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Innate Immune Activation

Overview of attention for book
Cover of 'Innate Immune Activation'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Emerging Concepts in Innate Immunity
  3. Altmetric Badge
    Chapter 2 Bioinformatic Assessment of Macrophage Activation by the Innate Immune System
  4. Altmetric Badge
    Chapter 3 Generation of Genetic Knockouts in Myeloid Cell Lines Using a Lentiviral CRISPR/Cas9 System
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    Chapter 4 Modeling Primary Human Monocytes with the Trans–Differentiation Cell Line BLaER1
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    Chapter 5 Measurement of NF-κB Activation in TLR-Activated Macrophages
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    Chapter 6 Biochemical Isolation of the Myddosome from Murine Macrophages
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    Chapter 7 Generation of Innate Immune Reporter Cells Using Retroviral Transduction
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    Chapter 8 Examining Myddosome Formation by Luminescence-Based Mammalian Interactome Mapping (LUMIER)
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    Chapter 9 Inflammatory Caspases: Activation and Cleavage of Gasdermin-D In Vitro and During Pyroptosis
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    Chapter 10 Detection of ASC Speck Formation by Flow Cytometry and Chemical Cross-linking
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    Chapter 11 Measuring Innate Immune Responses to Bacterial Viability
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    Chapter 12 Methods to Study Cell Swelling-Induced Inflammasome Activation
  14. Altmetric Badge
    Chapter 13 Detecting Release of Bacterial dsDNA into the Host Cytosol Using Fluorescence Microscopy
  15. Altmetric Badge
    Chapter 14 Quantitative Proteomics of Secreted Proteins
  16. Altmetric Badge
    Chapter 15 Simultaneous Detection of Cellular Viability and Interleukin-1β Secretion from Single Cells by ELISpot
  17. Altmetric Badge
    Chapter 16 Detection and Quantification of MAVS Aggregation via Confocal Microscopy
Attention for Chapter 14: Quantitative Proteomics of Secreted Proteins
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Chapter title
Quantitative Proteomics of Secreted Proteins
Chapter number 14
Book title
Innate Immune Activation
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7519-8_14
Pubmed ID
Book ISBNs
978-1-4939-7518-1, 978-1-4939-7519-8
Authors

Annika Frauenstein, Felix Meissner

Abstract

Secreted proteins such as cytokines, interleukins, growth factors, and hormones have pleiotropic functions and facilitate intercellular communication in organisms. Quantification of these proteins conventionally relies on antibody-based methods, i.e., enzyme-linked immunosorbent assays (ELISA), whose large-scale use is limited by availability, specificity, and affordability.Here, we describe an experimental and bioinformatics workflow to comprehensively quantify cellular protein secretion by mass spectrometry. Secreted proteins are collected in vitro or ex vivo, digested with proteases and the resulting peptide mixtures are analyzed in single liquid chromatography-mass spectrometry (LC-MS/MS) runs. Label-free quantification and bioinformatics analysis is conducted in the MaxQuant and Perseus computational environment. Our workflow allows the quantification of thousands of secreted proteins spanning a concentration range of four orders of magnitude and permits the systems-level characterization of secretory programs as well as the discovery of proteins with unexpected extracellular functions.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 17%
Student > Master 7 17%
Student > Ph. D. Student 6 14%
Student > Bachelor 4 10%
Professor > Associate Professor 3 7%
Other 3 7%
Unknown 12 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 33%
Agricultural and Biological Sciences 3 7%
Immunology and Microbiology 3 7%
Chemistry 2 5%
Neuroscience 2 5%
Other 4 10%
Unknown 14 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 December 2017.
All research outputs
#17,920,654
of 23,009,818 outputs
Outputs from Methods in molecular biology
#7,286
of 13,157 outputs
Outputs of similar age
#310,320
of 442,295 outputs
Outputs of similar age from Methods in molecular biology
#868
of 1,498 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,157 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,295 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.