Advanced immunophenotyping: A powerful tool for immune profiling, drug screening, and a personalized treatment approach

Abstract

Various autoimmune diseases are characterized by distinct cell subset distributions and activation profiles of peripheral blood mononuclear cells (PBMCs). PBMCs can therefore serve as an ideal biomarker material, which is easily accessible and allows for screening of multiple cell types. A detailed understanding of the immune landscape is critical for the diagnosis of patients with autoimmune diseases, as well as for a personalized treatment approach. In our study, we investigate the potential of multi-parameter spectral flow cytometry for the identification of patients suffering from autoimmune diseases and its power as an evaluation tool for in vitro drug screening approaches (advanced immunophenotyping). We designed a combination of two 22-color immunophenotyping panels for profiling cell subset distribution and cell activation. Downstream bioinformatics analyses included percentages of individual cell populations and median fluorescent intensity of defined markers which were then visualized as heatmaps and in dimensionality reduction approaches. In vitro testing of epigenetic immunomodulatory drugs revealed an altered activation status upon treatment, which supports the use of spectral flow cytometry as a high-throughput drug screening tool. Advanced immunophenotyping might support the exploration of novel therapeutic drugs and contribute to future personalized treatment approaches in autoimmune diseases and beyond.

Keywords: PBMC (peripheral blood mononucleated cells); T cells; drug screening; flow cytometry; immunophenotyping.

Figure 1

Schematic illustration of the workflow for panel design and verification. (A) Theoretical panel design of the T cell immunophenotyping panel. Readout of the Cytek Full Spectrum Viewer (Cytek Biosciences) displaying the spectral signatures of the 22 fluorophores in the 3L configuration of the Cytek Aurora (left). Optical layout of the used markers and fluorophores showing the approximate peak emission wavelengths (nm) (right). (B) Overview of the verification strategy for the T cell immunophenotyping panel. Representative example of the antibody titration depicting CD28 conjugated to BV650. Left: Pseudo-color plot of the concatenated files of the 1:2 serial dilutions of CD28. On the x-axis, the respective concentration is shown as µl/test. The red box indicates the final titration result. Right top: The Stain Index (SI) is calculated as the difference between the median fluorescence intensity (MFI) of the positive and negative populations, divided by two times the robust standard deviation (rSD) of the negative population. MFI and rSD were extracted from FlowJo in.csv file format. Right bottom: Diagram depicting the calculated SI of CD28-BV650 over 1:2 serial dilution steps. The x-axis displays the respective concentration as µl/test. The red circle indicates the final titration result. (C) Dot plots showing an exemplary illustration of “unmixing” accuracy of CXCR5-BV750 and CCR6-BV711 utilizing single stained compensation beads compared to single stained whole PBMCs. (D) NxN plots depicting the quality control of “unmixing” accuracy in the multi-color stained sample, gated on lymphocytes. CCR7-BV421 is shown as a representative example of correct “unmixing”. In every NxN plot, CCR7 is represented on the y-axis and all other fluorophores of the panel on the x-axis. (E) Staining resolution of the single stained (SS) tube compared to the fully stained/multi-color (MC) tube on the example of CXCR5-BV750. (F) Loss of staining resolution resulted in fine-tuning of the staining protocol, such as separate staining steps and changes in the antibody concentrations. (G) Immunophenotyping results were manually gated and compared to published studies to evaluate the quality of the data.

Figure 2

Gating strategy and presentation of PBMC and T cell subsets (A, D) Manual gating approaches of the major (A) PBMC subsets and (D) T cell subsets, respectively, following 24 hours of in vitro cultivation. Red arrows depict the relationships across plots. Numbers indicate the percentages of cells in the quadrants or gates. Individual parent gates are referred to on top of the plots when necessary. One representative donor is shown. (B, E, G) High-dimensional data analysis using Uniform Manifold Approximation and Projection (UMAP) trained on (B) whole PBMCs, (E) CD3⁺ T cells, and (G) CD4⁺ T cells, respectively, of concatenated donors depicting the accurate separation of the manually gated subsets following 24 hours in vitro cultivation. UMAPs were generated in FlowJo. (C, F, H) Bar charts illustrating the percentages of indicated subsets within (C) viable PBMCs (left), CD19⁺ B cells (right), (F) CD3⁺ T cells (top), CD4⁺ and CD8⁺ T cells (bottom), as well as (H) CD4⁺ T cells of 6 donors following 24 hours in vitro cultivation. Each symbol indicates 1 independent biological sample. Horizontal bars indicate the mean, error bars show the standard deviation. (A–H) Data are representative (A, D) or show a summary (B, C, E–H) of at least 6 independent experiments. DC, dendritic cells; pDC, plasmacytoid DC; mDC, myeloid DC; NK cells, natural killer; Bc, B cells; N, naïve; UM, sunswitched memory; SwMe, witched memory; DN, double-negative; DP, double-positive; PB/PC, plasmablasts/plasma cells; Mo, monocytes; int, intermediate; non-c, non-classical; Tc, T cells; TN, naïve T cells; TCM, central memory T cells; TEMRA, terminally differentiated effector T cells; TEM, effector memory T cells; Th, T helper cells; Treg, regulatory T cells; Tfh, T follicular helper cells; Tph, T peripheral helper cells.

Figure 3

Drug screening approach for whole PBMCs. (A) Schematic illustration of the workflow. PBMCs of 6 healthy controls (HCs) were PHA-stimulated and cultured for 24 hours in the presence of 5 immunomodulatory drugs and DMSO as control, respectively. (B) Summary heatmap depicting fractional differences (FD) of the marker expression as median fluorescent intensity (MFI) of drug-treated PBMCs compared to DMSO control in the indicated PBMC subsets of 6 pooled HCs. Each column represents the individual drugs (drugs 1-5), and rows show the marker expression in the distinct PBMC compartments. Heatmaps were generated in R using the “Complex Heatmap” package; data are represented as the FD in percentages of the marker MFI of the individual compounds compared to DMSO and clustered by column. DMSO control was set to 0. Outliers (x<-150%, x>150%) were removed from the matrix. The color code depicts activation (green), inhibitory (purple), and lineage-specific (black) markers. (C) High-dimensional data analysis on viable PBMCs from concatenated HCs using the t-Stochastic Neighbor Embedding plot (tSNE) plugin in FlowJo displaying manually gated clusters of the respective PBMC subsets (first row) and density plots illustrating the global marker expression of CD69 and CD38, respectively, in the DMSO condition and in response to treatment with drug 4 and drug 5 (second and third row). Yellow-orange colors depict areas of high marker expression, whereas dark green-blue areas indicate areas of lower marker expression. (D) Histograms showing CD69 and CD38 expression, respectively, in the indicated PBMC subsets. Colors illustrate the treatment condition, as DMSO is green, drug 4 treatment is blue and drug 5 treatment is red. (E) Summary box plots showing the FD of the marker MFI of drug 1-5 compared to DSMO in the indicated PBMC subsets. The intercept line equates to DMSO control. (*P < 0.05, **P < 0.01, and ***P < 0.001). (A–E) The DC subset encompasses pDCs and mDCs. Data are representative (D) or show a summary (B, C, E) of at least 6 independent experiments. DC, dendritic cells; pDC, plasmacytoid DC; mDC, myeloid DC; Mo, monocytes; NKs, natural killer cells; Bc, B cells.

Figure 4

Drug screening approach for T cell subsets. (A) Summary heatmap depicting fractional differences (FD) of the marker expression as median fluorescent intensity (MFI) of drug-treated PBMCs compared to DMSO control in the indicated T cell/Th cell subsets of 6 pooled HCs. Each column represents the individual drugs (drugs 1-5), and rows show the marker expression in the distinct PBMC compartments. Heatmaps were generated in R using the “Complex Heatmap” package; data are represented as the FD in percentages of the marker MFI of the individual compounds compared to DMSO and clustered by column. DMSO control was set to 0. Outliers (x<-150%, x>150%) were removed from the matrix. The color code depicts activation (green), inhibitory (purple), and maturation (turquoise) markers. (B) High-dimensional data analysis on CD3⁺ T cells of PBMCs from concatenated HCs using the t-Stochastic Neighbor Embedding plot (tSNE) plugin in FlowJo displaying manually gated clusters of the respective T cell subsets (first row) and density plots illustrating the global marker expression of CD69 and CD25, respectively, in the DMSO condition and in response to treatment with drug 4 and drug 5 (second and third row). Yellow-orange colors depict areas of high marker expression, whereas dark green-blue areas indicate areas of lower marker expression. (C) Histograms showing CD69 and CD25 expression, respectively, in the indicated T cell/Th cell subsets. Colors illustrate the treatment condition, as DMSO is green, drug 4 treatment is blue and drug 5 treatment is red. (D) Summary box plots showing the FD of the marker MFI of drugs 1-5 compared to DSMO in the indicated T cell/Th cell subsets. The intercept line equates to DMSO control. (*P < 0.05, **P < 0.01, and ***P < 0.001) (A–D) Data are representative (C) or show a summary (A, B, D) of at least 6 independent experiments. Th, T helper; Treg, regulatory T cells; Tfh, T follicular helper and Tph, T peripheral helper.

Figure 5

Immunophenotyping of patients suffering from autoimmune diseases. (A) Schematic illustration of the workflow. Frozen PBMCs of 5 naïve, untreated, female RA patients and 5 sex-matched and age-matched HCs were thawed and directly subjected to immunophenotyping using the established PBMC and T cell panel. (B) Bar charts depicting the percentages of the indicated subsets within viable PBMCs (left, top), CD19⁺ B cells (middle, top), CD3⁺ T cells (right, top), CD4⁺ T cells (left, bottom), CD4⁺ T cells (middle, bottom), and CD8⁺ T cells (right, bottom). Each symbol indicates 1 independent biological sample. Statistical comparisons were done by Student’s t-test, comparing the 5 individual replicates of RA patients to HC. Significance was defined as p-value (*P < 0.05 and ***P < 0.001). (C) 3-dimensional PCA depicting the combined activation marker expression levels resulting from the immunophenotyping using the PBMC and T cell panel. Each symbol indicates 1 independent biological sample. Blue dots show HCs and red dots illustrate RA patients. (D, F) Spectral flow cytometry analysis showing CD69 expression in the indicated (D) PBMC and (F) T cell subsets of 1 representative HC and RA patient, respectively. Numbers indicate the percentage of cells in the quadrants or gates. (E, G) Histograms showing (E) CD95 expression or (G) ICOS expression in the indicated (E) PBMC and (G) T cell subsets of 1 representative HC and RA patient, respectively. (B–E) The DC subset encompasses pDCs and mDCs. Data are representative (D–G) or show a summary (B, C) of at least 3 independent experiments. DC, dendritic cells; Mos, monocytes; NKs, natural killer cells; Bc, B cells; Tc, T cells; N, naïve; UnMe, unswitched memory; SwMe, switched memory; DN, double-negative; DP, double-positive; T_N, naïve T cells; T_CM, central memory T cells; T_EMRA, terminally differentiated effector T cells; T_EM, effector memory T cells; Th, T helper cells; Treg, regulatory T cells; Tfh, T follicular helper cells; Tph, T peripheral helper cells; RA, Rheumatoid arthritis; HC, healthy control; PCA, Principal Component Analysis.