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Single-cell mass cytometry for analysis of immune system functional states

Open access
Bjornson, Zach B, Nolan, Garry P, Fantl, Wendy J·2013·Current Opinion in Immunology

Abstract

Single cell mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on cell populations at single-cell resolution. Datasets are generated with antibody panels (upwards of 40) in which each antibody is conjugated to a polymer chelated with a stable metal isotope, usually in the Lanthanide series of the periodic table. Isotope labelled antibodies recognize surface markers to delineate cell types and intracellular signaling molecules to provide a measure of the network state—and thereby demarcating multiple cell state functions such as apoptosis, DNA damage and cell cycle. By measuring all these parameters simultaneously, the signaling state of an individual cell can be measured at its network state. This review will cover the basics of mass cytometry as well as outline steps already taken to allow it to stand aside traditional fluorescence based cytometry in the immunologist’s analytical arsenal in their study of immune states during infection.

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Metadata

Authors
Bjornson, Z. B., Nolan, G. P., & Fantl, W. J.
Venue
Current Opinion in Immunology
Publisher
Elsevier BV
Published
2013-08
Volume
25
Issue
4
Pages
484-494
ISSN
0952-7915 (ISSN-L)
OpenAlex
W2061729626
Cited by
224
References
10
FWCI
1.28
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Single-cell mass cytometry for analysis of immune system functional states. https://app.cochat.ai/papers/10.1016/j.coi.2013.07.004
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Bjornson, Z. B., Nolan, G. P., & Fantl, W. J. (2013). Single-cell mass cytometry for analysis of immune system functional states. Current Opinion in Immunology. https://doi.org/10.1016/j.coi.2013.07.004

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.