Transcriptional Code and Disease Map for Adult Retinal Cell Types
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RE so UR C E Transcriptional code and disease map for adult retinal cell types Sandra Siegert1,7, Erik Cabuy1,7, Brigitte Gross Scherf1, Hubertus Kohler1, Satchidananda Panda2, Yun-Zheng Le3,4, Hans Jörg Fehling5, Dimos Gaidatzis1,6, Michael B Stadler1,6 & Botond Roska1 Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of an individual adult brain region differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes in one brain region, the mouse retina. We found that each adult cell type expressed a specific set of genes, including a unique set of transcription factors, forming a ‘barcode’ for cell identity. Cell type transcriptomes carried enough information to categorize cells into morphological classes and types. Several genes that were specifically expressed in particular retinal circuit elements, such as inhibitory neuron types, are associated with eye diseases. The resource described here allows gene expression to be compared across adult retinal cell types, experimenting with specific transcription factors to differentiate stem or somatic cells to retinal cell types, and predicting cellular targets of newly discovered disease-associated genes. The brain is composed of many neuronal cell types that are deter- across the retina with a mosaic-like distribution (Supplementary Text mined during development by a dynamic transcriptional program1–5. and Supplementary Fig. 1). The cellular architecture of the retina is In adults, neurons sampled from different brain areas such as the highly conserved among mammals14–17. cortex, cerebellum and hippocampus maintain differences in their By constructing and analyzing a transcriptome atlas for retinal cell expressed genomes6,7. However, the extent to which intermingled cell types, we show that adult retinal cell types have highly diverse gene types within a particular brain region differ in their transcriptomes expression patterns. Our data uncover a transcription factor code is not understood6,8. for the cell types studied. Mapping known disease-associated genes Dissecting cell type transcriptomes within brain areas could also shed to retinal cell types revealed that inhibitory cells, as well as retinal light on the relationship between cell type and disease. Human genetic microglia, are cellular targets of inherited diseases. studies have identified hundreds of gene mutations correlated to diseases © 2012 Nature America, Inc. All rights reserved. America, Inc. © 2012 Nature of the nervous system. Although the affected brain regions can be deter- RESULTS mined in human subjects or mutant mice, the expression of disease- Retina cell type transcriptomes associated genes had not been systematically mapped to regional cell We assembled a library of 22 transgenic mouse lines18 in which each line npg types. This mapping is important because understanding disease mecha- had a group of retinal cells marked with fluorescent proteins (Fig. 1b, nisms, as well the design of therapeutic strategies, may vary according Supplementary Table 1 and Supplementary Text). We generated the to how widely the disease-associated gene is expressed across cell types. library with the goal of having some mouse lines in which single retinal Recent studies have demonstrated the feasibility of reprogramming stem cell types and others in which combinations of types from a single class cells and somatic cells to become neuronal cell types by expressing cell were labeled. The library had mouse lines with labeled cells represent- type–specific transcription factors9–11. Knowing these factors and ing each of the six retinal cell classes. Retinal cells were character- having reference transcriptomes for the different neuronal cell types of ized by physiological recording and immunohistochemical staining a brain region would facilitate cell-type engineering. (Supplementary Table 1 and Supplementary Figs. 2–6). We isolated The retina offers opportunities to investigate the relationship 200 fluorescent protein–labeled retinal cells (“cell groups”) from at least between the cellular elements of neuronal circuits and the genes that three different mice of each mouse line by fluorescence-activated cell they express12,13. On the basis of morphological and physiological sorting5,19,20 (Supplementary Figs. 7–9). The transcripts of each cell criteria, cells can be grouped into six classes: photoreceptor, hori- group of these biological triplicates were independently amplified in zontal, bipolar, amacrine, ganglion and non-neuronal cells2 (Fig. 1a). batches. Each batch contained an internal control cell group from the Each class can be further subdivided into cell types; these spread Arc line (Supplementary Fig. 7 and Supplementary Text). The Arc 1Neural Circuit Laboratories, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. 2Regulatory Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA. 3Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA. 4Harold Hamm Oklahoma Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA. 5Institute of Immunology, University Clinics Ulm, Ulm, Germany. 6Swiss Institute of Bioinformatics, Basel, Switzerland. 7Present addresses: The Picower Institute, Massachusetts Institute for Technology, Cambridge, Massachusetts, USA (S.S.), Reliable Cancer Therapies, Energy-based Therapies, Strombeek-Bever, Belgium (E.C.). Correspondence should be addressed to B.R. ([email protected]). Received 18 August 2011; accepted 20 December 2011; published online 22 January 2012; doi:10.1038/nn.3032 NATURE NEUROSCIENCE VOLUME 15 | NUMBER 3 | MARCH 2012 487 RE so UR C E cell group is a GABAergic, ON amacrine cell population18. A reason Linear RNA amplification is desirable for quantitative analysis of RNA we used the Arc cells as internal control was that we established the content. To test whether amplifications were indeed linear, we exam- protocols for dissociation, sorting and amplification with this line. ined the relationship between gene expression values and the amounts a b Photoreceptors Horizontal cells b2 Chrnb4 d4 Gja10 Photo- Outer nuclear layer receptors (ONL) Horizontal cells Bipolar Inner nuclear layer ONL cells (INL) Amacrine cells Glial INL cells Ganglion Ganglion cell layer GCL cells (GCL) Bipolar cells Amacrine cells mGluR6 Kcng4 Arc Igfbp2 Rgs5 Crh ONL INL GCL Pcp2 Lhx4 ChAT Chrna3 Fam81a Fbxo32 Ier5 ONL INL GCL Ganglion cells Microglia © 2012 Nature America, Inc. All rights reserved. America, Inc. © 2012 Nature PV Drd4 Grik4 Opn4 Csf2rb2 ONL INL npg INL GCL GCL Mean Mean Gene R Gene R c expression expression Fabp7 256 0.997 Htr2b 120 0.992 2.0 Olfr1372-ps1 199 0.989 Rprml 81 0.989 Pde6c 314 0.989 Frmd7 53 0.986 Gnat2 1821 0.988 1.5 P2rx2 53 0.982 Mogat1 569 0.984 Pxmp2 72 0.980 1.5 Osgep 469 0.979 Slc18a3 1615 0.978 Gulo 44 0.977 Pomc 116 0.971 Otop3 45 0.976 Cmtm8 396 0.960 Ppm1j 293 0.976 1.0 Gabrd 189 0.949 1.0 Clca3 43 0.975 Tgfb3 108 0.941 Expression value/ 0.5 mean expression value 0.5 Chrnb4 ChAT 0 0 0 50 100 150 200 200 150 100 50 0 Number of cells in the mixture Figure 1 Retinal inventory for cell type comparative transcriptome analysis. (a) Schematic overview of the retina. (b) Immunohistochemical staining of vibratome sections from the retinas of mice with fluorescent protein expression in cell groups. Blue, DAPI; green, fluorescently labeled cells; purple, the stratum marker choline acetyltransferase (ChAT; arrows). Scale bars, 10 µm. (c) Expression value changes of ten cone photoreceptor (Chrnb4)-specific and starburst cell (ChAT)-specific genes for graduated variations in ratios of the two cell groups in a mixture. 488 VOLUME 15 | NUMBER 3 | MARCH 2012 NATURE NEUROSCIENCE RE so UR C E of RNA in a cell group (see Supplementary Text). We mixed varying The linearity and repeatability of amplification allows quantitative ratios of green fluorescent protein (GFP)-positive cones from the Chrnb4 analysis of the cell group transcriptomes. mouse line and red fluorescent protein (RFP)-positive starburst amacrine cells from the ChAT line to yield a total of 200 cells (Supplementary A genetic barcode for retinal cell types Fig. 10). Gene expression in the mixtures was analyzed by exploiting We then asked whether there are only graded or combinatorial differ- the finding that both cones and starburst cells each express ~20 genes ences between cell type transcriptomes or whether a set of genes exists at least threefold higher than in any other cell group. Both for genes for each cell type that is only expressed in that type. First, we organ- enriched in cones and those enriched in starburst cells, gene expression ized the cell groups into the six cell classes and ranked the genes for values increased linearly with an increase in the number of cones or each class according to a specificity ratio (s.r.); that is, the ratio of the starburst cells in the mixtures (Fig. 1c). The linear correlation coefficients mean expression within the class compared with the maximal expres- were independent of the extent to which genes were expressed in sion across all other classes. For each class, we found transcripts that the pure cone and starburst cell groups (Supplementary Fig. 10). were enriched in that class (Fig. 2a). To quantify class enrichment, This suggests that the expression values a Photo- Bipolar Amacrine Ganglion b obtained were proportional to the RNA con- receptor cell cell cell 183 Pde6b tent. As a further independent test of propor- Rcvrn Krt18 tionality, we estimated the cell type composition Pde6g Gpr65 Fscn2 60 of three independent mixtures. This estimate Fabp4 Sag was based on a linear algorithm that compared 2610034M16Rik 1700008G05Rik Rtbdn cone-specific gene expression in a mixture and C79127 4930430E16Rik 30 in a pure cone sample (Supplementary Fig. 10). Aipl1 Specificity ratio Rdh12 Mosc1 The mean error was 12 ± 3% (s.d.) when ten Mpp4 20 Guca1a genes were used to estimate the composition Nrn1 Mosc1 10 7 Gabrr2 Slc32a1 5-fold of the mixture (Supplementary Fig.