Figure 4.
Spatial transcriptomic analysis of megakaryocytes within the bone marrow. MK-ROI were selected depending on their position: ASV- or NASV-MK. (A) Volcano plots (Mock ASV-MK [n = 6] vs Mock NASV-MK [n = 6]) shows the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes depending on their position. Thresholds are indicated with dotted lines. (B) UMAP plot of the short gene signature of MK-ROI colored by class in Mock condition (blue circles: Mock ASV [n = 6]; red circles: Mock NASV [n = 6]). (C) Volcano plot of COVID_7d ASV-MK (n = 6) vs Mock ASV-MK (n = 6) (left) and COVID_7d NASV-MK (n = 6) vs Mock NASV-MK (n = 6) (right) showing the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes during COVID-19. Thresholds are indicated with dotted lines. (D) UMAP plot of the short gene signature of MK-ROI colored by class in Mock ASV-MK (blue circles [n = 6]; and COVID_7d ASV-MK red circles [n = 6]). (E) Volcano plot (COVID_7d NASV-MK [n = 6] vs Mock NASV-MK [n = 6]) show the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes depending on their position. Thresholds are indicated with dotted lines. (F) UMAP plot of the short gene signature of MK-ROI colored by class in Mock NASV-MK (blue circles [n = 6]) and COVID_7d NASV-MK (red circles [n = 6]). (G) Heat map showing differences of the 15-gene signature between ASV-MK in Mock and COVID_7d. Each row represents a MK-ROI and each column represents a gene obtained from the short signature. The 15-gene signature was obtained using a machine learning approach. BioDiscML was used to classify “Mock ASV-MK” and “COVID_7d ASV-MK.” The best model was the Functions Logistic optimized with BER (Balanced Error Rate), and MCC for the model was 0.922 ± 0.113. (H) Heat map showing differences of the 30-gene signature between NASV-MK in Mock and COVID_7d. Each row represents a MK-ROI, and each column represents a gene obtained from the short signature. The 15-gene signature was obtained using a machine learning approach. BioDiscML was used to classified “Mock NASV-MK” and “COVID_7d NASV-MK.” The best model was the complement Naïve Bayes with FDR; MCC for the model was 0.922 ± 0.114.

Spatial transcriptomic analysis of megakaryocytes within the bone marrow. MK-ROI were selected depending on their position: ASV- or NASV-MK. (A) Volcano plots (Mock ASV-MK [n = 6] vs Mock NASV-MK [n = 6]) shows the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes depending on their position. Thresholds are indicated with dotted lines. (B) UMAP plot of the short gene signature of MK-ROI colored by class in Mock condition (blue circles: Mock ASV [n = 6]; red circles: Mock NASV [n = 6]). (C) Volcano plot of COVID_7d ASV-MK (n = 6) vs Mock ASV-MK (n = 6) (left) and COVID_7d NASV-MK (n = 6) vs Mock NASV-MK (n = 6) (right) showing the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes during COVID-19. Thresholds are indicated with dotted lines. (D) UMAP plot of the short gene signature of MK-ROI colored by class in Mock ASV-MK (blue circles [n = 6]; and COVID_7d ASV-MK red circles [n = 6]). (E) Volcano plot (COVID_7d NASV-MK [n = 6] vs Mock NASV-MK [n = 6]) show the log2 fold-change of each gene plotted against its statistical significance (−log10 P value). Red dots represent genes significantly upregulated and blue dots, genes significantly downregulated in megakaryocytes depending on their position. Thresholds are indicated with dotted lines. (F) UMAP plot of the short gene signature of MK-ROI colored by class in Mock NASV-MK (blue circles [n = 6]) and COVID_7d NASV-MK (red circles [n = 6]). (G) Heat map showing differences of the 15-gene signature between ASV-MK in Mock and COVID_7d. Each row represents a MK-ROI and each column represents a gene obtained from the short signature. The 15-gene signature was obtained using a machine learning approach. BioDiscML was used to classify “Mock ASV-MK” and “COVID_7d ASV-MK.” The best model was the Functions Logistic optimized with BER (Balanced Error Rate), and MCC for the model was 0.922 ± 0.113. (H) Heat map showing differences of the 30-gene signature between NASV-MK in Mock and COVID_7d. Each row represents a MK-ROI, and each column represents a gene obtained from the short signature. The 15-gene signature was obtained using a machine learning approach. BioDiscML was used to classified “Mock NASV-MK” and “COVID_7d NASV-MK.” The best model was the complement Naïve Bayes with FDR; MCC for the model was 0.922 ± 0.114.

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