A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration

Orozco, Luz D.; Owen, Leah A.; Hofmann, J; Stockwell, Amy; Tao, Jianhua; Haller, Susan; Mukundan, Vineeth T.; Clarke, Christine L.; Lund, Jessica; Sridhar, Akshayalakshmi; Mayba, Oleg; Barr, Julie L.; Zavala, Rylee A.; Graves, Elijah C.; Zhang, Charles; Husami, Nadine; Finley, Robert C.; Au, Elizabeth; Lillvis, John H.; Farkas, Michael H.; Shakoor, Akbar; Sherva, Richard; Kim, Ivana K.; Kaminker, Joshua S.; Townsend, Michael J.; Farrer, Lindsay A.; Yaspan, Brian L.; Chen, Hsu-Hsin; DeAngelis, Margaret M.

doi:10.1016/j.xgen.2023.100302

Cited by 13 publications

(20 citation statements)

References 112 publications

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“…2c), and DOK5 in DB5 bipolar cells (Fig. 2d), consistent with previous findings 21 . Utilizing paired snRNA-Seq and snATAC-Seq data, we identified putative candidates for master regulators in the different cell types using Epiregulon 24 (Fig.…”

Section: Flexibility In Multimodal Single Cell Profilingsupporting

confidence: 92%

“…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

Preprint

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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: Flexibility In Multimodal Single Cell Profilingsupporting

confidence: 92%

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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“…Systems biology provides tools to measure comprehensive representations of biological processes by measuring holistic features in the same cell. 14,15 There are many different ways to measure systems biology, each providing complementary views into cell function. Scientists have used systems biology to measure cell death complexities.…”

Section: Introductionmentioning

confidence: 99%

A morphology and secretome map of pyroptosis

Lippincott,

Tomkinson,

Bunten

et al. 2024

Preprint

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Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and healthy cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

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“…A large AMD genome-wide association study (GWAS) identified significant association at 34 loci [7] and directed us to potential pathways underlying disease mechanism(s). Studies that utilize single-cell, single-nuclei and/or bulk RNAseq are agnostic and unbiased approaches to examine gene expression, further adding value by demonstrating whether disease-associated loci are expressed in disease-affected tissue [8][9][10][11][12][13][14][15][16][17][18]. Additionally, RNA-Seq can uncover differentially spliced genes and non-coding RNAs [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…We focused the present study on tissues specifically affected by AMD, the macula of the retinal pigment epithelium/choroid (RPE)/choroid and the macula of the neural retina, between iAMD, NEO and, separately, normal condition. To address the complexity of a multi-faceted disease like AMD, we utilized a systems biology approach, as previously employed [13,[29][30][31]. In addition, motivated by previous studies showing evidence of allele-specific expression (ASE) [32] in genes associated with a risk of autism, stroke progression, Alzheimer disease and cancer [33][34][35][36][37][38], we interrogated the DNA of each donor for previously reported AMD GWAS coding variants [7].…”

Section: Introductionmentioning

confidence: 99%

Patterns of Gene Expression, Splicing, and Allele-Specific Expression Vary among Macular Tissues and Clinical Stages of Age-Related Macular Degeneration

Shwani,

Zhang,

Owen

et al. 2023

Cells

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Age-related macular degeneration (AMD) is a leading cause of blindness, and elucidating its underlying disease mechanisms is vital to the development of appropriate therapeutics. We identified differentially expressed genes (DEGs) and differentially spliced genes (DSGs) across the clinical stages of AMD in disease-affected tissue, the macular retina pigment epithelium (RPE)/choroid and the macular neural retina within the same eye. We utilized 27 deeply phenotyped donor eyes (recovered within a 6 h postmortem interval time) from Caucasian donors (60–94 years) using a standardized published protocol. Significant findings were then validated in an independent set of well-characterized donor eyes (n = 85). There was limited overlap between DEGs and DSGs, suggesting distinct mechanisms at play in AMD pathophysiology. A greater number of previously reported AMD loci overlapped with DSGs compared to DEGs between disease states, and no DEG overlap with previously reported loci was found in the macular retina between disease states. Additionally, we explored allele-specific expression (ASE) in coding regions of previously reported AMD risk loci, uncovering a significant imbalance in C3 rs2230199 and CFH rs1061170 in the macular RPE/choroid for normal eyes and intermediate AMD (iAMD), and for CFH rs1061147 in the macular RPE/choroid for normal eyes and iAMD, and separately neovascular AMD (NEO). Only significant DEGs/DSGs from the macular RPE/choroid were found to overlap between disease states. STAT1, validated between the iAMD vs. normal comparison, and AGTPBP1, BBS5, CERKL, FGFBP2, KIFC3, RORα, and ZNF292, validated between the NEO vs. normal comparison, revealed an intricate regulatory network with transcription factors and miRNAs identifying potential upstream and downstream regulators. Findings regarding the complement genes C3 and CFH suggest that coding variants at these loci may influence AMD development via an imbalance of gene expression in a tissue-specific manner. Our study provides crucial insights into the multifaceted genomic underpinnings of AMD (i.e., tissue-specific gene expression changes, potential splice variation, and allelic imbalance), which may open new avenues for AMD diagnostics and therapies specific to iAMD and NEO.

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A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration

Cited by 13 publications

References 112 publications

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

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

A morphology and secretome map of pyroptosis

Patterns of Gene Expression, Splicing, and Allele-Specific Expression Vary among Macular Tissues and Clinical Stages of Age-Related Macular Degeneration

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