Cd19 pe cy5 clone hib19

PBMC were washed and stained with 5 μl Live/Dead Violet (Invitrogen) followed by surface staining with the following titrated antibodies: 0.5 μl CD3-AF700 (clone UCHT1, Ebioscience), 2 μl CD4-APC (clone RPA-T4, BioLegend), 2 μl CD8-Efluor605 (clone RPA-T8, Ebioscience), 2 μl CD14-PE/Cy7 (clone HCD14, BioLegend), 2 μl CD16-AF488 (clone 3G8, BioLegend), 1 μl CD19-PE/Cy5 (clone HIB19, BioLegend), 2 μl CD25-APC/Cy7 (clone BC96, BioLegend), 2 μl CD127-NC650 (clone eBioRDR5, Ebioscience), 15 μl HLA-DR-PE (clone L243 BioLegend). Fluorescence minus one controls were used to set gates for CD25, CD127 and HLA-DR. Samples were acquired on a BD LSR II flow cytometer. Results are presented as percentages of cells after gating out of dead cells and doublets. CD4⁺ and CD8⁺ T cells were identified as CD3⁺ cells, whereas CD14^+/− and CD16^+/− cells were identified as CD3⁻ and CD19⁻ populations. CD25⁺ CD27⁻ populations were gated on the CD4⁺ cells.

As previously described (6 (link)), PBMC were washed and stained with 5 μL Live/Dead Violet (Invitrogen) followed by surface staining with the following titrated antibodies: 0.5 μL CD3-AF700 (clone UCHT1, eBioscience), 2 μL CD4-APC (clone RPA-T4, BioLegend), 2 μL CD8-eFluor605 (clone RPA-T8, eBioscience), 2 μL CD14-PE/Cy7 (clone HCD14, BioLegend), 2 μL CD16-AF488 (clone 3G8, BioLegend), 1 μL CD19-PE/Cy5 (clone HIB19, BioLegend), 2 μL CD25-APC/Cy7 (clone BC96, BioLegend), 2 μL CD127-NC650 (clone eBioRDR5, eBioscience), and 15 μL HLA-DR–PE (clone L243, BioLegend). Fluorescence minus one (FMO) controls were used to set gates for CD25, CD127, and HLA-DR. Samples were acquired on a BD LSR II flow cytometer. Results are presented as percentages of cells after excluding dead cells and doublets. CD4⁺ and CD8⁺ T cells were identified as CD3⁺ cells, while CD14^+/− and CD16^+/− cells were identified as CD3⁻ and CD19⁻ populations. CD25⁺CD27⁻ populations were gated on the CD4⁺ cells. The network representation of cell populations positively correlated among all infants was done using the igraph package in R. To identify closely related clusters (communities) within the network, the cluster_optimal function was used, implementing an algorithm described in Brandes et. al.(26 (link)).