A multi-omics atlas of the human retina at single-cell resolution

Liang, Qingnan; Cheng, Xuesen; Wang, Jun; Owen, Leah A.; Shakoor, Akbar; Lillvis, John L.; Zhang, Charles; Farkas, Michael H.; Kim, Ivana K.; Li, Yumei; DeAngelis, Margaret M.; Chen, Rui

doi:10.1016/j.xgen.2023.100298

Cited by 19 publications

(33 citation statements)

References 56 publications

(84 reference statements)

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“…Effectively capturing types and states of all cellular subtypes is key to understanding health and disease of the eye; however, the overwhelming presence of rod photoreceptors (around 60% of cells) often impedes the efficient recovery of other cells, such as retinal ganglion cells (less than 1%). Some techniques can be employed for depleting rod cells, however this adds experimental complexity and may unintentionally affect representation of other cell types, for example depletion of bipolar cells 20 . Utilizing a method such as OAK to perform paired snRNA-Seq and snATAC-Seq on retinal samples enables generation of large scale high-resolution data from these precious samples, which are obtained only from careful dissection of post-mortem donations.…”

Section: Resultsmentioning

confidence: 99%

“…2a). With a single donor sample, we obtained thousands of rod, cone, Müller glia, amacrine and bipolar cells, as well as hundreds of horizontal cells, astrocytes and retinal ganglion cells, representing the major cell types of the retina [20][21][22] . We used the snRNA-Seq annotations with the OAK snATAC-Seq data (Extended Data Fig.…”

Section: Flexibility In Multimodal Single Cell Profilingmentioning

confidence: 99%

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Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Wu,

Bennett,

et al. 2024

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Multiomic profiling of single cells by sequencing is a powerful technique for investigating cellular diversity in complex biological systems. Although the existing droplet-based microfluidic methods have advanced single-cell sequencing, they produce a plethora of cell-free droplets and underutilize barcoding capacities due to their low cell concentration prerequisites. Meanwhile, combinatorial indexing on microplates can index cells in a more effective way; however, it requires time-consuming and laborious protocols involving multiple splitting and pooling steps. Addressing these constraints, we have developed "Overloading And unpacKing" (OAK). With reduced labor intensity, OAK can provide cost-effective multiomic profiling for hundreds of thousands of cells, offering detection sensitivity on par with commercial droplet-based methods. To demonstrate OAK's versatility, we conducted single-cell RNA sequencing (scRNA-Seq) as well as joint single-nucleus RNA sequencing (snRNA-Seq) and single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-Seq) using cell lines. We further showcased OAK's performance on more complex samples, including in vitro differentiated bronchial epithelial cells and primary retinal tissues. Finally, we examined transcriptomic responses of 408,000 melanoma cells across around 1,000 starting lineages over a 90-day treatment with a RAF inhibitor, belvarafenib. We discovered a rare cell population (0.12%) that underwent a sequence of transcriptomic changes, resulting in belvarafenib resistance. Ultra-high throughput, broad compatibility with diverse molecular modalities, high detection sensitivity, and simplified experimental procedures distinguish OAK from previous methods, and render OAK a powerful tool for large-scale analysis of molecular signatures, even for rare cells.

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Section: Resultsmentioning

confidence: 99%

Section: Flexibility In Multimodal Single Cell Profilingmentioning

confidence: 99%

Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Wu,

Bennett,

et al. 2024

Preprint

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“…To obtain a comprehensive atlas of cell types in the human retina, we integrated seven publicly available datasets 7, 15, 19–23 with newly generated unpublished data (Fig. 1A-B).…”

Section: Resultsmentioning

confidence: 99%

“…Over 422,000 bipolar single nuclei included in the current atlas can be divided into 14 cell types based on marker genes 7, 9 (Fig. 2A).…”

Section: Resultsmentioning

confidence: 99%

“…Recent studies have generated atlases of the anterior and posterior segments of the human eye 5, 6 . Other studies have generated retinal atlases from multiple species, including mouse, chick, macaque, and human 7–15 . The goal of the work reported here is to augment previous datasets with additional donors, cells, and methods to generate the first version of a comprehensive cell atlas of the human retina.…”

Section: Introductionmentioning

confidence: 99%

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Integrated multi-omics single cell atlas of the human retina

Li,

Wang,

Ibarra

et al. 2023

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Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types. Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.

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