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 Patient-specific cell communication networks associate with disease progression in cancer

2021 ◽  
Author(s):  
David L Gibbs ◽  
Boris Aguilar ◽  
Vésteinn Thorsson ◽  
Alexander V Ratushny ◽  
Ilya Shmulevich
Keyword(s):  
Gene Expression ◽  
Communication Networks ◽  
Cancer Cell ◽  
Probabilistic Method ◽  
Cell Communication ◽  
Tumor Stage ◽  
Patient Specific ◽  
Specific Cell ◽  
Pan Cancer ◽  
Cell Cell

AbstractThe 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. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define 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 TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular 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 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 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 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.

scholarly journals Minireview: The Neuroendocrine Regulation of Puberty: Is the Time Ripe for a Systems Biology Approach?

Endocrinology ◽  
2006 ◽  
Vol 147 (3) ◽  
pp. 1166-1174 ◽  
Author(s):  
Sergio R. Ojeda ◽  
Alejandro Lomniczi ◽  
Claudio Mastronardi ◽  
Sabine Heger ◽  
Christian Roth ◽  
...  
Keyword(s):  
Global Analysis ◽  
Single Gene ◽  
Functional Integration ◽  
Cell Communication ◽  
Cell Interaction ◽  
Specific Cell ◽  
Gnrh Secretion ◽  
Cell Functions ◽  
Hierarchical Arrangement ◽  
Cell Cell

The initiation of mammalian puberty requires an increase in pulsatile release of GnRH from the hypothalamus. This increase is brought about by coordinated changes in transsynaptic and glial-neuronal communication. As the neuronal and glial excitatory inputs to the GnRH neuronal network increase, the transsynaptic inhibitory tone decreases, leading to the pubertal activation of GnRH secretion. The excitatory neuronal systems most prevalently involved in this process use glutamate and the peptide kisspeptin for neurotransmission/neuromodulation, whereas the most important inhibitory inputs are provided by γ-aminobutyric acid (GABA)ergic and opiatergic neurons. Glial cells, on the other hand, facilitate GnRH secretion via growth factor-dependent cell-cell signaling. Coordination of this regulatory neuronal-glial network may require a hierarchical arrangement. One level of coordination appears to be provided by a host of unrelated genes encoding proteins required for cell-cell communication. A second, but overlapping, level might be provided by a second tier of genes engaged in specific cell functions required for productive cell-cell interaction. A third and higher level of control involves the transcriptional regulation of these subordinate genes by a handful of upper echelon genes that, operating within the different neuronal and glial subsets required for the initiation of the pubertal process, sustain the functional integration of the network. The existence of functionally connected genes controlling the pubertal process is consistent with the concept that puberty is under genetic control and that the genetic underpinnings of both normal and deranged puberty are polygenic rather than specified by a single gene. The availability of improved high-throughput techniques and computational methods for global analysis of mRNAs and proteins will allow us to not only initiate the systematic identification of the different components of this neuroendocrine network but also to define their functional interactions.

scholarly journals IL-6 loss causes ventricular dysfunction, fibrosis, reduced capillary density, and dramatically alters the cell populations of the developing and adult heart

2009 ◽  
Vol 296 (5) ◽  
pp. H1694-H1704 ◽  
Author(s):  
Indroneal Banerjee ◽  
John W. Fuseler ◽  
Arti R. Intwala ◽  
Troy A. Baudino
Keyword(s):  
Cardiac Function ◽  
Cardiac Fibroblasts ◽  
Cell Communication ◽  
Cell Types ◽  
Capillary Density ◽  
Cell Populations ◽  
Cardiac Cell ◽  
Altered Expression ◽  
Cell Cell

Interleukin-6 (IL-6) is a pleiotropic cytokine responsible for many different processes including the regulation of cell growth, apoptosis, differentiation, and survival in various cell types and organs, including the heart. Recent studies have indicated that IL-6 is a critical component in the cell-cell communication between myocytes and cardiac fibroblasts. In this study, we examined the effects of IL-6 deficiency on the cardiac cell populations, cardiac function, and interactions between the cells of the heart, specifically cardiac fibroblasts and myocytes. To examine the effects of IL-6 loss on cardiac function, we used the IL-6 −/− mouse. IL-6 deficiency caused severe cardiac dilatation, increased accumulation of interstitial collagen, and altered expression of the adhesion protein periostin. In addition, flow cytometric analyses demonstrated dramatic alterations in the cardiac cell populations of IL-6 −/− mice compared with wild-type littermates. We observed a marked increase in the cardiac fibroblast population in IL-6 −/− mice, whereas a concomitant decrease was observed in the other cardiac cell populations examined. Moreover, we observed increased cell proliferation and apoptosis in the developing IL-6 −/− heart. Additionally, we observed a significant decrease in the capillary density of IL-6 −/− hearts. To elucidate the role of IL-6 in the interactions between cardiac fibroblasts and myocytes, we performed in vitro studies and demonstrated that IL-6 deficiency attenuated the activation of the STAT3 pathway and VEGF production. Taken together, these data demonstrate that a loss of IL-6 causes cardiac dysfunction by shifting the cardiac cell populations, altering the extracellular matrix, and disrupting critical cell-cell interactions.

scholarly journals Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

2020 ◽  
Author(s):  
Rui Hou ◽  
Michael Small ◽  
Alistair R. R. Forrest
Keyword(s):  
Communication Networks ◽  
Community Detection ◽  
Cell Communication ◽  
Cell Types ◽  
Community Structures ◽  
Simple Graphs ◽  
Detection Algorithms ◽  
Complex Processes ◽  
Community Identification ◽  
Directed Hypergraph

AbstractCell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligandreceptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

scholarly journals Spatiotemporal mosaic self-patterning of pluripotent stem cells using CRISPR interference

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ashley RG Libby ◽  
David A Joy ◽  
Po-Lin So ◽  
Mohammad A Mandegar ◽  
Jonathon M Muncie ◽  
...  
Keyword(s):  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Self Organization ◽  
Specific Cell ◽  
Transient Wave ◽  
Form Complex ◽  
Cortical Tension ◽  
Crispr Interference ◽  
Cell Cell

Morphogenesis involves interactions of asymmetric cell populations to form complex multicellular patterns and structures comprised of distinct cell types. However, current methods to model morphogenic events lack control over cell-type co-emergence and offer little capability to selectively perturb specific cell subpopulations. Our in vitro system interrogates cell-cell interactions and multicellular organization within human induced pluripotent stem cell (hiPSC) colonies. We examined effects of induced mosaic knockdown of molecular regulators of cortical tension (ROCK1) and cell-cell adhesion (CDH1) with CRISPR interference. Mosaic knockdown of ROCK1 or CDH1 resulted in differential patterning within hiPSC colonies due to cellular self-organization, while retaining an epithelial pluripotent phenotype. Knockdown induction stimulates a transient wave of differential gene expression within the mixed populations that stabilized in coordination with observed self-organization. Mosaic patterning enables genetic interrogation of emergent multicellular properties, which can facilitate better understanding of the molecular pathways that regulate symmetry-breaking during morphogenesis.

scholarly journals Predicting cell-to-cell communication networks using NATMI

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Hou ◽  
Elena Denisenko ◽  
Huan Ting Ong ◽  
Jordan A. Ramilowski ◽  
Alistair R. R. Forrest
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Cost Effective ◽  
Cell Types ◽  
Sequencing Technologies ◽  
Multiple Cell ◽  
Multicellular Interactions ◽  
Autocrine Signalling ◽  
Different Cell Types

Abstract Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

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