Fig 2: TCR sequencing of Wuhan-YLQPRTFLL tetramer sorted T cells from convalescent patients and Spike transduced A549 cells used to test the reactivity of CD8 T cell clones, related to Figures 3 and 4(A) TCR sequences of the Wuhan-YLQPRTFLL specific T cells from COVID-19 patients. M012 and F021 analysis performed on a different day. CD8 T cell clones grown from some of the donors annotated according to the key.(B) HLA A*02:01 (A2) and spike protein expression of the Wuhan and P272L spike transduced A549-A2 cells. HLA A*02 staining with BB7.2 Ab clone. Unconjugated isotype and anti-spike antibodies used in conjunction with a PE conjugated secondary antibody on Y axis.

Fig 3: YLQPRTFLL-specific T cell clones from SARS-CoV-2 convalescent patients are unable to recognize P272LYLQPRTFLL peptide reactive CD8 clones were grown from convalescent patients indicated in the brackets. a and ß TCR chain usage and CDR3s of each clone are shown and the public or private status of each indicated according to the key. Upper: peptide sensitivity assay with Wuhan-YLQPRTFLL and P272L-YLQLRTFLL peptides. MIP-1ß ELISA and error bars depict SD of duplicates. Middle: peptide-HLA tetramer staining. HLA A*02:01 SLYNTVATL (SLY) peptide from HIV used as an irrelevant tetramer (Cole et al., 2017). Lower: A549 cells expressing HLA A*02:01 (A549-A2) and full-length P272L Spike were not recognized, whereas endogenously expressed Wuhan Spike was recognized. MIP-1ß ELISA and error bars depict SD of duplicates. See also Figure S4B for HLA A2 and Spike staining of A549 cells.See also Figures S3 and S4.

Fig 4: Comparison of age [years] (A) and BMI [kg/m2] (B) between control cohort (CTRL, n = 10) and baseline cohort with morbid obesity (Baseline, n = 38) and comparison of abundance of PBMCs [% singlets] including CD3+ T cells (CD3+), CD4+ T cells (CD3+, CD56-, CD4+), CD8+ T cells (CD3+, CD56-, CD8+), B cells (CD19+), NK cells (Lin-, CD56+), HLA-DR- CD14+, Monocytes (HLA-DR+, CD14+, CD16var) divided in non-classical monocytes (NCM)(CD14low, CD16high), intermediate monocytes (IM) (CD14high, CD16high) and classical monocytes (CM) (CD14high, CD16low) (C–L). Statistical analysis was performed using a two-sided unpaired t-test (Age, CD3+ T cells, CD4+ T cells, CD8+ T cells, NCM, IM and CM), a Welch’s test (BMI, B cells) or a Mann-Whitney test (HLA-DR- CD14+, Monocytes, NK cells) with α = 0.05. *p-value ≤ 0.05, ***p-value ≤ 0.001, ****p-value ≤ 0.0001.

Fig 5: Analysis of whole blood samples using high-dimensional mass cytometry. FlowSOM-UMAP Overlay with 20,000 cells per sample using the markers CD11c, CD123, CD19, CD14, CD64, CD66b, CD11b, CD4, CD56, NKG2D, CD3, HLA-DR, CD8a, CD33, CD16 and CD45 was performed in Cytobank and enabled identification of main peripheral blood immune cell populations (A). Manually annotated cell populations were confirmed by evaluating surface marker expression (B). Processed files of the same group were concatenated and illustrated as UMAP density plots over time for CTRL, baseline, 1 - 2 months p.s., 3 - 5 months p.s., 6 - 8 months p.s. and 9 - 11 months p.s. (C). Abundances of identified immune cell populations are depicted as time-progression heatmaps whereby each row was normalized by z-score (D). Cross verification was performed by comparing time-progression of NK cells analyzed with mass cytometry and flow cytometry (mean ± SD) (E). Statistical analysis was performed compared to the CTRL using one-way ANOVA with Fisher’s LSD test with α = 0.05. *p-value ≤ 0.05, **p-value ≤ 0.01.