RNA sequencing identifies gene regulatory networks controlling extracellular matrix synthesis in intervertebral disk tissues

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.

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.765578 ◽

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.

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tuxnet : a simple interface to process RNA sequencing data and infer gene regulatory networks

The Plant Journal ◽

10.1111/tpj.14558 ◽

2019 ◽

Vol 101 (3) ◽

pp. 716-730 ◽

Cited By ~ 1

Author(s):

Ryan J. Spurney ◽

Lisa Van den Broeck ◽

Natalie M. Clark ◽

Adam P. Fisher ◽

Maria A. de Luis Balaguer ◽

...

Keyword(s):

Rna Sequencing ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Sequencing Data ◽

Simple Interface ◽

Gene Regulatory

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Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

10.1101/661058 ◽

2019 ◽

Cited By ~ 1

Author(s):

Qiao Wen Tan ◽

Marek Mutwil

Keyword(s):

Transcription Factors ◽

Rna Sequencing ◽

Gene Regulatory Networks ◽

Gene Function ◽

Credit Card ◽

Regulatory Networks ◽

Artemisia Annua ◽

Sequencing Data ◽

Gene Regulatory ◽

Artemisinin Biosynthesis

0.ABSTRACTPrediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as the major bottleneck in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for less than 50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics.HighlightsProcessing of large scale transcriptomic data with affordable single-board computersTranscription factors can be found in the same network as their targetsCo-expression of transcription factors and genes in secondary cell wall biosynthesisCo-expression of transcription factors and genes involved in artemisinin biosynthesis

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

10.1101/581678 ◽

2019 ◽

Cited By ~ 6

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.

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Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms ◽

10.1016/j.bbagrm.2019.194429 ◽

2020 ◽

Vol 1863 (6) ◽

pp. 194429 ◽

Cited By ~ 6

Author(s):

Qiao Wen Tan ◽

Marek Mutwil

Keyword(s):

Rna Sequencing ◽

Gene Regulatory Networks ◽

Credit Card ◽

Regulatory Networks ◽

Artemisia Annua ◽

Sequencing Data ◽

Gene Regulatory

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

10.1101/2021.09.13.460168 ◽

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.

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TuxNet: A simple interface to process RNA sequencing data and infer gene regulatory networks

10.1101/337147 ◽

2018 ◽

Cited By ~ 1

Author(s):

Maria Angels de Luis Balaguer ◽

Ryan J. Spurney ◽

Natalie M. Clark ◽

Adam P. Fisher ◽

Rosangela Sozzani

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Course ◽

Expression Profiles ◽

Expression Data ◽

Sequencing Data ◽

Simple Interface ◽

Gene Regulatory

ABSTRACTPredicting gene regulatory networks (GRNs) from gene expression profiles has become a common approach for identifying important biological regulators. Despite the increase in the use of inference methods, existing computational approaches do not integrate RNA-sequencing data analysis, are often not automated, and are restricted to users with bioinformatics and programming backgrounds. To address these limitations, we have developed TuxNet, an integrated user-friendly platform, which, with just a few selections, allows to process raw RNA-sequencing data (using the Tuxedo pipeline) and infer GRNs from these processed data. TuxNet is implemented as a graphical user interface and, using expression data from any organism with an existing reference genome, can mine the regulations among genes either by applying a dynamic Bayesian network inference algorithm, GENIST, or a regression tree-based pipeline that uses spatiotemporal data, RTP-STAR. To illustrate the use of TuxNet while getting insight into the regulatory cascade downstream of the Arabidopsis root stem cell regulator PERIANTHIA (PAN), we obtained time course gene expression data of a PAN inducible line and inferred a GRN using GENIST. Using RTP-STAR, we then inferred the network of a PAN secondary downstream gene, ATHB13, for which we obtained wildtype and mutant expression profiles. Our case studies feature the versatility of TuxNet to infer networks using different types of gene expression data (i.e time course and steady-state data) as well as how inference networks are used to identify important regulators.SUMMARYTuxNet offers a simple interface for non-computational biologists to infer GRNs from raw RNA-seq data.

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