Table 1.
Comparison of currently available software for flow cytometry analysis.
Fig 1.
Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.
Fig 2.
Workflow of T cell activation analysis.
T cells (Modified Jurkats, see Methods) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.