scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

2019 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Reconstruction ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals A single cell RNA sequencing resource for early sea urchin development

Development ◽  
2020 ◽  
Vol 147 (17) ◽  
pp. dev191528 ◽  
Author(s):  
Stephany Foster ◽  
Nathalie Oulhen ◽  
Gary Wessel
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Germ Cells ◽  
Regulatory Network ◽  
Germ Line ◽  
Primordial Germ Cells ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

ABSTRACTIdentifying cell states during development from their mRNA profiles provides insight into their gene regulatory network. Here, we leverage the sea urchin embryo for its well-established gene regulatory network to interrogate the embryo using single cell RNA sequencing. We tested eight developmental stages in Strongylocentrotus purpuratus, from the eight-cell stage to late in gastrulation. We used these datasets to parse out 22 major cell states of the embryo, focusing on key transition stages for cell type specification of each germ layer. Subclustering of these major embryonic domains revealed over 50 cell states with distinct transcript profiles. Furthermore, we identified the transcript profile of two cell states expressing germ cell factors, one we conclude represents the primordial germ cells and the other state is transiently present during gastrulation. We hypothesize that these cells of the Veg2 tier of the early embryo represent a lineage that converts to the germ line when the primordial germ cells are deleted. This broad resource will hopefully enable the community to identify other cell states and genes of interest to expose the underpinning of developmental mechanisms.

scholarly journals Single-Cell RNA Sequencing Analysis of the Heterogeneity in Gene Regulatory Networks in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui-Qi Wang ◽  
Wei Zhao ◽  
Hai-Kui Yang ◽  
Jia-Mei Dong ◽  
Wei-Jie Lin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Communication Analysis ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Colorectal cancer (CRC) manifests as gastrointestinal tumors with high intratumoral heterogeneity. Recent studies have demonstrated that CRC may consist of tumor cells with different consensus molecular subtypes (CMS). The advancements in single-cell RNA sequencing have facilitated the development of gene regulatory networks to decode key regulators for specific cell types. Herein, we comprehensively analyzed the CMS of CRC patients by using single-cell RNA-sequencing data. CMS for all malignant cells were assigned using CMScaller. Gene set variation analysis showed pathway activity differences consistent with those reported in previous studies. Cell–cell communication analysis confirmed that CMS1 was more closely related to immune cells, and that monocytes and macrophages play dominant roles in the CRC tumor microenvironment. On the basis of the constructed gene regulation networks (GRNs) for each subtype, we identified that the critical transcription factor ERG is universally activated and upregulated in all CMS in comparison with normal cells, and that it performed diverse roles by regulating the expression of different downstream genes. In summary, molecular subtyping of single-cell RNA-sequencing data for colorectal cancer could elucidate the heterogeneity in gene regulatory networks and identify critical regulators of CRC.

Random Forest Factorization Reveals Latent Structure in Single Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Boris M. Brenerman ◽  
Benjamin D. Shapiro ◽  
Michael C. Schatz ◽  
Alexis Battle
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Data ◽  
Random Forest Regression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes.

scholarly journals Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

2021 ◽  
Author(s):  
Matthew Stone ◽  
Sunnie Grace McCalla ◽  
Alireza Fotuhi Siahpirani ◽  
Viswesh Periyasamy ◽  
Junha Shin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Cost Effective ◽  
Gene Regulatory Network Inference ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory ◽  
Inference Methods

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional pro- grams of different cellular states by measuring the transcriptome of thousands individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory net- works and a number of methods with different learning frameworks have been developed. Here we present a expanded benchmarking study of eleven recent network inference methods on six published single-cell RNA-sequencing datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that while no method is a universal winner and most methods have a modest recovery of experimentally derived interactions based on global metrics such as AUPR, methods are able to capture targets of regulators that are relevant to the system under study. Based on overall performance we grouped the methods into three main categories and found a combination of information-theoretic and regression-based methods to have a generally high perfor- mance. We also evaluate the utility of imputation for gene regulatory network inference and find that a small number of methods benefit from imputation, which further depends upon the dataset. Finally, comparisons to inferred networks for comparable bulk conditions showed that networks inferred from scRNA-seq datasets are often better or at par to those from bulk suggesting that scRNA-seq datasets can be a cost-effective way for gene regulatory network inference. Our analysis should be beneficial in selecting algorithms for performing network inference but also argues for improved methods and better gold standards for accurate assessment of regulatory network inference methods for mammalian systems.

Sign in / Sign up

Export Citation Format

Share Document