scholarly journals Critical genetic program for Drosophila imaginal disc regeneration revealed by single-cell analysis

2021 ◽  
Author(s):  
Melanie I Worley ◽  
Nicholas Everetts ◽  
Riku Yasutomi ◽  
Nir Yosef ◽  
Iswar K Hariharan
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disc Regeneration ◽  
Discernible Effect ◽  
Genes Encoding ◽  
Wing Discs ◽  
Gene Regulatory

Whether regeneration is primarily accomplished by re-activating gene regulatory networks used previously during development or by activating novel regeneration-specific transcriptional programs remains a longstanding question. Currently, most genes implicated in regeneration also function during development. Using single-cell transcriptomics in regenerating Drosophila wing discs, we identified two regeneration-specific cell populations within the blastema. They are each composed of cells that upregulate multiple genes encoding secreted proteins that promote regeneration. In this regenerative secretory zone, the transcription factor Ets21C controls the expression of multiple regeneration-promoting genes. While eliminating Ets21C function has no discernible effect on development, it severely compromises regeneration. This Ets21C-dependent gene regulatory network is also activated in blastema-like cells in tumorous discs, suggesting that pro-regenerative mechanisms can be co-opted by tumors to promote aberrant growth.

scholarly journals Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

2019 ◽  
Vol 2 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jinchu Vijay ◽  
Marie-Frédérique Gauthier ◽  
Rebecca L. Biswell ◽  
Daniel A. Louiselle ◽  
Jeffrey J. Johnston ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Human Adipose Tissue ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disease Specific

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.

Single-Cell Analysis Reveals Cellular Heterogeneity and Regulatory Networks of Hypothalamic Leptin-Receptor Cells

2020 ◽  
Author(s):  
Nefeli Kakava-Georgiadou ◽  
Jeppe Severens ◽  
Anja Jørgensen ◽  
Keith Garner ◽  
Mieneke Luijendijk ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Leptin Receptor ◽  
Single Cell Analysis ◽  
Cellular Heterogeneity ◽  
Cell Analysis ◽  
Receptor Cells

scholarly journals A single-cell expression simulator guided by gene regulatory networks

2019 ◽  
Author(s):  
Payam Dibaeinia ◽  
Saurabh Sinha
Keyword(s):  
Experimental Data ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Sets ◽  
Statistical Measures ◽  
Gene Regulatory

AbstractA common approach to benchmarking of single-cell transcriptomics tools is to generate synthetic data sets that resemble experimental data in their statistical properties. However, existing single-cell simulators do not incorporate known principles of transcription factor-gene regulatory interactions that underlie expression dynamics. Here we present SERGIO, a simulator of single-cell gene expression data that models the stochastic nature of transcription as well as linear and non-linear influences of multiple transcription factors on genes according to a user-provided gene regulatory network. SERGIO is capable of simulating any number of cell types in steady-state or cells differentiating to multiple fates according to a provided trajectory, reporting both unspliced and spliced transcript counts in single-cells. We show that data sets generated by SERGIO are comparable with experimental data in terms of multiple statistical measures. We also illustrate the use of SERGIO to benchmark several popular single-cell analysis tools, including GRN inference methods.

scholarly journals ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

2021 ◽  
Vol 53 (3) ◽  
pp. 403-411 ◽  
Author(s):  
Jeffrey M. Granja ◽  
M. Ryan Corces ◽  
Sarah E. Pierce ◽  
S. Tansu Bagdatli ◽  
Hani Choudhry ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Cell Analysis ◽  
Software Suite ◽  
Cell Clustering ◽  
Gene Regulatory ◽  
Regulatory Landscapes ◽  
Scalable Software

AbstractThe advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

Author response for "Immunocyte single cell analysis of vaccine‐induced narcolepsy"

2020 ◽  
Author(s):  
Alexander Lind ◽  
Falastin Salami ◽  
Anne‐Marie Landtblom ◽  
Lars Palm ◽  
Åke Lernmark ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Author Response ◽  
Cell Analysis
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