Genome-wide association studies (GWAS) are crucial for understanding how genetic variations relate to diseases and traits. When integrated into a platform like Polly, the datasets offer valuable insights into diverse conditions, guide therapeutic discoveries, and promote multi-omics research.

GWAS has significantly advanced disease research by identifying specific genetic variants associated with conditions such as ulcerative colitis and age-related macular degeneration (AMD). These findings enable more precise diagnostics and personalized treatments. GWAS also sheds light on conditions like aneurysmal human aortic tissue, offering insights for prevention and therapy.

The ‘Monthly Dataset Roundup’ series introduces scRNASeq datasets that leverage the findings from GWAS studies, with the primary aim of promoting the sharing and utilization of comprehensive genetic data. In this latest edition, we are excited to present datasets enhancing our understanding of genetic variations identified through GWAS. Dive into Polly’s scRNASeq datasets to gain deeper insights into the genetic underpinnings of various traits and conditions, ultimately advancing the possibilities in personalized medicine and scientific discovery.

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Dataset 1

Intra- and inter-cellular rewiring of the human colon during ulcerative colitis .

Dataset ID: SCP259_1
‍Year of Publication: 2019
‍Total Samples: 30
‍Experiment type: Single-cell RNA Sequencing
‍Organism: Homo sapiens‍
‍Reference link: Publication

Summary:

Utilizing a multi-omics approach, GWAS has highlighted the risk alleles associated with ulcerative colitis (UC). However, the intricate cell type specificities and underlying pathways often remain enigmatic. In this study, the authors meticulously constructed an atlas encompassing 366,650 cells from the colon mucosa of 18 UC patients and 12 healthy individuals. This comprehensive analysis unveils 51 distinct subsets of epithelial, stromal, and immune cells, among which notable entities like BEST4+ enterocytes, microfold-like cells, and IL13RA2+IL11+ inflammatory fibroblasts emerge. Remarkably, these findings tie specific cell subsets, including inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and CD8+IL17+ co-expressing T cells, to disease expansion and resistance to anti-TNF treatment, forming pivotal hubs of intercellular interactions.

Many genes implicated in UC susceptibility exhibit cell type-specific expression patterns and are co-regulated within limited gene modules, hinting at a convergence upon select cell types and pathways. Leveraging this insight, we decipher the roles and functions of specific risk genes across GWAS loci, thus shedding light on their potential contributions.

Our approach leverages Polly for data visualization, enabling a clearer understanding of the intricate molecular interactions underlying UC risk alleles. Metadata tables and charts enrich the study with patient data, contextualizing cell findings. Accurate cell annotation clarifies roles. Using Polly for visualization aids understanding of UC risk allele interactions. Our platform forms a foundation to decode intricate disease mechanisms, linking genetics to precise cells and pathways and offering insights into UC and complex diseases. Ultimately, this atlas establishes a robust framework for probing complex human diseases, mapping genetic variants to precise cell types and pathways, and unraveling the intricacies of UC pathogenesis.

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Dataset 2

Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.

Dataset ID: GSE137537_GPL18573
‍Year of Publication: 2019
‍Total Samples: 6
‍Experiment type: Single-cell RNA Sequencing
‍Organism: Homo sapiens‍
‍Reference link: Publication

Summary:

GWAS has successfully pinpointed genetic variants linked to AMD, a leading cause of elderly blindness. Yet, discerning the implicated cell types has been challenging due to the genetic intricacy of the disease. To address this, the study executes high-throughput scRNA-seq on human retinas using two distinct platforms, delivering the inaugural single-cell transcriptomic atlas of the human retina. Employing a multi-resolution network analysis, it was observed that the comprehensive spectrum of major retinal cell types alongside their unique gene expression signatures.

Remarkably, the authors unearth heterogeneity within macroglia, suggesting a previously underestimated diversity in human retinal glial cells. Importantly, using GWAS-driven enrichment analysis, the identification of glia, vascular cells, and cone photoreceptors as associated with AMD risk was conducted. These observations offer an intricate depiction of the human retina's cellular landscape while showcasing how scRNA-seq, a multi-omics technique, can illuminate cell types intricately linked to complex and inflammatory genetic disorders.

In our data presentation, Polly's data visualization dynamically elucidates the intricate interplay among cell types, genetics, and susceptibility to AMD. The metadata integration provides a contextual backdrop, enhancing understanding of genetic contributions to AMD. The interactive sunburst chart visually dissects the relationships and proportions of retinal cell types linked to AMD while meticulous cell annotation clarifies their functional roles. Polly's visual tools collectively provide an accessible platform to comprehend the amalgamation of genetics, cellular dynamics, and disease susceptibility, ultimately enhancing the clarity of our findings.

Dataset 3

Single-cell transcriptome analysis reveals dynamic cell populations and differential gene expression patterns in control and aneurysmal human aortic tissue.

Dataset ID: GSE155468_GPL24676
‍Year of Publication: 2020
‍Total Samples: 11
‍Experiment type: Single-cell RNA Sequencing
‍Organism: Homo sapiens
‍‍Reference link: Publication

Summary:

Ascending thoracic aortic aneurysm (ATAA) arises from progressive aortic wall weakening, posing severe risks like aortic dissection and rupture. To deepen one's grasp of ATAA's origin, the authors used a holistic approach to characterize the cellular makeup of the ascending aortic wall and spotlight molecular changes within each cell population in human ATAA tissues. Employing cutting-edge sc-RNAseq, the study analyzed ascending aortic tissues from 11 participants, including 8 ATAA patients (4 women and 4 men) and 3 controls (2 women and 1 man). This enabled detailed categorization of cells via cluster identification using sc-RNAseq data. Comparative analysis of cell type proportions and gene expressions unveiled ATAA-related shifts.

The approach further involved integrating sc-RNAseq data with public GWAS and promoter capture Hi-C data for comprehensive insights. This approach revealed 11 major cell types in the aortic tissue, including 40 subtypes and diverse functional populations of cells like smooth muscle cells, macrophages, and T lymphocytes. ATAA tissues exhibited altered cell compositions, particularly a rise in immune cells such as T lymphocytes and pronounced mitochondrial dysfunction. Integrating sc-RNAseq and public data highlighted ERG as pivotal for maintaining aortic wall function.

The study offers a panoramic view of the ascending aortic wall's cellular data, elucidating how gene expressions shift in human ATAA tissues. Our approach, synergizing multi-omics methods facilitated by Polly's data visualizations, significantly advance our comprehension of ATAA's genesis and progression.

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Understanding the intricate connections in diverse multi-omics studies is crucial for revealing shared molecular insights. Polly revolutionizes access to "model-quality" data, ensuring validated, high-quality results. Incorporating critical metadata aligned with your research enhances data integrity through uniform processing and comprehensive annotation. Its adaptable curation engine scales to accommodate tailored metadata, cohorts, and diverse data types, fostering precision.

Polly accelerates research milestones, potentially reducing analysis time by 80% in gene and pathway analysis across indications. Its meta-analysis capabilities overcome multi-omics dataset limitations, dissect result variations, identify heterogeneity sources, and mitigate mixing risks.