June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Spatial single cell atlas of the mouse retina using MERFISH
Author Affiliations & Notes
  • Salma Ferdous
    Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Jongsu Choi
    Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Jin Li
    Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Rui Chen
    Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Footnotes
    Commercial Relationships   Salma Ferdous None; Jongsu Choi None; Jin Li None; Rui Chen None
  • Footnotes
    Support  T32 NLM and R01EY028970
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2317. doi:
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    • Get Citation

      Salma Ferdous, Jongsu Choi, Jin Li, Rui Chen; Spatial single cell atlas of the mouse retina using MERFISH. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2317.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : With the advance in single-cell omics technologies in the last decade, studies have identified more than 100 different subtypes of retinal cells in the mouse retina. One of the major drawbacks in the current technologies is that they require the dissociation of the tissue, resulting in the loss of spatial information. The purpose of this study is to establish the first single cell spatial atlas of the mouse retina using spatial transcriptome technology.

Methods : To generate the spatial atlas of the mouse retina, we performed single cell spatial transcriptomics analysis on the wild type C57Bl/6J mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). Based on single cell RNA-seq (scRNA-seq) data from the mouse retina, probes against a panel of 368 cell subtype marker genes were designed and synthesized. To achieve accurate cell segmentation in the highly packed retina, a set of oligo-conjugated antibodies specific to a cell membrane protein was co-stained with MEFISH probes. Deep-learning segmentation algorithms were then used to identify cell boundaries and assign transcripts to single cells. Using single-cell analysis tools such as scVI, tangram, and Giotto, cell type annotation and further downstream spatial analysis were performed.

Results : Six MERFISH experiments each containing 4-9 tissue sections were performed to generate spatial transcriptomic profiles of ~200,000 cells in total. By leveraging scRNA-seq data through data co-embedding, all major cell types and 120 cell subtypes in the retina have been identified. Investigation of the C57Bl/6J single cell spatial atlas revealed that a group of 8 amacrine cell subtypes are mis-localized in the ganglion cell layer, including the previously reported starburst amacrine cells. Spatial proximity analysis has further identified cell subtype pairs that exhibit spatial interaction, such as bipolar cell subtypes 1A and 1B (BC1A and BC1B). Lastly, co-embedding with scRNA-seq data was used to impute gene expression of MERFISH cells, generating spatial expression pattern of the entire transcriptome.

Conclusions : In short, we have generated the first comprehensive spatial single cell reference map of the mouse retina, an essential step toward gaining a comprehensive understanding of the mechanism of retinal function.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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