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Single Cell Omics
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Single Cell Omics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 24826

Special Issue Editor

Prof. Dr. Stefan Thor

E-Mail Website
Guest Editor
Developmental Biology, School of Biomedical Sciences, Skerman Bldg (65), The University of Queensland Brisbane Qld 4072, Australia
Interests: brain development; neurodevelopmental disorders; gene regulation; epigenetics

Special Issue Information

Dear Colleagues,

Single cell analysis is revolutionising many areas of modern biology and medicine. A number of technologies have been developed during recent years for analysis of the DNA, RNA, protein, or epigenomic content of single cells. These analysis methods follow two basic principles: the analysis of dissociated single cells or the in situ analysis of cells. These experimental advances have been accompanied by intense activity in the bioinformatics area, and a large number of computational tools have been developed with which to analyse single cells omics. One particularly dynamic area has been the development of computational tools that help determine lineage trajectories. Both the experimental and computational efforts are furthermore being aided by the power of genetic barcoding, which has also been developed in a multitude of “flavours”. This Special Issue aims to highlight recent advances in both the experimental and computational single cell omics space, and discuss how this helps unravel hitherto unchartered areas of biology and medicine.

Prof. Stefan Thor
Guest Editor

Manuscript Submission Information

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Keywords

  • single-cell transcriptomics
  • droplet transcriptomics
  • spatial transcriptomics
  • lineage trajectories
  • genetic barcoding
  • computational tools for single-cell transcriptomics

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Published Papers (4 papers)

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Research

16 pages, 4835 KiB  
Article
ExpressHeart: Web Portal to Visualize Transcriptome Profiles of Non-Cardiomyocyte Cells
by Gang Li, Changfei Luan, Yanhan Dong, Yifang Xie, Scott C. Zentz, Rob Zelt, Jeff Roach, Jiandong Liu, Li Qian, Yun Li and Yuchen Yang
Int. J. Mol. Sci. 2021, 22(16), 8943; https://doi.org/10.3390/ijms22168943 - 19 Aug 2021
Cited by 3 | Viewed by 3696
Abstract
Unveiling the molecular features in the heart is essential for the study of heart diseases. Non-cardiomyocytes (nonCMs) play critical roles in providing structural and mechanical support to the working myocardium. There is an increasing amount of single-cell RNA-sequencing (scRNA-seq) data characterizing the transcriptomic [...] Read more.
Unveiling the molecular features in the heart is essential for the study of heart diseases. Non-cardiomyocytes (nonCMs) play critical roles in providing structural and mechanical support to the working myocardium. There is an increasing amount of single-cell RNA-sequencing (scRNA-seq) data characterizing the transcriptomic profiles of nonCM cells. However, no tool allows researchers to easily access the information. Thus, in this study, we develop an open-access web portal, ExpressHeart, to visualize scRNA-seq data of nonCMs from five laboratories encompassing three species. ExpressHeart enables comprehensive visualization of major cell types and subtypes in each study; visualizes gene expression in each cell type/subtype in various ways; and facilitates identifying cell-type-specific and species-specific marker genes. ExpressHeart also provides an interface to directly combine information across datasets, for example, generating lists of high confidence DEGs by taking the intersection across different datasets. Moreover, ExpressHeart performs comparisons across datasets. We show that some homolog genes (e.g., Mmp14 in mice and mmp14b in zebrafish) are expressed in different cell types between mice and zebrafish, suggesting different functions across species. We expect ExpressHeart to serve as a valuable portal for investigators, shedding light on the roles of genes on heart development in nonCM cells. Full article
(This article belongs to the Special Issue Single Cell Omics)
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23 pages, 5363 KiB  
Article
Cigarette Smoke Specifically Affects Small Airway Epithelial Cell Populations and Triggers the Expansion of Inflammatory and Squamous Differentiation Associated Basal Cells
by Christian T. Wohnhaas, Julia A. Gindele, Tobias Kiechle, Yang Shen, Germán G. Leparc, Birgit Stierstorfer, Heiko Stahl, Florian Gantner, Coralie Viollet, Jürgen Schymeinsky and Patrick Baum
Int. J. Mol. Sci. 2021, 22(14), 7646; https://doi.org/10.3390/ijms22147646 - 16 Jul 2021
Cited by 20 | Viewed by 9338
Abstract
Smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and causes remodeling of the small airways. However, the exact smoke-induced effects on the different types of small airway epithelial cells (SAECs) are poorly understood. Here, using air–liquid interface (ALI) cultures, [...] Read more.
Smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and causes remodeling of the small airways. However, the exact smoke-induced effects on the different types of small airway epithelial cells (SAECs) are poorly understood. Here, using air–liquid interface (ALI) cultures, single-cell RNA-sequencing reveals previously unrecognized transcriptional heterogeneity within the small airway epithelium and cell type-specific effects upon acute and chronic cigarette smoke exposure. Smoke triggers detoxification and inflammatory responses and aberrantly activates and alters basal cell differentiation. This results in an increase of inflammatory basal-to-secretory cell intermediates and, particularly after chronic smoke exposure, a massive expansion of a rare inflammatory and squamous metaplasia associated KRT6A+ basal cell state and an altered secretory cell landscape. ALI cultures originating from healthy non-smokers and COPD smokers show similar responses to cigarette smoke exposure, although an increased pro-inflammatory profile is conserved in the latter. Taken together, the in vitro models provide high-resolution insights into the smoke-induced remodeling of the small airways resembling the pathological processes in COPD airways. The data may also help to better understand other lung diseases including COVID-19, as the data reflect the smoke-dependent variable induction of SARS-CoV-2 entry factors across SAEC populations. Full article
(This article belongs to the Special Issue Single Cell Omics)
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13 pages, 2474 KiB  
Article
Construction of Whole Genomes from Scaffolds Using Single Cell Strand-Seq Data
by Mark Hills, Ester Falconer, Kieran O’Neill, Ashley D. Sanders, Kerstin Howe, Victor Guryev and Peter M. Lansdorp
Int. J. Mol. Sci. 2021, 22(7), 3617; https://doi.org/10.3390/ijms22073617 - 31 Mar 2021
Cited by 11 | Viewed by 4333
Abstract
Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments [...] Read more.
Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments such as contigs and scaffolds in relation to each other is a critical and often challenging step in the construction of robust genome references. We previously identified misoriented regions in the mouse and human reference assemblies using Strand-seq, a single cell sequencing technique that preserves DNA directionality Here we demonstrate the ability of Strand-seq to build and correct full-length chromosomes by identifying which scaffolds belong to the same chromosome and determining their correct order and orientation, without the need for overlapping sequences. We demonstrate that Strand-seq exquisitely maps assembly fragments into large related groups and chromosome-sized clusters without using new assembly data. Using template strand inheritance as a bi-allelic marker, we employ genetic mapping principles to cluster scaffolds that are derived from the same chromosome and order them within the chromosome based solely on directionality of DNA strand inheritance. We prove the utility of our approach by generating improved genome assemblies for several model organisms including the ferret, pig, Xenopus, zebrafish, Tasmanian devil and the Guinea pig. Full article
(This article belongs to the Special Issue Single Cell Omics)
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19 pages, 3971 KiB  
Article
DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
by Salim Ghannoum, Waldir Leoncio Netto, Damiano Fantini, Benjamin Ragan-Kelley, Amirabbas Parizadeh, Emma Jonasson, Anders Ståhlberg, Hesso Farhan and Alvaro Köhn-Luque
Int. J. Mol. Sci. 2021, 22(3), 1399; https://doi.org/10.3390/ijms22031399 - 30 Jan 2021
Cited by 4 | Viewed by 6084
Abstract
The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in [...] Read more.
The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations. Full article
(This article belongs to the Special Issue Single Cell Omics)
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