Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

Download Full-text

Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Frontiers in Plant Science ◽

10.3389/fpls.2021.661361 ◽

2021 ◽

Vol 12 ◽

Author(s):

Laura Serrano-Ron ◽

Javier Cabrera ◽

Pablo Perez-Garcia ◽

Miguel A. Moreno-Risueno

Keyword(s):

Single Cell ◽

Cell Fate ◽

Gene Regulatory Networks ◽

Root Development ◽

Regulatory Networks ◽

Primary Root ◽

Cell Fate Specification ◽

Rna Seq ◽

Fate Specification ◽

Gene Regulatory

Over the last decades, research on postembryonic root development has been facilitated by “omics” technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

Download Full-text

Effect of imputation on gene network reconstruction from single-cell RNA-seq data

10.1101/2021.04.13.439623 ◽

2021 ◽

Author(s):

Lam-Ha Ly ◽

Martin Vingron

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Network Reconstruction ◽

Data Sets ◽

Rna Seq ◽

Imputation Methods ◽

Before And After ◽

Gene Regulatory ◽

The Difference

AbstractDespite the advances in single-cell transcriptomics the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA-seq data make it hard to infer networks from transcriptome data. Data imputation can be applied in order to enhance gene-gene correlations and facilitate downstream data analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks.To study this question, we evaluate the effect of imputation methods on the performance and structure of the reconstructed networks in different experimental single-cell RNA-seq data sets. We use state-of-the-art algorithms for both imputation and network reconstruction and evaluate the difference in results before and after imputation. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. Yet, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms, while warning against the indiscriminate use of imputation before network reconstruction in general.

Download Full-text

Single-cell RNA-seq dissects the intratumoral heterogeneity of triple negative breast cancer based on gene regulatory networks

Molecular Therapy — Nucleic Acids ◽

10.1016/j.omtn.2020.12.018 ◽

2021 ◽

Author(s):

Shunheng Zhou ◽

Yu-e Huang ◽

Haizhou Liu ◽

Xu Zhou ◽

Mengqin Yuan ◽

...

Keyword(s):

Breast Cancer ◽

Single Cell ◽

Gene Regulatory Networks ◽

Triple Negative Breast Cancer ◽

Regulatory Networks ◽

Triple Negative ◽

Intratumoral Heterogeneity ◽

Rna Seq ◽

Gene Regulatory

Download Full-text

Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

10.1101/2020.08.30.273839 ◽

2020 ◽

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Metabolic Diseases ◽

Large Data ◽

Data Repositories ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

AbstractAnalyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

Download Full-text

Characterization of rare spindle and root cell transcriptional profiles in the stria vascularis of the adult mouse cochlea

Scientific Reports ◽

10.1038/s41598-020-75238-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Shoujun Gu ◽

Rafal Olszewski ◽

Ian Taukulis ◽

Zheng Wei ◽

Daniel Martin ◽

...

Keyword(s):

Hearing Loss ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Adult Mouse ◽

Stria Vascularis ◽

Cell Types ◽

Rna Seq ◽

Root Cells ◽

Transcriptional Profiles ◽

Gene Regulatory

Abstract The stria vascularis (SV) in the cochlea generates and maintains the endocochlear potential, thereby playing a pivotal role in normal hearing. Knowing transcriptional profiles and gene regulatory networks of SV cell types establishes a basis for studying the mechanism underlying SV-related hearing loss. While we have previously characterized the expression profiles of major SV cell types in the adult mouse, transcriptional profiles of rare SV cell types remained elusive due to the limitation of cell capture in single-cell RNA-Seq. The role of these rare cell types in the homeostatic function of the adult SV remain largely undefined. In this study, we performed single-nucleus RNA-Seq on the adult mouse SV in conjunction with sample preservation treatments during the isolation steps. We distinguish rare SV cell types, including spindle cells and root cells, from other cell types, and characterize their transcriptional profiles. Furthermore, we also identify and validate novel specific markers for these rare SV cell types. Finally, we identify homeostatic gene regulatory networks within spindle and root cells, establishing a basis for understanding the functional roles of these cells in hearing. These novel findings will provide new insights for future work in SV-related hearing loss and hearing fluctuation.

Download Full-text

Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks

Cell ◽

10.1016/j.cell.2018.11.022 ◽

2019 ◽

Vol 176 (1-2) ◽

pp. 361-376.e17 ◽

Cited By ~ 59

Author(s):

Adam J. Rubin ◽

Kevin R. Parker ◽

Ansuman T. Satpathy ◽

Yanyan Qi ◽

Beijing Wu ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Causal Gene ◽

Gene Regulatory

Download Full-text

Mapping gene regulatory networks from single-cell omics data

Briefings in Functional Genomics ◽

10.1093/bfgp/elx046 ◽

2018 ◽

Vol 17 (4) ◽

pp. 246-254 ◽

Cited By ~ 71

Author(s):

Mark W E J Fiers ◽

Liesbeth Minnoye ◽

Sara Aibar ◽

Carmen Bravo González-Blas ◽

Zeynep Kalender Atak ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Omics Data ◽

Mapping Gene ◽

Gene Regulatory

Download Full-text

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Bioinformatics ◽

10.1093/bioinformatics/btx575 ◽

2017 ◽

Vol 34 (2) ◽

pp. 258-266 ◽

Cited By ~ 34

Author(s):

Nan Papili Gao ◽

S M Minhaz Ud-Dean ◽

Olivier Gandrillon ◽

Rudiyanto Gunawan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Transcriptional Expression ◽

Gene Regulatory

Download Full-text

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

10.1101/2021.02.03.429484 ◽

2021 ◽

Author(s):

Jiaxing Chen ◽

Chinwang Cheong ◽

Liang Lan ◽

Xin Zhou ◽

Jiming Liu ◽

...

Keyword(s):

Neural Network ◽

Single Cell ◽

Regulatory Networks ◽

Deep Neural Network ◽

Neighborhood Context ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Rna Seq ◽

Cell Type Specific ◽

Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

Download Full-text