Applications of single-cell genomics and computational strategies to study common disease and population-level variation

Auerbach, Benjamin D.; Hu, Jian; Reilly, Muredach P; Li, Mingyao

doi:10.1101/gr.275430.121

Cited by 15 publications

(13 citation statements)

References 178 publications

(190 reference statements)

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“…Singlecell genomics allows the accurate detection of genomic variants, clonal decomposition, and DNA replication states in single cells (eg, genetic mosaicism). 15 Through single-cell whole-genome sequencing of wild-type and mutational states of individual disease cells, cell-to-cell structural variation that contributes to the origins of phenotypic and evolutionary diversity can be discovered. 16…”

Section: Genomicsmentioning

confidence: 99%

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Wang

Maron

Loscalzo

2023

ATVB

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Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

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Section: Genomicsmentioning

confidence: 99%

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Wang

Maron

Loscalzo

2023

ATVB

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“…For instance, while environmental exposure may affect only a small proportion of cells within an organism, it can still instigate significant health changes that might be overlooked when evaluated at the population level. 5 Single-cell exposomics offers significant potential because it provides insights into how individual cells respond to environmental stimuli, aiding in the understanding of the initiation, progression, or prevention of diseases. The cells' interactions with their environment form the basis for understanding the organism's overall health and disease state.…”

Section: Introductionmentioning

confidence: 99%

Exploring Single-Cell Exposomics by Mass Spectrometry

Gao

2023

Environ. Sci. Technol.

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Single-cell exposomics, a revolutionary approach that investigates cell−environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

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“…It is also thought that the origin of a disease may be sourced to a subpopulation of cells or perhaps even a single cell. While this claim remains under debate, it emphasizes the importance of being able to confidently capture rare cell populations for clinical applications of scRNA-seq [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

Watson

Mora

Fard

et al. 2022

Briefings in Bioinformatics

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Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the ‘best-performing’ metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data.

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Applications of single-cell genomics and computational strategies to study common disease and population-level variation

Cited by 15 publications

References 178 publications

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Exploring Single-Cell Exposomics by Mass Spectrometry

How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

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