Identification of CD4+ T cell subsets using FlowSOM and UMAP
After gating for CD4+ T cells using FlowJo, pre-processed data were considered for the analysis. All FlowSOM-based k-NN clustering was performed on the combined dataset to enable identification of small populations. For CD4+ TEMs, resulting nodes were meta-clustered with the indicated k-values (based on the elbow criterion) and annotated manually. FlowSOM k-NN clustering and two-dimensions UMAP projections were calculated using the CyTOF workflow package (v. 1.2)97 (link).
Suzzi S., Croese T., Ravid A., Gold O., Clark A.R., Medina S., Kitsberg D., Adam M., Vernon K.A., Kohnert E., Shapira I., Malitsky S., Itkin M., Brandis A., Mehlman T., Salame T.M., Colaiuta S.P., Cahalon L., Slyper M., Greka A., Habib N, & Schwartz M. (2023). N-acetylneuraminic acid links immune exhaustion and accelerated memory deficit in diet-induced obese Alzheimer’s disease mouse model. Nature Communications, 14, 1293.
Flow cytometry-based clustering (FlowSOM) with k-nearest neighbor (k-NN) algorithm
UMAP projections
dependent variables
Identification and annotation of CD4+ T effector memory (TEM) cell populations
control variables
CD4+ T cell gating using FlowJo
Combined dataset used for FlowSOM-based k-NN clustering to enable identification of small populations
Annotations
Based on most similar protocols
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