MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration

Abstract

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

Figure 1.. MetaGate data analysis workflow.

(A) A biological sample, such as patient blood, is analyzed using a mass or flow cytometer, which produces FCS data files. Manual gating is performed in FlowJo or Cytobank, creating a data file with specifications of each gate. (B) Gate data and FCS files are imported into MetaGate, where a graphical user interface allows defining populations based on combinations of gates. Through a data reduction algorithm, a MetaGate data file is created, which contains marker expression and event frequencies of combinations of populations. (C) The self-containing MetaGate data file is opened in the MetaGate graphical user interface for interactive production of statistics and plots, such as heatmaps, volcano plots and bar plots.

Figure 2.. DLBCL immune characterization workflow.

(A) Peripheral blood was collected from healthy blood donors (n=17) and from patients diagnosed with diffuse large B-cell lymphoma (n=28) before and after chemotherapy. (B) Blood samples were split and analyzed using two mass cytometry panels. Data from each panel was imported separately in MetaGate and later merged.

Figure 3.. Peripheral blood immune cell composition in DLBCL.

(A) Heat map showing expression of key markers in subsets of analyzed cell types, visualizing how subsets were defined for downstream analysis. (B) Volcano plot showing size differences of 36 key immune cell types between healthy donors and all patients before chemotherapy. (C– E) Bar plots showing percentages of (C) M–MDSC (defined as HLA-DR⁻ CD14⁺ CD19⁻ CD3⁻ CD56⁻ cells), (D) T cells and (E) CD56^bright NK cells, within various parent populations in healthy controls (n=17) and all patients before therapy (n=28). (F) Heatmap showing differences in marker expression between healthy controls (n=17) and patients before therapy (n=21–28) within multiple immune cell subsets, with colors indicating direction of difference and statistical significance from nonparametric tests without p value adjustment. Values are mean intensity values unless otherwise indicated. (G) Box plots showing selected readouts from (F).

Figure 4.. Effects of treatment on immune cell phenotypes.

(A) B cell frequencies as percentage of all CD45⁺ in healthy controls (n=17) and all patients (n=28) before and after treatment. (B) Volcano plot showing differences in absolute counts of 28 cell subsets before and after treatment (n=28). (C–D) Selected comparisons from (B). (E) Heatmap showing median frequencies of key NK cell subsets as percentage of bulk NK cells in healthy controls (n=17) and patients (n=28) before and after therapy. (F) Heatmap showing differences in marker expression within multiple immune cell subsets between patients before and after treatment (n=20–28), with colors indicating direction of difference and statistical significance from paired nonparametric tests without p value adjustment. (G) Mean CD38 expression in multiple NK cell subsets of healthy controls (n=15–17) and patients (n=25–28) before and after treatment.

Figure 5.. Immune cell repertoires stratified on patient characteristics.

(A–C, F) Volcano plots showing differences in 33 absolute cell counts in peripheral blood of patients before therapy, stratified on (A) age, (B) subtype, (C) stage and (F) disease progression within the follow-up time. (D–E, G–H) Selected readouts from (C) and (F).