How Do Non-Coding Risk Variants and Cis-Regulatory Elements Contribute to Retinal Disease-associated Vision Loss?
The growing global incidence of retinal diseases and the associated loss of vision, thanks (in part) to the increase in the aged population and the prevalence of diabetes, requires the rapid development of effective strategies to improve detection, prevention, and treatment approaches. The genetic component of retinal diseases mainly involves risk variants residing in non-coding regions of the genome (Wang et al.), which may impact disease development by altering how cis-regulatory elements (CREs, which include gene enhancers) regulate gene expression in retinal cells (Preissl et al., Gusev et al., and Finucane et al.). Integrating single-cell analytical technologies with data from genome-wide association studies has supported the comprehensive annotation of CREs and their target genes and the association of non-coding risk variants to specific regulatory sequences in specific cells. Such approaches offer the means to decipher the complex regulatory mechanisms present in the single cells that make up complex tissues and better understand disease development.
In a recent BioRxiv preprint article, researchers from the laboratories of Bing Ren and Radha Ayyagari (UC San Diego) comprehensively characterized the epigenome and 3D chromatin architecture of retinal cells isolated from fresh post-mortem tissues from three human donors aged 20-40 (Yuan et al.). Specifically, they employed single-nucleus multiome (snATAC-seq/snRNA-seq) and single-nucleus methyl-3C sequencing (snm3C-seq) (Li et al. and Tian et al.) to profile gene expression, chromatin accessibility, DNA methylation, and chromatin conformation in over 58,000 retinal cells.
Employing these analytical tools supports the simultaneous analysis of chromatin accessibility and transcriptomic profiles/DNA methylation and chromatin conformation profiles in the same cell/nucleus, thereby increasing the robustness of the generated datasets (as compared to the integration of datasets from individual assays that are not carried out on the same cells). In addition to the above-mentioned techniques, parallel analysis of individual cells for RNA expression and DNA from targeted tagmentation by sequencing or "Paired-Tag" from Epigenome Technologies generates joint epigenetic and gene expression profiles at the single-cell resolution and detects histone modifications and RNA transcripts in individual nuclei with an efficiency comparable to single-nucleus RNA-seq/ChIP-seq assays. Applying Paired-Tag technology may enable researchers to take huge leaps forward in our understanding of gene regulatory mechanisms and significantly improve disease management, such as those associated with vision loss.
Here, the findings from this "eye-opening" new study report single-cell transcriptome, epigenome, and 3D genome atlases of retina cells and provide the basis for a better understanding of the complex regulatory mechanisms present in retinal cells, how retinal diseases may arise, and how we may battle vision loss.
Creating, Integrating, and Analyzing Multi-omic Atlases of Retinal Cells
Yuan et al. conducted integrated single-cell multiomics analyses to investigate gene expression, chromatin accessibility, DNA methylation, and 3D chromatin architecture in the human retina, macula, and retinal pigment epithelium (RPE)/choroid, which offers a more holistic view of retinal biology and regulatory mechanisms. Applying freshly collected retina tissues from young donors minimized post-mortem artifacts and provided robustness to the findings; meanwhile, leveraging data across 23 cell subtypes allowed the development of a resource vital for studying cell type-specific gene regulation of human retinal diseases.
They identified approximately 420,000 unique candidate (c)CREs, defined their cell-type-specific activity and target genes, and characterized their chromatin states; furthermore, an extensive list of candidate transcription factors and motifs (particularly those enriched in rare cell populations such as the RPE) laid the foundation for reconstructing detailed gene-regulatory networks and conducting targeted studies on retinal cell function and disease mechanisms. This first report of cell-type-specific DNA single-cell methylation profiles in the human retina also represents a valuable resource that can link differentially-methylated regions with pairs of interacting cCREs and genes and disease-related loci. The team also leveraged genome-wide association studies data to identify cell types relevant to vision loss-associated diseases and determined likely causal single nucleotide polymorphisms for important vision loss-associated diseases (age-related macular degeneration and macular telangiectasia).
A subsequent comparative analysis of chromatin landscapes between human and mouse retina cells revealed evolutionarily conserved and evolutionarily divergent retinal gene-regulatory programs, uncovering the rapid turnover of gene regulatory elements during evolution; of note, these data also provide a framework to assess the utility of mouse models for human retinal diseases.
Finally, the team leveraged recent advances in deep-learning techniques to develop a sequence-based deep neural network model to predict the regulatory functions of non-coding risk variants of retina diseases; they validated these predictions via CRISPR editing in an immortalized RPE cell line, which helped to uncover the genetic underpinnings of eye disease, highlighting the role of conserved and human-specific non-coding regulatory elements in polygenic traits.
Conclusions and the Next Steps?
The integrated approach described in this study represents an advance in our understanding of retinal diseases; the description of single-cell transcriptome, epigenome, and 3D genome atlases of retina cells may transform patient care in the future by supporting the development of more personalized, genetically informed therapies.
The authors hope to expand their analyses further than the three individuals of varying ages, which may make their findings more generalizable across broader demographic groups. Indeed, expanding sample diversity and size in subsequent studies remains crucial to improving our understanding of chromatin landscape variability across populations. They also note that integrating single-cell multiomics with spatial transcriptomics will aid in identifying rare cell types and elucidating complex gene-regulatory networks, further enhancing our ability to pinpoint how genetic variants influence disease phenotypes.
Future studies in this area may be supported by single-cell technologies such as Paired-tag - a complementary analytic platform that creates joint epigenetic and gene expression profiles at single-cell resolution and detects histone modifications and RNA transcripts in individual nuclei. This huge advance was first developed by a team guided by Bing Ren, one of the lead authors of this new study; now, Epigenome Technologies offers optimized Paired-Tag kits and services to researchers in the epigenetics field under an exclusive license from the Ludwig Institute for Cancer Research.
For more on how the development of single-cell multi-omic atlases of retinal cells has transformed our understanding of cell-specific regulatory mechanisms and vision loss, see bioRxiv, December 2024.
By Stuart P. Atkinson
Comentarios