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Social network architecture of human immune cells unveiled by quantitative proteomics

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TLDR
High-resolution mass-spectrometry-based proteomics revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions and discovering fundamental intercellular communication structures and previously unknown connections between cell types.
Abstract
The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.

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CD4 + T cell help in cancer immunology and immunotherapy

TL;DR: This Review highlights the cellular dynamics and membrane receptors that mediate CD4+ T cell help and the molecular mechanisms of the enhanced antitumour activity of CTLs and discusses the molecular nature of help signals and how they can be harnessed to improve cancer immunotherapy.
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The gasdermins, a protein family executing cell death and inflammation

TL;DR: This Review provides a comprehensive overview of the gasdermin family, the mechanisms that control their activation and their role in inflammatory disorders and cancer.
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Deciphering cell-cell interactions and communication from gene expression.

TL;DR: This Review highlights discoveries enabled by analyses of cell–cell interactions from transcriptomic data and reviews the methods and tools used in this context.
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ImmPort, toward repurposing of open access immunological assay data for translational and clinical research.

TL;DR: The ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings and allows research data to be repurposed to accelerate the translation of new insights into discoveries.
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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data.

TL;DR: This work has developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
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WGCNA: an R package for weighted correlation network analysis.

TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
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Regularization Paths for Generalized Linear Models via Coordinate Descent

TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.
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MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

TL;DR: MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data, detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space and achieves mass accuracy in the p.p.b. range.
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