scholarly journals Revealing the critical regulators of cell identity in the mouse cell atlas

2018 ◽  
Author(s):  
Shengbao Suo ◽  
Qian Zhu ◽  
Assieh Saadatpour ◽  
Lijiang Fei ◽  
Guoji Guo ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Network ◽  
Small Set ◽  
Gene Regulatory ◽  
Additional Support ◽  
User Friendly ◽  
Different Cell Types ◽  
First Time

AbstractRecent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 gene regulatory networks whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each of over 800 cell types in mouse for the first time. Systematic validation by automated literature- and data-mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive, web-portal to enable users to navigate this mouse cell network atlas.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals Evidence of Rentian Scaling of Functional Modules in Diverse Biological Networks

2018 ◽  
Vol 30 (8) ◽  
pp. 2210-2244
Author(s):  
Javier J. How ◽  
Saket Navlakha
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Broad Class ◽  
Graph Model ◽  
Cell Types ◽  
Mouse Cell ◽  
Molecular Networks ◽  
Functional Modules ◽  
Gene Regulatory ◽  
External Connections

Biological networks have long been known to be modular, containing sets of nodes that are highly connected internally. Less emphasis, however, has been placed on understanding how intermodule connections are distributed within a network. Here, we borrow ideas from engineered circuit design and study Rentian scaling, which states that the number of external connections between nodes in different modules is related to the number of nodes inside the modules by a power-law relationship. We tested this property in a broad class of molecular networks, including protein interaction networks for six species and gene regulatory networks for 41 human and 25 mouse cell types. Using evolutionarily defined modules corresponding to known biological processes in the cell, we found that all networks displayed Rentian scaling with a broad range of exponents. We also found evidence for Rentian scaling in functional modules in the Caenorhabditis elegans neural network, but, interestingly, not in three different social networks, suggesting that this property does not inevitably emerge. To understand how such scaling may have arisen evolutionarily, we derived a new graph model that can generate Rentian networks given a target Rent exponent and a module decomposition as inputs. Overall, our work uncovers a new principle shared by engineered circuits and biological networks.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Vol 3 (11) ◽  
pp. e202000658 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Multiple Cell ◽  
Gene Regulatory

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

scholarly journals Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

2022 ◽  
Author(s):  
Chirag Gupta ◽  
Jielin Xu ◽  
Ting Jin ◽  
Saniya Khullar ◽  
Xiaoyu Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Network Biology ◽  
Cell Type ◽  
Cell Network ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Type Gene ◽  
And Control

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell-type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular. Several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment). Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals mechanisms on cell-type gene dyregulation in AD.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Regulation of Division and Differentiation of Plant Stem Cells

2018 ◽  
Vol 34 (1) ◽  
pp. 289-310 ◽  
Author(s):  
Edith Pierre-Jerome ◽  
Colleen Drapek ◽  
Philip N. Benfey
Keyword(s):  
Stem Cell ◽  
Cell Division ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Stem Cell Maintenance ◽  
Cell Position ◽  
Dependent Cell ◽  
Gene Regulatory ◽  
Plant Stem

A major challenge in developmental biology is unraveling the precise regulation of plant stem cell maintenance and the transition to a fully differentiated cell. In this review, we highlight major themes coordinating the acquisition of cell identity and subsequent differentiation in plants. Plant cells are immobile and establish position-dependent cell lineages that rely heavily on external cues. Central players are the hormones auxin and cytokinin, which balance cell division and differentiation during organogenesis. Transcription factors and miRNAs, many of which are mobile in plants, establish gene regulatory networks that communicate cell position and fate. Small peptide signaling also provides positional cues as new cell types emerge from stem cell division and progress through differentiation. These pathways recruit similar players for patterning different organs, emphasizing the modular nature of gene regulatory networks. Finally, we speculate on the outstanding questions in the field and discuss how they may be addressed by emerging technologies.

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

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.

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

2020 ◽  
Author(s):  
Fu-Ying Dao ◽  
Hao Lv ◽  
Dan Zhang ◽  
Zi-Mei Zhang ◽  
Li Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Methods ◽  
Learning Algorithm ◽  
Cell Types ◽  
Chromatin Loops ◽  
Deep Learning Algorithm ◽  
Dna Elements ◽  
Chromatin Folding ◽  
User Friendly ◽  
Different Cell Types

Abstract The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer–promoter interaction is the general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs$\ge$0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1.

scholarly journals Differences in the products of mitochondrial protein synthesis in vivo in human and mouse cells and their potential use as markers for the mitochondrial genome in human–mouse cell hybrids

1974 ◽  
Vol 144 (1) ◽  
pp. 161-164 ◽  
Author(s):  
Alec Jeffreys ◽  
Ian Craig
Keyword(s):  
Protein Synthesis ◽  
Mitochondrial Protein ◽  
Detailed Comparison ◽  
Cell Types ◽  
Mouse Cell ◽  
Human Cells ◽  
Mitochondrial Protein Synthesis ◽  
Cell Hybrids ◽  
Different Cell Types

The proteins synthesized in the mitochondria of mouse and human cells grown in tissue culture were examined by electrophoresis in polyacrylamide gels. The proteins were labelled by incubating the cells in the presence of [35S]methionine and an inhibitor of cytoplasmic protein synthesis (emetine or cycloheximide). A detailed comparison between the labelled products of mouse and human mitochondrial protein synthesis was made possible by developing radioautograms after exposure to slab-electrophoresis gels. Patterns obtained for different cell types of the same species were extremely similar, whereas reproducible differences were observed on comparison of the profiles obtained for mouse and human cells. Four human–mouse somatic cell hybrids were examined, and in each one only components corresponding to mouse mitochondrially synthesized proteins were detected.

Sign in / Sign up

Export Citation Format

Share Document