Chapter title |
Deciphering T Cell Immunometabolism with Activity-Based Protein Profiling
|
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Chapter number | 124 |
Book title |
Activity-Based Protein Profiling
|
Published in |
Current topics in microbiology and immunology, August 2018
|
DOI | 10.1007/82_2018_124 |
Pubmed ID | |
Book ISBNs |
978-3-03-011142-7, 978-3-03-011143-4
|
Authors |
Adam L. Borne, Tao Huang, Rebecca L. McCloud, Boobalan Pachaiyappan, Timothy N. J. Bullock, Ku-Lung Hsu, Borne, Adam L., Huang, Tao, McCloud, Rebecca L., Pachaiyappan, Boobalan, Bullock, Timothy N. J., Hsu, Ku-Lung |
Abstract |
As a major sentinel of adaptive immunity, T cells seek and destroy diseased cells using antigen recognition to achieve molecular specificity. Strategies to block checkpoint inhibition of T cell activity and thus reawaken the patient's antitumor immune responses are rapidly becoming standard of care for treatment of diverse cancers. Adoptive transfer of patient T cells genetically engineered with tumor-targeting capabilities is redefining the field of personalized medicines. The diverse opportunities for exploiting T cell biology in the clinic have prompted new efforts to expand the scope of targets amenable to immuno-oncology. Given the complex spatiotemporal regulation of T cell function and fate, new technologies capable of global molecular profiling in vivo are needed to guide selection of appropriate T cell targets and subsets. In this chapter, we describe the use of activity-based protein profiling (ABPP) to illuminate different aspects of T cell metabolism and signaling as fertile starting points for investigation. We highlight the merits of ABPP methods to enable target, inhibitor, and biochemical pathway discovery of T cells in the burgeoning field of immuno-oncology. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 5 | 56% |
Unknown | 4 | 44% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 67% |
Scientists | 3 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 3 | 19% |
Student > Master | 3 | 19% |
Researcher | 2 | 13% |
Student > Bachelor | 1 | 6% |
Other | 1 | 6% |
Other | 1 | 6% |
Unknown | 5 | 31% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 4 | 25% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
Computer Science | 1 | 6% |
Psychology | 1 | 6% |
Medicine and Dentistry | 1 | 6% |
Other | 2 | 13% |
Unknown | 6 | 38% |