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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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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Updated: Sep 3, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

L F Piochi1, A T Gaspar1, N Rosário-Ferreira2

  • 1Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal.

Advances in Protein Chemistry and Structural Biology
|July 25, 2022
PubMed
Summary

Investigating cellular responses to therapeutic agents is crucial. Single-cell omics and computational tools help analyze ligand-induced perturbations for improved drug discovery.

Keywords:
Drug developmentIntegrative analysisLigandsMulti-omicsPerturbationsSingle-cell

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Area of Science:

  • Cellular biology
  • Computational biology
  • Pharmacology

Background:

  • Cellular perturbations from stimuli, including therapeutic agents, alter cell profiles and functions, potentially impacting tissues.
  • External ligand exposure induces changes in cell profiles across various single-omics levels.
  • Understanding these molecular changes is vital for assessing therapeutic agent effects.

Purpose of the Study:

  • To review recent advances in computational tools for analyzing ligand-induced perturbations using single-cell omics data.
  • To discuss the limitations of current computational approaches and data integration methods.
  • To explore how large datasets can enhance drug research and development.

Main Methods:

  • Single-cell RNA-sequencing (scRNA-seq) for cell profiling and perturbation analysis.
  • Integration of single-cell transcriptomics with other omics data (proteomics, epigenomics).
  • Focus on computational tools and algorithms for processing and integrating multi-omics data.

Main Results:

  • Single-cell omics enables detailed analysis of cellular responses to perturbations.
  • Integration of multi-omics data provides a comprehensive view of perturbation mechanisms.
  • Advances in computational tools are essential for handling and interpreting complex single-cell data.

Conclusions:

  • Computational tools are critical for processing and integrating diverse single-cell omics data.
  • Improved analysis of ligand-induced perturbations can accelerate drug discovery and development.
  • Addressing current limitations in computational methods is key to leveraging big data in pharmacology.