Flowjo linux5/20/2023 ![]() Axis parameters correspond to fluorescence from bound antibodies, thus allowing population identification. At bottom, the first six hierarchical gates on a representative sample to identify cells after an initial burst that may have sample crossover, singlet cells, live (left to right, row 1), peripheral blood mononuclear cells (PBMC), T cells and CD4+ and CD8+ T cells (left to right, row 2). ![]() In flow cytometry, populations are identified with “gates,” which are shapes drawn on cell clusters. Top right: derived versus raw data produced from the entire study and analysis. Top left: summary of patients, time points, acquisition runs and statistics and cell populations of interest in the SHINE study. ![]() In this study, the manual analysis of each file and review of each of the 120 gates and statistics on all 632 clinical samples and controls would take 110 cumulative days.įigure 2 – Clinical study analysis challenge. In addition, while FlowJo provides reproducible and rapid analysis, validation in this workflow is performed by manual review (Figure 2 shows only six gates from a representative sample). Manual analysis via gating and review were an enormous computing challenge because raw data comprise the majority of the memory footprint (Figure 2 b). The study included 120 cell phenotypes and statistics with 632 clinical samples, 3–4 time points over a one-year period and 163 unique study subjects. The analysis challenge is summarized in Figure 2a. The authors used a dataset from the Supporting Health by Integrating Nutrition and Exercise (SHINE) study, which examined neuroendocrine modulation of immune function. However, the acquisition of large numbers of samples and the need to run a 96-well plate in under 4 minutes and a 1536-well plate in an hour 1–4 have created a data analysis bottleneck that limits the potential of available analysis tools.Ĭlearly, a better way to facilitate automated analysis had to be found. FlowJo cell analysis software (FlowJo, LLC, Ashland, Ore.) is able to analyze flow cytometry data at the experiment level, enabling researchers to quickly and reproducibly cluster cell phenotypes by “gating” (Figure 1 d). As cytometers evolved into the 1990s, a more powerful and user-friendly analysis and display program was needed. Modern flow cytometry originated in the late 1960s in the Herzenberg Laboratory at Stanford University (Stanford, Calif.). The resulting fluorescence data may be visualized as a 2D histogram in a FlowJo pseudocolor dot-plot graph, which heat maps fluorescence intensities of greatest frequency in red. ![]() In this manner, fluorescence may be correlated with the protein to which the antibody bound specifically and cells identified (d). This excites fluorescence at unique emission wavelengths that can be detected by a flow cytometer, which in turn produces (c) list-mode data files. From left to right (a) a single cell suspension stained with different fluorophore-bound antibodies is interrogated (b) with light from one or more lasers (illustrated as blue and green). The data produced by these instruments is stored as event-level data and associated metadata, which gives researchers the ability to determine the phenotype of many thousands of cells (Figure 1 d).įigure 1 – Flow cytometry. A core technology in single-cell phenomics is flow cytometry, in which cells are labeled with fluorescent-bound antibodies in suspension, interrogated with laser light individually at several thousands of cells per second in a stream of fluid and measured using a flow cytometer that detects both scatter and fluorescence parameters ( Figure 1b). While genomics and transcriptomics provide insight into the potential of a cell, single-cell phenomic data reflect its current reality, allowing researchers to determine how genomic variants affect phenotypes and providing deep insight into the causes of disease.
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