Decision letter: A cell atlas of the chick retina based on single-cell transcriptomics
2020-12-02

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Retinal structure and function have been studied in many vertebrate orders, but molecular characterization has been largely confined to mammals. We used single-cell RNA sequencing (scRNA-seq) to generate a cell atlas of the chick retina. We identified 136 cell types plus 14 positional or developmental intermediates distributed among the six classes conserved across vertebrates – photoreceptor, horizontal, bipolar, amacrine, retinal ganglion, and glial cells. To assess morphology of molecularly defined types, we adapted a method for CRISPR-based integration of reporters into selectively expressed genes. For Müller glia, we found that transcriptionally distinct cells were regionally localized along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. We also identified immature photoreceptor, horizontal cell, and oligodendrocyte types that persist into late embryonic stages. Finally, we analyzed relationships among chick, mouse, and primate retinal cell classes and types. Our results provide a foundation for anatomical, physiological, evolutionary, and developmental studies of the avian visual system. eLife digest The evolutionary relationships of organisms and of genes have long been studied in various ways, including genome sequencing. More recently, the evolutionary relationships among the different types of cells that perform distinct roles in an organism, have become a subject of inquiry. High throughput single-cell RNA sequencing is a technique that allows scientists to determine what genes are switched on in single cells. This technique makes it possible to catalogue the cell types that make up a tissue and generate an atlas of the tissue based on what genes are switched on in each cell. The atlases can then be compared among species. The retina is a light-sensitive tissue that animals with a backbone, called vertebrates, use to see. The basic plan of the retina is very similar in vertebrates: five classes of neurons – the cells that make up the nervous system – are arranged into three layers. The chicken is a highly visual animal and it has frequently been used to study the development of the retina, from understanding how unspecialized embryonic cells become neurons to examining how circuits of neurons form. The structure and role of the retina have been studied in many vertebrates, but detailed descriptions of this tissue at the molecular level have been largely limited to mammals. To bridge this gap, Yamagata, Yan and Sanes generated the first cell atlas of the chicken retina. Additionally, they developed a gene editing-based technique based on CRISPR technology called eCHIKIN to label different cell types based on genes each type switched on selectively, providing a means of matching their shape and location to their molecular identity. Using these methods, it was possible to subdivide each of the five classes of neurons in the retina into multiple distinct types for a total of 136. The atlas provided a foundation for evolutionary analysis of how retinas evolve to serve the very different visual needs of different species. The chicken cell types could be compared to types previously identified in similar studies of mouse and primate retinas. Comparing the relationships among retinal cells in chickens, mice and primates revealed strong similarities in the overall cell classes represented. However, the results also showed big differences among species in the specific types within each class, and the genes that were switched on within each cell type. These findings may provide a foundation to study the anatomy, physiology, evolution, and development of the avian visual system. Until now, neural development of the chicken retina was being studied without comprehensive knowledge of its cell types or the developmentally important genes they express. The system developed by Yamagata, Yan and Sanes may be used in the future to learn more about vision and to investigate how neural cell types evolve to match the repertoire of each species to its environment. Introduction The retina is about as complex as other regions of the vertebrate central nervous system. It differs from many regions, however, in being particularly accessible to study. For example, its neurons can be imaged live without surgical intervention, visual stimuli can be precisely controlled in time and space, and the paucity of long-distance inputs facilitates analysis of the entire circuit ex vivo. These and other technical advantages, along with the intrinsic importance of the retina and the fact that most blinding diseases arise from retinal dysfunction, have combined to make the retina a popular model for analysis of neural structure, function, development, and disease (Wässle, 2004; Dowling, 2012; Hoon et al., 2014). Accordingly, retinas of many vertebrate species have been studied in detail, including those of rodents (e.g. mice and rats), carnivores (e.g. cats and ferrets), primates (e.g. macaques, marmosets, and humans), birds (e.g. chickens and pigeons), fish (e.g. zebrafish and goldfish), reptiles (e.g. turtles and lizards), and amphibia (e.g. salamanders and frogs) (Dowling, 2012; Thoreson and Dacey, 2019; Baden et al., 2020). All these studies depend on classification and characterization of the cell types that comprise the retina. Recently, this enterprise has been greatly enhanced by the introduction of methods for high-throughput single-cell transcriptomic profiling (scRNA-seq), which enable comprehensive and minimally biased sampling of cell types. To date, however, with the exception of a single study on zebrafish RGCs (Kölsch et al., 2020), these methods have been applied only to mice and primates (Macosko et al., 2015; Shekhar et al., 2016; Rheaume et al., 2018; Liang et al., 2019; Menon et al., 2019; Peng et al., 2019; Tran et al., 2019; Cowan et al., 2020; Lu et al., 2020; Orozco et al., 2020; Yan et al., 2020a; Yan et al., 2020b; reviewed in Shekhar and Sanes, 2021), restricting use of non-mammalian models and making it difficult to draw evolutionary relationships among species at the molecular level. Here, we address this limitation by using scRNA-seq to generate a cell atlas of the chick retina. The basic plan of the retina is highly conserved among all vertebrates (Baden, 2020). Five neuronal classes are arranged in three cellular (nuclear) layers separated by two synaptic (plexiform) layers: photoreceptors (PRs) in an outer nuclear layer (ONL), three sets of interneurons (horizontal, bipolar, and amacrine cells; HCs, BCs, ACs) in an inner nuclear layer (INL), and output neurons (retinal ganglion cells, RGCs), along with some ACs, in a ganglion cell layer (GCL; Cajal, 1892; Masland, 2012; Figure 1A–B). PRs form synapses with HCs and BCs in an outer plexiform layer (OPL), while RGCs, BCs, and ACs form synapses in an inner plexiform layer (IPL). Axons of RGCs then exit the eye and travel through the optic nerve to a variety of retinorecipient areas in the brain (Dhande et al., 2015; Martersteck et al., 2017). Each of these classes is divided into types with specific patterns of connectivity among them endowing distinct RGC types with sensitivities to different visual stimuli, such as edges, moving or oriented objects, and color contrasts (Sanes and Masland, 2015). The retina also contains glial cells: Müller glia in the INL, and in many species, astrocytes and oligodendrocytes in and beneath the GCL (Reichenbach and Bringmann, 2013; Vecino et al., 2016). Altogether, transcriptomic and morphological studies have identified a total of >130 neural (neuronal and glial) cell types in mice and ~70 in primates (Yan et al., 2020a; Yan et al., 2020b; Peng et al., 2019). Figure 1 with 1 supplement see all Download asset Open asset Datasets used to generate a chick retinal atlas. (A) Cross-section of chick retina stained with NeuroTrace to mark somata. The retina consists of three cellular layers: outer nuclear layer (ONL), inner nuclear layer (INL), and ganglion cell layer (GCL) separated by two synaptic layers, outer plexiform (OPL) and inner plexiform (IPL). Bar, 10 μm. (B) Sketch showing retinal cell classes. The ONL contains photoreceptors (PR): double cones, single cones, and rods. The INL contains horizontal, bipolar, and amacrine cells (HC, BC, and AC) and Müller glia (MG). The GCL contains retinal ganglion cells (RGCs) and ACs. Oligodendrocytes (OL) are present in an axonal layer beneath the GCL. (C) Birthdates of each class, from Prada et al., 1991. Hatching (P0) is at embryonic day (E)21. Arrows denote ages at which cells were obtained for scRNA-seq. To generate the cell atlas, E16 data were used for RGCs and E18 data for all other classes. (D) Expression of a subset of marker genes used to allocate E18 retinal cells to classes. Plot shows scaled expression level in a randomly down-sampled subset of all cells. (E) UMAP of E16+18 scRNA-seq data with class names based on D. (F) Fraction of E18 cells in each cell class, as determined by expression of canonical markers in D. (G) Fraction of E16 RGC-enriched cells in each cell class, determined as in D, F. (H) Number of clusters (putative cell types) in each retinal cell class, based on reclustering each class separately. (I) Fraction of E12 RGC-enriched cells in each cell class, determined as in D, F. Although the basic retinal plan is conserved, cell types and patterns of connectivity vary among species, serving their visual needs (Baden et al., 2020). Birds are highly visual animals with sizable eyes, generally high acuity and sophisticated retinas (Cook, 2000; Seifert et al., 2020). For example, although most mammals have two cone PR types, most birds are tetrachromatic, with cone PRs selectively sensitive to red, green, blue, and ultraviolet light (Hart, 2001; Baden and Osorio, 2019). Many have regions specialized for high acuity vision, akin to the fovea found in primates. Indeed, histological and immunohistochemical studies have suggested that there may be more cell types in avian retinas than in those of mammals (Cajal, 1892; Mariani and Leure-DuPree, 1977; Hayes, 1982; Quesada et al., 1988; Naito and Chen, 2004; Karten and Brecha, 1983; Brecha et al., 1984; Karten et al., 1990). Among birds, the retina of the domestic chicken (Gallus gallus domesticus) has been the most intensively studied. In particular, it has been a favored model for developmental analyses, including studies on the generation and migration of retinal neurons, their diversification into classes and types, the growth and guidance of RGC axons to the optic tectum, and the capacity of retinal neurons and their axons to regenerate (Adler, 2000; Mey and Thanos, 2000; Thanos and Mey, 2001; Wilken and Reh, 2016; Wisely et al., 2017). To complement and facilitate these studies, we used scRNA-seq to profile cells from the chick retina. From ~40,000 single-cell transcriptomes, we identified cells of all six classes named above (PR, HC, BC, AC, RGC, and glia) and used unsupervised methods to divide them into ~150 clusters. We show that 136 of the groups represent putative cell types, with others corresponding to developmental intermediates. We then devised a method for CRISPR-based somatic cell integration of fluorescent reporters into genes shown by scRNA-seq to be expressed by specific types. Using this technique along with other histological methods, we matched molecular profiles to morphology for many neuronal types. We also found a positional signature in Müller glia, with distinct expression patterns based on their location along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. Finally, we compared the cell classes and types of chick retina with those of three mammalian species – mouse, macaque, and human – demonstrating conserved molecular features of all classes and some types, along with multiple differences between chick and mammals. Together, our results provide new insights into retinal structure and evolution, as well as a foundation for anatomical, physiological, and developmental studies of avian retina. Results Profiling chick retinal cells All known chick retinal cell types are born by E14, retinal structure is relatively mature by E18, and birds are visually competent at hatching (E21; Figure 1C; Hamburger and Hamilton, 1951; Prada et al., 1991; Cepko et al., 1996; Mey and Thanos, 2000; Yamagata and Sanes, 1995a; Yamagata and Sanes, 1995b; Drenhaus et al., 2003). We used a droplet-based method (Zheng et al., 2017) to obtain 30,022 high-quality single-cell transcriptomes from embryonic day 18 (E18) chick retina (Figure 1—figure supplement 1A). We assigned cells to classes based on expression of previously established markers, using methods described in Peng et al., 2019 and Yan et al., 2020a. We identified five neuronal classes (PRs, HCs, BCs, ACs, and RGCs) as well as two glial types, Müller glia and oligodendrocytes (Figure 1D,E,F). Of the E18 retinal cells profiled, 620 were RGCs (Figure 1F). This fraction (~2%) was similar to that observed in other species (Macosko et al., 2015; Peng et al., 2019) but was insufficient for extensive classification of what we anticipated would be a highly heterogeneous class. We therefore used magnetic beads coated with antibodies to the pan-RGC cell surface marker Thy1 (French and Jeffrey, 1986; Yamagata et al., 2002) to enrich RGCs from E16 retina before scRNA-seq. We obtained 9159 single-cell transcriptomes, of which 8107 (89%) were RGCs (Figure 1G; Figure 1—figure supplement 1B) that were used for subsequent analysis. We combined single-cell transcriptomes for all cells except RGCs from the E18 dataset and for all and only RGCs from the E16 dataset to generate a cell atlas. To resolve cell types within these classes, we reclustered each one separately, and obtained a total of 150 clusters (Figure 1H). Each presumably corresponded to a cell type, a small group of closely related types, a positional variant or, since we profiled embryonic cells, a developmental intermediate. Quality metrics for each clusters are shown in Supplementary file 3. As one approach to separating definitive types from developmental intermediates, we collected cells from E12 retina, obtaining single-cell transcriptomes from PRs, HCs, BCs, RGCs, and MGs as well as several putative precursor populations (Figure 1I; Figure 1—figure supplement 1C–E). These cells were not used to generate the atlas, but rather to test developmental hypotheses. CRISPR-based cell type characterization Generation of a retinal cell atlas for mouse benefited from prior knowledge, numerous well-characterized antibodies, and many transgenic lines that express a reporter in one or a few types (Macosko et al., 2015; Shekhar et al., 2016; Tran et al., 2019; Yan et al., 2020a). Lacking these advantages for chick, we tested methods for inserting fluorescent reporters into genes shown by scRNA-seq to be expressed by specific types, thereby allowing us to visualize those cells’ morphology. In a method called SLENDR, guide RNAs, and plasmids encoding Cas9 and a reporter are delivered to somatic cells to insert the reporter into a chromosomal site determined by the sequence of the guide RNAs and homologous sequences appended to the reporter (Mikuni et al., 2016). We were unsuccessful in applying this method to chick retina, and therefore modified it. We used in ovo electroporation to deliver a mixture consisting of guide RNAs, Cas9 protein, and a single-strand DNA containing a reporter sequence flanked by ~70 bases gene-specific homology arms (Gurumurthy et al., 2019; Figure 2A). We co-electroporated this mixture with piggyBac transposon reporter/transposase constructs encoding a spectrally distinct reporter to monitor the site and efficiency of electroporation, and optimized reagents to enhance homologous recombination (see Materials and methods). We call the method eCHIKIN for electroporation- and CRISPR-mediated Homology-Instructed Knock-IN. Figure 2 Download asset Open asset Introduction of tags and reporters to specific loci using eCHIKIN. (A) The eCHIKIN method. In ovo electroporation of Cas9/guideRNA ribonucleoprotein complexes edits the gene specified by the guide RNA, inserting a sequence encoding HA tag, GFP, or CRE. To identify transfected areas in tissues, a second fluorescent protein (GFP or mCherry) is expressed using the piggyBac transposon system. (B–D) Insertion of HA epitope tag into the nuclear protein encoded by VSX2. Cells stained by the anti-HA antibody are in the inner (upper) portion of the INL where bipolar cells (labeled with anti-VSX2 in C) are located at E12. CAG-driven GFP is expressed in all the layers. No HA-labeled cells are present in the lower portion of the INL, which contains TFAP2-positive amacrine cells (D), and all HA-positive cells are TFAP2-negative. In this and subsequent figures, sections were stained with NeuroTrace (blue) during mounting. (E, E’, E’’) GFP with a termination codon was inserted at the initiation codon of TFAP2A, an amacrine cell (AC) marker. In this case, GFP is not fused to TFAP2A protein, resulting in filling cytoplasm including neurites in the IPL at E12. TFAP2A protein is expressed by GFP-expressing cells, all of which are TFAP2A-positive ACs. (Note that the eCHIKIN construct disrupts the TFAP2A open-reading frame, so double labeling results from expression of endogenous TFAP2A and indicates that only a single TFAP2A allele was edited). (F) Insertion of HA epitope tag into the cytoplasmic protein encoded by RGC-specific RBPMS2 gene. Labeled cells are in the GCL at E10. (G) Insertion of Cre recombinase into the TFAP2A gene. The insertion construct was coelectroporated with a CAG- loxP-STOP-loxP-GFP construct, labeling a small number of ACs with GFP at E14. (H) Insertion of Cre recombinase into the RBPMS2 gene. The insertion construct was coelectroporated with CAG- loxP-STOP-loxP-GFP as in F, labeling a small number of RGCs with GFP at E14. Bar in H, 10 μm for B-E,G; 5 μm for F, H. To test eCHIKIN, we targeted genes expressed in most cells of three retinal classes: VSX2 (Chx10) in BCs, TFAP2A in ACs and RBPMS2 in RGCs (Chen and Cepko, 2000; Bassett et al., 2007; Piri et al., 2006). We inserted a nine amino acid hemagglutinin (HA) tag at the N-terminus of VSX2 and RBPMS2 and inserted GFP into the TFAP2A locus. In each case, the homologously inserted reporter specifically labeled the expected cell class, as verified by soma position and immunostaining: VSX2-HA-tagged cells were restricted in the outer part of the INL, as appropriate for BCs, and were VSX2-positive and TFAP2A-negative (Figure 2B–D). TFAP2A-GFP cells were present in the inner portion of the INL as appropriate for TFAP2A-expressing ACs (Figure 2E). In this case, GFP was not fused to the endogenous proteins, so it filled the cytoplasm, revealing dendrites of ACs in the IPL. In addition, the labeled cells still express the endogenous gene (Figure 2E), suggesting that only one allele was edited in most cells. Likewise, RBPMS2-HA-tagged cells were confined to the ganglion cell layer, as appropriate for RGCs (Figure 2F). In each case, the spectrally distinct co-electroporated reporter, which had its own regulatory elements and integrated non-homologously, was expressed in cells of all layers (Figure 2B–F). To label cells that expressed genes at we inserted Cre recombinase into and RBPMS2 and co-electroporated with a fluorescent protein the of strong and expressed and class was similar in these to those in which the loci were with a reporter (Figure eCHIKIN a means of matching molecular to cellular morphology and without are tetrachromatic, with cone types that express or et al., et al., 2015). In to these the retina also contains that express and of and cells, which comprise about of all PRs et al., et al., et al., 1990). 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