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Transcriptional code and disease map for adult retinal cell types

Nature Neuroscience volume 15, pages 487–495 (2012)Cite this article

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Abstract

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.

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Figure 1: Retinal inventory for cell type comparative transcriptome analysis.
Figure 2: Transcriptome comparisons of cell groups that belong to a cell class.
Figure 3: Transcriptome comparisons of cell groups.
Figure 4: Cell group–specific transcription factors.
Figure 5: Retinal disease-associated genes in adult cell types.

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Acknowledgements

We thank S. Djaffer, J. Jüttner, J. Hall and Y. Shimada for technical assistance, E. Oakeley for comments on the experimental design, D. Balya for providing help with programming, and K. Farrow, S. Rompani, K. Yonehara, V. Busskamp, S. Oakeley, P. King, A. Matus, S. Arber and F. Rijli for comments on the manuscript. We thank Z. Raics for making the webpage, and L. Kus and S. Gong (Rockefeller University) for help and for BACs from the GENSAT project. We thank I. Provencio (University of Virginia) for providing the melanopsin antibody. The study was supported by Friedrich Miescher Institute funds, Alcon award, a National Center of Competence in Research Genetics grant, a European Research Council grant, a Swiss-Hungarian grant, and RETICIRC, TREATRUSH, SEEBETTER and OPTONEURO grants from the European Union to B.R.

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Author notes

  1. Sandra Siegert & Erik Cabuy

    Present address: Present addresses: The Picower Institute, Massachusetts Institute for Technology, Cambridge, Massachusetts, USA (S.S.), Reliable Cancer Therapies, Energy-based Therapies, Strombeek-Bever, Belgium (E.C.).,

Authors and Affiliations

  1. Neural Circuit Laboratories, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Sandra Siegert, Erik Cabuy, Brigitte Gross Scherf, Hubertus Kohler, Dimos Gaidatzis, Michael B Stadler & Botond Roska

  2. Regulatory Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA

    Satchidananda Panda

  3. Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA

    Yun-Zheng Le

  4. Harold Hamm Oklahoma Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA

    Yun-Zheng Le

  5. Institute of Immunology, University Clinics Ulm, Ulm, Germany

    Hans Jörg Fehling

  6. Swiss Institute of Bioinformatics, Basel, Switzerland

    Dimos Gaidatzis & Michael B Stadler

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Contributions

S.S. designed experiments; characterized mouse lines with immunohistochemistry and electrophysiology; performed confocal microscopy, image processing and quantification; dissociated retinas; performed fluorescence-activated cell sorting; normalized gene array data; analyzed gene and exon array data; created figures; and wrote the manuscript. E.C. performed RNA isolation, amplification and gene profiling for gene and exon arrays. B.G.S. generated BAC transgenic mouse lines and performed in situ hybridization. H.K. assisted with fluorescence-activated cell sorting. S.P. provided the Opn4-Cre mouse. Y.-Z.L. provided b2-Cre and d4-Cre mice. H.J.F. provided the Rosa26-LSL-RFP reporter mouse. D.G. normalized exon array data. M.B.S. assisted with gene array data analysis and provided scripts for pairwise correlation analysis and hierarchical clustering. B.R. helped with data analysis; designed experiments; and wrote the manuscript.

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Correspondence to Botond Roska.

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Siegert, S., Cabuy, E., Scherf, B. et al. Transcriptional code and disease map for adult retinal cell types. Nat Neurosci 15, 487–495 (2012). https://doi.org/10.1038/nn.3032

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