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Mass Cytometry

Overview of attention for book
Mass Cytometry
Springer New York

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 1 Setting Up Mass Cytometry in a Shared Resource Lab Environment
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    Chapter 2 Citizenship and Money: Historical Snapshots
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    Chapter 2 Acquisition, Processing, and Quality Control of Mass Cytometry Data
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    Chapter 3 To Sell or Not to Sell: The Ethics of ius pecuniae
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    Chapter 3 Visualization of Mass Cytometry Signal Background to Enable Optimal Core Panel Customization and Signal Threshold Gating
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    Chapter 4 Method for Tagging Antibodies with Metals for Mass Cytometry Experiments
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    Chapter 4 A Classification of Investment-Based Citizenship Programmes
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    Chapter 5 ‘Long-Distance Citizens’: Strategies and Interests of States, Companies and Individuals in the Global Race for Wealth
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    Chapter 5 Scalable Conjugation and Characterization of Immunoglobulins with Stable Mass Isotope Reporters for Single-Cell Mass Cytometry Analysis
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    Chapter 6 Ius pecuniae in a Multilevel System: The European Experience
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    Chapter 6 Titration of Mass Cytometry Reagents
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    Chapter 7 Surface Barcoding of Live PBMC for Multiplexed Mass Cytometry
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    Chapter 8 Automated Cell Processing for Mass Cytometry Experiments
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    Chapter 9 Live Cell Barcoding for Efficient Analysis of Small Samples by Mass Cytometry
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    Chapter 10 Staining of Phosphorylated Signalling Markers Protocol for Mass Cytometry
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    Chapter 11 Multiplex MHC Class I Tetramer Combined with Intranuclear Staining by Mass Cytometry
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    Chapter 12 Analysis of the Murine Bone Marrow Hematopoietic System Using Mass and Flow Cytometry
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    Chapter 13 Mass Cytometric Cell Cycle Analysis
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    Chapter 14 Picturing Polarized Myeloid Phagocytes and Regulatory Cells by Mass Cytometry
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    Chapter 15 Quantitative Measurement of Cell-Nanoparticle Interactions Using Mass Cytometry
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    Chapter 16 Data-Driven Flow Cytometry Analysis
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    Chapter 17 Analysis of Mass Cytometry Data
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    Chapter 18 Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry
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    Chapter 19 Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples
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    Chapter 20 Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery
Attention for Chapter 5: Scalable Conjugation and Characterization of Immunoglobulins with Stable Mass Isotope Reporters for Single-Cell Mass Cytometry Analysis
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