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What is Cell Signaling?02:03

What is Cell Signaling?

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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.
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Overview of Cell Signaling01:23

Overview of Cell Signaling

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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
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Cell-surface Signaling01:21

Cell-surface Signaling

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Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects.
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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
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Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint

Lihong Peng1, Jingwei Tan2, Wei Xiong2

  • 1School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.

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This study introduces CellComNet, a deep learning framework for inferring cell-cell communication from single-cell data. CellComNet accurately identifies ligand-receptor interactions, aiding in understanding tumor progression and developing targeted therapies.

Keywords:
Cell–cell communicationDeep neural networkFeature extractionHeterogeneous Newton boosting machineLigand–receptor interaction

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Cell-cell communication is crucial in the tumor microenvironment, influencing cancer growth, progression, and metastasis.
  • Understanding intercellular communication aids in deciphering molecular mechanisms driving these processes.

Purpose of the Study:

  • To develop an ensemble deep learning framework, CellComNet, for inferring ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data.
  • To enhance the accuracy of intercellular communication inference for cancer research.

Main Methods:

  • CellComNet integrates data processing, feature extraction, dimension reduction, and classification using a hybrid Newton boosting and deep neural network model.
  • Ligand-receptor interactions (LRIs) are identified and screened using single-cell RNA sequencing (scRNA-seq) data.
  • Cell-cell communication is inferred by combining scRNA-seq data, screened LRIs, and a joint scoring strategy.

Main Results:

  • CellComNet outperformed existing models in LRI classification, achieving superior AUCs and AUPRs on multiple datasets.
  • Application to melanoma and head and neck squamous cell carcinoma (HNSCC) revealed significant communication pathways: cancer-associated fibroblasts with melanoma cells, and endothelial cells with HNSCC cells.

Conclusions:

  • CellComNet effectively identifies credible LRIs and improves cell-cell communication inference.
  • The framework holds potential for advancing anticancer drug design and developing tumor-targeted therapies.