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. 2019 Apr 30;8:e43598. doi: 10.7554/eLife.43598

Human macular Müller cells rely more on serine biosynthesis to combat oxidative stress than those from the periphery

Ting Zhang 1,†, Ling Zhu 1,†,✉, Michele C Madigan 1,2, Wei Liu 3, Weiyong Shen 1, Svetlana Cherepanoff 4, Fanfan Zhou 5, Shaoxue Zeng 1, Jianhai Du 6,7, Mark C Gillies 1
Editors: Gary L Westbrook8, Jeremy Nathans9
PMCID: PMC6533082  PMID: 31036157

Abstract

The human macula is more susceptible than the peripheral retina to developing blinding conditions such as age-related macular degeneration, diabetic retinopathy. A key difference between them may be the nature of their Müller cells. We found primary cultured Müller cells from macula and peripheral retina display significant morphological and transcriptomic differences. Macular Müller cells expressed more phosphoglycerate dehydrogenase (PHGDH, a rate-limiting enzyme in serine synthesis) than peripheral Müller cells. The serine synthesis, glycolytic and mitochondrial function were more activated in macular than peripheral Müller cells. Serine biosynthesis is critical in defending against oxidative stress. Intracellular reactive oxygen species and glutathione levels were increased in primary cultured macular Müller cells which were more susceptible to oxidative stress after inhibition of PHGDH. Our findings indicate serine biosynthesis is a critical part of the macular defence against oxidative stress and suggest dysregulation of this pathway as a potential cause of macular pathology.

Research organism: Other

Introduction

The macula, a specialized region located at the posterior pole of the primate retina, has the greatest density of cone photoreceptors for the highest visual acuity (Sharon et al., 2002). The impact of common pathologies such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular telangiectasia type 2 (MacTel) are most devastating at the macula, leading to vision loss and blindness. Understanding the unique biochemical and anatomic specializations of the macula may provide new ways to prevent and treat these diseases.

A major difference between the macula and peripheral retina is the density of the retinal neurons. Although the macula occupies less than 1.4% of the retina area, it contains ~8.4% of cones,~3.4% of rods and ~60% of the ganglion cells (Curcio and Allen, 1990). The ratio of cones to rods is much higher in the macula (1:8) than that in the peripheral retina (1:20) (Curcio et al., 1990). The high density of neurons in the macula is associated with a high metabolic rate and increased levels of oxidative stress (Handa, 2012).

Müller cells, the major glial cells of retina, are responsible for retinal redox homeostasis and metabolic support of retinal neurons (Reichenbach and Bringmann, 2013). They control the composition of retinal extracellular fluids by mediating the transcellular transport of ions, water and bicarbonate (Bringmann et al., 2005; Kofuji and Newman, 2004; Newman, 1996). They may play an important role in retinal glucose metabolism and provide substrates to photoreceptors, one of the most metabolically active cell types in the body (Poitry-Yamate et al., 1995). Müller cells also promote photoreceptor survival by providing neurotrophic substances and removing metabolic end products (Poitry et al., 2000). Whether these functional requirements of Müller cells differ in the macula and the retinal periphery is unknown.

Müller cells in the macula display unique morphological features. The apical processes of all Müller cells envelop the photoreceptor cell bodies within the outer nuclear layer as the outer limiting membrane, extending distally to form the ‘fibre baskets’ (Bringmann et al., 2006). Müller cells in the periphery share most of these features. Foveal Müller cells are Z-shaped: their processes run vertically from the inner limiting membrane (ILM) to the inner nuclear layer (INL), then horizontally away from the fovea within Henle’s fibre layer (HFL) then vertically again to the outer limiting membrane (OLM). A specialized, cone-shaped zone of Müller cells described in the central and inner part of the fovea has recently received renewed attention (Gass, 1999; Yamada, 1969).

Macular and peripheral Müller cells may also differentially express several functional proteins. Müller cells from the macula and the periphery both express CD117 but only peripheral Müller cells express CD44 (Too et al., 2017). Macular Müller cells also express more Aquaporin-4 than peripheral Müller cells, which may contribute to a specialized ‘glymphatic system’ of the macula (Daruich et al., 2018).

The importance of Müller cells for retinal homeostasis suggests that their dysfunction contributes to many retinal diseases. Retinal oedema and neuronal degeneration, the common features of many retinal diseases, may occur secondary to abnormal retinal glutamate metabolism and ionic disturbances caused by Müller cell dysfunction (Bringmann et al., 2006). Müller cell dysfunction has been implicated in MacTel) (Powner et al., 2010) and in a form of autoimmune retinopathy (Peek et al., 1998). Any primary or secondary impairment of Müller cells may cause or at least aggravate the degeneration of retinal neurons by increasing their susceptibility to noxious stimuli. Recent report suggests that Müller cells are the major sites in the retina of de novo synthesis of serine and glutathione which support retinal neurons and protect them from oxidative stress (Zhang et al., 2018). Therefore, we investigated molecular and functional differences of Müller cells in the macula and peripheral regions of retina, with particular interest in serine and glycine metabolism.

Results

Isolation, culturing and validation of primary Müller cells from human macula and peripheral retina

We isolated macular and mid-peripheral retinal pieces from healthy human donors’ eyes and cultured the Müller cells as previously described (Zhu et al., 2015) (Figure 1A). The anatomical boundary of the macula is commonly recognized as a region approximately 5.5-mm-diameter, centred at the fovea (Chopdar et al., 2003). We thus trephined 5 mm diameter disks centred at the fovea and similarly sized pieces from mid-peripheral retina (4.5 mm to 7.5 mm from the fovea) (Mori et al., 2016). After 4–6 weeks in culture, Müller cells that grew out from the macular (macular-human primary Müller cells [M-huPMCs]) and peripheral (peripheral-human primary Müller cells [P-huPMCs]) retinal pieces had distinct cell morphologies (Figure 1D & F). M-huPMCs were small, spindle- or stellate-shaped cells with a low cytoplasm/nucleus ratio (Figure 1B & C); the P-huPMCs had large cell bodies, multiple cytoplasmic processes and a higher cytoplasm/nucleus ratio (Figure 1B & E). Both M-huPMCs and P-huPMCs without sub-culturing (P0) demonstrated positive immunolabeling for four Müller-cell-specific markers: Glial fibrillary acidic protein (GFAP) (Figure 1G–H), carbonic anhydrase II (Figure 1I–J), SOX9 (Figure 1K–L) (Song et al., 2013) and Cellular Retinaldehyde Binding Protein (CRALBP) (Figure 1—figure supplement 1). GFAP is a non-specific response of Müller cell stress which they usually express in cell culture (Augustine et al., 2018). This confirmed that these two morphologically distinct cell populations are Müller cells.

Figure 1. Morphology of primary macular and peripheral Müller cells.

(A) A wide-field fundus image showing the retinal areas used for primary Müller cell culture. Retinal tissue at Area one was used to derive primary Müller cells from the macula while the punches from Areas 2 and 3 were pooled to derive primary Müller cells from the peripheral retina. (B) Cytoplasm/nucleus ratio of primary Müller cells from the macula (M-huPMCs) and peripheral (P-huPMCs) human retina (n = 8). (C-F) Bright field images of huPMCs isolated from the macula (C-D) or peripheral (E-F) retina. (D) Primary Müller cells that grew out from macular retinal piece. (F) Primary Müller cells that grew out from peripheral retinal piece. (G-L) M-huPMCs (upper panel) and P-huPMCs (lower panel) (P0) without sub-culturing expressed three specific protein markers of Müller cells: GFAP (G-H), carbonic anhydrase II (I-J) and SOX9 (K-L). Black scale bar = 200 µM, white scale bar = 100 µM.

Figure 1—source data 1. Source data for Figure 1B.
elife-43598-fig1-data1.xlsx (9.2KB, xlsx)
DOI: 10.7554/eLife.43598.004

Figure 1.

Figure 1—figure supplement 1. CRALBP immunostaining of M-huPMCs and P-huPMCs.

Figure 1—figure supplement 1.

M-huPMCs (A) and P-huPMCs (B) at P0 passage both expressed CRALBP, the specific protein marker of Müller cells. Scale bar = 50 µM (M = macula; p=peripheral).
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Transcription profiles of macular and peripheral Müller cells

RNA-sequencing analysis of matched M-huPMCs (n = 8) vs P-huPMCs (n = 8) from each individual retina of eight donors revealed significant differences between the two cell types. Reads expression values of all 16 samples suggested a highly similar gene expression pattern (Figure 2A). Scatter plots show the correlation of the gene expression profiles between M-huPMCs and P-huPMCs (Figure 2B). In Figure 2C heatmap of gene expressions of M-huPMCs and P-huPMCs (R, DESeq2) is used to visualize the top 100 genes with the smallest q-values. Pairwise sample distances were estimated using the Euclidean distance and sample clustering was done with the Ward algorithm. Principal component analysis (PCA) shows the clustering of M-huPMCs and P-huPMCs (Figure 2D). A total of 7588 differentially expressed genes (DEGs) were identified in Müller cells isolated from human macula and peripheral retina with a 1.5-fold or more increase or decrease and with an FDR corrected p-value<0.05 (Figure 2F, Supplementary S1 and S2). Bland–Altman (MA) plot and Volcano plot show the differentially expressed genes in M-huPMCs and P-huPMCs (Figure 2E–F). Based on gene ontology (GO) enrichment analysis (ClusterProfiler), these upregulated genes were significantly associated with numerous GO Biological process terms, including ‘response to oxygen levels’, ‘response to decreased oxygen levels’ and ‘response to hypoxia’ (Figure 2G).

Figure 2. Transcription profiles of macular and peripheral Müller cells.

Figure 2.

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(A) Boxplot of the Reads expression values of the M-huPMCs and P-huPMCs cultured from eight donor retinas. (B) Scatter Plot for correlations of genes expressed in the M-huPMCs and P-huPMCs. (C) Heatmap of differentially expressed genes between the M-huPMCs and P-huPMCs of eight donors shows the top 100 genes with the smallest q-values. (D) Principal component analysis (PCA) was performed with the RNA-seq data derived from the M-huPMCs and P-huPMCs. (E) Bland–Altman (MA) plot of differentially expressed genes in the M-huPMCs and P-huPMCs. (F) Volcano plot of differentially expressed genes in the M-huPMCs and P-huPMCs. The log 10 (q values) were plotted against the log 2 (Fold Change) in gene expression. The genes upregulated (n = 1475) 1.5-fold or more and with an FDR corrected p-value<0.05 were depicted as red dots; while the genes downregulated (n = 6113) by 1.5-folds or more and with a FDR corrected p-value<0.05 were depicted as blue dots. (G) Gene ontology (GO) analysis of upregulated genes in M-huPMCs relative to P-huPMCs. The top 14 GO terms in the biological process category are displayed and ordered by enriched gene number and adjusted p value.

RNA sequencing found that both the M-huPMCs and P-huPMCs strongly expressed (high RPKM number [Reads Per Kilobase of transcript per Million mapped reads]) gene markers for the Müller cell: vimentin, glutamine synthetase (GS), CRALBP, clusterin, carbonic anhydrase II and GFAP (Table 1) and negligibly expression (low RPKM number) of gene markers of the other retinal cell types: rhodopsin for photoreceptors; mGluR6 for the bipolar cells, syntaxin 1A for the amacrine cells, Iba1 for the microglia cells and PECAM1 for vascular endothelial cells (Table 1). These findings are consistent with Müller cells retaining their original genotype in primary tissue culture. Notably, the expression of Müller cell markers was higher in the P-huPMCs compared to M-huPMCs.

Table 1. Gene expression of retinal cells marker in identified cells from macula and peripheral retina.

Marker Gene name Gene ID Length M-rpkm P-rpkm Log2ratio(P/M) Regulation(P/M) p-value
Müller cell Vimentin 7431 2151 2923.82 3492.22 0.26 up <2.22E-308
 Glutamine synthetase 2752 4737 23.46 95.31 2.02 up <2.22E-308
 RLBP1 (CRALBP) 6017 1752 4.50 34.79 2.95 up <2.22E-308
 CLU clusterin 1191 3012 78.80 763.72 3.28 up <2.22E-308
 Carbonic anhydrase II 760 1666 15.52 138.45 3.16 up <2.22E-308
 GFAP 2670 3097 31.87 944.58 4.89 up <2.22E-308
Other cell types
Photoreceptors RHO rhodopsin 6010 2768 0.01 0.05 2.13 up 1.43E-07
Biopolar cells mGluR6 2916 6143 0.03 0.31 3.2 up 3.13E-134
Amacrine cells STX1A syntaxin 1A 6804 2138 4.35 3.89 −0.16 down 3.43E-06
Microglia Iba1 199 1491 1.44 3.67 1.35 up 3.99E-129
Endothelial PECAM1 5175 6831 0.25 0.72 1.52 up 1.21E-138
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Differential expression of genes related to the de novo serine synthesis pathway in Müller cells isolated from the human macula and peripheral retina

MacTel is a retinal disease which may be caused primarily by dysfunction of macular Müller cells. A recent study reported that the de novo serine metabolic pathway may play an important role in MacTel through defects of phosphoglycerate dehydrogenase (PHGDH), a key rate-limiting enzyme in de novo serine metabolism (Scerri et al., 2017). We have reported that PHGDH is predominantly expressed in human Müller cells and that inhibition of PHGDH aggravates mitochondrial and oxidative stress (Zhang et al., 2018). We found in the present study that the expression of key genes related to de novo serine synthesis (e.g. PHGDH, PSAT1 and SHMT2) was higher in M-huPMCs than in P-huPMCs (Table 2).

Table 2. Serine synthesis pathway-related gene expression in Müller cell isolated from macula and peripheral retina.

Gene name Gene ID Length M-rpkm P-rpkm Log2ratio (M/P) Regulation (M/P) p-value
PHGDH 26227 2021 60.36 40.09 0.59 up <2.22E-308
PSAT1 29968 2221 101.15 79.10 0.35 up <2.22E-308
PSPH 5723 2142 12.51 14.20 −0.18 down 2.56E-21
SHMT1 6470 2540 3.72 7.05 −0.92 down 2.99E-228
SHMT2 6472 2357 64.83 61.14 0.08 up 1.27E-23
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Western blot studies found that M-huPMCs expressed significantly more PHGDH compared to P-huPMCs (Figure 3A–B, normalized to β-actin, n = 4 donor retinas). We further explored the topographical protein expression of PHGDH using a series of 2 mm fresh retinal tissue punches at different locations across the human retina (Figure 3C). We found that macula expressed significantly more PHGDH (Figure 3D–F, normalized to α/β-Tubulin or Figure 3—figure supplement 1, normalized to total loaded retinal protein) than punches from the peripheral retina, although there were inter-individual variations between the five retinas. We believe that PHGDH expression from the retina largely reflects its expression by Müller cells since they are the main source of PHGDH in the human retina (Zhang et al., 2018).

Figure 3. PHGDH expression in cultured Müller cells and retinal tissue from macula and periphery.

(A) Representative immunoblot showing PHGDH protein expression in primary cultures of human Müller cells from macula and peripheral retina; (B) Quantitative analysis of PHGDH relative to β-actin for immunoblots (n = 4 donor retinas); (C) Schema of trephined retina area used for Western blotting shown in (F); (D-E) Quantitative analysis of the expression of PHGDH correlated to different areas of human retina (five donors); (F) Expression of PHGDH in different retinal locations shown using western blotting (n = 5 donors).

Figure 3—source data 1. Source data for Figure 3B,D,E,F.
elife-43598-fig3-data1.xlsx (9.7KB, xlsx)
DOI: 10.7554/eLife.43598.010

Figure 3.

Figure 3—figure supplement 1. PHGDH expression in the macula and peripheral retina.

Figure 3—figure supplement 1.

(A) PHGDH expression in different retinal locations was assessed with western blot (n = 5 donors). (B) Quantitative analysis of PHGDH expression (normalized to total loaded retinal protein) was correlated with different areas of human retina (n = 5 donors).
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Immunofluorescent staining of human macular (Figure 4A–H) and peripheral (Figure 4I–P) retina from the same eye revealed co-localization of CRALBP (red, a Müller cell marker) and PHGDH (green). This is consistent with our previous study that PHGDH in the retina is predominantly expressed in the Müller cells(Zhang et al., 2018). In contrast to the peripheral retina (Figure 4M–P), the macula has multiple layers of Müller cell nuclei in the inner nuclear layer and strongly expresses PHGDH in Henle fibre layer (Figure 4E–H).

Figure 4. Immunofluorescent staining of PHGDH in human retina.

Representative images of immunofluorescence visualization of PHGDH (green) and CRALBP (red, a Müller cell marker) in human macular (A-H) and peripheral retina (I-P). E-H Field enlarged image from B (white-dotted box) in macula; M-P. Enlarged images from J (white-dotted box) in peripheral retina. The specific immunoreactivity of PHGDH antibody was verified in PHGDH knockdown in MIO-M1 cells (Figure 4—figure supplement 1).

Figure 4.

Figure 4—figure supplement 1. Verification of PHGDH specific immunoreactivity by PHGDH knockdown in MIO-M1 cells.

Figure 4—figure supplement 1.

Cells expressing green fluorescence have been successfully transfected with control (A) or PHGDH (D) shRNA-GFP plasmid. PHGDH was immunostained with anti-PHGDH antibody, visualised with Alexa 594 (red) in the MIO-M1 cells (B and E). (C) Merged images of A and B showing normal PHGDH immunoreactivity in cells transfected with negative control shRNA-GFP plasmid (yellow arrows). (F) Merged images of D and E showing reduced PHGDH immunoactivity in the cells transfected with PHGDH-shRNA-GFP plasmid (white arrows). Scale bar = 50 µM.
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Metabolic differences between the macular and peripheral Müller cells

To evaluate the metabolism of differential expression of PHGDH in the macular and peripheral Müller cells, we treated M-huPMCs and P-huPMCs from four different donors with 13C-glucose to measure the metabolic flux of serine and glycine from glucose (Figure 5A–B). Following the treatment, different 13C labelled serine isotopologue (M0, M1, M2, and M3) and glycine isotopologue (M0, M1 and M2) were quantified with gas chromatography–mass spectrometry (GC-MS). After 3 hr labeling, around 18% of cellular serine (average of both M-huPMCs and P-huPMCs) was replaced by 13C-glucose (Figure 5C). M-huPMCs had higher 13C labelled serine (24%) and 13C-labeled glycine (12%) than of P-huPMCs (13% and 5% respectively, Figure 5C). We did not observe a significant change in the relative level of total serine or glycine in M-huPMCs and P-huPMCs (Figure 5—figure supplement 1).

Figure 5. Metabolic differences between the macular and peripheral Müller cells.

(A) de novo serine/glycine synthesis pathway. 3PG: 3-phosphoglycerate, PHGDH: 3-phosphoglycerate dehydrogenase, PSAT1: phosphoserine aminotransferase 1, PSPH: phosphoserine phosphatase, SHMT: serine hydroxymethyltransferase. (B) Simplified schematic of steps in de novo serine/glycine synthesis, showing 13C labeling patterns resulting from 13C6 glucose substrate. Red fills indicate 13C atoms. (C–D) 13C-serine/glycine levels in M-huPMCs and P-huPMCs after treatment with 13C-glucose (C) or 13C-serine (D). (E–H) Seahorse XF analysis of M-huPMCs and P-huPMCs. Glycolytic (E–F) and mitochondrial functions (G–H) were evaluated in human Müller cells in primary culture isolated from macula and peripheral regions (n = 4).

Figure 5—source data 1. Source data for Figure 5C,D,F,H.
elife-43598-fig5-data1.xlsx (11.3KB, xlsx)
DOI: 10.7554/eLife.43598.015

Figure 5.

Figure 5—figure supplement 1. The ratio of serine (A) or glycine (B) MS intensity to total amount of cellular protein in M-huPMCs and P-huPMCs from the same donor.

Figure 5—figure supplement 1.

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We also treated M-huPMCs and P-huPMCs with 13C-labeled serine to trace how human primary Müller cells use exogenous serine. After treatment with 13C-serine for 24 hr, we found that most 13C-serine was converted to 13C-glycine but we found no other glucose intermediates, in contrast to previous reports in cancer cells (Amelio et al., 2014). A modest but statistically significantly higher level of 13C-labeled M2 glycine (70% vs 59%) suggests that more serine is converted to glycine in M-huPMCs than in P-huPMCs. However, less 13C-labeled M1 glycine, M1 serine and M2 serine in M-huPMCs indicates that there is a lower rate of reverse serine/glycine reactions through SHMT occurring in these cells than in P-huPMCs (Figure 5D).

The expression of PHGDH is not only important to the de novo serine synthesis metabolic pathway, but also closely related to cellular glycolytic and mitochondrial functions. We further assessed the glycolytic and mitochondrial functions of M-huPMCs and P-huPMCs using Seahorse Assay. We found that M-huPMCs had significantly higher glycolytic capacity and reserve than P-huPMCs (Figure 5E–F). M-huPMCs also had significantly higher basal mitochondrial respiration and ATP production but lower spare respiratory capacity than P-huPMCs (Figure 5G–H).

Susceptibility of the M-huPMCs and P-huPMCs to oxidative stress after inhibition of PHGDH

To further explore the role of PHGDH in Müller cells from different regions, we used a PHGDH specific inhibitor, CBR-5884, to assess the different responses of Müller cells from different retinal regions. We first validated the inhibition efficiency of CBR-5884 in human primary Müller cells and found that treatment of CBR-5884 in huPMCs for 2 hr resulted in 61% and 42% reductions in serine and glycine levels, respectively (Figure 6A). We also found that M-huPMCs and P-huPMCs had similar levels of cell viability and cytotoxicity after exposure to CBR-5884 (30 µM) for 6 hr. M-huPMCs had significantly lower cell viability and higher cytotoxicity levels than P-huPMCs when they were exposed to exogenous oxidative stress (100 µM H2O2) (Figure 6B–C). Inhibiting PHGDH using CBR-5884 when cells were exposed to oxidative stress led to more pronounced Müller cells cytotoxicity in the macula compared to the periphery.

Figure 6. Responses to oxidative stress of primary cultured Müller cells from macula and peripheral retina.

(A) Measurement of 13C-serine and -glycine after PHGDH inhibition in human primary Müller cells; B-C. Cell metabolic activity (B) and cellular damage (C) in the M-huPMCs and P-huPMCs with or without PHGDH inhibition under oxidative stress; D-I. Flow cytometry analysis for M-huPMCs and P-huPMCs stained with Thiol Green Dye to detect GSH (D-F) and with CM-H2DCFDA to evaluate ROS (G-I) for cells under mild oxidative stress (100 μM H2O2), with or without PHGDH inhibition (n = 5).

Figure 6—source data 1. Source data for Figure 6A–C,F,I.
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DOI: 10.7554/eLife.43598.018

Figure 6.

Figure 6—figure supplement 1. Quantitative re-analysis of the flow cytometry data in Figure 6D–E,G–H.

Figure 6—figure supplement 1.

(A-B)ROS (CM-H2DCFDA) and GSH (Thiol Green Dye) levels in M-huPMCs and P-huPMCs in response to induced hyperoxia with and without CBR, an inhibitor of PHDGH, relative to untreated controls (n = 5 per group).
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Since de novo serine synthesis is closely related to the production of glutathione (GSH, the major antioxidant in retina), we speculated that the greater susceptibility of Müller cells to oxidative stress when PHGDH was inhibited, was due to disturbance of the balance between GSH and ROS. We measured GSH and ROS levels in M-huPMCs and P-huPMCs under oxidative stress with or without PHGDH inhibition using flow cytometry (Figure 6D–I). We found significantly higher intracellular levels of GSH in M-huPMCs than in P-huPMCs (Figure 6F). When the cells were also subjected to mild oxidative stress (100 μM H2O2), P-huPMCs had a more pronounced decrease of GSH levels and elevated ROS levels than M-huPMCs. Inhibition of PHGDH using CBR-5884 induced a more significant GSH reduction in M-huPMCs than in P-huPMCs; ROS levels significantly increased in both M-huPMCs and P-huPMCs (Figure 6F–I, normalized to control of P-huPMCs or Figure 6—figure supplement 1, normalized to P-huPMCs/M-huPMCs own control).

Discussion

To understand macula pathology, we studied the differences between Müller cells from the peripheral and central retina in primary culture. We found that Müller cells from the macula showed a morphologically distinct response to primary tissue culture compared to those from the peripheral retina. Previous reports have highlighted the morphological differences between Müller cells in the human macula and the retinal periphery in vivo (Bringmann et al., 2018). In situ Müller cells in the foveal region display a Z shape; their elongated processes run with the cone axons in Henle’s fibre layer, most conspicuously in the parafovea (Lujan et al., 2011). In culture, we found that Müller cells isolated from macula were small spindle to stellate shaped cells with lower cytoplasm/nucleus ratios and shorter processes, while the Müller cells from peripheral retina had larger cell bodies, multiple cytoplasmic processes, higher cytoplasm/nucleus ratios. This might be due to the different responses of Müller cells to in vitro culture conditions. These phenotypic differences were retained in the two sub-populations of Müller cells up to the second passage (P2), after which cells showed evidence of division more towards immortalized ‘MIO-M1’-shaped cells. This suggested that these two populations of cells can maintain distinct characteristics up to the second passage, after which they revert to a common phenotype. Therefore, all our following experiments were strictly controlled to use the primary cells up to two passages. Loss of surrounding cells and other in vivo conditions is an acknowledged limitation of tissue culture; however, the different responses of macular vs. peripheral Müller cells to the same in vitro conditions suggests a fundamental difference in mechanisms of survival and homeostasis.

While the two types of cells had distinct morphological appearances, immunofluorescent studies confirmed that Müller cells from the macula and retinal periphery both expressed the same range of Müller cell markers (Figure 1); thus, differences in the cytoskeletal and other proteins underpinning these distinct morphologies are yet to be characterized. Our RNA-seq analysis significantly revealed differential gene expression patterns in Müller cells from the macula and peripheral retina. However, both populations of cells strongly expressed Müller cell markers and only weakly expressed markers of other retinal cell types. Interestingly, peripheral Müller cells expressed Müller cell markers more strongly than those from the macula, perhaps in keeping with their ability to maintain a more ‘Müller-like’ morphology in primary culture. Müller cells from the peripheral retina of humans may be more like Müller cells in other species, most of which do not have a macula. It has been reported that human Müller cells from the macula expressed less GS than those from the retinal periphery (Daruich et al., 2018).

We have previously reported that de novo serine synthesis plays important roles in Müller cellular redox homeostasis and mitochondrial function (Zhang et al., 2018). RNA-seq analysis in the present study found that macular Müller cells expressed higher levels of several key enzymes (with high RPKM numbers) involved in this metabolic pathway, such as PHGDH, than peripheral Müller cells. Our Western blot analysis confirmed that the macula expressed more PHGDH than the peripheral retina (Figure 3). The co-localization of PHGDH with the Müller cells marker CRALBP (Figure 4) is also consistent with a previous report that PHGDH is predominantly expressed by Müller cells in retina (Zhang et al., 2018). The strong expression of PHGDH in Henle’s fibre layer, a specialised region of the macula, may explain why macular Müller cells express more PHGDH than those from the retinal periphery.

The macula has the highest metabolic activity in the retina related to the very high density of neurons, especially cone photoreceptors within the fovea (Okawa et al., 2008; Perkins et al., 2004; Perkins and Frey, 2000). As the major supporting glial cell in the retina, it is not surprising that human macular Müller cells display a more active glycolytic and mitochondrial metabolism than peripheral Müller cells. Our Seahorse XF experiments found that macular Müller cells had a significantly higher capacity for basal mitochondrial respiration, and ATP production, as well as higher glycolytic capacity and reserve than peripheral Müller cells (Figure 5). In both experiments, the readings (ECAR or OCR) started at a similar level, indicating that both populations of cells in primary culture had compatible baseline metabolism rates.

The higher expression of PHGDH in macular Müller cells is consistent with more active de novo serine synthesis in the macula than in the retinal periphery. Studies using C13-glucose tracer revealed that Müller cells in primary cell culture had very high de novo serine/glycine enrichment (around 18% within 3 hr), much higher than those reported for melanoma cells, where only around 12% of serine was labeled with C13-glucose within 24 hr (Scott et al., 2011). We also found that M-huPMCs shifted glucose carbons more toward de novo serine synthesis but less to glycolysis and the TCA cycle than P-huPMCs. Increased activity of the serine pathway in macular Müller cells is consistent with the generally higher metabolic activity in the macula.

We further explored whether the topographic variation in PHGDH expression will affect the responses of Müller cells to exogenous stimuli. Treatment with the PHGDH-specific inhibitor CBR-5884 led to more pronounced cytotoxicity in macular Müller cells than in peripheral Müller cells, after exposure to oxidative stress. This suggests that defects in de novo serine synthesis increase the vulnerability of M-huPMCs to oxidative stress relative to P-huPMCs. This observation aligns with a recent report that PHGDH dysfunction may be related to the pathogenesis of MacTel, a retinal disease characterised by pronounced degeneration of Müller cells that is confined to the macula (Scerri et al., 2017). This study also found serum levels of serine in MacTel patients to be significantly decreased (Scerri et al., 2017). Increased levels of oxidative stress, combined with PHGDH dysfunction, could thus increase the susceptibility of the macula to damage.

The greater rate of de novo serine synthesis in M-huPMCs may be linked to their increased levels of reduced GSH and ROS; this is consistent with higher metabolic activity and oxidative stress in the macula. Inhibition of de novo serine synthesis resulted in a more significant decrease in reduced GSH levels and increase in ROS levels in M-huPMCs than in P-huPMCs. This further supports the hypothesis that impaired de novo serine synthesis can affect ROS balance in macular Müller cells more than in the peripheral retina.

These findings reveal new lines of investigation for understanding the pathogenesis of AMD and DR; the role of Müller cells in both these disease entities has received relatively little attention. The implications for MacTel, potentially a prototypic Müller cell disease (Charbel Issa et al., 2013), are arguably easier to draw. Although MacTel has been reported to be present in 0.1% of the population over 40 years of age, the actual prevalence may be higher (Coorey et al., 2012; Klein et al., 2010). MacTel affects both eyes with a predilection for the temporal parafovea (Gass and Blodi, 1993). Recent clinicopathological studies have consistently found loss of Müller cells markers from the macula. The area of macular luteal pigment depletion, which is a specific and sensitive marker of MacTel clinically, was found to match the area of Müller cell loss, which was in turn associated with photoreceptor degeneration (Powner et al., 2010; Powner et al., 2013). It is now suspected MacTel is caused by a derangement of glial-neural function with a secondary vasculopathy that is not the prime cause of loss of vision. Defects of genes associated with the PHGDH gene have recently been implicated in patients with MacTel (Scerri et al., 2017). Our results indicate that impairment of PHGDH would have more significant effects on macular Müller cells, especially under oxidative stress. Interestingly, we found PHGDH is highly expressed in macular Henle fibre layer, which may explain why MacTel is tightly associated with HFL pathology (Powner et al., 2010).

We have explored the differences between Müller cells from the macula and peripheral human retina. The distinct transcriptional and functional differences we found between these two populations of primary Müller cells contributes to explaining the unique biochemical and metabolic specializations of the human macula. We accept Müller cells in vitro, are different to Müller cells in vivo, since they have lost contact with neurons and blood vessels. Nevertheless, the Müller cells that had been isolated from the different retinal regions responded consistently and differently to the conditions they were exposed to, indicating some molecular differences between the Müller cells from these two different locations. The macula is devastatingly affected by several common blinding conditions including AMD and DR, major causes of vision impairment and loss worldwide. The differences in serine biosynthesis between Müller cells from two regions is consistent with the hypothesis that disease of Macular Müller cells causes metabolic dysfunction which is the primary cause of MacTel. A better understanding of the unique biology of the human macula, both in terms of its resiliency and vulnerability, will significantly inform future approaches to disease prevention and treatments.

Materials and methods

Key resources table.

Reagent type
(species) or
resource		 Designation Source or reference Identifiers Additional information
Antibody Rabbit polyclonal anti-3-PGDH Millipore Cat# ABS571, RRID:AB_2783876 IF(1:1000), WB (1:1000)
Antibody Rabbit polyclonalanti-α/β tubulin Cell Signaling Technology Cat# 2148, RRID:AB_2288042 WB (1:1000)
Antibody Mouse monoclonal anti-CRALBP Abcam Cat# ab15051, RRID:AB_2269474 IF(1:500)
Antibody Goat polyclonalanti-GFAP Abcam Cat# ab53554, RRID:AB_880202 IF(1:1000)
Antibody Rabbit polyclonal anti-carbonicanhydrase II Abcam Cat# ab6621, RRID:AB_305602 IF(1:1000)
Antibody Rabbit polyclonal anti-Sox9 Millipore Cat# AB5535, RRID:AB_2239761 IF(1:1000)
Recombinant DNA reagent PHGDH-shRNA-GFP plasmid Sigma Aldrich MISSION SHRNA PLASMID DNA
NM_006623/TRCN0000233032 / -hPGK-Neo - CMV-tGFP
Recombinant DNA reagent Negative control-shRNA-GFP plasmid Sigma Aldrich MISSION SHRNA CUSTOM DNA
NEGATIVE CONTROL - nontarget shRNA (SHC016) - pLKO.1-Neo-CMV-tGFP vector
Commercial assay or kit Intracellular GSH detection kit Abcam ab112132
Commercial assay or kit CM-H2DCFDA Molecular Probes C6827
Commercialassay or kit Pierce LDH cytotoxicity assay kit ThermoFisher Scientific 88953
Commercialassay or kit AamarBlue cellviability reagent ThermoFisher Scientific DAL1100
Chemicalcompound, drug CBR-5884 Sigma Aldrich SML1656
Software,algorithm SPSS SPSS RRID:SCR_002865
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Isolation, culture and identification of primary Müller cells from the macula and peripheral human retina

Human retinas were obtained from post-mortem donor eyes with ethical approval from Human Research Ethics Committee of the University of Sydney (HREC#:16/282). Human retinas without known retinal diseases were isolated as described previously (Zhang et al., 2018). The dissected retina was immersed in DMEM medium in a 92 mm culture dish with transparent background. The macula lutea was readily visualized with bright yellow macula pigment. As demonstrated in Figure 1, a 5 mm tissue punch centred on the central retina as well as the superior and inferior mid-periphery was taken. The mid-periphery was defined as the mid-point between the edge of the macula lutea and the ora serrata. Primary Müller cells were cultured according to our established laboratory protocol (available upon request). After retinal pieces from macula and peripheral region were cultured in DMEM medium for 6–8 weeks (with twice weekly medium change), immunofluorescent staining of Müller cell markers (GFAP, carbonic anhydrase II, SOX9 and CRALBP) was performed on the matched sets of primary Müller cells (P0, without subculturing) isolated from the macula and peripheral regions of each donor eye. Images were taken with the Olympus microscope (IX71).

RNA sequencing

After extracting the total RNA from M-huPMCs and P-huPMCs (n = 8), mRNA was enriched using the oligo (dT) magnetic beads. The library preparation, sequencing and quality control were commercially contracted to BGI (https://www.bgi.com/global/). The mRNA was fragmented into short fragments (200 ~ 700 bp) in the fragmentation buffer. The first-strand cDNA was synthesized with random hexamer-primer using the mRNA fragments as templates, followed by the second strand synthesis. The double stranded cDNA was purified with a QiaQuick PCR extraction kit and then used for end repair and base A addition. Finally, sequencing adapters were ligated to the fragments. The fragments were purified by SPRI bead size selection and enriched by PCR amplification. The library products were sequenced using Illumina HiSeq 2500 with paired end 100 bp read length.

RNA data analysis

Primary sequencing data was generated by Illumina HiSeq 2500. Raw reads were filtered to remove adaptor sequences and PCR duplicates. The filtered clean reads were aligned to the reference sequences with SOAP2. The alignment data was utilized to calculate distribution of reads on reference genes and perform coverage analysis. Downstream analysis was performed including gene differential expression analysis (DESeq v1.18.0), alternative splicing (tophat v2.0.8+cufflinks v2.0.2) and SNP detection (using SOAPsnp v1.05). Results of gene expression included gene expression levels and differential expression analysis was performed using DESeq2 in R (version 3.5.1). P-values were adjusted for multiple testing using the Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995). A false discovery rate (FDR) adjusted p-value (i.e. q-value)<0.05 was set for the selection of differential expression genes. Two dimensional plots of principal components were calculated with principal component analysis using R software. We used ClusterProfiler (Bioconductor; https://bioconductor.org/pack-ages/release/bioc/html/clusterProfiler.html) which is an R package to analyse gene clusters and classify biological terms.

Seahorse XF analysis

Macular and peripheral Müller cells were seeded at a density of 2 × 104 cells/well in DMEM medium into the Seahorse XF96 cell culture microplates (Seahorse Bioscience, Agilent Technologies, Santa Clara, CA, USA). The mitochondrial stress assay was carried out in assay medium containing XF base medium (Aligent). Assay medium was freshly prepared and adjusted to pH 7.4. After 24 hr incubation at 37°C in 5% CO2, the confluent cells were ready to be tested. After programming in accordance with manufacturer’s recommendation, the testing plate was placed in Seahorse XF Analyser. Oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) were measured approximately every 8 min. The stress reaction system in each well contained 175 µl assay buffer, 25 µl Oligomycin, 25 µl FCCP and 25 µl Rotenone/Antimycin A to achieve the working concentrations of 1.26 µM, 1 µM, 0.5 µM respectively, and the drugs were sequentially added to each well at different time points according to manufacturer instructions. The OCR and ECAR curves were obtained as the reads of stress resulted from drugs at different time points. The rate is normalized to the amount of protein in each well. The differences in glycolytic and mitochondrial functions were compared between the two primary Müller cell populations (macula and peripheral retina).

LDH cell cytotoxicity assay and AlamarBlue cell viability assay

M-huPMCs and P-huPMCs (P0) were detached from culture dishes using TrypLE enzyme (Thermo Fisher Scientific) and seeded at a density of 1 × 104 cells/well in 96-well plates. Cells were cultured to 80% confluence in DMEM/10% FBS and then starved overnight with Basal Medium Eagle (Gibco) supplemented with 1% FBS, 1% penicillin-streptomycin (P/S) and 1% Insulin-Transferrin-Selenium (ITS, Thermo Fisher Scientific). M-huPMCs and P-huPMCs (P1) were then treated with 100 μM H2O2, 15 μM CBR-5884 and 100 μM H2O2 + 15 μM CBR-5884 for 6 hr, respectively. A medium only, no treatment group was included as a control. The LDH concentration in each group was assayed by LDH cytotoxicity assay kit (Pierce). In brief, 20 µl supernatant of the 96-well plate was transferred into a 386-well plate. 20 µl reaction mixture was added into each well of the plate. The mixture was incubated at room temperature for 30 min. 20 µl stop solution was added to cease the reaction. The absorbance was measured at 490 nm and 680 nm with the plate reader (Safire2, Tecan). To assess cell viability, 15 µl alamarBlue reagent was added to 150 µl medium in each well of the 96-well plate. After a 2 hr incubation at 37°C, the absorbance was read at 570 and 600 nm with the Tecan Safire plate reader (TECAN).

Western blot

The cells were washed with PBS and lysed in RIPA buffer (Sigma Aldrich) with Protease/Phosphatase Inhibitor Cocktail (Cell Signalling). The lysed cells were incubated at 4°C for 10 min and then centrifuged at 12,000 g at 4°C for 10 min. The supernatants were collected and protein concentrations were determined using a BCA protein assay kit (Thermo Fisher Scientific). Samples were heated with reducing buffer and NuPAGE 4–12% Bis-Tris Protein Gels (Life Technologies) at 70°C for 10 min. The samples were loaded onto NuPAGE 4–12% Bis-Tris Protein gels (Thermo Fisher Scientific) and electrophoresed at 180V, 4°C for 70 min. The proteins were transferred using the iBlot gel transfer device (Thermo Fisher Scientific). The PVDF membranes (Millipore, 0.42 uM) were blocked with 5% bovine serum albumin (BSA), Tris-Buffered Saline and 0.1% Tween 20 (TBST) for 1 hr at room temperature (RT) and then incubated with either primary antibody overnight at 4°C: PHGDH (Millipore, #ABS571, 1:1000) or α/β tubulin (Cell Signalling, #2148, 1:1000). The membranes were washed three times with PBST and incubated with the HRP-conjugated secondary antibody (1:5000) at RT for 2 hr on the following day. Then the membranes were washed with TBST for 5 min, three times and TBS for 5 min once. After washing, the membranes were incubated in Clarity Western ECL Substrate for 5 min and imaged using the G-Box imaging system (In Vitro Technologies).

Vibratome sectioning

Retinal punches from the macula and peripheral regions of donor eyes were fixed in 4% paraformaldehyde (PFA) in PBS for 2 hr and then transferred to PBS for 1 hr. Retinal pieces were embedded in low melting point agarose (42°C, 3% agarose (Lonza) in PBS). Tissue blocks were cooled down at 4°C until the agarose solidified. Agarose blocks were trimmed and glued to the metal chuck of a vibratome (Leica, VT1200S). Vibratome sections (100 μm thick) were obtained from retinal tissues and stored in PBS at 4°C.

Immunofluorescent staining

HuPMCs in 24-well plates were fixed in 4% PFA for 1 hr, then rinsed and stored in PBS for later immunofluorescence staining. Retinal vibratome sections and huPMCs were initially blocked in 5% normal goat serum (Sigma) overnight at 4°C. Sections were immunostained with primary antibodies: PHGDH (Millipore, ABS571, 1:1000), CRALBP (Abcam, ab15051, 1:500), GFAP (Abcam, ab53554, 1:1000), Carbonic Anhydrase II (Abcam, ab6621, 1:1000), SOX9 (Millipore, ab5535, 1:1000) diluted in PBS containing 1% normal goat serum and 0.5% Triton X-100. Vibratome sections were incubated for 4 nights at 4°C. Thereafter, huPMCs were incubated with species-specific secondary antibodies conjugated with Alexa Fluor 488 (green) or 594 (red) (Molecular Probes) at a 1:1000 dilution for 2 hr at room temperature; while the vibratome sections were incubated at 4°C overnight. Cells and sections were then rinsed with PBS and nuclei were stained with Hoechst 33342 (Thermo Fisher Scientific). After mounting in VECTASHIELD antifade mounting medium (Vector Laboratories), immunofluorescence labelling of huPMCs and vibratome sections was captured with the ZEISS confocal laser-scanning microscope (LSM700, Carl Zeiss) and ZEN Blue software.

13C Glucose labeling experiments

M-huPMCs and P-huPMCs (P0) in T25 flasks (Corning) were starved overnight with BME supplemented with 1%FBS, 1% penicillin-streptomycin, and 1% Insulin-Transferrin-Selenium (ITS). M-huPMCs and P-huPMCs were treated with 30 µM CBR-5884 for 4 hr. Cells were incubated with 13C-glucose (5 mM, CLM-1396–0.5, Cambridge Isotope Lab) with or without CBR-5884 (30 µM) in KRB (Krebs-Ringer-Bicarbonate buffer) for 2 hr in a CO2 incubator. Before the incubation, KRB containing the 13C-glucoce was pre-warmed at 37°C in a CO2 incubator for 2 hr. 1 ml cold 0.9% sodium chloride was used to rinse the cells, followed by 800 µl extraction buffer (methanol: ddH2O, 800:200). Cells were then scraped off the flask on dry ice and transferred into Eppendorf tubes. The cell/extraction buffer mix was homogenized and placed on dry ice for 30 min, then centrifuged at 13,000 g for 10 min at 4°C, and the supernatant transferred into the clean tubes. 0.1 M NaOH was added into the pellet of and incubate at 37°C for 16 hr. Protein concentration was measured by BCA method (Thermo Fisher Scientific) (Du et al., 2015; Zhu et al., 2018).

Flow cytometry

We detected the intracellular ROS and GSH in M-huPMCs and P-huPMCs (P0) after different treatments, using CM-H2DCFDA (Molecular Probes) and Intracellular GSH Detection Kit (Abcam). The huPMCs were washed with PBS to terminate the treatment and TrypLE enzyme (Thermo Fisher Scientific) was used to detach cells from culture dishes. After centrifugation (340 g), cells from different treatments were separated into two groups and incubated with either 2 µM CM-H2DCFDA or 1 × Thiol Green Dye for 1 hr in HBSS at 5% CO2, 37°C, respectively. Cells were centrifuged at 340 g for 5 min, washed with PBS and loaded onto a Guava easycyte flow cytometer (Merck Millipore). Cells were gated for singlets (GRN-B-Alog vs. GRN-B-Hlog). The fluorescent event counts were recorded, and the data was analysed using Guava easycyte software 3.1.

Statistical analysis

The data are expressed as the means ± SEM. Statistical analyses were performed using the SPSS version 17.0 for Windows software. The differences between mean values were evaluated using one-way ANOVA, followed by Tukey post hoc test. Student’s t tests were used to compare differences between any given two groups throughout the study. A p-value less than 0.05 was considered to indicate a statistically significant difference.

Acknowledgements

We acknowledge the Lions NSW Eye Bank and Australian Ocular Biobank tissue co-ordinators and scientistsand eye donors and their families for the human donor eye tissue used in this project. The Bosch Institute Molecular Biology Facility provided support and training for the techniques and equipment used in this research. This study was supported by an Australian National Health and Medical Research Council project grant (APP1145121), The Ophthalmic Research Institute of Australia, a grant from the Lowy Medical Research Institute and NIH Grant EY026030 (JD). Professor Mark C Gillies is a Sydney Medical School Fellow and supported by an Australian National Health and Medical Research Council Practitioner Fellowship. Dr. Ling Zhu is supported by The Claffy Foundation, Sydney Hospital/Sydney Eye Hospital.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Ling Zhu, Email: ling.zhu@sydney.edu.au.

Gary L Westbrook, Vollum Institute, United States.

Jeremy Nathans, Johns Hopkins University School of Medicine, United States.

Funding Information

This paper was supported by the following grants:

  • Australian National Health and Medical Research Council APP1145121 to Ling Zhu, Weiyong Shen, Mark C Gillies.

  • The Ophthalmic Research Institute of Australia to Ling Zhu, Mark C Gillies.

  • National Institutes of Health EY026030 to Jianhai Du.

  • Lowy Medical Research Institute to Ting Zhang, Ling Zhu, Weiyong Shen, Mark C Gillies.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Software, Formal analysis, Writing—original draft.

Conceptualization, Data curation, Supervision, Funding acquisition, Project administration, Writing—review and editing.

Conceptualization, Writing—review and editing.

Data curation.

Conceptualization.

Conceptualization, Writing—review and editing.

Conceptualization, Writing—review and editing.

Data curation.

Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing—review and editing.

Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Project administration, Writing—review and editing.

Ethics

Human subjects: Human retinas were obtained from post-mortem donor eyes with ethical approval from Human Research Ethics Committee of the University of Sydney (HREC#:16/282).

Additional files

Supplementary file 1. RNA sequencing raw data.
elife-43598-supp1.xlsx (1.9MB, xlsx)
DOI: 10.7554/eLife.43598.019
Supplementary file 2. RNA sequencing filtered data.
elife-43598-supp2.xlsx (466.9KB, xlsx)
DOI: 10.7554/eLife.43598.020

Data availability

RNA sequencing data are included in the manuscript and Supplementary files. These data are also available at Dryad (https://doi.org/10.5061/dryad.hp60p89).

The following dataset was generated:

Zhang T, Zhu L, Madigan M, Liu W, Shen W, Cherepanoff S, Zhou F, Du J, Gillies M. 2018. Data from: Human macular Müller cells rely more on serine biosynthesis to combat oxidative stress than those from the periphery. Dryad Digital Repository.

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Decision letter

Editor: Jeremy Nathans1
Reviewed by: James B Hurley2, Paul Bernstein

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Human macular Müller cells rely more on serine biosynthesis to combat oxidative stress than those from the periphery" for consideration by eLife. Your article has been reviewed by Gary Westbrook as the Senior Editor, a Reviewing Editor, and three reviewers. The following individuals involved in review of your submission have agreed to reveal their identity: James B Hurley (Reviewer #1); Paul Bernstein (Reviewer #2). The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Three experts reviewed your manuscript, and their assessments, together with my own, form the basis of this letter. As you will see, all of the reviewers were impressed with the importance and novelty of your work. I am including the three reviews at the end of this letter, as there are a variety of specific and useful suggestions in them. One that we think is of particular importance (noted by reviewers #1 and #3) is the quantification on the immunoblot in Figure 3. We appreciate that the reviewers' comments cover a broad range of suggestions for improving the manuscript. Please use your best judgment in deciding which of these can be accommodated in a reasonable period of time. We look forward to receiving your revised manuscript

Reviewer #1:

This manuscript characterizes differences between cultured primary Müller cells isolated from either the macula or from the periphery of human retinas. The study acknowledges and addresses limitations of cultured Müller cells as representative of bona fide Müller cells in the retina of an eye. The study finds many structural and functional differences. Much of the paper focuses on a quantitative difference in serine metabolism. The report, with its acknowledged limitations, is an important contribution that will help to advance the understanding of degenerative diseases that preferentially affect the macula.

The authors should address the following concerns and suggestions.

1) Figure 1B shows the "Cytoplasma Neucleus" ratio, but the text in subsection “Isolation, culturing and validation of primary Müller cells from human macula and 95 peripheral retina” refers to the nucleus/cytoplasm ratio. Please make these consistent.

2) Subsection “Isolation, culturing and validation of primary Müller cells from human macula and peripheral retina”. It would be useful to include other well-known markers for Müller cells, like CRALBP and Glutamine Synthetase. It should be noted or commented on that GFAP expression increases when Müller cells are stressed.

3) The quantification of the western blot data shown in Figure 3B is very useful. However, in the raw data shown in Figure 3A it looks like there is a decrease in the amount of tubulin that is perhaps even greater than the increase in the amount of PHGDH. Since the data in Figure 3B is expressed as a ratio I think there needs to be a more convincing way to show that PHGDH levels are different when normalized to total protein or to another marker. It is possible that the tubulin content in the macular vs peripheral Müller cells is different. That would be significant and should be discussed.

4) Subsection “Differential expression of genes related to the de novo serine synthesis pathway in Müller cells isolated from the human macula and peripheral retina” – the reference to Zhange et al., 2018 is confusing here – is that cited as an example of inter-individual variations? Please clarify what this citation is supporting.

5) Please include more information about how the various antibodies were validated. This is especially important for Figures like Figure 4. Has this immunoreactivity been confirmed to be negative against knockouts? Does the staining pattern depend on the concentration of antibody? How did the authors decide what antibody concentration to use? Was a titration done?

6) For Figure 5 please report the total amounts of serine and glycine per mg tissue as this might also change (in addition to the changes in% enrichment).

7) Subsection “Metabolic differences between the macular and peripheral Müller cells”: This is a confusing statement because the level of 13C labeled M2 glycine seems to be only slightly higher. (~70% vs ~60%) in the macular cells than in the peripheral cells.

8) The data in Figure 5A-D might be easier to interpret if there was more than one time point. It is hard to know whether these numbers represent kinetic differences or if they represent differences in the sizes of the pools that are accessible to the added labeled glucose or labeled serine.

9) For the oximetry experiments in Figure 5E-FH, how were the rates normalized – was it per number of cells, per mg of protein? Please describe this either in the Figure itself or in the Figure legend.

10) Figure 6A. How was the optimal concentration of CBR-5884 determined? In Figure 6A it appears that it is not very effective. Please comment on this. Also, does it affect the total amount of serine and glycine or just the% that is labeled? Can higher concentrations be used and is it known if it affects other reactions?

11) Please interpret the flow cytometry data. The Macular-derived cells show a stronger signal in Figure 6E but is that simply because they have a different size or morphology? Is there a control that can be used to dismiss that explanation?

12) In the Figure legend for Figure 6 please state the methods used in flow cytometry to detect GSH and ROS.

13) There are obvious concerns about the relevance of differences between cells that are isolated from other cells, proteolyzed and exposed to culture conditions. A strength of this paper is that the authors acknowledge and address these concerns. They discuss the caveats and point out how this study is useful and relevant in the Discussion section of the manuscript. (no response required for this point).

14) Please cite a reference or data that support the statement in the Discussion section: "The macula has the highest metabolic activity in the retina related to the very high density of neurons, especially cone photoreceptors within the fovea."

15) One way to provide further validation of the cultured Müller cells would be to measure the rate of 13C Serine synthesis from glucose in intact retina and compare it to the rate in the cultured cells. If there is a way to normalize the samples so that they contain approximately the same number of Müller cells then those two types of preparation should also make about the same amount of 13C serine. If the cultured cells make more serine then there would be a problem. But if they make about the same, then the validity of the preparation is supported. If this is a feasible experiment it would strengthen the paper.

16) Please correct the grammar in the sentence “We further explored whether the topographic variation in PHGDH expression of 348 affected the responses of Müller cells to exogenous stimuli.”. (Discussion section)

17) Subsection “13C pulse‐chase labeling Experiments”: The subtitle here reports that it is a "pulse-chase" experiment, but it seems more like the 13C glucose label is present throughout the experiment. If that is correct it should not be referred to as a "pulse-chase".

18) Subsection “13C pulse‐chase labeling Experiments”: "glucoce"

Reviewer #2:

This manuscript by Zhang and colleagues comprehensively and rigorously addresses the question of whether or not Muller cells of the macula substantially differ from their compatriots in the periphery with particular attention to serine metabolism and its role in combating oxidative stress. This is an important question because the macula is selectively affected by a number of blinding conditions, and although Muller cells are present throughout the retina, there is accumulating morphological and biochemical evidence that macular Muller cells differ from their counterparts in the periphery. Additionally, we are now beginning to recognize that in diseases such as MacTel, the Muller cells are intimately involved in the underlying pathogenesis. Since MacTel has been linked to abnormalities of serine metabolism and PHGDH defects, their focus on this particular pathway and gene product makes sense.

Overall, the logic and execution of the presented work are excellent. They first show that they can culture the macular and peripheral Muller cells from human donors successfully and that their primary culture cells express the expected markers and show markedly different morphologies depending on their region of origin. Gene expression studies confirm these findings and show that PHGDH is relatively upregulated in the macula, especially in the fovea. They then go on to show biochemically that serine-related pathways may contribute resistance to oxidative stress in the macula.

All in all, at the conclusion of reading this manuscript, I felt convinced that the authors have demonstrated that macular Muller cells are unique and that they have plausible explanations that can help elucidate the underlying cause of maculopathies such as MacTel. This is well done and important work.

Reviewer #3:

This manuscript compares human primary Muller cell cultures from macular and peripheral retina in morphology, transcriptome, serine biosynthesis, metabolism, and response to oxidative stress. The author argues that macular Muller cells have an augmented de novo serine synthesis system with higher expression levels of key enzymes. This is necessary for macular Muller cells to handle graver oxidative stress associated with higher metabolic rates in the macular region. Overall, it is an interesting and important subject pertaining to common human retinal diseases such as AMD and diabetic retinopathy, and it also has a broader impact regarding the role of glial metabolism in the health of neural tissues in general.

One obvious caveat is that Muller cells in vitro may be different from those in retinal tissues. Especially the process of generating Muller cell cultures is a form of stress that kills off all neurons and other cells. However, for human/primate cells, other than single cell sequencing (such as the work in https://doi.org/10.1101/428110), and potential studies using retinal organoids, this primary culture system is a reasonably good alternative for a relatively pure Muller cell population study.

My major concerns are mostly technical in nature. Overall, there is a lack of necessary details in methods that casts doubt on some of the results.

On the sequencing part, the authors neglected to report important details: what is the read length? single or paired end reads?

Subsection “Transcription profiles of macular and peripheral Müller cells”, "A total of 21,338 differentially expressed genes (DEGs) were identified in Müller cells isolated from human macular and peripheral retina." This number is quite high, nearly as high as the total number of annotated genes in humans.

Subsection “Transcription profiles of macular and peripheral Müller cells”, "14,380 DEGs with a q-value (a p-value that has been adjusted for the False Discovery Rate) < 0.05 were detected". So about 2/3 of all human genes are differentially expressed in these two populations?

On Western blot, the loading controls are problematic. In Figure 3A (western blots on cultured cells) α/β tubulin was used as loading control. However, from their sequencing data, TUBB is 1.37x higher in M-huPMC, but TUBA1A is 1.64x lower. In the rest of Figure 3 (western blots from retinal tissues), tubulin was again used as loading control. However, it is clear that the tubulin band varies significantly across different samples, with an overall tendency to be higher in the macular regions. If tubulin and PHGDH vary independently across retinal regions, it cannot be a reasonably good loading control, thus the result in Figure 3D becomes somewhat questionable. Unfortunately, this is the critical data connecting cultured results with retinal tissues.

On mitochondria respiration measurements, judging from baseline data points before oligomycin in Figure 5G, it is difficult to relate it to the "basal respiration" data in Figure 5H, which shows a significant difference. Overall, the ECAR data looks much more convincing than the OCR data.

[Editors’ note: minor comments from Reviewers 2 and 3 have not been included.]

eLife. 2019 Apr 30;8:e43598. doi: 10.7554/eLife.43598.026

Author response

Summary:

Three experts reviewed your manuscript, and their assessments, together with my own, form the basis of this letter. As you will see, all of the reviewers were impressed with the importance and novelty of your work. I am including the three reviews at the end of this letter, as there are a variety of specific and useful suggestions in them. One that we think is of particular importance (noted by reviewers #1 and #3) is the quantification on the immunoblot in Figure 3. We appreciate that the reviewers' comments cover a broad range of suggestions for improving the manuscript. Please use your best judgment in deciding which of these can be accommodated in a reasonable period of time. We look forward to receiving your revised manuscript

Reviewer #1:

This manuscript characterizes differences between cultured primary Müller cells isolated from either the macula or from the periphery of human retinas. The study acknowledges and addresses limitations of cultured Müller cells as representative of bona fide Müller cells in the retina of an eye. The study finds many structural and functional differences. Much of the paper focuses on a quantitative difference in serine metabolism. The report, with its acknowledged limitations, is an important contribution that will help to advance the understanding of degenerative diseases that preferentially affect the macula.

The authors should address the following concerns and suggestions.

1) Figure 1B shows the "Cytoplasma Neucleus" ratio, but the text in subsection “Isolation, culturing and validation of primary Müller cells from human macula and 95 peripheral retina” refers to the nucleus/cytoplasm ratio. Please make these consistent.

We have revised the text to cytoplasm/nucleus ratio. (subsection “Isolation, culturing and validation of primary Müller cells from human macula and - peripheral retina”) (Discussion section.)

2) Subsection “Isolation, culturing and validation of primary Müller cells from human macula and peripheral retina”. It would be useful to include other well-known markers for Müller cells, like CRALBP and Glutamine Synthetase. It should be noted or commented on that GFAP expression increases when Müller cells are stressed.

Several representative Müller cells markers, including CRALBP and glutamine synthetase (GS), were analysed in our RNA sequence results (Table 1). We have also performed immunostaining for CRALBP in a matched pair of huPMCs from the macula and peripheral retina as shown below (Figure 1—figure supplement 1). We have also included a sentence related to Müller cells and GFAP in the revised manuscript as follows “GFAP is a non-specific response of Müller cell stress which they usually express in cell culture (Augustine et al., 2018).” (subsection “Isolation, culturing and validation of primary Müller cells from human macula and - peripheral retina”).

3) The quantification of the western blot data shown in Figure 3B is very useful. However, in the raw data shown in Figure 3A it looks like there is a decrease in the amount of tubulin that is perhaps even greater than the increase in the amount of PHGDH. Since the data in Figure 3B is expressed as a ratio I think there needs to be a more convincing way to show that PHGDH levels are different when normalized to total protein or to another marker. It is possible that the tubulin content in the macular vs peripheral Müller cells is different. That would be significant and should be discussed.

We did not state clearly in the figure legend that Figure 3A is an immunoblot for Donor 1 only, there were three technical repeats, as a representative image for this experiment. Figure 3B is the quantitative analysis of the Western blots for all four donors. We chose an antibody anti-α/β-Tubulin (Cell signalling, #2148) as the loading control to be consistent with our western blot of retinal total protein in Figure 3F since it detects endogenous levels of both α- and β-tubulin total protein. We have now provided the western blots of all four sets of human primary Müller cells from four donors using β-actin as the loading control, with similar findings shown in the Figure 3A and an updated quantitative analysis based on this blot (Figure 3B).

4) Subsection “Differential expression of genes related to the de novo serine synthesis pathway in Müller cells isolated from the human macular and peripheral retina” – the reference to Zhange et al., 2018 is confusing here – is that cited as an example of inter-individual variations? Please clarify what this citation is supporting.

Thank you. The reference was a miss insert and this has been revised in the manuscript.

5) Please include more information about how the various antibodies were validated. This is especially important for Figures like Figure 4. Has this immunoreactivity been confirmed to be negative against knockouts? Does the staining pattern depend on the concentration of antibody? How did the authors decide what antibody concentration to use? Was a titration done?

Most antibodies have already been validated by many reports (CRALBP (Yu et al., 2010), GFAP (Sarfare et al., 2015); Carbonic Anhydrase II (Cui et al., 2012); SOX9 (Muranishi and Furukawa, 2012)). We have now transfected PHGDH-shRNA-GFP (TRCN0000233032: NM_006623.2-1563s21c1) plasmid into the MIO-M1 Müller cell Line to knockdown PHGDH expression to assess PHGDH immunolabelling. We performed immunostaining three days after the transfection using anti-PHGDH antibody (1:1000) on fixed cells (Figure 4—figure supplement 1). Cells expressing PHGDH-shRNA-GFP (green fluorescence, white arrow, D) expressed less PHGDH (red fluorescence, white arrow, E). In comparison, there was normal PHGDH immunoreactivity (red fluorescence, yellow arrow, B) observed in cells transfected with negative control shRNA plasmid (Scramble sequence, green fluorescence, yellow arrow, A). This finding is consistent with the anti-PHGDH antibody we used specifically recognise human PHGDH protein.

We used serial dilution of antibodies to determine the optimal antibody concentration to be used in the immunostaining.

6) For Figure 5 please report the total amounts of serine and glycine per mg tissue as this might also change (in addition to the changes in% enrichment).

Total serine MS intensity divided by total amount of cellular protein was used to compare the relative levels of serine or glycine in macular and peripheral-huPMCs isolated from the same donor retina. We believe that this ratio should detect any difference in serine or glycine levels between Müller cells from the 2 different regions (Figure 5—figure supplement 1). We did not observe a significant difference in the ratio of serine (A) or glycine (B) to total protein in M-huPMCs and P-huPMCs.

7) Subsection “Metabolic differences between the macular and peripheral Müller cells”: This is a confusing statement because the level of 13C labeled M2 glycine seems to be only slightly higher. (~70% vs ~60%) in the macular cells than in the peripheral cells.

We thank the reviewer for the comment. We have revised the manuscript as follows: “A modest but statistically significantly higher level of 13C-labeled M2 glycine (70% vs 59%) suggests that more serine is converted to glycine in M-huPMCs than in P-huPMCs.” (Subsection “Metabolic differences between the macular and peripheral Müller cells”).

8) The data in Figure 5A-D might be easier to interpret if there was more than one time point. It is hard to know whether these numbers represent kinetic differences or if they represent differences in the sizes of the pools that are accessible to the added labeled glucose or labeled serine.

We strictly used only P0 human primary Müller cells in this experiment. The number of primary Müller cells isolated from each macula is very limited and we must use the macular and peripheral cells from the same donor to compare, so technically we were unable to test more time points with the limited amount of P0 cells with current protocols.

9) For the oximetry experiments in Figure 5E-FH, how were the rates normalized – was it per number of cells, per mg of protein? Please describe this either in the Figure itself or in the Figure legend.

The rate is normalized to the amount of protein in each well. We have revised the Materials and methods section to indicate this accordingly. (Subsection “Seahorse XF Analysis”).

10) Figure 6A. How was the optimal concentration of CBR-5884 determined? In Figure 6A it appears that it is not very effective. Please comment on this. Also, does it affect the total amount of serine and glycine or just the% that is labeled? Can higher concentrations be used and is it known if it affects other reactions?

We selected a concentration that we thought would be effective without reducing cellular metabolic activity based on a report from Mullarky et al., (2016). We are not aware of off-target effects of CBR-5884, but we still minimized the treatment duration to avoid any possible irrelevant off-target effects. CBR-5884 was reported to decrease de novo serine synthesis by 30% (changes of ion intensity, 13C-glucose labelling experiment, (Mullarky et al., 2016)), which is consistent with our findings in human primary Müller cells.

11) Please interpret the flow cytometry data. The Macular-derived cells show a stronger signal in Figure 6E but is that simply because they have a different size or morphology? Is there a control that can be used to dismiss that explanation?

Forward Scatter (FSC) and Side Scatter (SSC) were used to identify cells based on size and granularity, which are reflections of complexity. Both M-huPMCs and P-huPMCs had a similar profile of FSC/SSC distribution after trypsinization, suggesting that they were of a similar size and complexity. We used the same gating for both FSC and SSC to exclude cells of different size or complexity in the sample cohorts. We also re-analysed the different responses of M-huPMCs and P-huPMCs to induced stress. The extent of the increase of ROS and decrease of GSH in treated M-huPMCs and P-huPMCs has been normalized to their own control (Figure 6—figure supplement 1).

12) In the Figure legend for Figure 6 please state the methods used in flow cytometry to detect GSH and ROS.

We have now provided details in the figure legend: “Flow cytometry analysis for M-huPMCs and P-huPMCs stained with Thiol Green Dye to detect GSH (D-F) and with CM-H2DCFDA to evaluate ROS (G-I) for cells under mild oxidative stress (100 μM H2O2), with or without PHGDH inhibition (n=5).”

14) Please cite a reference or data that support the statement in the Discussion section: "The macula has the highest metabolic activity in the retina related to the very high density of neurons, especially cone photoreceptors within the fovea."

We have now included references (Okawa et al., 2008; Perkins et al., 2004; Perkins and Frey, 2000) as suggested (Discussion section). Cone photoreceptors have been reported to have higher energy demands than rod photoreceptors. In view of the highest density of cones, it may be speculated that the macula has the highest metabolic activity in the retina.

15) One way to provide further validation of the cultured Müller cells would be to measure the rate of 13C Serine synthesis from glucose in intact retina and compare it to the rate in the cultured cells. If there is a way to normalize the samples so that they contain approximately the same number of Müller cells then those two types of preparation should also make about the same amount of 13C serine. If the cultured cells make more serine then there would be a problem. But if they make about the same, then the validity of the preparation is supported. If this is a feasible experiment it would strengthen the paper.

We have now established a method to measure the rate of 13C Serine synthesis from glucose and performed the experiments on four donor pairs of post-mortem human macula and mid-peripheral retina (5mm diameter punches) as suggested by the reviewer. We found that 3 out of 4 macula explants had higher rates of serine metabolism when compared with peripheral retina from the same donor. (Author response image 1).

Author response image 1. 13C-labelled serine level in the macula and peripheral retina following the treatment of 13C-glucose (n=4 donors).

Author response image 1.

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Donors 1, 2 and 3 showed a higher 13C-labelled serine level in the macula vs peripheral retina. Donor 4 showed reduced 13C-labelled serine in the macula.

Due to the limited sample size, statistical analysis was not possible. The post-mortem delay, age of donor and the cause of death may also contribute to the variations between the different donor retinas. Further analyses of larger numbers would be helpful.

Another challenge is how much serine in each cell and which cell uses de novo synthesis. For example, if there is more serine in other cells that can dilute the rate of enrichment. Also, the dead cells in culture can generate serine by degrading protein, further complicating this analysis. We also need to adopt a method of counting the density of Müller cells within the explants to estimate the number of Müller cells in macula or mid-peripheral region. One of our colleagues’ team is now counting the different types of retinal cells in different area of human retina however, this will be a separate publication and beyond the scope of the current study.

16) Please correct the grammar in the sentence “We further explored whether the topographic variation in PHGDH expression of 348 affected the responses of Müller cells to exogenous stimuli.”. (Discussion section)

Thank you. This has been revised as suggested (Discussion section).

17) Subsection “13C pulse‐chase labeling Experiments”: The subtitle here reports that it is a "pulse-chase" experiment, but it seems more like the 13C glucose label is present throughout the experiment. If that is correct it should not be referred to as a "pulse-chase".

Thank you. We have revised this accordingly “13C Glucose Labeling Experiments”.

18) Subsection “13C pulse‐chase labeling Experiments”: "glucoce"

Thank you. We have revised accordingly.

Reviewer #3:

[…] My major concerns are mostly technical in nature. Overall, there is a lack of necessary details in methods that casts doubt on some of the results.

On the sequencing part, the authors neglected to report important details: what is the read length? single or paired end reads?

Subsection “Transcription profiles of macular and peripheral Müller cells”, "A total of 21,338 differentially expressed genes (DEGs) were identified in Müller cells isolated from human macular and peripheral retina." This number is quite high, nearly as high as the total number of annotated genes in humans.

Subsection “Transcription profiles of macular and peripheral Müller cells”, "14,380 DEGs with a q-value (a p-value that has been adjusted for the False Discovery Rate) < 0.05 were detected". So about 2/3 of all human genes are differentially expressed in these two populations?

We apologize for not applying the appropriate cut-off for the number of DEGs here. The correct number with the proper cut-off applied is now shown in Figure 2F. The correction was made as follows: " A total of 7,588 differentially expressed genes (DEGs) were identified in Müller cells isolated from human macular and peripheral retina with a 1.5-fold or more increase or decrease and with an FDR corrected p-value < 0.05.” (Figure 2F, Supplementary file 1 and Supplementary file 2 updated).

On Western blot, the loading controls are problematic. In Figure 3A (western blots on cultured cells) α/β tubulin was used as loading control. However, from their sequencing data, TUBB is 1.37x higher in M-huPMC, but TUBA1A is 1.64x lower. In the rest of Figure 3 (western blots from retinal tissues), tubulin was again used as loading control. However, it is clear that the tubulin band varies significantly across different samples, with an overall tendency to be higher in the macular regions. If tubulin and PHGDH vary independently across retinal regions, it cannot be a reasonably good loading control, thus the result in Figure 3D becomes somewhat questionable. Unfortunately, this is the critical data connecting cultured results with retinal tissues.

As noted in the comments for question 3 of the reviewer 1 above, for the western blot of total retinal protein, we loaded the same amount of total retinal protein (20ug) for each sample and normalized the data to total loaded protein amount; a similar finding was observed as shown in the revised Figure 5—figure supplement 1.

On mitochondria respiration measurements, judging from baseline data points before oligomycin in Figure 5G, it is difficult to relate it to the "basal respiration" data in Figure 5H, which shows a significant difference. Overall, the ECAR data looks much more convincing than the OCR data.

The basal respiration was derived by subtracting non-mitochondrial respiration from baseLine cellular OCR. Although the baseLine data points of M-hPMCs and P-hPMCs are similar before oligomycin, their non-mitochondrial respiration was significantly different after treatment with antimycin A and rotenone treatment, inhibitors of complex III and I used to shut down ETC function.

Associated Data

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

    Data Citations

    1. Zhang T, Zhu L, Madigan M, Liu W, Shen W, Cherepanoff S, Zhou F, Du J, Gillies M. 2018. Data from: Human macular Müller cells rely more on serine biosynthesis to combat oxidative stress than those from the periphery. Dryad Digital Repository. [DOI] [PMC free article] [PubMed]

    Supplementary Materials

    Figure 1—source data 1. Source data for Figure 1B.
    elife-43598-fig1-data1.xlsx (9.2KB, xlsx)
    DOI: 10.7554/eLife.43598.004
    Figure 3—source data 1. Source data for Figure 3B,D,E,F.
    elife-43598-fig3-data1.xlsx (9.7KB, xlsx)
    DOI: 10.7554/eLife.43598.010
    Figure 5—source data 1. Source data for Figure 5C,D,F,H.
    elife-43598-fig5-data1.xlsx (11.3KB, xlsx)
    DOI: 10.7554/eLife.43598.015
    Figure 6—source data 1. Source data for Figure 6A–C,F,I.
    elife-43598-fig6-data1.xlsx (10.6KB, xlsx)
    DOI: 10.7554/eLife.43598.018
    Supplementary file 1. RNA sequencing raw data.
    elife-43598-supp1.xlsx (1.9MB, xlsx)
    DOI: 10.7554/eLife.43598.019
    Supplementary file 2. RNA sequencing filtered data.
    elife-43598-supp2.xlsx (466.9KB, xlsx)
    DOI: 10.7554/eLife.43598.020

    Data Availability Statement

    RNA sequencing data are included in the manuscript and Supplementary files. These data are also available at Dryad (https://doi.org/10.5061/dryad.hp60p89).

    The following dataset was generated:

    Zhang T, Zhu L, Madigan M, Liu W, Shen W, Cherepanoff S, Zhou F, Du J, Gillies M. 2018. Data from: Human macular Müller cells rely more on serine biosynthesis to combat oxidative stress than those from the periphery. Dryad Digital Repository.

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

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