Single-Cell and Spatial Multi-Omics Technologies in Human Health

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 5399

Special Issue Editors

Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
Interests: single-cell multi-omics; computational systems biology; deep learning; immuno-oncology microbiome; drug resistance

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Guest Editor
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
Interests: single-cell and spatial multi-omics; graph neural network; gene regulatory mechanism; immuno-informatics; senescent cells
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Special Issue Information

Dear Colleagues,

Recent advances in single-cell and spatial-sequencing technologies have enabled the characterization of cellular- and tissue-level heterogeneity and biological processes in complex diseases. This provides unprecedented opportunities to understand disease pathology at the single-cell level to find new diagnostic markers or new therapeutic targets. The analyses of single-cell and spatially resolved sequencing data allow mechanistic classification and development, providing a practical basis for improving the diagnosis and treatment of diseases. 

In this Special Issue, we invite you to share computational methods and laboratory innovations that enable the concurrent and integrative analyses of single-cell and spatially resolved multi-omics data. We embrace any novel statistical or deep-learning-based tools, pipelines, and databases that are developed for conquering computational challenges and linking with human health using relevant data. We would also like to showcase novel findings as a result of applications of multi-omics to address significant questions related to human health, including, but not limited to, immuno-oncology, Alzheimer’s disease, host–microbiome interactions, aging, drug resistance, cancer diagnosis, etc. Original research, databases/webservers, and review papers are welcome.

We look forward to reading your contributions to this rapidly advancing, interdisciplinary field.

Dr. Anjun Ma
Prof. Dr. Qin Ma
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • single-cell multi-omics
  • spatial multi-omics
  • bioinformatics and biomedical informatics
  • human health
  • deep-learning methods

Published Papers (1 paper)

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Review

17 pages, 1692 KiB  
Review
Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives
by Hyeongseon Jeon, Juan Xie, Yeseul Jeon, Kyeong Joo Jung, Arkobrato Gupta, Won Chang and Dongjun Chung
Biomolecules 2023, 13(2), 221; https://doi.org/10.3390/biom13020221 - 24 Jan 2023
Cited by 4 | Viewed by 4463
Abstract
Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically [...] Read more.
Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis. Full article
(This article belongs to the Special Issue Single-Cell and Spatial Multi-Omics Technologies in Human Health)
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