scholarly journals Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulator ◽  
Cell Analysis ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

scholarly journals Effects of poor sleep on the immune cell landscape as assessed by single-cell analysis

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiuxing Liu ◽  
Binyao Chen ◽  
Zhaohao Huang ◽  
Runping Duan ◽  
He Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Tumor Development ◽  
Public Health Issue ◽  
Poor Sleep ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

AbstractPoor sleep has become an important public health issue. With loss of sleep durations, poor sleep has been linked to the increased risks for diseases. Here we employed mass cytometry and single-cell RNA sequencing to obtain a comprehensive human immune cells landscape in the context of poor sleep, which was analyzed in the context of subset composition, gene signatures, enriched pathways, transcriptional regulatory networks, and intercellular interactions. Participants subjected to staying up had increased T and plasma cell frequency, along with upregulated autoimmune-related markers and pathways in CD4+ T and B cells. Additionally, staying up reduced the differentiation and immune activity of cytotoxic cells, indicative of a predisposition to infection and tumor development. Finally, staying up influenced myeloid subsets distribution and induced inflammation development and cellular senescence. These findings could potentially give high-dimensional and advanced insights for understanding the cellular and molecular mechanisms of pathologic conditions related to poor sleep.

scholarly journals Single-cell Sequencing Reveals Brain/Spinal Cord Oligodendrocyte Precursor Heterogeneity and Requirement for mTOR in Cholesterol Biosynthesis and Myelin Maintenance

2020 ◽  
Author(s):  
Luipa Khandker ◽  
Marisa A. Jeffries ◽  
Yun-Juan Chang ◽  
Marie L. Mather ◽  
Jennifer N. Bourne ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cholesterol Biosynthesis ◽  
Adult Brain ◽  
Cell Analysis ◽  
Sequencing Studies ◽  
Oligodendrocyte Precursor ◽  
Brain And Spinal Cord ◽  
The Brain

AbstractBrain and spinal cord oligodendroglia have distinct functional characteristics, and cell autonomous loss of individual genes can result in different regional phenotypes. However, sequencing studies to date have not revealed distinctions between brain and spinal cord oligodendroglia. Using single-cell analysis of oligodendroglia during myelination, we demonstrate that brain and spinal cord precursors are transcriptionally distinct, defined predominantly by cholesterol biosynthesis. We further identify mechanistic target of rapamycin (mTOR) as a major regulator promoting cholesterol biosynthesis in oligodendroglia. Oligodendroglial-specific loss of mTOR compromises cholesterol biosynthesis in both the brain and spinal cord. Importantly, mTOR loss has a greater impact on cholesterol biosynthesis in spinal cord oligodendroglia that corresponds with more pronounced developmental deficits. However, loss of mTOR in brain oligodendroglia ultimately results in oligodendrocyte death, spontaneous demyelination, and impaired axonal function, demonstrating that mTOR is required for myelin maintenance in the adult brain.

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Single-cell analysis identifies a key role for Hhip in murine coronal suture development

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Greg Holmes ◽  
Ana S. Gonzalez-Reiche ◽  
Madrikha Saturne ◽  
Susan M. Motch Perrine ◽  
Xianxiao Zhou ◽  
...  
Keyword(s):  
Single Cell ◽  
Hedgehog Signaling ◽  
Bone Growth ◽  
Single Cell Analysis ◽  
Coronal Suture ◽  
Cell Analysis ◽  
Rna Seq ◽  
Wild Type ◽  
Calvarial Bone ◽  
Osteogenic Cells

AbstractCraniofacial development depends on formation and maintenance of sutures between bones of the skull. In sutures, growth occurs at osteogenic fronts along the edge of each bone, and suture mesenchyme separates adjacent bones. Here, we perform single-cell RNA-seq analysis of the embryonic, wild type murine coronal suture to define its population structure. Seven populations at E16.5 and nine at E18.5 comprise the suture mesenchyme, osteogenic cells, and associated populations. Expression of Hhip, an inhibitor of hedgehog signaling, marks a mesenchymal population distinct from those of other neurocranial sutures. Tracing of the neonatal Hhip-expressing population shows that descendant cells persist in the coronal suture and contribute to calvarial bone growth. In Hhip−/− coronal sutures at E18.5, the osteogenic fronts are closely apposed and the suture mesenchyme is depleted with increased hedgehog signaling compared to those of the wild type. Collectively, these data demonstrate that Hhip is required for normal coronal suture development.

scholarly journals RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

2019 ◽  
Vol 21 (1) ◽  
pp. 167 ◽  
Author(s):  
Isiaka Ibrahim Muhammad ◽  
Sze Ling Kong ◽  
Siti Nor Akmar Abdullah ◽  
Umaiyal Munusamy
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Networks ◽  
Data Sets ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Specific Biological Process ◽  
Transcriptional Regulatory ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Sequencing Platforms

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

Breast Cancer Transcriptional Regulatory Network Reprogramming by using the CRISPR/Cas9 system: An Oncogenetics Perspective

2021 ◽  
Vol 21 ◽  
Author(s):  
Desh Deepak Singh ◽  
Ravi Verma ◽  
Subhash K. Tripathi ◽  
Rajnish Sahu ◽  
Poonam Trivedi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Treatment Options ◽  
Diagnostic Tools ◽  
Transcriptional Regulatory Networks ◽  
Development Of Resistance ◽  
Transcriptional Regulatory ◽  
Network Reprogramming

: Breast cancer (BC) is the second most commonly diagnosed cancer in the world. BC develops due to dysregulation of transcriptional profiles, substantial interpatient variations, genetic mutations, and dysregulation of signaling pathways in breast cells. These events are regulated by many genes such as BRCA1/2, PTEN, TP53, mTOR, TERT, AKT, PI3K and others genes. Treatment options for BC remain a hurdle, which warrants a comprehensive understanding that establishes an interlinking connection between these genes in BC tumorigenesis. Consequently, there is an increasing demand for alternative treatment approaches and the design of more effective treatments. In this regard, it is crucial to build the corresponding transcriptional regulatory networks governing BC by using advanced genetic tools and techniques. In the past, several molecular editing technologies have been used to edit genes with several limitations. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated Protein 9 (CRISPR/Cas9) recently received a profound attention due to its potential in biomedical and therapeutic applications. Here, we review the role of various molecular signalling pathways dysregulated in BC development such as PTEN/PI3K/AKT/mTOR as well as BRCA1/BRCA2/TP53/TERT and their interplay between the related gene networks in BC initiation, progression and development of resistance against available targeted therapeutic agents. Use of CRISPR/Cas9 gene-editing technology to generate BC gene-specific transgenic cell lines and animal models to decipher their role and interactions with other gene products has been employed successfully. Moreover, the significance of using CRISPR/Cas9 technology to develop early BC diagnostic tools and treatments is discussed here.

scholarly journals Single Cell Sequencing Reveals Early PGC-like Intermediates During Mouse Primed to Naïve Transition

2021 ◽  
Author(s):  
Jing Liu ◽  
Shengyong Yu ◽  
Chunhua Zhou ◽  
Jiangping He ◽  
Xingnan Huang ◽  
...  
Keyword(s):  
Germ Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cell Analysis ◽  
Primordial Germ Cell ◽  
Model System ◽  
Cell Analysis ◽  
Rna Seq ◽  
Single Cell Sequencing

Abstract Single cell analysis provides clarity unattainable with bulk approaches. Here we apply single cell RNA-seq to a newly established BMP4 induced mouse primed to naive transition (Bi-PNT) system and show that the reset is not a direct reversal of cell fate but through developmental intermediates. We first show that mEpiSCs bifurcate into c-Kit+ naïve and c-Kit- placenta-like cells, among which, the naive branch undergoes further transition through a primordial germ cell-like cells (PGCLCs) intermediate capable of spermatogenesis in vivo. Indeed, deficiency of Prdm1/Blimp1, the key regulator for PGC specification, blocks the induction of PGCLCs and naïve cells. Instead, Gata2 knockout arrests placenta-like fate, but facilitates the generation of PGCLCs. Our results not only reveal a newly cell fate dynamics between primed and naive states at single-cell resolution, but also provide a model system to explore mechanisms involved in regaining germline competence from primed pluripotency.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

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