Figure 3.
Immature erythrocytic PV-derived clusters decrease following IFN-α treatment, and differences between PV and HC cannot be attributed to IFNAR expression. (A) The absolute numbers of cells in the different erythrocytic maturation stages divided into treated and untreated HC or PV samples. The differences were analyzed using paired t tests after passing the Shapiro-Wilk test for normal distribution. (B) Ridge plots for the hallmark IFN-α response in the erythrocytic clusters, divided into 4 groups, namely HC untreated (orange), HC IFN-α treated (teal), PV untreated (red), and PV IFN-α treated (violet). The heat map on the right displays the differences in the IFN-α response between the treated PV and treated HC cells. The colors indicate the normalized enrichment score (NES). (C) FC following IFN-α treatment was calculated for DEGs in PV- and HC-derived erythrocytic cells. The FC in HC cells is shown on the x-axis and PV on y-axis. Genes that did not show a similar FC deviate from the diagonal. All genes below the blue line are more strongly regulated in HC, whereas genes above the red line show stronger regulation in PV cells. (D) STAT1 expression is shown in for patients with MPN and HCs. RNA was isolated from at least 2 colonies per patient and condition after the CFU assay. Complimentary DNA was generated, and the gene expression analyzed using qPCR. The mean STAT1 expression per patient and condition is shown. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn test for multiple comparisons. (E) IFIT2 expression as percentage of MT-ATP6 is shown for patients with MPN and HCs. RNA was isolated from at least 2 colonies per patient and per condition and analyzed using qPCR. The mean IFIT2 expression per patient and condition is shown. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn test for multiple comparisons. (F) The expression of IFNAR1 from scRNA-seq data was analyzed. Violin plots show the expression in PV cells and HC cells per cell type. Statistical analyses were conducted using the raw values, and MAGIC imputation was used for visualization. (G) Surface expression of IFNAR1 analyzed by flow cytometry. Blood after incomplete lysis of erythrocytes was stained for CD45, CD71, and CD235a to identify the different maturation stages. The gating strategy is shown in supplemental Figure 4. In a second panel, IFNAR1 was stained together with CD45 and CD235a. The mean fluorescence intensity of the different populations was calculated. Three patients with PV and 3 HCs were analyzed. Statistical analyses were conducted using a row-matched 1-way analysis of variance, followed by Tukey multiple comparisons tests. (H) Heat map of DEGs (P < .05; FC < or > 0.25) in JAK2V617F, JAK2WT, and HC cells following IFN-α stimulation. The gene ontology analysis was subsequently done, and the derived pathways are indicated. ns, not significant; ∗P < .05; ∗∗ P < .01; ∗∗∗P < .001; ∗∗∗∗P < .0001.

Immature erythrocytic PV-derived clusters decrease following IFN-α treatment, and differences between PV and HC cannot be attributed to IFNAR expression. (A) The absolute numbers of cells in the different erythrocytic maturation stages divided into treated and untreated HC or PV samples. The differences were analyzed using paired t tests after passing the Shapiro-Wilk test for normal distribution. (B) Ridge plots for the hallmark IFN-α response in the erythrocytic clusters, divided into 4 groups, namely HC untreated (orange), HC IFN-α treated (teal), PV untreated (red), and PV IFN-α treated (violet). The heat map on the right displays the differences in the IFN-α response between the treated PV and treated HC cells. The colors indicate the normalized enrichment score (NES). (C) FC following IFN-α treatment was calculated for DEGs in PV- and HC-derived erythrocytic cells. The FC in HC cells is shown on the x-axis and PV on y-axis. Genes that did not show a similar FC deviate from the diagonal. All genes below the blue line are more strongly regulated in HC, whereas genes above the red line show stronger regulation in PV cells. (D) STAT1 expression is shown in for patients with MPN and HCs. RNA was isolated from at least 2 colonies per patient and condition after the CFU assay. Complimentary DNA was generated, and the gene expression analyzed using qPCR. The mean STAT1 expression per patient and condition is shown. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn test for multiple comparisons. (E) IFIT2 expression as percentage of MT-ATP6 is shown for patients with MPN and HCs. RNA was isolated from at least 2 colonies per patient and per condition and analyzed using qPCR. The mean IFIT2 expression per patient and condition is shown. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn test for multiple comparisons. (F) The expression of IFNAR1 from scRNA-seq data was analyzed. Violin plots show the expression in PV cells and HC cells per cell type. Statistical analyses were conducted using the raw values, and MAGIC imputation was used for visualization. (G) Surface expression of IFNAR1 analyzed by flow cytometry. Blood after incomplete lysis of erythrocytes was stained for CD45, CD71, and CD235a to identify the different maturation stages. The gating strategy is shown in supplemental Figure 4. In a second panel, IFNAR1 was stained together with CD45 and CD235a. The mean fluorescence intensity of the different populations was calculated. Three patients with PV and 3 HCs were analyzed. Statistical analyses were conducted using a row-matched 1-way analysis of variance, followed by Tukey multiple comparisons tests. (H) Heat map of DEGs (P < .05; FC < or > 0.25) in JAK2V617F, JAK2WT, and HC cells following IFN-α stimulation. The gene ontology analysis was subsequently done, and the derived pathways are indicated. ns, not significant; ∗P < .05; ∗∗ P < .01; ∗∗∗P < .001; ∗∗∗∗P < .0001.

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