Resource22 August 2019free access Transparent process A single-cell transcriptome atlas of the adult human retina Samuel W Lukowski orcid.org/0000-0002-8598-7902 Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, Australia Search for more papers by this author Camden Y Lo Monash University, Melbourne, Vic., Australia Search for more papers by this author Alexei A Sharov National Institute for Aging, National Institutes of Health, Baltimore, MD, USA Search for more papers by this author Quan Nguyen orcid.org/0000-0001-7870-5703 Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, Australia Search for more papers by this author Lyujie Fang orcid.org/0000-0002-2286-0023 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Jinan University, Guangzhou, China Search for more papers by this author Sandy SC Hung Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Search for more papers by this author Ling Zhu orcid.org/0000-0003-0776-1630 The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Ting Zhang orcid.org/0000-0001-8074-8999 The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Ulrike Grünert The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Tu Nguyen orcid.org/0000-0002-6165-1822 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Search for more papers by this author Anne Senabouth Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia Search for more papers by this author Jafar S Jabbari Australian Genome Research Facility, Melbourne, Vic., Australia Search for more papers by this author Emily Welby Stem Cells and Regenerative Medicine Section, NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK Search for more papers by this author Jane C Sowden Stem Cells and Regenerative Medicine Section, NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK Search for more papers by this author Hayley S Waugh Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Adrienne Mackey Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Graeme Pollock Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Trevor D Lamb John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia Search for more papers by this author Peng-Yuan Wang Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, Vic., Australia Center for Human Tissues and Organs Degeneration, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China Search for more papers by this author Alex W Hewitt Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Menzies Institute for Medical Research, University of Tasmania, Hobart, Tas., Australia Search for more papers by this author Mark C Gillies The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Joseph E Powell orcid.org/0000-0001-9031-6356 Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia Search for more papers by this author Raymond CB Wong Corresponding Author [email protected] orcid.org/0000-0002-8092-9455 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Shenzhen Eye Hospital, Shenzhen University School of Medicine, Shenzhen, China Search for more papers by this author Samuel W Lukowski orcid.org/0000-0002-8598-7902 Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, Australia Search for more papers by this author Camden Y Lo Monash University, Melbourne, Vic., Australia Search for more papers by this author Alexei A Sharov National Institute for Aging, National Institutes of Health, Baltimore, MD, USA Search for more papers by this author Quan Nguyen orcid.org/0000-0001-7870-5703 Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, Australia Search for more papers by this author Lyujie Fang orcid.org/0000-0002-2286-0023 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Jinan University, Guangzhou, China Search for more papers by this author Sandy SC Hung Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Search for more papers by this author Ling Zhu orcid.org/0000-0003-0776-1630 The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Ting Zhang orcid.org/0000-0001-8074-8999 The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Ulrike Grünert The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Tu Nguyen orcid.org/0000-0002-6165-1822 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Search for more papers by this author Anne Senabouth Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia Search for more papers by this author Jafar S Jabbari Australian Genome Research Facility, Melbourne, Vic., Australia Search for more papers by this author Emily Welby Stem Cells and Regenerative Medicine Section, NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK Search for more papers by this author Jane C Sowden Stem Cells and Regenerative Medicine Section, NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK Search for more papers by this author Hayley S Waugh Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Adrienne Mackey Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Graeme Pollock Lions Eye Donation Services, Melbourne, Vic., Australia Search for more papers by this author Trevor D Lamb John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia Search for more papers by this author Peng-Yuan Wang Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, Vic., Australia Center for Human Tissues and Organs Degeneration, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China Search for more papers by this author Alex W Hewitt Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Menzies Institute for Medical Research, University of Tasmania, Hobart, Tas., Australia Search for more papers by this author Mark C Gillies The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia Search for more papers by this author Joseph E Powell orcid.org/0000-0001-9031-6356 Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia Search for more papers by this author Raymond CB Wong Corresponding Author [email protected] orcid.org/0000-0002-8092-9455 Centre for Eye Research Australia, Melbourne, Vic., Australia Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia Shenzhen Eye Hospital, Shenzhen University School of Medicine, Shenzhen, China Search for more papers by this author Author Information Samuel W Lukowski1,‡, Camden Y Lo2,‡, Alexei A Sharov3, Quan Nguyen1, Lyujie Fang4,5,6, Sandy SC Hung4,5, Ling Zhu7, Ting Zhang7, Ulrike Grünert7, Tu Nguyen4,5, Anne Senabouth8, Jafar S Jabbari9, Emily Welby10, Jane C Sowden10, Hayley S Waugh11, Adrienne Mackey11, Graeme Pollock11, Trevor D Lamb12, Peng-Yuan Wang13,14, Alex W Hewitt4,5,15, Mark C Gillies7, Joseph E Powell8,16,‡ and Raymond CB Wong *,4,5,17,‡ 1Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, Australia 2Monash University, Melbourne, Vic., Australia 3National Institute for Aging, National Institutes of Health, Baltimore, MD, USA 4Centre for Eye Research Australia, Melbourne, Vic., Australia 5Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic., Australia 6Jinan University, Guangzhou, China 7The University of Sydney, Faculty of Medicine, Save Sight Institute, Sydney, NSW, Australia 8Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia 9Australian Genome Research Facility, Melbourne, Vic., Australia 10Stem Cells and Regenerative Medicine Section, NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK 11Lions Eye Donation Services, Melbourne, Vic., Australia 12John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia 13Department of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, Vic., Australia 14Center for Human Tissues and Organs Degeneration, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China 15Menzies Institute for Medical Research, University of Tasmania, Hobart, Tas., Australia 16UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia 17Shenzhen Eye Hospital, Shenzhen University School of Medicine, Shenzhen, China ‡These authors contributed equally to this work as first authors ‡These authors contributed equally to this work as senior authors *Corresponding author. Tel: +613 85321962; E-mail: [email protected] EMBO J (2019)38:e100811https://doi.org/10.15252/embj.2018100811 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The retina is a specialized neural tissue that senses light and initiates image processing. Although the functional organization of specific retina cells has been well studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile the human retina, we performed single-cell RNA sequencing on 20,009 cells from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct cell populations representing all known neural retinal cells: rod photoreceptors, cone photoreceptors, Müller glia, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, astrocytes, and microglia. Our data captured molecular profiles for healthy and putative early degenerating rod photoreceptors, and revealed the loss of MALAT1 expression with longer post-mortem time, which potentially suggested a novel role of MALAT1 in rod photoreceptor degeneration. We have demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to understanding retinal biology and disease. Synopsis The transcriptome of human neural retina at a single-cell level defines the gene expression profile in major cell types in the neural retina and can be used as a benchmark to assess the quality of stem cell-derived cells or primary retinal cells. The presented transcriptome atlas of human neural retina comprises single-cell RNA-sequencing data from 20,009 human retinal cells. Unsupervised cell clustering analysis allows identification of 18 transcriptionally distinct cell populations that represent all known neural retinal cell types. Reduced expression of the long non-coding RNA MALAT1 correlates with longer post-mortem time in putative early degenerating rod photoreceptors. The retina transcriptome atlas can be used to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. Introduction The eye is a highly specialized sensory organ in the human body. Sight is initiated by the conversion of light into an electrical signal in the photoreceptors of the neurosensory retina. The rod photoreceptors are responsible for light detection at extremely low luminance, while the cone photoreceptors are responsible for color detection and operate at moderate and higher levels. Following preprocessing, by horizontal, bipolar, and amacrine cells, the resultant signal is transferred via ganglion cells to the brain. Neurotransmitter support is provided by Müller glia, retinal astrocytes, and microglial cells. Inherited retinal diseases are becoming the leading cause of blindness in working-age adults, with loci in over 200 genes associated with retinal diseases (RetNet: https://sph.uth.edu/retnet/), often involving specific retinal cell types. Knowledge of the transcriptome profile of individual retinal cell types in humans is important to understand the cellular diversity in the retina, as well as the study of retinal genes that contribute to disease in individual retinal cell types (Hornan et al, 2007; Farkas et al, 2013; Whitmore et al, 2014; Mustafi et al, 2016; Pinelli et al, 2016). The transcriptome profiles of whole human retina from adults (Hornan et al, 2007; Farkas et al, 2013; Whitmore et al, 2014; Mustafi et al, 2016; Pinelli et al, 2016) and during fetal development (Kozulin et al, 2009; Hoshino et al, 2017) have been previously described. However, these studies only assayed the averaged transcriptional signatures across all cell types, meaning that information on the cellular heterogeneity in the retina is lost. As such, the transcriptional pathways that underlie the highly specialized function of many human retinal cell types remain unclear, including the rod and cone photoreceptors, Müller glial cells, horizontal cells, and amacrine cells. Recent advances in RNA sequencing and microfluidic platforms have dramatically improved the accessibility of single-cell transcriptomics, with increased throughput at a lower cost. Critically, single-cell microfluidics and low-abundance RNA library chemistries allow accurate profiling of the transcriptome of individual cell types. This has been demonstrated in the mouse, where transcriptome profiles of the mouse retina (Macosko et al, 2015) and retinal bipolar cells (Shekhar et al, 2016) have been described at the single-cell level using the Drop-seq method (Macosko et al, 2015). These studies provided a molecular classification of the mouse retina and identified novel markers for specific cell types, as well as novel candidate cell types in the retina. Recently, single-cell transcriptomics was used to analyze the human retina. Phillips et al (2018) have profiled a total of 139 cells from adult retina using the C1 Fluidigm platform, but the limited number of profiled cells presents challenges in the annotation and accurate identification of individual retinal cell types. Moreover, a flow cytometry approach was used to isolate 65 human fetal cone photoreceptors followed by scRNA-seq profiling (Welby et al, 2017). During the preparation of this manuscript, Voigt et al (2019) reported scRNA-seq profiling of 8,217 cells from human retina obtained from a mixed pool of donors that included a healthy patient, a patient with early glaucoma, and one with unknown ocular history. Herein, we report the generation of a human neural retina transcriptome atlas using 20,009 single cells collected from three healthy donors. Our data provide new insights into the transcriptome profile of major human retinal cell types and establish a high cellular-resolution reference of the human neural retina, which will have implications for identification of biomarkers and understanding retinal cell biology and diseases. Results Preparation of human neural retina samples and generation of single-cell transcriptome atlas We obtained post-mortem human adult eyes approved for research purposes following corneal transplantation. As the transcriptome profile of human retinal pigment epithelial cells has already been reported (Liao et al, 2010; Strunnikova et al, 2010), we focused solely on the neural retina layers. In this study, we extracted the neural retina from 12 donor eyes (Appendix Table S1). We observed consistent cell viability across retinal tissues retrieved within 15 h post-mortem (Appendix Fig S1A) and found that donor age does not impact negatively on cell viability in the extracted neural retina (Appendix Fig S1B). To minimize potential risk of mRNA degradation due to reduced cell viability, we selected three donor samples retrieved within 15 h post-mortem and analyzed them with single-cell RNA sequencing (scRNA-seq) using the 10X Genomics Chromium platform. Sequence data from five scRNA-seq libraries derived from the three neural retinal samples were pooled for processing and analysis. From 23,000 cells, we obtained an average of 40,232 reads per cell and 1,665 UMIs (unique transcripts) per cell. Following quality control and filtering using the Seurat package (Butler et al, 2018), our final dataset contained 20,009 cells, which were taken forward for further analysis. The scRNA-seq data were initially analyzed using an unsupervised graph clustering approach implemented in Seurat (version 2.2.1) to classify individual cells into cell populations according to similarities in their transcriptome profiles. Overall, the cells were classified into 18 transcriptionally distinct clusters (Appendix Fig S2). We first assessed the variation between donor samples (Appendix Table S2). Interestingly, although many of the identified clusters are well represented in all three donor retinal samples, we also observed several donor-specific clusters corresponding to rod photoreceptors (Appendix Fig S3A). In contrast, we observed minimal variation between two different libraries prepared from the same donor sample, supporting the quality of the scRNA-seq datasets in this study (Appendix Fig S3B). The average expression for all detected genes in each cluster is listed in Dataset EV1. Identification of major cell types in the human retina using scRNA-seq Based on known markers (Blackshaw et al, 2001; Imanishi et al, 2002; Corbo et al, 2007; Soto et al, 2008; Klimova et al, 2015; Macosko et al, 2015; Shekhar et al, 2016; Vecino et al, 2016), we were able to assign cell identities to 16 of the 18 clusters (Fig 1A–D), corresponding to rod photoreceptors (PDE6A, CNGA1, RHO), cone photoreceptors (ARR3, GNGT2, GUCA1C), Müller glia (RLBP1/CRALBP), retinal astrocytes (GFAP), microglia (HLA-DPA1, HLA-DPB1, HLA-DRA), bipolar cells (VSX2, OTX2), retinal ganglion cells (NEFL, GAP43, SNCG), amacrine cells (GAD1, CALB1, CHAT), and horizontal cells (ONECUT1, ONECUT2). The expression of selected marker genes is displayed in t-SNE plots (Fig 1D). Two clusters (C5 and C14) express markers from multiple retinal cell types (Appendix Fig S4); thus, we were unable to assign cell identities to these two clusters and they were excluded from further analysis. Interestingly, our data demonstrated multiple transcriptionally distinct clusters within the rod photoreceptors (six clusters) and bipolar cells (three clusters). In contrast, only one cluster was detected for cone photoreceptors, Müller glia, retinal ganglion cells, horizontal cells, amacrine cells, retinal astrocytes, and microglia, respectively. Correlation analysis confirmed the similarity between clusters within the same cell type (Fig 1E). As expected, we observed high correlations between the expression levels of transcripts within photoreceptor cell types (rod and cones), as well as glial cells (retinal astrocytes and Müller glia) and other retinal neurons (bipolar cells, retinal ganglion cells, amacrine cells, and horizontal cells). The composition of cell populations across our three donors shows that the majority of the cells in human neural retina were rod photoreceptors (~74%) followed by bipolar cells (~10%). These results are similar to those reported in mice, where rod photoreceptors and bipolar cells form the majority of cells in the retina (Jeon et al, 1998; Macosko et al, 2015). Figure 1. Single-cell transcriptome atlas for human neural retina A, B. t-SNE visualization of 20,009 human retinal cells colored by (A) annotation of 18 transcriptionally distinct clusters (C0-C17) and (B) their distribution in three donor retina samples. C. Feature expression heatmap showing expression patterns of major retinal class markers across 16 retinal cell clusters. The size of each circle depicts the percentage of cells expressing the marker within the cluster. Brown color indicates ≥ 10 nTrans (number of transcripts). D. t-SNE plots showing expression of a set of selected marker genes for major retinal classes. E. Correlation matrix for the identified 18 clusters. The upper triangle depicts the z-value for correlation, and the lower triangle depicts the correlation coefficient for gene expression in clusters. F. Heatmap of differentially expressed genes used to classify cell types for each cluster compared to all other clusters for the 18 retinal cell clusters. The rows correspond to the top 10 genes most selectively upregulated in individual clusters (P < 0.01, Benjamini–Hochberg correction), and the columns show individual cells ordered by cluster (C0-C17). Download figure Download PowerPoint To identify genes whose expression was specific to a given cell type, we performed differential gene expression analysis to identify marker genes for each cluster (Fig 1F). We subsequently extracted membrane-related proteins from gene ontology annotations to identify potential surface markers, which can be used to develop immuno-based methods to isolate primary culture of individual retinal cell types. Appendix Table S3 lists the identified markers for individual retinal cell types. We also assessed the gene expression of a panel of commonly known markers in amacrine cells and bipolar cells (Figs EV1 and EV2), as well as a panel of markers for subtype identification recently identified in mouse scRNA-seq studies (Macosko et al, 2015; Shekhar et al, 2016). In particular, the bipolar clusters can be classified as OFF-bipolar cells (GRIK1+: C6) and ON-bipolar cells (ISL1+: C8, C11). Further analysis showed that C8 represents rod bipolar cells based on the marker PRKCA, while C11 expresses the marker TTR corresponding to a diffuse bipolar subtype DB4 (Fig EV1). In summary, we profiled the transcriptomes of all major cell types in the human retina in the presented dataset. Due to their abundance, for the subsequent analyses we focused on the photoreceptors and glial cells. Click here to expand this figure. Figure EV1. Bipolar marker gene expression in the compiled human neural retina transcriptome atlas (20,009 cells) Feature expression heatmap of VSX2 (pan-bipolar), ISL1 (ON-bipolar), GRIK1 (OFF-bipolar), PRKCA (rod bipolar cells), and TTR (DB4 bipolar). t-SNE plots showing gene expression for 14 new markers for individual bipolar subtypes identified in a previous mouse scRNA-seq study (Shekhar et al, 2016). Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Amacrine marker gene expression in the compiled human neural retina transcriptome atlas (20,009 cells) A, B. t-SNE plots showing gene expression in the compiled human retina transcriptome atlas (20,009 cells). (A) 10 commonly used amacrine markers and (B) new markers for amacrine subtypes identified in a previous mouse scRNA-seq study (Macosko et al, 2015). Download figure Download PowerPoint Profiling healthy and degenerating human rod photoreceptor subpopulations We profiled 14,759 rod photoreceptors and showed that they can be classified into six populations with distinct gene expressions (C0, C1, C2, C3, C4, and C7). We assessed these six clusters with a panel of seven known rod or pan-photoreceptor markers (Fig 2A). Our results suggest differential expression patterns among the seven markers. All seven rod markers are highly abundant, consistent with previous scRNA-seq studies of mouse and human retina (Macosko et al, 2015; Phillips et al, 2018). The seven markers showed differential expression patterns in the six identified rod photoreceptor clusters. In particular, RHO, GNGT1, and SAG have the highest levels of rod marker detected, followed by NRL, ROM1, GNAT1, and CNGA1. We also noted that ROM1 is expressed in both rod and cone photoreceptors, which is consistent with previous studies (Boon et al, 2008). Importantly, many rod photoreceptor clusters consist of a majority of cells from a single donor (> 90% for C0, C2, and C4 and > 80% for C1 and C7; Fig 2B). It is possible that this observation is due to the systematic biases such as differences in tissue retrieval time, age of donors, or other sample preparation variation. The exception is cluster C3, which is well represented by all three donors. Figure 2. Identification of MALAT1-hi and MALAT1-lo subpopulations of rod photoreceptors Feature expression heatmap of a panel of known marker genes for rod photoreceptors across the identified 16 retinal cell clusters. Brown color indicates ≥ 100 nTrans (number of transcripts). Representation of the three donor retina samples in the six rod photoreceptor clusters. Violin plot showing high or low expression levels of MALAT1 in rod photoreceptor clusters. Distribution of rod photoreceptor populations with high MALAT1 expression (MALAT1-hi) or low MALAT1 expression (MALAT1-lo) in three donor retina samples. Fluorescent in situ hybridization analysis of human peripheral retina showing heterogeneous levels of MALAT1 expression in the rod photoreceptors located in the outer nuclear layer (ONL). INL, inner nuclear layer; OPL, outer plexiform layer. Scale bar = 20 μm. Download figure Download PowerPoint Next, we set out to further define and classify heterogeneity in rod photoreceptors. We observed that MALAT1, a long non-coding RNA that plays a role in retinal homeostasis and disease (Wan et al, 2017), was robustly expressed in ~66% of the identified rod photoreceptors (9,750 cells), while the rest had little to no expression (5,009 cells; Fig 2C). As such, we utilized MALAT1 expression as a discriminator and investigated differences between rod photoreceptors with high expression (MALAT1-hi; > 4.68 normalized transcripts per cell) or low expression (MALAT1-lo; < 4.68 normalized transcripts per cell). MALAT1-hi and MALAT1-lo rod photoreceptors were consistently found in each donor and library samples, with MALAT1-hi accounting for ~66, 90, and 36% of the rods in donors #1, #2, and #3, respectively (Fig 2D). To further validate this finding, we performed RNA in situ hybridization in another three donor retinal samples. We consistently observed the presence of MALAT1-hi and MALAT1-lo subpopulations of rod photoreceptors in all retinal samples (Figs 2E and EV3A). Together, these results showed the presence of heterogeneity within rod photoreceptors that can be distinguished by MALAT1 expression. Click here to expand this figure. Figure EV3. MALAT1 expression in human retina Fluorescent in situ hybridization showing expression of MALAT1 in three donor retina samples (Retina 4–6). Retina 5 from Fig 2E is also displayed here for easier comparison. Green arrows highlight rod photoreceptors with low levels of MALAT1 in Retina 4 and Retina 6, and white arrows highlight rod photoreceptors with high levels of MALAT1 in Retina 5. Scale bars = 20 μm. Correlation of proportion of MALAT1-hi rod populations with time of retina retrieval after death for Retina 1–3. Download figure Download PowerPoint To rule out the possibility that the presence of MALAT1 rod subpopulations was due to donor sample variations, we applied canonical correlation analysis (CCA) to correct for technical and batch artifacts. We found that CCA effectively corrected the donor-specific effect on rod photoreceptor clusters (Fig 3A and B). The average expression for all detected genes in each cluster is listed in Dataset EV2. Following CCA corr
