Supplementary Data from: Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity, Potts et al.
Full associated publication: 'Proteomic analysis of circulating immune cells identifies cellular phenotypes associated with COVID-19 severity', Potts et al (2023). Provided supplementary data includes: Table S2. Details of donors used to generate whole-blood RNA-seq data in Bergamaschi et al and re-analysed here for comparison with proteomic data, related to Figures 3 and 4. Table S3. Details of donors analysed by flow cytometry panels in Bergamaschi et al and re-analysed here to determine sample neutrophil contamination, related to Figure 3. Table S5. Functional enrichment analysis of proteomic data, related to Figure 2. Enrichment of functional pathways in clusters of cellular proteins upregulated during COVID. DAVID enrichment terms and corresponding Benjamini-Hochberg-corrected p-values are shown for each cluster in Fig. 2B. Table S6. Interactive spreadsheet of all proteomic and transcriptomic data in the manuscript, related to Figures 2, 3, 4. (A) Interactive searchable spreadsheet containing all data and statistics from whole cellular (WCL), plasma membrane (PM) and RNAseq analyses (B) Proteomic data from all WCL analyses (C) Proteomic data from WCL analyses for proteins quantified across all three WCL experiments (D) Results of statistical tests comparing relative abundance of each protein quantified in WCL analyses. (E) Proteomic data from second PM analysis (F) Proteomic data from all PM analyses (G) Results of statistical tests comparing relative abundance of each protein quantified in second PM analysis. (H) Transcriptomic data from all donors generated in Bergamaschi et al at day 0 timepoint. Data expressed as Log2(RPKM). (I) Transcriptomic data from donors also analysed in proteomic analyses, generated in Bergamaschi et al at day 0 timepoint. Data expressed as Log2(RPKM).