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Single Cell Protein Analysis

Overview of attention for book
Cover of 'Single Cell Protein Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Single-Cell Western Blotting
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    Chapter 2 A Microfluidic Device for Immunoassay-Based Protein Analysis of Single E. coli Bacteria
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    Chapter 3 Enzyme-Linked ImmunoSpot (ELISpot) for Single-Cell Analysis
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    Chapter 4 Single Cell Protein Analysis
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    Chapter 5 Single Cell Protein Analysis
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    Chapter 6 Microfluidic Flow Cytometry for Single-Cell Protein Analysis
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    Chapter 7 Microfluidic Image Cytometry for Single-Cell Phenotyping of Human Pluripotent Stem Cells
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    Chapter 8 Characterizing Phenotypes and Signaling Networks of Single Human Cells by Mass Cytometry.
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    Chapter 9 Multiplexed Peptide-MHC Tetramer Staining with Mass Cytometry.
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    Chapter 10 Imaging and Mapping of Tissue Constituents at the Single-Cell Level Using MALDI MSI and Quantitative Laser Scanning Cytometry
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    Chapter 11 SPLIFF: A Single-Cell Method to Map Protein-Protein Interactions in Time and Space.
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    Chapter 12 Microfluidic Proximity Ligation Assay for Profiling Signaling Networks with Single-Cell Resolution
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    Chapter 13 Dynamics and Interactions of Individual Proteins in the Membrane of Single, Living Cells
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    Chapter 14 Microfluidics-Enabled Enzyme Activity Measurement in Single Cells
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    Chapter 15 Microfluidic Chemical Cytometry for Enzyme Assays of Single Cells
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    Chapter 16 Quantitative Detection of Nucleocytoplasmic Transport of Native Proteins in Single Cells
Attention for Chapter 8: Characterizing Phenotypes and Signaling Networks of Single Human Cells by Mass Cytometry.
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Chapter title
Characterizing Phenotypes and Signaling Networks of Single Human Cells by Mass Cytometry.
Chapter number 8
Book title
Single Cell Protein Analysis
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2987-0_8
Pubmed ID
Book ISBNs
978-1-4939-2986-3, 978-1-4939-2987-0
Authors

Leelatian, Nalin, Diggins, Kirsten E, Irish, Jonathan M, Nalin Leelatian, Kirsten E. Diggins, Jonathan M. Irish

Abstract

Single cell mass cytometry is revolutionizing our ability to quantitatively characterize cellular biomarkers and signaling networks. Mass cytometry experiments routinely measure 25-35 features of each cell in primary human tissue samples. The relative ease with which a novice user can generate a large amount of high quality data and the novelty of the approach have created a need for example protocols, analysis strategies, and datasets. In this chapter, we present detailed protocols for two mass cytometry experiments designed as training tools. The first protocol describes detection of 26 features on the surface of human peripheral blood mononuclear cells. In the second protocol, a mass cytometry signaling network profile measures 25 node states comprised of five key signaling effectors (AKT, ERK1/2, STAT1, STAT5, and p38) quantified under five conditions (Basal, FLT3L, SCF, IL-3, and IFNγ). This chapter compares manual and unsupervised data analysis approaches, including bivariate plots, heatmaps, histogram overlays, SPADE, and viSNE. Data files in this chapter have been shared online using Cytobank ( http://www.cytobank.org/irishlab/ ).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 27%
Professor > Associate Professor 4 11%
Student > Ph. D. Student 4 11%
Student > Master 4 11%
Student > Bachelor 3 8%
Other 6 16%
Unknown 6 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 30%
Biochemistry, Genetics and Molecular Biology 7 19%
Immunology and Microbiology 7 19%
Medicine and Dentistry 3 8%
Sports and Recreations 1 3%
Other 1 3%
Unknown 7 19%

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 November 2015.
All research outputs
#7,465,925
of 9,674,536 outputs
Outputs from Methods in molecular biology
#3,823
of 7,408 outputs
Outputs of similar age
#173,584
of 251,955 outputs
Outputs of similar age from Methods in molecular biology
#169
of 350 outputs
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