4.8 Article

Linking transcriptomes with morphological and functional phenotypes in mammalian retinal ganglion cells

Journal

CELL REPORTS
Volume 40, Issue 11, Pages -

Publisher

CELL PRESS
DOI: 10.1016/j.celrep.2022.111322

Keywords

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Funding

  1. National Key R&D Program of China [2018YFA0108300]
  2. Natural Science Foundation of China [81870682, 81961128021, 81721003]
  3. Guangdong Provincial Key RD Programs [2018B030335001, 2018B030337001]
  4. Science and Technology Program of Guangzhou [202007030010, 202007030011]
  5. Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program [2017BT01S138]
  6. CAMS Innovation Fund for Medical Sciences [2019-I2M-5-005]
  7. Major Project on Brain Science and Brain -Like Computing of the Ministry of Science and Technology of the People's Republic of China [2021ZD0200103]
  8. China Postdoctoral Science Foundation [2019M663256, 2020M672978]

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In this study, we characterized the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch-seq. The findings provide functional and morphological annotation for different transcriptomic-defined cell types in a previously established RGC atlas. The research highlights the convergence of different modalities in defining RGC identity and identifies differentially expressed genes among RGC subtypes, which could serve as candidate marker genes for functional studies.
Retinal ganglion cells (RGCs) are the brain's gateway to the visual world. They can be classified into different types on the basis of their electrophysiological, transcriptomic, or morphological characteristics. Here, we characterize the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch sequencing (Patch-seq), providing functional and morphological annotation of many transcriptomic-defined cell types of a previously established RGC atlas. We show a convergence of different modalities in defining the RGC identity and reveal the degree of correspondence for well-characterized cell types across multi -modal data. Moreover, we complement some RGC types with detailed morphological and functional proper-ties. We also identify differentially expressed genes among ON, OFF, and ON-OFF RGCs such as Vat1l, Slitrk6, and Lmo7, providing candidate marker genes for functional studies. Our research suggests that the molecularly distinct clusters may also differ in their roles of encoding visual information.

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