scholarly journals FunRes: resolving tissue-specific functional cell states based on a cell–cell communication network model

2020 ◽  
Author(s):  
Sascha Jung ◽  
Kartikeya Singh ◽  
Antonio del Sol
Keyword(s):  
Communication Network ◽  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Computational Method ◽  
Tissue Specific ◽  
Great Utility ◽  
A Cell ◽  
Cell Cell

Abstract The functional specialization of cell types arises during development and is shaped by cell–cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell–cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.

scholarly journals Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
David L. Gibbs ◽  
Boris Aguilar ◽  
Vésteinn Thorsson ◽  
Alexander V. Ratushny ◽  
Ilya Shmulevich
Keyword(s):  
Communication Networks ◽  
Probabilistic Method ◽  
Cell Communication ◽  
Cell Types ◽  
Tumor Stage ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Cell ◽  
High Level ◽  
Cell Cell

The maintenance and function of tissues in health and disease depends on cell–cell communication. This work shows how high-level features, representing cell–cell communication, can be defined and used to associate certain signaling “axes” with clinical outcomes. We generated a scaffold of cell–cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for The Cancer Genome Atlas (TCGA) patient samples, each representing likely channels of intercellular communication in the tumor microenvironment (TME). It was shown that cell–cell edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types, and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

scholarly journals Inference and analysis of cell-cell communication using CellChat

2020 ◽  
Author(s):  
Suoqin Jin ◽  
Christian F. Guerrero-Juarez ◽  
Lihua Zhang ◽  
Ivan Chang ◽  
Peggy Myung ◽  
...  
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Mouse Skin ◽  
Molecular Complexes ◽  
Mass Action ◽  
A Cell ◽  
Context Specific ◽  
Global Communications ◽  
Intercellular Communications ◽  
Cell Cell

AbstractUnderstanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

scholarly journals CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes

2019 ◽  
Author(s):  
Mirjana Efremova ◽  
Miquel Vento-Tormo ◽  
Sarah A. Teichmann ◽  
Roser Vento-Tormo
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Published Data ◽  
Data Sets ◽  
Cellular Interactions ◽  
Receptor Ligand ◽  
Link Type ◽  
Transcriptomics Data ◽  
Cell Cell

AbstractCell-cell communication mediated by receptor-ligand complexes is crucial for coordinating diverse biological processes, such as development, differentiation and responses to infection. In order to understand how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions1. Our repository takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, the procedures for inferring cell-cell communication networks from scRNA-seq data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v2.0 is a novel version of our resource that incorporates additional functionalities to allow users to introduce new interacting molecules and reduce the time and resources needed to interrogate large datasets. CellPhoneDB v2.0 is publicly available at https://github.com/Teichlab/cellphonedb and as a user-friendly web interface at http://www.cellphonedb.org/. In our protocol, we demonstrate how to reveal meaningful biological discoveries from CellPhoneDB v2.0 using published data sets.

scholarly journals EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanyu Zhang ◽  
Ruoyi Cai ◽  
James Dai ◽  
Wei Sun
Keyword(s):  
Dna Methylation ◽  
Tumor Cells ◽  
T Regulatory Cells ◽  
Simulated Data ◽  
Cell Types ◽  
Computational Method ◽  
Methylation Data ◽  
Cell Type ◽  
A Cell ◽  
Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

scholarly journals Analysis of the RNA content of the exosomes derived from blood serum and urine and its potential as biomarkers

2014 ◽  
Vol 369 (1652) ◽  
pp. 20130502 ◽  
Author(s):  
Mu Li ◽  
Emily Zeringer ◽  
Timothy Barta ◽  
Jeoffrey Schageman ◽  
Angie Cheng ◽  
...  
Keyword(s):  
Blood Serum ◽  
Cell Communication ◽  
Cell Culture Model ◽  
Culture Model ◽  
Wide Range ◽  
Abnormal Cells ◽  
A Cell ◽  
Invasive Diagnostics ◽  
Serum And Urine

Exosomes are tiny vesicles (30–150 nm) constantly secreted by all healthy and abnormal cells, and found in abundance in all body fluids. These vesicles, loaded with unique RNA and protein cargo, have a wide range of biological functions, including cell-to-cell communication and signalling. As such, exosomes hold tremendous potential as biomarkers and could lead to the development of minimally invasive diagnostics and next generation therapies within the next few years. Here, we describe the strategies for isolation of exosomes from human blood serum and urine, characterization of their RNA cargo by sequencing, and present the initial data on exosome labelling and uptake tracing in a cell culture model. The value of exosomes for clinical applications is discussed with an emphasis on their potential for diagnosing and treating neurodegenerative diseases and brain cancer.

scholarly journals Cloning and functional characterization of the human fractalkine receptor promoter regions

2002 ◽  
Vol 368 (3) ◽  
pp. 753-760 ◽  
Author(s):  
Alexandre GARIN ◽  
Philippe PELLET ◽  
Philippe DETERRE ◽  
Patrice DEBRÉ ◽  
Christophe COMBADIÈRE
Keyword(s):  
Functional Characterization ◽  
Cell Types ◽  
Promoter Regions ◽  
Exon 2 ◽  
Reading Frame ◽  
Fractalkine Receptor ◽  
A Cell ◽  
Exon 4 ◽  
Coronary Events

We have previously shown that reduced expression of the fractalkine receptor, CX3CR1, is correlated with rapid HIV disease progression and with reduced susceptibility to acute coronary events. In order to elucidate the mechanisms underlying transcriptional regulation of CX3CR1 expression, we structurally and functionally characterized the CX3CR1 gene. It consists of four exons and three introns spanning over 18kb. Three transcripts are produced by splicing the three untranslated exons with exon 4, which contains the complete open reading frame. The transcript predominantly found in leucocytes corresponds to the splicing of exon 2 with exon 4. Transcripts corresponding to splicing of exons 1 and 4 are less abundant in leucocytes and splicing of exons 3 and 4 are rare longer transcripts. A constitutive promoter activity was found in the regions extending upstream from untranslated exons 1 and 2. Interestingly, exons 1 and 2 enhanced the activity of their respective promoters in a cell-specific manner. These data show that the CX3CR1 gene is controlled by three distinct promoter regions, which are regulated by their respective untranslated exons and that lead to the transcription of three mature messengers. This highly complex regulation may allow versatile and precise expression of CX3CR1 in various cell types.

scholarly journals New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

2020 ◽  
Vol 11 (12) ◽  
pp. 866-880 ◽  
Author(s):  
Xin Shao ◽  
Xiaoyan Lu ◽  
Jie Liao ◽  
Huajun Chen ◽  
Xiaohui Fan
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Receptor Interaction ◽  
Communication Studies ◽  
Transcriptomics Data ◽  
Phenotype Heterogeneity ◽  
Multicellular Organisms ◽  
Communication Study ◽  
Cell Cell

AbstractFor multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

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