levels in aged Müller cells than in young Müller cells (fold change > 2, FDR < 0.05) (Fig. 2C; Supplementary Table S3). Interestingly, most genes that were upregulated or downregulated in aged Müller cells were also upregulated or downregulated in Müller cells upon injury (Fig. 2D), indicating that aging and injury share some common pathological properties. Thirty-eight genes showed significant upregulation and 10 genes showed significant downregulation only in aged Müller cells (Fig. 2D; Supplementary Table S3). These genes included molecules involved in cell adhesion (such as Tenm4, Lama3, Itgb7), signaling pathway components (such as Wnt6, Hgf, Plcb1, Npy), and cell cycle and stress response regulators (such as Bach2, Trp63, Perp, Impdh2, Chaf1a). Though none of these genes have been implicated in regulating the physiology of Müller cells, studying the roles of these genes as well as those of genes most dramatically changed in aged Müller cells might reveal mechanisms regulating retinal aging and age-related retinal diseases. 
Expression Dynamics of Key Regulators for Retinal Regeneration
In lower vertebrates, Müller cells respond to injury by actively reentering the cell cycle, dedifferentiating to an RPC-like state and regenerating all types of retinal neurons, while in mammals, this regeneration activity is reduced to a negligible level.8 Taking advantage of the quantitively characterized transcriptomes of RPCs and Müller cells in both normal and injury conditions in this study, we examined the expression dynamics of key regulators of retinal regeneration in Müller cells during development, injury responses, and aging. Transcriptome analysis revealed that a large number of cell cycle regulators were significantly downregulated in Müller cells compared to those in RPCs (Supplementary Fig. S5A; Fig. 3A). Interestingly, many of these cell cycle regulators, for example, Ccnb1, Mki67, Cdk1, and Aurka, were upregulated in Müller cells upon injury (Fig. 3A; Supplementary Table S2), consistent with the known proliferation tendency of Müller cells in mice in response to injury (Supplementary Fig. S6).8 However, although the upregulation of these cell cycle regulators was significant, their expression levels in Müller cells after injury were still far lower than the levels in RPCs (Fig. 3A), indicating that Müller cells failed to reactivate the cell cycle machinery to a necessary level, thus mostly remaining proliferation quiescent. 
Figure 3
 
Expression dynamics of key regulators of retinal regeneration during Müller cell development, injury response, and aging. (A) Expression dynamics of cell cycle regulatory genes (same list of genes as in Fig. 1E) in the different groups of cells. The fold change refers to the fold change of a gene in the respective group of cells versus that in young Müller cells (Normal). (B) Expression levels of important RPC and retinal regeneration regulators in the different groups of cells. (C) Expression levels of the Notch pathway components in the different groups of cells. (D) Expression levels of the Wnt pathway components in the different groups of cells. RPCs: retinal progenitor cells of E12.5 mouse embryos; Normal: young Müller cells; NMDA: Müller cells from retinas treated with NMDA intravitreal injection; Aged: Müller cells from 1.5-year-old mice.
Figure 3
 
Expression dynamics of key regulators of retinal regeneration during Müller cell development, injury response, and aging. (A) Expression dynamics of cell cycle regulatory genes (same list of genes as in Fig. 1E) in the different groups of cells. The fold change refers to the fold change of a gene in the respective group of cells versus that in young Müller cells (Normal). (B) Expression levels of important RPC and retinal regeneration regulators in the different groups of cells. (C) Expression levels of the Notch pathway components in the different groups of cells. (D) Expression levels of the Wnt pathway components in the different groups of cells. RPCs: retinal progenitor cells of E12.5 mouse embryos; Normal: young Müller cells; NMDA: Müller cells from retinas treated with NMDA intravitreal injection; Aged: Müller cells from 1.5-year-old mice.
Cellular activity and cell fates are controlled by cell type–specific TFs. RPC proliferation and multipotency regulatory TFs, such as Rax, Pax6, Vsx2, Sox2, and Six3, were abundantly expressed in Müller cells, and their expression remained high in Müller cells in both injury and aging conditions (Fig. 3B). Ascl1 is a TF expressed in a subpopulation of RPCs that give rise to all kinds of retinal neurons except retinal ganglion cells.49 During regeneration, Ascl1 is essential for the dedifferentiation and regeneration activities of zebrafish Müller cells50,51 and capable of reprogramming mouse Müller cells into retinal neurons in vitro and in vivo.9,52 Our RNA sequencing data showed that Ascl1, as well as its downstream targets Lin28a/b, were highly expressed in RPCs, but were absent in Müller cells and remained unexpressed under injury and aging conditions, consistent with the published report.52 In addition, most early RPC-enriched neurogenic genes, including key TFs for retinal neuron differentiation (Supplementary Fig. S7, marked by the red arrow), remained unexpressed in Müller cells in the injured retina, indicating that Müller cells were kept in a terminally differentiated glial cell state instead of a retinogenic RPC-like state. 
The Notch pathway has been widely studied for its important roles in Müller cell development and regeneration.11,5360 To examine how the Notch pathway changes in Müller cells during development, injury, and aging, we extracted the expression data for the four Notch receptors and several key downstream target genes. Of the four Notch receptors, Notch1 and Notch2 are the predominant receptors in the retina that were highly expressed in both RPCs and Müller cells. Notch3 was highly expressed in RPCs but significantly downregulated in Müller cells, whereas Notch4 was marginally expressed in both RPCs and Müller cells (Fig. 3C). Of the five major Notch pathway targets and pathway activity indicators, Hes1, Hes5, Hes6, Hey1, and Hey2, all except Hey2 were expressed in both RPCs and Müller cells, while Hey2 was expressed only in Müller cells (Fig. 3C). Upon injury, Hes5 was significantly downregulated in Müller cells, while all other Notch pathway components remained relatively unchanged (Fig. 3C). These data demonstrate that the Notch signaling components are expressed in both RPCs and Müller cells, and minor adjustments occur during Müller cell development and response to injury. 
The Wnt signaling pathway plays critical roles in zebrafish Müller cell regeneration activity61 and mouse Müller cell proliferation activity.62 We extracted the expression data for Wnt ligands, receptors, downstream mediators, and targets in RPCs and Müller cells. The data showed that only Wnt5b is expressed in RPCs, while Müller cells did not express any Wnt ligands (Fig. 3D). On the other hand, various Fzd-family Wnt receptors were abundantly expressed in both RPCs and Müller cells (Fig. 3D). In addition, negative regulators of the pathway, including axins and Dkk3, were also abundantly expressed in both RPCs and Müller cells (Fig. 3D). The pathway downstream TF and activity indicator Lef1 (the only Wnt downstream TF detected in our sequencing data) was expressed in RPCs but was nearly absent in Müller cells (Fig. 3D). Upon injury, Müller cells slightly upregulated several Wnt ligands, including Wnt5a/b, Wnt7b, and Wnt9a, and significantly downregulated Fzd9, but Lef1 remained unexpressed (Fig. 3D). 
Zebrafish Müller cells react to retinal injury by dedifferentiation to an RPC-like state and regenerate the retina. Sifuentes et al.63 used RNA-Seq to examine the transcriptome changes in zebrafish Müller cells after retinal injury. To get an idea of the status of basal fish Müller cell transcriptome, we also extracted the transcriptomes of 36hpf zebrafish eyes, when most retinal cells are at the RPC stage, from a public database,64 and compared them with the transcriptomes of fish Müller cells without injury (0 hpls in Sifuentes et al.63). The analyses showed that, at the basal situation, the fish Müller cell transcriptome lacked the expression for a large number of important cell cycle regulators and neurogenic factors, while enriched for Müller cell functional genes such as ion transporters (Supplementary Fig. S8), resembling that of mouse Müller cells and consistent with the differentiated glia status of the cells. We compared our lists of DEGs in mouse Müller cells after NMDA injury with DEGs in zebrafish Müller cells 8 hours after lesion (8 hpl) and 16 hpl but found only small overlap between the two species (Supplementary Fig. S9), and most zebrafish Müller cell regeneration-associated genes and those genes most dramatically changed in zebrafish Müller cells after a light lesion remained unchanged in mouse Müller cells (among the 12 genes listed in the main text Table of Sifuentes et al.,63 only Stat3 and Fab7 were slightly upregulated in mouse Müller cells, and of the most dramatically changed genes in zebrafish Müller cells from Table s2 and s3 of Sifuentes et al.,63 only Tspo, Adam8, Mvp, Mef2a, and Bik changed expression in mouse Müller cells in the same trends) (Supplementary Table S2), demonstrating that the transcriptomes of Müller cells of the two species react very differently to retinal injury, consistent with the dramatically different reaction modes of Müller cells to retinal injury in the two species. 
Finally, we also examined the expression of DNA methyltransferases and demethylating enzymes. As described above, of the four DNA methyltransferases, Müller cells expressed only Dnmt1 (Supplementary Figs. S5C, S6). Interestingly, injury significantly stimulated the expression of Dnmt1 and Dnmt3a in Müller cells, and a similar trend was also observed in aged Müller cells (Supplementary Fig. S10). On the other hand, three Tet family DNA demethylating enzymes were expressed at relatively constant levels in RPCs and Müller cells, while Apobec2, a cytidine deaminase required for zebrafish Müller cells to reenter the regeneration state,65 was not expressed in mouse RPCs and Müller cells (Supplementary Fig. S10). The dynamic expression patterns of Dnmt family methyltransferases suggest that Müller cells may adjust their DNA methylome in response to injury and during aging. 
Noncoding RNA Expression in Müller Cells and RPCs
Our RNA sequencing also detected noncoding RNAs (ncRNAs), and expression level comparisons showed that many ncRNAs changed their expression levels during Müller cell differentiation from RPCs. We detected 355 upregulated ncRNAs and 731 downregulated ncRNAs in Müller cells (fold change > 2, FDR < 0.05) compared with those in early RPCs (Fig. 4A). Noncoding RNAs, especially lncRNAs, play important roles in controlling the transcription of neighboring genes.66,67 Cis-analysis correlating lncRNAs with their neighboring protein-coding genes showed that changes in the expression levels of some lncRNAs were accompanied by changes in the expression levels of their neighboring mRNAs, either positively or inversely (Fig. 4D). Among 287 differentially expressed lncRNAs, 52 (18%) were accompanied by coordinated expression level changes of neighboring mRNAs, with most located upstream of corresponding mRNAs and positively coregulated (Supplementary Fig. S11). For example, Rrm2 is a ribonucleotide reductase that catalyzes the formation of deoxyribonucleotides from ribonucleotides that is essential for genomic DNA replication. Rrm2 expression was 310 times lower in Müller cells than in RPCs (the last mRNA in Fig. 4D). LOC102639931 is a lncRNA located 7637 upstream of Rrm2 gene. LOC102639931 expression was 15.9 times higher in Müller cells than in RPCs (the last lncRNA in Fig. 4D). The correlated changes in lncRNAs and mRNAs indicate that some mRNA expression in Müller cells and RPCs might be regulated by their neighboring lncRNAs. Similarly, we also found that 667 ncRNAs were upregulated and 386 ncRNAs were downregulated in Müller cells in the injured retina (Fig. 4B). Among them, 244 were lncRNAs, and the expression level changes in 19 (8%) of them were accompanied by neighboring mRNA gene expression changes (Fig. 4E; Supplementary Fig. S11). Aging did not markedly affect ncRNA expression level changes; only 178 ncRNAs and 41 ncRNAs were upregulated and downregulated, respectively, in aged Müller cells (Fig. 4C). Studying the regulatory relationship of these correlated ncRNAs and mRNAs during Müller cell development and physiological processes may reveal new molecular mechanisms governing gene expression in Müller cells. 
Figure 4
 
Noncoding RNA expression changes in Müller cells during development, injury. and aging. (A) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells versus early RPCs. (B) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells 2 days after NMDA intravitreal injection versus in normal Müller cells. (C) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells from 1.5-year-old mice versus in young normal Müller cells. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (D) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes between Müller cells and RPCs. (E) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes in Müller cells after injury. The color scale represents Log2FPKM.
Figure 4
 
Noncoding RNA expression changes in Müller cells during development, injury. and aging. (A) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells versus early RPCs. (B) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells 2 days after NMDA intravitreal injection versus in normal Müller cells. (C) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells from 1.5-year-old mice versus in young normal Müller cells. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (D) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes between Müller cells and RPCs. (E) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes in Müller cells after injury. The color scale represents Log2FPKM.
Methylomes of Müller Cells and RPCs
DNA methylation is a crucial form of epigenetic modification that controls gene expression and genome stability.68,69 Our gene expression comparison showed that DNA methyltransferases were more abundantly expressed in RPCs than in Müller cells (Supplementary Figs. S5C, S10), indicating that DNA methylation was more active in RPCs than in Müller cells. However, systematic characterization and comparison of the methylomes of Müller cells with those of RPCs are lacking. Here, we used the RRBS technique to sequence the most informative CpG sites in the genomes of Müller cells and early RPCs. Similar to other cell types,70,71 the methylomes of both Müller cells and RPCs displayed a bimodal distribution in that most bases were either hypermethylated or hypomethylated (Supplementary Fig. S12A). Unsupervised hierarchical clustering analysis showed that Müller cells and RPCs are clearly separated from each other (Fig. 5A), demonstrating that Müller cells and RPCs are also distinct from each other at the methylome level. Interestingly, the methylome of Müller cells was more biased toward hypomethylation than that of RPCs (Fig. 5B), which coincided with the lower levels of expression of DNA methyltransferase in Müller cells than in RPCs (Supplementary Figs. S5C, S10). Over the gene body, the region around the TSS was depleted of CpG methylation (Supplementary Fig. S12B), consistent with the findings for other cell types.71,72 Across other regions of the gene body, Müller cells were generally less methylated than RPCs (Supplementary Fig. S12B). Comparison of methylation levels on 200-bp tiles of the genomes showed that 15,955 genomic regions were differentially methylated between Müller cells and RPCs (DMR, methylation level change > 25%) (Fig. 5C). Most DMRs showed demethylation in Müller cells (13,847 DMRs), while only 2108 DMRs were more methylated in Müller cells (Fig. 5C). Annotation of these DMRs showed that nearly half of these DMRs were distributed in intergenic regions; 32% were among introns, 13% were among exons, and 12% were located in promoter regions (Fig. 5D). We confirmed the methylation status of some DMRs by bisulfite conversion-PCR followed by Sanger sequencing (Supplementary Fig. S13). 
Figure 5
 
Methylomes of Müller cells and RPCs. (A) Unsupervised hierarchical clustering shows the separation of the methylomes of Müller cells from those of RPCs. (B) Distribution of CpG methylation levels in the genomes of Müller cells and RPCs. (C) The heat map shows the methylation levels of DMRs in Müller cells and RPCs. (D) Distribution of the genomic annotations of DMRs between Müller cells and RPCs.
Figure 5
 
Methylomes of Müller cells and RPCs. (A) Unsupervised hierarchical clustering shows the separation of the methylomes of Müller cells from those of RPCs. (B) Distribution of CpG methylation levels in the genomes of Müller cells and RPCs. (C) The heat map shows the methylation levels of DMRs in Müller cells and RPCs. (D) Distribution of the genomic annotations of DMRs between Müller cells and RPCs.
Correlation Between Promoter Methylation and Gene Expression During Müller Cell Development
One important function of CpG methylation is to suppress gene transcription by promoter methylation.68,69 We wanted to know how promoter CpG methylation is correlated with gene transcription in Müller cells and RPCs. We focused on genes expressed in Müller cells or RPCs 5-fold more than those in the other cell type as Müller cell–specific genes (1156 genes) or RPC-specific genes (1621 genes). How the expression of a gene is regulated by promoter methylation has been suggested to depend on the density of CpG in the promoter.68,73,74 Thus, we further divided Müller cell/RPC–specific genes into the following groups: genes with high CpG density promoter (HCP), intermediate CpG density promoter (ICP), and low CpG density promoter (LCP). We then calculated the CpG methylation levels of the promoter of each gene in Müller cells and RPCs. Promoters in the HCP group were mostly hypomethylated in both Müller cells and RPCs (Figs. 6Aa and Ab), and promoters in the ICP group were also generally hypomethylated in both cell types (Figs. 6Ac, 6Ad). Promoters in the LCP group of RPC-specific genes exhibited variable methylation status from hypomethylated to hypermethylated in both Müller cells and RPCs (Fig. 6Ae). Interestingly, promoters of Müller cell–specific genes in the LCP group showed a trend of demethylation in Müller cells compared to those in RPCs (Fig. 6Af), suggesting that promoter demethylation might be involved in the upregulation of this group of genes during the RPC to Müller cell differentiation process. For example, Rlbp1 and Prss56 are two key Müller cell functional genes that are highly expressed in Müller cells but absent from RPCs. The promoters of Rlbp1 and Prss56 contain 28% and 20% CpG density, respectively. The promoters of these two genes were methylated in RPCs but demethylated in Müller cells (Fig. 6B). To examine whether promoter methylation was involved in the dramatic downregulation of cell cycle regulators in Müller cells, we calculated the CpG methylation levels of the promoters of cell cycle genes. The results showed that promoters of cell cycle regulators are generally hypomethylated in both Müller cells and RPCs (Supplementary Fig. S14), suggesting that the expression of cell cycle regulators is not regulated at the DNA methylation level. 
Figure 6
 
Correlation between promoter methylation and gene expression during Müller cell development. (A) The box plots show the distribution of the methylation levels of the promoters of RPC- or Müller cell–enriched genes in Müller cells and RPCs. Genes were divided into three groups based on the density of the CpG sites in the promoter: HCP: promoter with high CpG density; ICP: promoter with intermediate CpG density; LCP: promoter with low CpG density. (B) Integrative Genomics Viewer (IGV) (Broad Institute, Cambridge, MA, USA) views of the DNA methylation status of the genomic regions of two representative genes. The dotted red boxes highlight the differentially methylated promoter regions. (C) The box plots show the distribution of the methylation levels of the binding sites of the individual TFs in Müller cells and RPCs.
Figure 6
 
Correlation between promoter methylation and gene expression during Müller cell development. (A) The box plots show the distribution of the methylation levels of the promoters of RPC- or Müller cell–enriched genes in Müller cells and RPCs. Genes were divided into three groups based on the density of the CpG sites in the promoter: HCP: promoter with high CpG density; ICP: promoter with intermediate CpG density; LCP: promoter with low CpG density. (B) Integrative Genomics Viewer (IGV) (Broad Institute, Cambridge, MA, USA) views of the DNA methylation status of the genomic regions of two representative genes. The dotted red boxes highlight the differentially methylated promoter regions. (C) The box plots show the distribution of the methylation levels of the binding sites of the individual TFs in Müller cells and RPCs.
TF binding to their target sites and genomic DNA methylation have been proposed to reciprocally regulate each other.68,75 Thus, we extracted the available genomic binding sites of important RPC and Müller cell regulatory TFs, including Pax6, Sox2, Sox9, and Hes1, and calculated the DNA methylation levels of these sites. CpG methylation of the binding sites for these TFs was generally hypomethylated in both Müller cells and RPCs (Fig. 6C), suggesting that DNA methylation would not affect the genome targeting of these TFs in Müller cells. 
Müller Cell Methylome Changes During Injury and Aging
Gene expression examination revealed that Müller cells upregulated the expression of DNA methyltransferases Dnmt1 and Dnmt3a after injury and in aging (Supplementary Fig. S10), suggesting that DNA methylation patterns were dynamically regulated in Müller cells. Moreover, inhibition of DNA methylation perturbs the regeneration activity of Müller cells in zebrafish.76 To examine the CpG methylation patterns in Müller cells during injury and aging, we performed RRBS sequencing of the genomes of Müller cells isolated from retinas 2 days after NMDA treatment and from 1.5-year-old mice. Searching for DMRs of 200-bp tiles of Müller cell genomes showed that a number of regions exhibited altered DNA methylation levels in response to injury (Fig. 7A, methylation level change > 25%), while aging had a mild effect on the DNA methylation patterns of Müller cells (Fig. 7B). DMRs in both injury and aging conditions were largely distributed in the intergenic region, introns and exons, and 8% and 16% were distributed in the promoter regions in the injury condition and aging condition, respectively. Ascl1 is an essential TF for the regeneration of the zebrafish retina and is quickly upregulated in Müller cells after injury,50,51 while it fails to do so in mammals (Fig. 3B).52 To test whether Ascl1 expression is sequestered by promoter methylation in Müller cells in mice, we examined the genomic region of Ascl1. The data showed that the promoter region was hypomethylated in Müller cells in all the conditions tested, similar to the methylation pattern in RPCs (Fig. 7E). Similarly, Lin28a and Lin28b, two other important regeneration regulators for Müller cells,51 also had similar promoter methylation patterns in Müller cells and RPCs (Fig. 7E), suggesting that promoter methylation is not the blockade for the expression of Ascl1, as well as its downstream effectors, Lin28a and Lin28b, in Müller cells, consistent with the previously published restriction PCR result.76 To examine whether Ascl1 binding to genomic targets might be influenced by DNA methylation, we calculated the CpG methylation levels of genomic Ascl1-targeting sites in Müller cells under different conditions, as well as in RPCs. Ascl1-targeting sites were slightly less methylated in Müller cells than in RPCs, while this methylation level was slightly upregulated in injury conditions (Fig. 7F), suggesting that Ascl1-targeting sites in Müller cells were not locked by DNA methylation. 
Figure 7
 
Müller cell methylome changes during injury and aging. (A) The heat map shows the methylation levels of DMRs in Müller cells before and after NMDA treatment. (B) The heat map shows the methylation levels of DMRs in young and aged Müller cells. (C) Distribution of the genomic annotations of DMRs in Müller cells before and after NMDA treatment. (D) Distribution of the genomic annotations of DMRs between young and aged Müller cells. (E) IGV views of the DNA methylation status of the genomic regions of three key regulators of retinal regeneration. (F) The box plot shows the methylation levels of the Ascl1-targeting sites in Müller cells under different conditions.
Figure 7
 
Müller cell methylome changes during injury and aging. (A) The heat map shows the methylation levels of DMRs in Müller cells before and after NMDA treatment. (B) The heat map shows the methylation levels of DMRs in young and aged Müller cells. (C) Distribution of the genomic annotations of DMRs in Müller cells before and after NMDA treatment. (D) Distribution of the genomic annotations of DMRs between young and aged Müller cells. (E) IGV views of the DNA methylation status of the genomic regions of three key regulators of retinal regeneration. (F) The box plot shows the methylation levels of the Ascl1-targeting sites in Müller cells under different conditions.
Discussion
Müller cells play essential roles in maintaining the structural and functional homeostasis of the neural retina and hold great promise for regenerating retinal neurons in situ after damage. This study systematically quantified the transcriptomes of Müller cells in both physiological and pathological conditions, which are distinct from early RPCs and reflect the physiological functions and regeneration capacity of Müller cells. Genomic DNA methylome analyses showed that Müller cells have a less methylated genome than early RPCs. Transcriptome and DNA methylome correlation analyses suggested that CpG methylation might regulate the expression of a subset of Müller cell–specific functional genes during development, but likely is not involved in restricting the regeneration activity of Müller cells. 
The Transcriptomes of Müller Cells and Early RPCs Are Distinct
Though studies have shown overlaps of gene expression patterns between Müller cells and early RPCs,15 our transcriptome comparison clearly demonstrates that the gene expression profiles of Müller cells and early RPCs are distinct on a genome-wide level. Consistent with the different retinogenic statuses and physiological functions of each cell type, genes involved in neurotransmitter and photopigment metabolism, ion and water transportation, cell–cell interactions, and angiogenesis were highly and specifically expressed in Müller cells, while genes participating in cell cycle progression and nervous system development were highly expressed in RPCs. Although Müller cells express a number of RPC TFs, including Pax6, Sox2, Rax, Six3, and Vsx2, many other TFs required for specific retinal neuron differentiation, for example, Atoh7, Foxn4, Ascl1, Neurog2, and Isl1, were absent in Müller cells, which means that certain TFs need to be reactivated or supplemented exogenously for Müller cells to regenerate retinal neurons. Gene functional association network analysis indicated that certain TFs, for example, Mafk, Klf4, Tef, Nr1d1, and Ddit3 in Müller cells and Barhl2, Tbx20, Ebf3, Mybl2, and Hmga2 in RPCs, might play more roles in determining the developmental and physiological properties of Müller cells or RPCs than other cell type–specific TFs, which is worth further investigation. 
Cell Cycle Re-Entry Might Be a Key Barrier for Müller Cells To Enter the Regeneration State After Injury
In zebrafish, Müller cells regenerate the retina in response to injury by reentering the cell cycle, proliferating, and then differentiating into retinal neurons.6 In mice, cell cycle regulators were indeed upregulated in Müller cells after injury, reflecting the tendency to reenter the cell cycle. However, although upregulated, the expression levels of the cell cycle regulators were still far lower in Müller cells than in RPCs, which explains the rare proliferation events of Müller cells after injury. The dramatic differences in the expression patterns of a large group of genes necessary for driving cell cycle progression and the failed upregulation of these genes to a necessary level after injury suggest that reactivating the cell cycle machinery might be a key barrier for Müller cells to enter an RPC-like state to regenerate retinal neurons. 
Notch and Wnt Signaling in Müller Cells During Development and Injury Response
Notch signaling has demonstrated important roles during Müller cell development and regeneration.11,5360 Nelson et al.77 used RNA microarray to examine the transcriptomes of Müller cells from postnatal day 1 (P1) to P21 and demonstrated that the Notch pathway remains active in postmitotic Müller cells and stabilizes the glial fate. The authors grouped Notch1, Notch2, Hes1, and Hes5 to gene cluster 1 or 9 (highly expressed at all ages from P1 to P21); Notch3, Hes6, and Hey1 to cluster 7 or 8 (trend down from P1 to P21); Notch4 to cluster 6 (no expression at all ages), and Hey2 to cluster 10 (increase from P1 to P21). Consistent with this microarray analysis of postnatal maturing Müller cells, our analysis on the components of the Notch signaling pathway showed exactly the same trends of expression level changes in these genes between early RPCs and mature Müller cells (Fig. 3C). In zebrafish, the Notch pathway is suppressed in some Müller cells, which is required for the cells to enter the regeneration state.11,78,79 Our data showed that most Notch pathway components remained unchanged in Müller cells upon retina injury, except that Hes5 was downregulated, suggesting that mouse Müller cells might also downregulate Notch signaling in response to injury, yet the downregulation was far from sufficient to promote the regeneration activity of Müller cells in mice. Of note, Elsaeidi et al.11 used RT-qPCR on whole mouse retina tissues to show that Hes5 expression remained unchanged upon injury, which is different from our result. Because these authors used whole retina as the input, we believe the discrepancy might be caused by technical differences. 
Wnt signaling plays important roles in Müller cell regeneration.61,62 Our data showed that Müller cells did not express any Wnt ligands, and the pathway downstream TF and activity indicator Lef1 was nearly absent in Müller cells, in both normal and injury conditions, suggesting the pathway is very weak or absent in Müller cells. On the other hand, Müller cells abundantly expressed various Wnt receptors, suggesting the capability of the cells to react to Wnt signals. 
CpG Methylation Might Regulate the Expression of a Subset of Müller Cell–Specific Functional Genes During Development but Is Likely Not Involved in Restricting the Regeneration Activity of Müller Cells
Genomic DNA methylation is dynamically regulated during embryo development and shows different patterns in different cell types.6972 Whether and how DNA methylation is involved in Müller cell development and physiology are unknown. According to gene expression measurements, RPCs highly express both maintenance DNA methyltransferase Dnmt1 and de novo DNA methyltransferase Dnmt3b, while Müller cells express only low levels of Dnmt1, suggesting that DNA methylation activity is higher in RPCs. Consistent with this finding, the overall genomic methylation level is lower in Müller cells than in RPCs. When examining the relationship between genomic DNA methylation and gene expression, we found that the promoters of most cell type–specific genes were hypomethylated in both Müller cells and RPCs, thus seemingly not regulated by promoter methylation. However, promoters of a group of Müller cell–specific genes with a low density of CpG were less methylated in Müller cells than in RPCs, suggesting that demethylation is involved in the upregulation of this group of genes during differentiation from RPCs to Müller cells. 
Müller cells in lower vertebrates exhibit efficient regeneration activity, while these cells are mostly dormant in mammals. One possible mechanism is that genomic regions that must be activated during retinal regeneration are sequestered in a hypermethylated state in Müller cells in mammals. However, RRBS sequencing of the zebrafish Müller cell genome and methylation-sensitive restriction-PCR analysis of a number of regeneration-associated genes in mouse Müller cells indicated that regeneration-associated gene promoters are hypomethylated in quiescent Müller cells.76 Consistently, our genome-wide methylation analysis of the mouse Müller cell genome showed that promoters of most genes that are highly expressed in RPCs, including those that are involved in cell cycle progression and retinogenesis, are hypomethylated in both Müller cells and RPCs. The genome targeting sites of many RPC multipotency TFs, such as Pax6, Sox2, and Ascl1, are also hypomethylated in both cell types. Thus, our methylome sequencing data suggest that genomic DNA methylation is probably not a barrier for Müller cells to enter the RPC-like regeneration state. 
As the primary glia in the retina, Müller cells intimately interact with retinal neurons in both healthy and diseased conditions. Moreover, due to their special resistance to stressors and unique potential to regenerate retinal neurons, Müller cells are well suited as targets for therapeutic interventions to treat retinal diseases. Our study provides a comprehensive characterization of the molecular features of Müller cells at the transcriptome and DNA methylome levels, which should be valuable for future mechanism investigations, new retinal regeneration method exploration, and translational studies concerning Müller cells. 
Acknowledgments
The authors thank Edward M. Levine, PhD, from the University of Utah for providing the Rlbp1-GFP mice, Jingxuan Pan, PhD, and Bei Jin, PhD, from the Zhongshan Ophthalmic Center for their assistance with the fluorescence-activated cell sorting experiments, and Lai Wei, PhD, for assistance and suggestions on RNA-Seq and RRBS experiments. 
Supported by Guangdong Provincial Department of Science and Technology (2015B020225003), the National Natural Science Foundation of China (81870659), and the Ministry of Science and Technology of China 973 program (2015CB964600). 
Disclosure: S. Lin, None; J. Guo, None; S. Chen, None 
References
Bringmann A, Pannicke T, Grosche J, et al. Muller cells in the healthy and diseased retina. Prog Retin Eye Res. 2006; 25: 397–424.
Shen W, Fruttiger M, Zhu L, et al. Conditional Muller cell ablation causes independent neuronal and vascular pathologies in a novel transgenic model. J Neurosci. 2012; 32: 15715–15727.
Bachleda AR, Pevny LH, Weiss ER. Sox2-deficient muller glia disrupt the structural and functional maturation of the mammalian retina. Invest Ophthalmol Vis Sci. 2016; 57: 1488–1499.
Wohl SG, Jorstad NL, Levine EM, Reh TA. Muller glial microRNAs are required for the maintenance of glial homeostasis and retinal architecture. Nat Commun. 2017; 8: 1603.
Dyer MA, Cepko CL. Control of Muller glial cell proliferation and activation following retinal injury. Nat Neurosci. 2000; 3: 873–880.
Goldman D. Muller glial cell reprogramming and retina regeneration. Nat Rev Neurosci. 2014; 15: 431–442.
Pébay A, ed. Regenerative Biology of the Eye. Totowa, NJ: Humana Press; 2014.
Wilken MS, Reh TA. Retinal regeneration in birds and mice. Curr Opin Genet Dev. 2016; 40: 57–64.
Jorstad NL, Wilken MS, Grimes WN, et al. Stimulation of functional neuronal regeneration from Muller glia in adult mice. Nature. 2017; 548: 103–107.
Yao K, Qiu S, Wang YV, et al. Restoration of vision after de novo genesis of rod photoreceptors in mammalian retinas. Nature. 2018; 560: 484–488.
Elsaeidi F, Macpherson P, Mills EA, et al. Notch suppression collaborates with Ascl1 and Lin28 to unleash a regenerative response in fish retina, but not in mice. J Neurosci. 2018; 38: 2246–2261.
Agathocleous M, Harris WA. From progenitors to differentiated cells in the vertebrate retina. Annu Rev Cell Dev Biol. 2009; 25: 45–69.
Cepko CL, Austin CP, Yang X, et al. Cell fate determination in the vertebrate retina. Proc Natl Acad Sci U S A. 1996; 93: 589–595.
Jadhav AP, Roesch K, Cepko CL. Development and neurogenic potential of Muller glial cells in the vertebrate retina. Prog Retin Eye Res. 2009; 28: 249–262.
Blackshaw S, Harpavat S, Trimarchi J, et al. Genomic analysis of mouse retinal development. PLoS Biol. 2004; 2: E247.
Cepko C. Intrinsically different retinal progenitor cells produce specific types of progeny. Nat Rev Neurosci. 2014; 15: 615–627.
He J, Zhang G, Almeida AD, et al. How variable clones build an invariant retina. Neuron. 2012; 75: 786–798.
Vazquez-Chona FR, Clark AM, Levine EM. Rlbp1 promoter drives robust Muller glial GFP expression in transgenic mice. Invest Ophthalmol Vis Sci. 2009; 50: 3996–4003.
Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015; 12: 357–360.
Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015; 31: 166–169.
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43: e47.
Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4: 44–57.
Pujana MA, Han JD, Starita LM, et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet. 2007; 39: 1338–1349.
Prieto C, Risueno A, Fontanillo C, De las Rivas J. Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One. 2008; 3: e3911.
Kumaki Y, Oda M, Okano M. QUMA: quantification tool for methylation analysis. Nucleic Acids Res. 2008; 36: W170–W175.
Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011; 27: 1571–1572.
Akalin A, Kormaksson M, Li S, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012; 13: R87.
Yu G, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015; 31: 2382–2383.
Guo JU, Ma DK, Mo H, et al. Neuronal activity modifies the DNA methylation landscape in the adult brain. Nat Neurosci. 2011; 14: 1345–1351.
Roesch K, Jadhav AP, Trimarchi JM, et al. The transcriptome of retinal Muller glial cells. J Comp Neurol. 2008; 509: 225–238.
Ueno K, Iwagawa T, Ochiai G, et al. Analysis of Muller glia specific genes and their histone modification using Hes1-promoter driven EGFP expressing mouse. Sci Rep. 2017; 7: 3578.
Xiang M. Intrinsic control of mammalian retinogenesis. Cell Mol Life Sci. 2013; 70: 2519–2532.
Hatakeyama J, Kageyama R. Retinal cell fate determination and bHLH factors. Semin Cell Dev Biol. 2004; 15: 83–89.
Harada T, Harada C, Parada LF. Molecular regulation of visual system development: more than meets the eye. Genes Dev. 2007; 21: 367–378.
Chow RL, Lang RA. Early eye development in vertebrates. Annu Rev Cell Dev Biol. 2001; 17: 255–296.
Poche RA, Furuta Y, Chaboissier MC, et al. Sox9 is expressed in mouse multipotent retinal progenitor cells and functions in Muller glial cell development. J Comp Neurol. 2008; 510: 237–250.
Livesey FJ, Young TL, Cepko CL. An analysis of the gene expression program of mammalian neural progenitor cells. Proc Natl Acad Sci U S A. 2004; 101: 1374–1379.
Yamazaki H, Katsuoka F, Motohashi H, et al. Embryonic lethality and fetal liver apoptosis in mice lacking all three small Maf proteins. Mol Cell Biol. 2012; 32: 808–816.
Takahashi K, Yamanaka S. A decade of transcription factor-mediated reprogramming to pluripotency. Nat Rev Mol Cell Biol. 2016; 17: 183–193.
Takeuchi JK, Mileikovskaia M, Koshiba-Takeuchi K, et al. Tbx20 dose-dependently regulates transcription factor networks required for mouse heart and motoneuron development. Development. 2005; 132: 2463–2474.
Liberg D, Sigvardsson M, Akerblad P. The EBF/Olf/Collier family of transcription factors: regulators of differentiation in cells originating from all three embryonal germ layers. Mol Cell Biol. 2002; 22: 8389–8397.
Ding Q, Chen H, Xie X, et al. BARHL2 differentially regulates the development of retinal amacrine and ganglion neurons. J Neurosci. 2009; 29: 3992–4003.
Heinrichs S, Conover LF, Bueso-Ramos CE, et al. MYBL2 is a sub-haploinsufficient tumor suppressor gene in myeloid malignancy. Elife. 2013; 2: e00825.
Baker SJ, Ma'ayan A, Lieu YK, et al. B-myb is an essential regulator of hematopoietic stem cell and myeloid progenitor cell development. Proc Natl Acad Sci U S A. 2014; 111: 3122–3127.
Oh IH, Reddy EP. The myb gene family in cell growth, differentiation and apoptosis. Oncogene. 1999; 18: 3017–3033.
Fusco A, Fedele M. Roles of HMGA proteins in cancer. Nat Rev Cancer. 2007; 7: 899–910.
Copley MR, Babovic S, Benz C, et al. The Lin28b-let-7-Hmga2 axis determines the higher self-renewal potential of fetal haematopoietic stem cells. Nat Cell Biol. 2013; 15: 916–925.
Bringmann A, Iandiev I, Pannicke T, et al. Cellular signaling and factors involved in Muller cell gliosis: neuroprotective and detrimental effects. Prog Retin Eye Res. 2009; 28: 23–51.
Brzezinski IV JA, Kim EJ, Johnson JE, Reh TA. Ascl1 expression defines a subpopulation of lineage-restricted progenitors in the mammalian retina. Development. 2011; 138: 3519–3531.
Fausett BV, Gumerson JD, Goldman D. The proneural basic helix-loop-helix gene ascl1a is required for retina regeneration. J Neurosci. 2008; 28: 1109–1117.
Ramachandran R, Fausett BV, Goldman D. Ascl1a regulates Muller glia dedifferentiation and retinal regeneration through a Lin-28-dependent, let-7 microRNA signalling pathway. Nat Cell Biol. 2010; 12: 1101–1107.
Ueki Y, Wilken MS, Cox KE, et al. Transgenic expression of the proneural transcription factor Ascl1 in Muller glia stimulates retinal regeneration in young mice. Proc Natl Acad Sci U S A. 2015; 112: 13717–13722.
Hojo M, Ohtsuka T, Hashimoto N, et al. Glial cell fate specification modulated by the bHLH gene Hes5 in mouse retina. Development. 2000; 127: 2515–2522.
Yaron O, Farhy C, Marquardt T, et al. Notch1 functions to suppress cone-photoreceptor fate specification in the developing mouse retina. Development. 2006; 133: 1367–1378.
Jadhav AP, Mason HA, Cepko CL. Notch 1 inhibits photoreceptor production in the developing mammalian retina. Development. 2006; 133: 913–923.
Jadhav AP, Cho SH, Cepko CL. Notch activity permits retinal cells to progress through multiple progenitor states and acquire a stem cell property. Proc Natl Acad Sci U S A. 2006; 103: 18998–181003.
Mizeracka K, DeMaso CR, Cepko CL. Notch1 is required in newly postmitotic cells to inhibit the rod photoreceptor fate. Development. 2013; 140: 3188–3197.
Scheer N, Groth A, Hans S, Campos-Ortega JA. An instructive function for Notch in promoting gliogenesis in the zebrafish retina. Development. 2001; 128: 1099–1107.
Bernardos RL, Lentz SI, Wolfe MS, Raymond PA. Notch-Delta signaling is required for spatial patterning and Muller glia differentiation in the zebrafish retina. Dev Biol. 2005; 278: 381–395.
Lee HY, Wroblewski E, Philips GT, et al. Multiple requirements for Hes 1 during early eye formation. Dev Biol. 2005; 284: 464–478.
Ramachandran R, Zhao XF, Goldman D. Ascl1a/Dkk/beta-catenin signaling pathway is necessary and glycogen synthase kinase-3beta inhibition is sufficient for zebrafish retina regeneration. Proc Natl Acad Sci U S A. 2011; 108: 15858–15863.
Yao K, Qiu S, Tian L, et al. Wnt regulates proliferation and neurogenic potential of Muller glial cells via a Lin28/let-7 miRNA-dependent pathway in adult mammalian retinas. Cell Rep. 2016; 17: 165–178.
Sifuentes CJ, Kim JW, Swaroop A, Raymond PA. Rapid, dynamic activation of Muller glial stem cell responses in zebrafish. Invest Ophthalmol Vis Sci. 2016; 57: 5148–5160.
Seritrakul P, Gross JM. Tet-mediated DNA hydroxymethylation regulates retinal neurogenesis by modulating cell-extrinsic signaling pathways. PLoS Genet. 2017; 3: e1006987.
Powell C, Elsaeidi F, Goldman D. Injury-dependent Muller glia and ganglion cell reprogramming during tissue regeneration requires Apobec2a and Apobec2b. J Neurosci. 2012; 32: 96–109.
Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs. Cell. 2018; 172: 393–407.
Engreitz JM, Haines JE, Perez EM, et al. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature. 2016; 539: 452–455.
Jones PA. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012; 13: 484–492.
Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013; 14: 204–220.
Meissner A, Mikkelsen TS, Gu H, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008; 454: 766–770.
Guo H, Zhu P, Yan L, et al. The DNA methylation landscape of human early embryos. Nature. 2014; 511: 606–610.
Sanosaka T, Imamura T, Hamazaki N, et al. DNA Methylome analysis identifies transcription factor-based epigenomic signatures of multilineage competence in neural stem/progenitor cells. Cell Rep. 2017; 20: 2992–3003.
Xie W, Schultz MD, Lister R, et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell. 2013; 153: 1134–1148.
Weber M, Hellmann I, Stadler MB, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007; 39: 457–466.
Stadler MB, Murr R, Burger L, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011; 480: 490–495.
Powell C, Grant AR, Cornblath E, Goldman D. Analysis of DNA methylation reveals a partial reprogramming of the Muller glia genome during retina regeneration. Proc Natl Acad Sci U S A. 2013; 110: 19814–19819.
Nelson BR, Ueki Y, Reardon S, et al. Genome-wide analysis of Muller glial differentiation reveals a requirement for Notch signaling in postmitotic cells to maintain the glial fate. PLoS One. 2011; 6: e22817.
Conner C, Ackerman KM, Lahne M, et al. Repressing notch signaling and expressing TNFalpha are sufficient to mimic retinal regeneration by inducing Muller glial proliferation to generate committed progenitor cells. J Neurosci. 2014; 34: 14403–14419.
Wan J, Goldman D. Opposing actions of Fgf8a on Notch signaling distinguish two Muller glial cell populations that contribute to retina growth and regeneration. Cell Rep. 2017; 19: 849–862.
Figure 1
 
Müller glia and early RPCs express distinct transcriptomes. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells versus early RPCs (from embryonic day 12.5 mouse embryos). The transcripts significantly enriched in RPCs (FC < 0.5, FDR < 0.05) and Müller cells (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated in Müller cells. (C) GO terms related to biological processes enriched in the genes upregulated in RPCs. (D) Functional association gene network in Müller cells. (E) Functional association gene network in RPCs. TFs with the highest degree of gene–gene interactions are highlighted in (D, E) by darker texts and lines. The color scale represents Log2FPKM.
Figure 1
 
Müller glia and early RPCs express distinct transcriptomes. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells versus early RPCs (from embryonic day 12.5 mouse embryos). The transcripts significantly enriched in RPCs (FC < 0.5, FDR < 0.05) and Müller cells (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated in Müller cells. (C) GO terms related to biological processes enriched in the genes upregulated in RPCs. (D) Functional association gene network in Müller cells. (E) Functional association gene network in RPCs. TFs with the highest degree of gene–gene interactions are highlighted in (D, E) by darker texts and lines. The color scale represents Log2FPKM.
Figure 2
 
Transcriptome changes in Müller cells during injury and aging. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells 2 days after NMDA intravitreal injection. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated and downregulated in Müller cells after NMDA injury. (C) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells from aged versus young mice. (D) The Venn diagram shows the overlap of the genes upregulated or downregulated in Müller cells from aged mice with the genes upregulated or downregulated in Müller cells after NMDA injury.
Figure 2
 
Transcriptome changes in Müller cells during injury and aging. (A) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells 2 days after NMDA intravitreal injection. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (B) GO terms related to biological processes enriched in the genes upregulated and downregulated in Müller cells after NMDA injury. (C) The volcano plot shows the distribution of the fold changes of each mRNA transcript in Müller cells from aged versus young mice. (D) The Venn diagram shows the overlap of the genes upregulated or downregulated in Müller cells from aged mice with the genes upregulated or downregulated in Müller cells after NMDA injury.
Figure 3
 
Expression dynamics of key regulators of retinal regeneration during Müller cell development, injury response, and aging. (A) Expression dynamics of cell cycle regulatory genes (same list of genes as in Fig. 1E) in the different groups of cells. The fold change refers to the fold change of a gene in the respective group of cells versus that in young Müller cells (Normal). (B) Expression levels of important RPC and retinal regeneration regulators in the different groups of cells. (C) Expression levels of the Notch pathway components in the different groups of cells. (D) Expression levels of the Wnt pathway components in the different groups of cells. RPCs: retinal progenitor cells of E12.5 mouse embryos; Normal: young Müller cells; NMDA: Müller cells from retinas treated with NMDA intravitreal injection; Aged: Müller cells from 1.5-year-old mice.
Figure 3
 
Expression dynamics of key regulators of retinal regeneration during Müller cell development, injury response, and aging. (A) Expression dynamics of cell cycle regulatory genes (same list of genes as in Fig. 1E) in the different groups of cells. The fold change refers to the fold change of a gene in the respective group of cells versus that in young Müller cells (Normal). (B) Expression levels of important RPC and retinal regeneration regulators in the different groups of cells. (C) Expression levels of the Notch pathway components in the different groups of cells. (D) Expression levels of the Wnt pathway components in the different groups of cells. RPCs: retinal progenitor cells of E12.5 mouse embryos; Normal: young Müller cells; NMDA: Müller cells from retinas treated with NMDA intravitreal injection; Aged: Müller cells from 1.5-year-old mice.
Figure 4
 
Noncoding RNA expression changes in Müller cells during development, injury. and aging. (A) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells versus early RPCs. (B) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells 2 days after NMDA intravitreal injection versus in normal Müller cells. (C) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells from 1.5-year-old mice versus in young normal Müller cells. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (D) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes between Müller cells and RPCs. (E) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes in Müller cells after injury. The color scale represents Log2FPKM.
Figure 4
 
Noncoding RNA expression changes in Müller cells during development, injury. and aging. (A) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells versus early RPCs. (B) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells 2 days after NMDA intravitreal injection versus in normal Müller cells. (C) The volcano plot shows the distribution of the fold changes of each ncRNA in Müller cells from 1.5-year-old mice versus in young normal Müller cells. The transcripts significantly downregulated (FC < 0.5, FDR < 0.05) and upregulated (FC > 2, FDR < 0.05) are highlighted by blue and red color, respectively. (D) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes between Müller cells and RPCs. (E) Heat maps of the lncRNAs (left) and mRNAs (right) that showed correlated expression level changes in Müller cells after injury. The color scale represents Log2FPKM.
Figure 5
 
Methylomes of Müller cells and RPCs. (A) Unsupervised hierarchical clustering shows the separation of the methylomes of Müller cells from those of RPCs. (B) Distribution of CpG methylation levels in the genomes of Müller cells and RPCs. (C) The heat map shows the methylation levels of DMRs in Müller cells and RPCs. (D) Distribution of the genomic annotations of DMRs between Müller cells and RPCs.
Figure 5
 
Methylomes of Müller cells and RPCs. (A) Unsupervised hierarchical clustering shows the separation of the methylomes of Müller cells from those of RPCs. (B) Distribution of CpG methylation levels in the genomes of Müller cells and RPCs. (C) The heat map shows the methylation levels of DMRs in Müller cells and RPCs. (D) Distribution of the genomic annotations of DMRs between Müller cells and RPCs.
Figure 6
 
Correlation between promoter methylation and gene expression during Müller cell development. (A) The box plots show the distribution of the methylation levels of the promoters of RPC- or Müller cell–enriched genes in Müller cells and RPCs. Genes were divided into three groups based on the density of the CpG sites in the promoter: HCP: promoter with high CpG density; ICP: promoter with intermediate CpG density; LCP: promoter with low CpG density. (B) Integrative Genomics Viewer (IGV) (Broad Institute, Cambridge, MA, USA) views of the DNA methylation status of the genomic regions of two representative genes. The dotted red boxes highlight the differentially methylated promoter regions. (C) The box plots show the distribution of the methylation levels of the binding sites of the individual TFs in Müller cells and RPCs.
Figure 6
 
Correlation between promoter methylation and gene expression during Müller cell development. (A) The box plots show the distribution of the methylation levels of the promoters of RPC- or Müller cell–enriched genes in Müller cells and RPCs. Genes were divided into three groups based on the density of the CpG sites in the promoter: HCP: promoter with high CpG density; ICP: promoter with intermediate CpG density; LCP: promoter with low CpG density. (B) Integrative Genomics Viewer (IGV) (Broad Institute, Cambridge, MA, USA) views of the DNA methylation status of the genomic regions of two representative genes. The dotted red boxes highlight the differentially methylated promoter regions. (C) The box plots show the distribution of the methylation levels of the binding sites of the individual TFs in Müller cells and RPCs.
Figure 7
 
Müller cell methylome changes during injury and aging. (A) The heat map shows the methylation levels of DMRs in Müller cells before and after NMDA treatment. (B) The heat map shows the methylation levels of DMRs in young and aged Müller cells. (C) Distribution of the genomic annotations of DMRs in Müller cells before and after NMDA treatment. (D) Distribution of the genomic annotations of DMRs between young and aged Müller cells. (E) IGV views of the DNA methylation status of the genomic regions of three key regulators of retinal regeneration. (F) The box plot shows the methylation levels of the Ascl1-targeting sites in Müller cells under different conditions.
Figure 7
 
Müller cell methylome changes during injury and aging. (A) The heat map shows the methylation levels of DMRs in Müller cells before and after NMDA treatment. (B) The heat map shows the methylation levels of DMRs in young and aged Müller cells. (C) Distribution of the genomic annotations of DMRs in Müller cells before and after NMDA treatment. (D) Distribution of the genomic annotations of DMRs between young and aged Müller cells. (E) IGV views of the DNA methylation status of the genomic regions of three key regulators of retinal regeneration. (F) The box plot shows the methylation levels of the Ascl1-targeting sites in Müller cells under different conditions.
Supplement 1
Supplement 2
Supplement 3
Supplement 4
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×