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Integrative Multi-Omics, Single-Cell and Spatial Approaches to Study Complex Diseases
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Integrative Multi-Omics, Single-Cell and Spatial Approaches to Study Complex Diseases

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (5 December 2022) | Viewed by 13414

Special Issue Editors

Dr. Clint L. Miller

E-Mail Website
Guest Editor
Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
Interests: cardiovascular diseases; human genetics; functional genomics; single-cell sequencing; bioinformatics; drug discovery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xia Yang

E-Mail Website
Guest Editor
Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
Interests: integrative multiomics; systems biology; complex diseases; cardiometabolic diseases; brain disorders

Special Issue Information

Dear Colleagues,

We are reaching out to request your participation in our Special Issue: “Integrative Multi-Omics, Single-Cell and Spatial Approaches to Study Complex Diseases” in Genes. 

Recent advances in genomic sequencing technologies have led to an explosion in multi-omics datasets (e.g., genomics, transcriptomics, epigenomics, metabolomics, proteomics, microbiomics, etc.), which has been accompanied by the development of novel computational genomics tools to analyse these data and perform integrative analysis. Together, these approaches have rapidly expanded the number of discoveries for complex diseases, such as  cancer, cardiometabolic disease, neurological diseases, and immune diseases. Many of these new methods have been adapted to the single-cell level, which has revealed new challenges to overcome related to data heterogeneity, signal-to-noise, sparsity, scalability, and validation. More recently, in situ imaging and perturbation methods have enabled spatial and temporal molecular profiling at single-cell and single-molecule level in disease-relevant tissues and cells.

In this Special Issue, we hope to bring together a diverse set of experts in genomics from multi-disciplinary backgrounds to share their collective expertise in a broad range of topics related to multi-omics profiling and data integration for various complex human diseases. We expect the themes to cover various topics, such as bulk and single-cell multi-omics and multi-modal data acquisition and analyses (e.g., scRNA-seq, scATAC-seq, CITE-seq), other epigenomic, proteomic and spatial genomics in both healthy and diseased tissues/cells. We will highlight new statistical and machine learning approaches for -omics data harmonization, multi-ancestry analyses, quantitative trait locus mapping, GWAS variant prioritization, as well as web application development for end-to-end analyses, data sharing, visualization, annotation, target validation and precision medicine.

We welcome applications to a broad range of cell/tissue and disease areas, involving either publicly available or custom datasets. We also welcome multiple manuscript formats, including original research articles, reviews or mini-reviews, opinions, hypotheses, or theories.

For example:

https://www.mdpi.com/journal/genes/special_issues/Genetics_Cardiovascular_Metabolism

Dr. Clint L. Miller
Prof. Dr. Xia Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-omics
  • single-cell
  • complex diseases
  • systems biology
  • machine learning
  • drug discovery

Published Papers (2 papers)

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Review

12 pages, 1542 KiB  
Review
The Power of Single-Cell RNA Sequencing in eQTL Discovery
by Maleeha Maria, Negar Pouyanfar, Tiit Örd and Minna U. Kaikkonen
Genes 2022, 13(3), 502; https://doi.org/10.3390/genes13030502 - 12 Mar 2022
Cited by 7 | Viewed by 7803
Abstract
Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing [...] Read more.
Genome-wide association studies have successfully mapped thousands of loci associated with complex traits. During the last decade, functional genomics approaches combining genotype information with bulk RNA-sequencing data have identified genes regulated by GWAS loci through expression quantitative trait locus (eQTL) analysis. Single-cell RNA-Sequencing (scRNA-Seq) technologies have created new exciting opportunities for spatiotemporal assessment of changes in gene expression at the single-cell level in complex and inherited conditions. A growing number of studies have demonstrated the power of scRNA-Seq in eQTL mapping across different cell types, developmental stages and stimuli that could be obscured when using bulk RNA-Seq methods. In this review, we outline the methodological principles, advantages, limitations and the future experimental and analytical considerations of single-cell eQTL studies. We look forward to the explosion of single-cell eQTL studies applied to large-scale population genetics to take us one step closer to understanding the molecular mechanisms of disease. Full article
(This article belongs to the Special Issue Integrative Multi-Omics, Single-Cell and Spatial Approaches to Study Complex Diseases)
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25 pages, 2250 KiB  
Review
Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models
by Yanning Zuo, Don Wei, Carissa Zhu, Ormina Naveed, Weizhe Hong and Xia Yang
Genes 2021, 12(7), 1101; https://doi.org/10.3390/genes12071101 - 20 Jul 2021
Cited by 6 | Viewed by 4962
Abstract
Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There [...] Read more.
Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches. Full article
(This article belongs to the Special Issue Integrative Multi-Omics, Single-Cell and Spatial Approaches to Study Complex Diseases)
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