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Figure 1: Study
Design and a Multi-parameter Disease Severity Metric (DSM)
(A) COVID-19 patient
cohort overview and sample collection timeline.
(B)
Experimental design, analysis questions, and approach.
(C) Heatmap of 18 clinical and
serum protein measurements of patients after correction for days since
hospital admission.
(D) Parameter importance of the
fitted coefficient values from partial-least-square (PLS) ordinal
regression models of disease severity.
(E) Distribution of patient
disease severity metric (DSM) grouped based on the disease severity-outcome
classification for the 30 patients with CITE-seq data (left panel) and an
independent set of 64 patients from Brescia (right panel).
Figure 2: A
Multimodal Single Immune Cell Atlas of COVID-19
(A) Average
protein expression in each coarse-level cell cluster.
(B) UMAP
visualization of single cells based on protein expression profiles for
innate and adaptive groupings of cells labelled by the name of the
corresponding coarse-level cluster.
(C) Average
expression of selected surface protein markers in example finer-resolution
CD4+ T cell clusters (CM = central memory, TM = transitional memory, EM.TE
= terminal/effector memory).
(D-I)
Transcript-based UMAP visualization of classical monocytes defined by
surface proteins.
Figure 3:
Cell-type-specific Gene Expression Signatures of COVID-19 Disease Status
and Severity
(A) Gene set
enrichment analysis (GSEA) result of COVID-19 versus HCs at T0.
(B) Similar
to (A) but the enrichment analysis was performed based on association with
DSM, and the model controlled for TSO.
(C) Heatmap
of type I IFN response in classical monocytes.
(D)
Per-sample gene set signature scores of the GO Response to type I IFN gene
set vs. TSO (in days) in DSM-low and DSM-high groups.
(E) Scatter
plot comparing the effect size (normalized enrichment score) of association
between TSO and the GO Response to type I IFN gene set in the DSM-high (y
axis) and DSM-low (x axis) patients.
(F) Heatmap
of apoptosis/cell death signature in pDCs.
(G) Heatmap
showing the sample-level pairwise Pearson correlations among serum
IFN-α2a level, pDC frequency, the apoptosis signature score in pDCs,
as well as the IFN-I and protein translation signature scores in classical
monocyte, CD56dimCD16hi NK, and CD8 memory T cell clusters (* p value <
0.05).
Figure 4: Conditional
Independence Network Analysis Points to IL-15-associated Fatty Acid
Metabolism and Attenuated Inflammation in CD56dimCD16hi NK Cells as Primary
Correlates of Disease Severity
(A) Disease severity
network showing cell type-specific gene set signatures directly connected
with DSM.
(B and C, G
and I) Scatter plots showing the correlation of circulating IL-15 level
versus REACTOME_Fatty acid metabolism signature score (B) and DSM (C);
REACTOME_Fatty acid metabolism score versus HALLMARK_TNFa signaling via
NF-kB score and GO_Cellular response to IL-1 score (G); REACTOME_Fatty acid
metabolism score versus HALLMARK_mTORC1 signaling score and normalized IFNG
mRNA expression (I).
(D) Heatmap
of REACTOME_Fatty acid metabolism LE genes from the GSEA analysis of DSM
association in CD56dimCD16hiNK cells (see Figure 3B).
(E and F)
Similar to (D). Heatmaps of inflammation related gene sets in CD56dimCD16hi
NK cells: HALLMARK_TNFa signaling via NF-kB (E), GO_Cellular response to
IL1 (see (A)), and KEGG_Chemokine signaling pathway (see (A)) (F).
(H) Average
IFNG mRNA expression of CD56dimCD16hi NK cells; (B-C, F, I) are aligned
column wise.
(J and K)
Per-sample gene set signature scores of REACTOME_Fatty acid metabolism (J),
HALLMARK_TNFa signaling via NFkB and GO_Cellular response to IL-1 (K) vs.
TSO (in days) in DSM-high (red) and DSM-low (blue) groups of CD56dimCD16hi
NK cells.
(L)
Circulating IL-15 levels in DSM-low and DSM-high groups vs. TSO.
Figure 5: Single Cell
and Clonal Expansion Analysis in CD8+ T cells and Exhaustion Assessment in
Clonal CD8+ T cells
(A) Heatmap
showing the gene expression profile of CD8+ T cell clusters identified
based on single-cell mRNA expression of the leading-edge genes of selected
pathways presented in Figure 3; only COVID-19 T0 samples are shown.
(B) Average
expression of selected surface proteins in the clusters from (A).
(C) Results
of linear model accounting for age, and experimental batch, comparing the
frequency of CD8+ T cell clusters from (A) between COVID-19 and HC samples.
(D) Fraction
of overlap between RNA based clustering (from A) and surface protein based
CD8+ naive and memory T cell cluster annotations (based on high resolution
clustering).
(E) CD8+ T
cell clonality in HC, DSM-low, DSM-high groups.
(F)
Coefficients from linear models of mean surface protein expression of
canonical exhaustion markers fitted to COVID-19 patients and HCs.
(G) GSEA
results for assessing enrichment for known exhaustion signatures in DE
genes for expanded CD8+ T cells in COVID-19 versus HCs and DSM-high versus DSM-low
comparisons.
Figure 6: Analyses of
Timing Effects Suggest a Late Immune Response Juncture
(A) Time
course of monocyte subset frequencies in DSM-low and DSM-high groups.
(B) Similar to
(A) but showing the absolute blood neutrophil and monocyte counts.
(C) Effect
size (normalized enrichment score from GSEA) comparison of the period
before day 17 (TSO < day 17, green) and during the TSO = days 17-23
period (purple) for inflammatory related gene sets.
(D) Similar
to Figure 3A, but here showing GSEA results for assessing differences
between the DSM-high versus DSM-low groups using only samples from days 17-23
since symptom onset.
(E) Time
course of gene set signature scores of inflammatory related gene sets in
DSM-low and DSM-high patient groups in CD56dimCD16hi NK cells and classical
monocytes.
(F) Time course
of serum protein levels from DSM-high and DSM-low patients respectively.
Figure 7: Divergence of Deceased and Recovered Patients at the Late Juncture
(A)
Approach for assessing and validating the late immune
response juncture hypothesis by using serum protein profiles of critical
ill patients with either recovery or deceased outcomes.
(B)
Effect size plots of circulating serum proteins comparing the difference
between critical deceased vs. recovered patients before (days 7-16), during
(days 17-23), and after (days 24-30) the juncture period.
(C)
Outcome prediction performance (recovered vs. fatal) at (17-23 days; purple) or post (24-30 days; blue) juncture using leave-one-out cross-validation.
(D)
Similar to Figure 6F but showing serum protein levels of critical ill patients
with recovery or deceased outcomes (see (A)).
(E)
Similar to (D) but for antibody measurements against SARS CoV-2 spike and nucleocapsid proteins in critically ill patients with recovered or deceased outcomes.
Supplemental Figure 1
(related to Figure 1): Timeline of Treatments Relative to Hospital
Admission Date and DSM Validation in an Independent Set of Patients
(A) Timeline
of treatments relative to hospital admission date.
(B)
Distribution of patient disease severity metric (DSM) grouped based on the
WHO ordinal disease severity score of the CITE-seq cohort at the earliest
time of PBMC sampling.
(C)
Distribution of validation cohort disease severity metric (DSM) grouped by whether they were ever admitted to the ICU during the course of hospitalization for all patients (left) and only patients classified as Critical-Alive (right).
Supplemental Figure 2
(related to Figure 2): Single Immune Cell Atlas of COVID-19 Reveals Cell
Populations Associated with COVID-19 Disease Status and Severity
(A)
Correlations of cell frequencies gated from CITE-seq and independently
obtained 27-color flow cytometry data of the same samples.
(B)
Frequencies of immune cell clusters/subsets in HC, DSM-low (less severe
disease; DSM at or below median of DSM) and DSM-high (more severe disease;
DSM above median) groups at T0 (near hospitalization).
(C) Heatmap
showing cell frequencies of major cell clusters/subsets in individual
subjects (columns), grouped by HC and DSM.
Supplemental Figure 3
(related to Figure 3): Cell-type-specific Gene Expression Signatures
Association with Time Since Symptom Onset and Disease Severity
(A and B)
Similar to Figure 3B, but showing GSEA results (of select gene sets) based
on association with time since symptom onset (TSO) in DSM-low (A) and
DSM-high (B).
(C) Similar
to Figure 3C. Heatmap of translation/ribosomal gene signature in classical
monocytes.
(D) Similar
to Figure 3D. Time course of gene set signature scores of
REACTOME_Translation and KEGG_Ribosome gene sets in DSM-low and DSM-high
groups, respectively.
(E and F)
Similar to Figure 3F. Heatmap of apoptosis/cell death gene signature in
pDCs of validation cohorts - Schulte-Schrepping et al., Cell, 2020 cohort
1 (E) and cohort 2 (F).
(G) GSEA
results of Schulte-Schrepping et al. cohorts for pDC apoptosis/cell
death signature identified in Brescia cohort.
Supplemental Figure 4
(related to Figure 4): Supporting Data for Dissecting Primary Gene
Expression Signature Correlates Inferred by Conditional Independence
Network Analysis
(A) Scatter
plot of REACTOME_Oxidative stress-induced senescence signature score and
GO_Apoptotic signaling signature score in pDCs.
(B) Similar
to (A), but between circulating IL-15 level and fatty acid metabolism
signature score in CD56dimCD16hi NK cells after regressing out their
associations with DSM.
(C and D)
Similar to Figure 4D. Heatmaps of REACTOME_Fatty acid metabolism in NK
cells of two validation cohorts - Schulte-Schrepping et al, Cell 2020
cohort 1 (C) and cohort 2 (D).
(E) GSEA
results of Schulte-Schrepping et al. cohorts for NK cell
REACTOME_Fatty acid metabolism.
(F) Similar
to Figure 4G. Scatter plot of REACTOME_Fatty acid metabolism score and
HALLMAKR_TNFa signaling via NF-kB score in the validation cohorts - Schulte-Schrepping
et al, 2020, Cell.
(G and H)
Similar to Figure 4E. Heatmaps of inflammation related gene sets in
classical monocytes: HALLMARK_TNFa signaling via NF-kB (G) and
HALLMARK_Inflammatory response (H).
Supplemental Figure 5
(related to Figure 4): Exogeneous Corticosteroid Treatment is Not a Major
Driver of Cell-type-specific Gene Expression Signatures Associated with
Disease Severity
(A) GSEA
results for glucocorticoid response signature in DSM association model.
(B and C)
Scatter plot showing the correlations between the indicated signature
scores (computed using GSVA) and the glucocorticoid response signature
score (B) or the TSC22D3 mRNA expression level (C) in CD56dimCD16hi NK
cells.
(D) TSC22D3
mRNA expression levels of CD56dimCD16hi NK cells and classical monocytes in
HC, no steroid-use and steroid-use COVID-19 patient groups.
Supplemental Figure 6
(related to Figure 5): Single Cell and Clonal Expansion Analysis in CD4+ T
cells and Exhaustion Assessment in CD8+ T cells
(A-B, C, D) Same as
Figures 5A-5D but for CD4+ T cells. 15 CD4+ T cell clusters were tested in
linear models.
(E) Similar
to Figure 5F but for pseudo-bulk mRNA expression of canonical exhaustion
markers.
(F)
Association of proportion of CD39+PD1+ cells with COVID-19 versus HCs and
DSM in clonal CD8+ memory T cells using different cutoffs for CD39 and PD1
surface protein expression DSB counts (0.5, 1).
(G) Association
of proportion of exhausted cells with COVID-19 versus HCs and DSM in clonal
CD8+ memory T cells based using different cutoffs for surface protein
expression DSB counts (0.5, 1, 1.5) and number of exhaustion markers above
DSB count cutoff (2 or 3 markers).
(H) Gene set
enrichment of Wherry et al. up and down genes in KEGG, GO BP,
REACTOME, and Li BTM's.
(I) GSEA
result of Schulte-Schrepping et al. cohorts for exhaustion
signatures of COVID-19 versus HCs and severe versus mild comparisons at T0.
(J) Similar
to Figure 5G. GSEA results of Schulte-Schrepping et al. cohorts for Wherry
et al. exhaustion signatures of COVID-19 versus HCs and severe versus
mild comparisons at T0.
Supplemental Figure 7
(related to Figures 6 and 7): Supporting Data for Critical Juncture Analysis
(A) Similar
to Figure 3A, but here showing GSEA results for assessing the differences
of delta between disease severity groups (DSM-high versus DSM-low) between
the days 17-23 time window and the period before (TSO < day 17).
(B) Time
course of blood neutrophil and monocyte counts in recovered and deceased
groups.
(C) Effect
size comparison of DSM-high versus DSM-low (CITE-seq cohort) and deceased
versus recovered (critical patients with distinct outcome subcohorts - see
Figure 6G) at the days 17-23 period.
(D) Similar
to (C). Effect size comparison of Brescia deceased versus recovered and an
independent US cohort (Yale cohort, Lucas et al, 2020, Nature)
deceased versus recovered patients (See Methods) for 38 overlapping
circulating proteins/cytokines at the juncture period (TSO days 17-23).
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