↓ Skip to main content

PLOS

Linear Fidelity in Quantification of Anti-Viral CD8+ T Cells

Overview of attention for article published in PLOS ONE, June 2012
Altmetric Badge

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
6 Mendeley
Title
Linear Fidelity in Quantification of Anti-Viral CD8+ T Cells
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039533
Pubmed ID
Authors

Inge E. A. Flesch, Natasha A. Hollett, Yik Chun Wong, David C. Tscharke

Abstract

Enumeration of anti-viral CD8(+) T cells to make comparisons between mice, viruses and vaccines is a frequently used approach, but controversy persists as to the most appropriate methods. Use of peptide-MHC tetramers (or variants) and intracellular staining for cytokines, in particular IFNγ, after a short ex vivo stimulation are now common, as are a variety of cytotoxicity assays, but few direct comparisons have been made. It has been argued that use of tetramers leads to the counting of non-functional T cells and that measurement of single cytokines will fail to identify cells with alternative functions. Further, the linear range of these methods has not been tested and this is required to give confidence that relative quantifications can be compared across samples. Here we show for two acute virus infections and CD8(+) T cells activated in vitro that DimerX (a tetramer variant) and intracellular staining for IFNγ, alone or in combination with CD107 to detect degranulation, gave comparable results at the peak of the response. Importantly, these methods were highly linear over nearly two orders of magnitude. In contrast, in vitro and in vivo assays for cytotoxicity were not linear, suffering from high background killing, plateaus in maximal killing and substantial underestimation of differences in magnitude of responses.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 17%
Unknown 5 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Professor 1 17%
Researcher 1 17%
Student > Doctoral Student 1 17%
Unknown 1 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 50%
Biochemistry, Genetics and Molecular Biology 1 17%
Immunology and Microbiology 1 17%
Unknown 1 17%