| Chapter title |
Mass Cytometry Assays for Antigen-Specific T Cells Using CyTOF
|
|---|---|
| Chapter number | 3 |
| Book title |
Flow Cytometry Protocols
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7346-0_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7344-6, 978-1-4939-7346-0
|
| Authors |
Dongxia Lin, Holden T. Maecker |
| Abstract |
T Cells specific for a single antigen tend to be rare, even after expansion of memory cells. They are commonly detected by in vitro stimulation with peptides or protein, followed by staining for intracellular cytokines. In this protocol, we use CyTOF(®) mass cytometry to collect single-cell data on a large number of cytokines/chemokines, as well as cell-surface proteins that characterize T cells and other immune cells. We also include a method for magnetic bead enrichment of antigen-stimulated T cells, based on their expression of CD154 and CD69. Coupling magnetic enrichment with highly multi-parameter mass cytometry, this method enables the ability to dissect the frequency, phenotype, and function of antigen-specific T cells in greater detail than previously possible. Rare cell subsets can be examined, while minimizing run times on the CyTOF. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 25% |
| United Kingdom | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 20% |
| Student > Ph. D. Student | 8 | 18% |
| Student > Bachelor | 3 | 7% |
| Student > Master | 3 | 7% |
| Student > Doctoral Student | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 14 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Immunology and Microbiology | 9 | 20% |
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| Agricultural and Biological Sciences | 5 | 11% |
| Engineering | 4 | 9% |
| Medicine and Dentistry | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 18 | 40% |