Interpreting flow cytometry data: a guide for the perplexed

By Leonore Herzenberg, J. Tung, W. Moore, L. A. Herzenberg, D. Parks. Nature Immunology, 2006

473 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f975aba5bd13e02871373e83521e5fdf111efc39

How researchers see this paper

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In Biomedical Engineering

1. L. A. Herzenberg
2. Leonore Herzenberg

In Molecular Biology

1. L. A. Herzenberg
2. Leonore Herzenberg
3. Garry P. Nolan
4. Josef Spidlen
5. D. Monack
6. Nikesh Kotecha

Citations in the corpus

• Two physically, functionally, and developmentally distinct peritoneal macrophage subsets (2010, methodology): “1; Alexa700labeled anti-IgD (11-26); APCCy7-labeled anti-CD11b (M1/70); Pacific Blue– labeled anti-F4/80 (BM8) or Gr-1 (RB6-8C5) and biotin-labeled anti-CD11c (HL3).”
• Activation-induced FOXP3 in human T effector cells does not suppress proliferation or cytokine production. (2007, unknown): “Based on recommendations from Herzenberg ( 18 ) and Roederer (19), gates for FOXP3-positive cells were set based on fluorescence minus one controls.”
• Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE (2011, unknown): “One common but cumbersome step is the selection of subsets of cells in a process called “gating” [3].”
• MIFlowCyt: The Minimum Information about a Flow Cytometry Experiment (2008, background): “Such technological advances, in the absence of reporting standards, also introduce considerable challenges for the producers, reviewers, and consumers of today’s biomedical and immunology literature (3).”