Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma

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Summary

To assess the transcriptomic profile of disease-specific cell populations, fibroblasts from patients with primary open-angle glaucoma (POAG) were reprogrammed into induced pluripotent stem cells (iPSCs) before being differentiated into retinal organoids and compared with those from healthy individuals. We performed single-cell RNA sequencing of a total of 247,520 cells and identified cluster-specific molecular signatures. Comparing the gene expression profile between cases and controls, we identified novel genetic associations for this blinding disease. Expression quantitative trait mapping identified a total of 4,443 significant loci across all cell types, 312 of which are specific to the retinal ganglion cell subpopulations, which ultimately degenerate in POAG. Transcriptome-wide association analysis identified genes at loci previously associated with POAG, and analysis, conditional on disease status, implicated 97 statistically significant retinal ganglion cell-specific expression quantitative trait loci. This work highlights the power of large-scale iPSC studies to uncover context-specific profiles for a genetically complex disease.

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Highlights

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Large-scale scRNA-seq study of human iPSC-derived retinal organoids
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23 subpopulations spread across 247,520 cells
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Identified 312 significant eQTLs specific to retinal ganglion cells
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Identified eQTLs associated with POAG

Abstract

Daniszewski et al. performed scRNA-seq on retinal organoids derived from 110 human iPSC lines. They identified 97 expression qualitative trait loci specific to the ganglion cell population and associated with POAG. The data are a valuable resource to improve our understanding of a genetically complex disease like glaucoma.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Author and article information

Contributors

Joseph E. Powell

Alice Pébay

Alex W. Hewitt

Journal

Journal ID (nlm-ta): Cell Genom

Journal ID (iso-abbrev): Cell Genom

Title: Cell Genomics

Publisher: Elsevier

ISSN (Electronic): 2666-979X

Publication date PMC-release: 08 June 2022

Publication date Collection: 08 June 2022

Publication date (Electronic): 08 June 2022

Volume: 2

Issue: 6

Electronic Location Identifier: 100142

Affiliations

[1 ]Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC 3010, Australia

[2 ]Department of Surgery, The University of Melbourne, Parkville, VIC 3010, Australia

[3 ]Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC 3002, Australia

[4 ]Garvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia

[5 ]QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia

[6 ]O’Brien Institute Department of St Vincent’s Institute of Medical Research, Melbourne, Fitzroy, VIC 3065, Australia

[7 ]Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Parkville, VIC 3010, Australia

[8 ]Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, SA 5042, Australia

[9 ]Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia

[10 ]Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Crawley, WA 6009, Australia

[11 ]School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7005, Australia

[12 ]UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia

Author notes

[∗ ]Corresponding author j.powell@ 123456garvan.org.au

[∗∗ ]Corresponding author apebay@ 123456unimelb.edu.au

[∗∗∗ ]Corresponding author hewitt.alex@ 123456gmail.com

[13]

These authors contributed equally

[14]

Lead contact

Article

Publisher Item ID: S2666-979X(22)00075-1 Publisher ID: 100142

DOI: 10.1016/j.xgen.2022.100142

PMC ID: 9903700

PubMed ID: 36778138

SO-VID: 8e711b90-50e8-4587-9892-44d3d85476e3

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 19 September 2020

Date revision received : 8 March 2021

Date accepted : 11 May 2022

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Retinal ganglion cell-specific genetic regulation in primary open-angle glaucoma

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Neuronal Signaling

Most cited references 113

limma powers differential expression analyses for RNA-sequencing and microarray studies

Comprehensive Integration of Single-Cell Data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

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Cited by 5

Most referenced authors 7,381