Single-cell landscape of immunological responses in COVID ......2020/07/23 · Respiratory...
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Single-cell landscape of immunological responses in COVID-19 patients
Ji-Yuan Zhang1,*, Xiang-Ming Wang2,*, Xudong Xing3,2,*, Zhe Xu1,*, Chao Zhang1, Jin-
Wen Song1, Xing Fan1, Peng Xia1, Jun-Liang Fu1, Si-Yu Wang1, Ruo-Nan Xu1, Xiao-
Peng Dai1, Lei Shi1, Lei Huang1, Tian-Jun Jiang1, Ming Shi1, Yuxia Zhang4, Alimuddin
Zumla5, Markus Maeurer6, Fan Bai2,†, Fu-Sheng Wang1,†
Institutional affiliations:
1Department of Infectious Diseases, Fifth Medical Center of Chinese PLA General
Hospital, National Clinical Research Center for Infectious Diseases, Beijing, 100039,
China. 2Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking
University, Beijing, 100871, China. 3Peking University-Tsinghua University-National Institute of Biological Sciences Joint
Graduate Program, School of Life Sciences, Tsinghua University, Beijing, 100084,
China. 4Guangzhou Women and Children's Medical Center, State Key Laboratory of
Respiratory Diseases, Guangzhou Medical University, Guangzhou, 510623,
Guangdong, China. 5Department of Infection, Division of Infection and Immunity, University College
London, London, UK; National Institute for Health Research Biomedical Research
Centre, University College London Hospitals NHS Foundation Trust, London, United
Kingdom. 6Immunotherapy Programme, Champalimaud Centre for the Unknown, Lisbon,
Portugal; I Med Clinic, University of Mainz, 55099, Mainz, Germany.
*These authors contributed equally.
†Correspondence:
Fu-Sheng Wang (email: [email protected]), Fan Bai (email: [email protected])
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Abstract
In COVID-19 caused by SARS-CoV-2 infection, the relationship between disease
severity and the host immune response is not fully understood. Here we performed
single-cell RNA sequencing in peripheral blood samples of five healthy donors and 13
COVID-19 patients including moderate, severe and convalescent cases. Through
determining the transcriptional profiles of immune cells, coupled with assembled T cell
receptor and B cell receptor sequences, we analyzed the functional properties of
immune cells. Most cell types in COVID-19 patients showed a strong interferon-alpha
response, and an overall acute inflammatory response. Moreover, intensive expansion
of highly cytotoxic effector T cell subsets, such as CD4+ Effector-GNLY (Granulysin),
CD8+ Effector-GNLY and NKT CD160, was associated with convalescence in
moderate patients. In severe patients, the immune landscape featured a deranged
interferon response, profound immune exhaustion with skewed T cell receptor
repertoire and broad T cell expansion. These findings illustrate the dynamic nature of
immune responses during the disease progression.
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The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) infection causes a spectrum of illness from
mild to severe disease and death1. Though SARS-CoV-2 like SARS-CoV uses
angiotensin-converting enzyme 2 (ACE2) as its receptor for entry in target cells2, 3, the
viral shedding pattern is different between the two viruses. The SARS-CoV-2 viral load
is detectable during the presymptomatic stage4-6 and peaks soon after disease onset7-9,
which is significantly earlier than that of SARS-CoV10. These factors contribute to the
high contagious nature of SARS-CoV-2 and its rapid spread, leading to the global
pandemic of COVID-19. Both SARS-CoV-2 infection and viral infection-mediated-
immune responses can directly and/or indirectly damage cells in the respiratory tract of
COVID-19 patients11, 12. The majority of the patients exhibit mild to moderate
symptoms, up to 15% progress to severe pneumonia and approximately 5% eventually
develop acute respiratory distress syndrome (ARDS) and/or multiple organ failure11.
Higher fatality rates have been observed in elderly individuals with co-morbidities and
those who are immunocompromised13-16.
Since there are no effective drugs or vaccines available at this time against SARS-
CoV-2, there is an urgent need to better understand the host immune response during
disease in order to better devise prognostic and diagnostic markers, and to design
appropriate therapeutic interventions for patients with severe disease presentation.
Viral infection and the antiviral host immune response interact in vivo and shape
disease severity as well as clinical outcomes, especially during acute viral infection.
Therefore, the immunopathology of COVID-19 has received much attention. Immune
responses in a COVID-19 patient with moderate disease presentation17, show that a
robust cellular and humoral immune response can be elicited upon acute SARS-CoV-2
infection. However, it remained unknown how the uncontrolled innate and impaired
adaptive immune responses were associated with pulmonary tissue damage. COVID-
19 patients with severe disease presentation showed pronounced lymphopenia and
elevation of serum pro-inflammatory cytokines18, 19. We recently reported a fatal case
where significant interstitial lymphocytic infiltrates in both lung tissues and
overactivation of T cells in peripheral blood were observed20. More recently,
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inflammatory FCN1+ macrophages were found to replace FABP4+ macrophages in the
bronchoalveolar lavage fluid from severe SARS-CoV-2 infected patients, whereas
highly expanded and functional competent tissue resident clonal CD8+ T cells were
observed in moderate patients with SARS-CoV-2 infection21. These observations have
revealed possible immunopathogenic mechanisms underlying COVID-19 progression
at the first glance. However, a global characterization of the antiviral or pathogenic
immune responses in different clinical settings is still lacking.
Here, we implemented single-cell RNA sequencing (scRNA-seq) to obtain an
unbiased and comprehensive visualization of the immunological responses in
peripheral blood mononuclear cells (PBMCs) from COVID-19 patients with moderate
to severe symptoms. Our study depicts a high-resolution transcriptomic landscape of
blood immune cells during the disease progression of COVID-19, which will facilitate
a better understanding of the protective and pathogenic immune responses of the
disease.
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Results
Single-cell transcriptional profiling of peripheral immune cells
To characterize the immunological features of patients with COVID-19, we
performed droplet-based scRNA-seq (10X Genomics) to study the transcriptomic
profiles of peripheral blood mononuclear cells (PBMCs) from 13 patients and five
healthy donors (HD: n=5) as controls (Fig. 1a). The 13 COVID-19 patients were
classified into three clinical conditions: moderate (Moderate: n=7), severe (Severe: n=4)
and convalescent (Conv: n=6 of whom four were paired with moderate cases) (Fig. 1a,
b, Table 1 and Supplementary Fig. 1). The clinical characteristics and laboratory
findings of enrolled patients are detailed in Table 1. Single cell T cell receptor (TCR)
and B cell receptor (BCR) sequencing were also performed for each subject. After the
unified single cell analysis pipeline (see Methods), ~0.6 billion unique transcripts were
obtained from 122,542 cells from PBMCs of all samples. Among these cells, 22,711
cells (18.5%) were from the HD condition, 37,901 cells (30.9%) were from the
Moderate condition, 24,640 cells (20.1%) were from the Severe condition, and 37,290
cells (30.4%) were from the Conv condition. All high-quality cells were integrated into
an unbatched and comparable dataset and subjected to principal component analysis
after correction for read depth and mitochondrial read counts (Supplementary Fig. 2a,
b).
Using graph-based clustering (uniform manifold approximation and projection,
UMAP), we captured the transcriptomes of 14 major cell types or subtypes according
to the expression of canonical gene markers (Fig. 1c-e and Supplementary Fig. 2a, b).
These cells included naive state T cells (Naive T: CD3+CCR7+), activated state T cells
(Activated T: CD3+PRF1+), MAIT cells (SLC4A10+TRAV1-2+), γδT cells
(TRGV9+TRDV2+), proliferative T cells (Pro T: CD3+MKI67+), natural killer (NK) cells
(KLRF1+), B cells (MS4A1+), plasma B cells (MZB1+), CD14+ monocytes (CD14+
Mono: LYZ+CD14+), CD16+ monocytes (CD16+ Mono: LYZ+FCGR3A+), monocyte-
derived dendritic cells (Mono DCs: CD1C+), plasmacytoid dendritic cells (pDCs:
LILRA4+), platelets (PPBP+) and hemopoietic stem cells (HSCs: CYTL1+GATA2+). As
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such, we clearly defined the composition of cell subpopulations in peripheral blood.
Differences in cell compositions across disease conditions
To reveal the differences in cell compositions across three conditions (Moderate,
Severe, and Conv), and to compare with that in HD, we calculated the relative
percentage of the 14 major cell types in the PBMCs of each individual on the basis of
scRNA-seq data (Fig. 2a-d). The relative percentage of activated T cluster peaked in
moderate patients and did not return to the normal level, even at convalescence. Of note,
the relative abundance of naive T cells, MAIT cells, and Mono DCs decreased with the
disease severity, and these populations later restored in Conv patients (Fig. 2d). In
contrast, the relative percentage of Pro T cells, plasma B cells, CD14+ Mono, and
platelets increased with disease severity and later declined in Conv patients (Fig. 2d).
The massive increase of CD14+ Mono in patients with severe disease was in accordance
with a recent study demonstrating that inflammatory monocytes, induced by pathogenic
T cells, incite the inflammatory storm in COVID-1922.
Next, to investigate the antiviral and pathogenic immune responses during SARS-
CoV-2 infection, we evaluated the expression levels of two important pathways (Gene
Ontology biological process terms: Response to interferon alpha, and Acute
inflammatory response) in major cell types across four conditions. We found that the
response to interferon alpha was uniformly and significantly up-regulated in all major
cell types from the PBMCs of COVID-19 patients and showed the highest value in
almost every major cell type in severe patients, with the exception of plasma B cells, in
which the interferon alpha response was greatest in moderate patients (Fig. 2e). In
addition, with the exception of Pro T cells, the acute inflammatory response showed
consistent and significant differences across conditions in the selected cell types.
Several cell types showed trends in the acute inflammatory response that roughly
corresponded with disease severity, including activated T, γδT, NK and CD16+ mono
(Fig. 2e). Moreover, the plasma levels of Type I IFN, IFN-γ and other inflammatory
cytokines displayed the highest levels in severe patients (Supplementary Fig. 2c). These
results suggest a strong overall pro-inflammatory response in COVID-19 patients (Fig.
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2e).
Strong interferon responses were observed in innate immune cells
To further investigate the transcriptomic changes of innate immune cells (Fig. 3a,
b) after SARS-CoV-2 infection, we compared the expression patterns of the Moderate
or Severe condition with that of the HD condition in CD14+ and CD16+ monocytes. We
found that the significantly differentially expressed genes (DEGs) were involved in
interferon responses, myeloid leukocyte activation, cytokine production and NF-κB
signaling pathway in COVID-19 patients (Fig. 3c, d). In addition, more DEGs in
monocytes from the Severe condition were enriched in molecule metabolic and
catabolic processes, as well as cytokine secretion (Supplementary Fig. 3a). For NK cells,
similar to monocytes, DEGs associated with interferon responses, cytokine production,
NF-κB signaling pathway and leukocyte cytotoxicity were significantly enriched in
COVID-19 patients (Fig. 3e, f), suggesting a consistent response by innate immune
cells to SARS-CoV-2 infection. Furthermore, in comparison with moderate patients,
the DEGs of the NK cells of the Severe condition, such as ITGB2, CCL5 and CXCR2,
were more closely related to migration associated processes (Supplementary Fig. 3b, c).
In line with the DEG enrichment results, we found that monocytes and NK cells
both showed significantly up-regulated IFN and acute inflammatory responses after
SARS-CoV-2 infection, especially in severe patients (Fig 2e). Levels of cellular
apoptosis and migration were also up-regulated in both monocytes and NK cells
compared to the HD condition (Fig. 3g). Unlike the comparable apoptosis levels in
monocytes and NK cells, the innate immune cells in severe patients were more prone
to migration than those in moderate patients (Fig. 3g and Supplementary Fig. 3c). These
results suggest that most innate immune cell types in COVID-19 patients show strong
interferon responses.
Features of T cell subsets in COVID-19 patients
To characterize changes in individual T cell subsets among individuals across four
conditions, we sub-clustered T cells from PBMCs and obtained 12 subsets according to
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the expression and distribution of canonical T cell markers (Fig. 4a, b): 6 subtypes of
CD4+ T cells (CD3E+CD4+), three subtypes of CD8+ T cells (CD3E+CD8A+) and 3
subtypes of NKT cells (CD3E+CD4–CD8A–TYROBP+).
Of the six subtypes of CD4+ T cell clusters, in addition to naive CD4+ T (CD4+
Naive: CCR7+SELL+), memory CD4+ T (CD4+ Memory: S100A4+GPR183+), effector
memory CD4+ T (CD4+ Effector Memory: S100A4+GPR183+GZMA+) and regulatory
T (Treg: FOXP3+IL2RA+) subtypes, we defined two effector CD4+ T subtypes, CD4+
Effector-GZMK and CD4+ Effector-GNLY. The CD4+ Effector-GNLY cluster was
characterized by high expression of genes associated with cytotoxicity, including NKG7,
GZMA, GZMB, GZMH and GNLY, while the CD4+ Effector-GZMK cluster showed a
relatively high expression level of the GZMK gene, but low expression of other
cytotoxic genes (Fig. 4b and Supplementary Fig. 4a, b). Furthermore, CD4+ Effector-
GNLY cells showed high expression of TBX21, implying that they were Type I T helper
(TH1) liked cells (Supplementary Fig. 4c). In contrast, CD4+ Effector-GZMK and CD4+
Effector Memory harbored Type Ⅱ T helper (TH2) liked features with high expression
of GATA3 (Supplementary Fig. 4c). The three subtypes of CD8+ T cell clusters included
naive CD8+ T subset (CD8+ Naive: CCR7+SELL+), and two effector CD8+ T subsets
(CD8+ Effector-GZMK and CD8+ Effector-GNLY), which both had high expression
levels of GZMA and NKG7. In detail, CD8+ Effector-GZMK uniquely expressed GZMK,
whereas CD8+ Effector-GNLY showed relatively high expression levels of GZMB/H
and GNLY (Fig. 4b and Supplementary Fig. 4a, b). The three subsets of NKT cell
clusters were defined as naive NKT cells (NKT Naive: CCR7+SELL+), CD56+ NKT
cells (NKT CD56) and CD160+ NKT cells (NKT CD160) (Fig. 4a, b).
To gain insights into features in T cell subsets, we evaluated the distribution of each
cluster across four conditions (Fig. 4c and Supplementary Fig. 4d, e). Notably, the
proportions of naive state T subsets including CD4+ Naive, CD4+ Memory, CD4+
Effector Memory, Treg, CD8+ Naive and NKT Naive subsets, decreased in COVID-19
patients in comparison with HDs (Fig. 4c and Supplementary Fig. 4d). Even in the Conv
condition, the proportions of CD4+ Naive, CD8+ Naive and Treg clusters did not restore
to the levels of HDs (Fig. 4c and Supplementary Fig. 4d). In contrast, the proportions
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of active state T subsets, including CD4+ Effector-GNLY, CD8+ Effector-GNLY, NKT
CD56 and NKT CD160 subsets, increased in COVID-19 patients, and these cytotoxic
subsets were present in high proportions even in Conv patients (Fig. 4c). Of particular
interest, the CD4+ Effector-GNLY subset was almost absent in HDs but highly enriched
in moderate, severe and Conv patients. In addition, the abundance of the NKT CD160
subset was significantly reduced in severe patients as compared to moderate patients.
We then evaluated the cytotoxicity and exhaustion scores of different effector state
T subsets across four conditions (Fig. 4d and Supplementary Fig. 4f). The CD4+
Effector-GNLY, CD8+ Effector-GNLY, NKT CD56 and NKT CD160 subsets showed
higher cytotoxicity scores than those of the other subsets. Within these highly cytotoxic
clusters, healthy donors all had the lowest cytotoxicity scores, whereas the Moderate
condition showed the highest cytotoxic state, with the exception of the CD4+ Effector-
GNLY subset (Fig. 4d, e and Supplementary Fig. 4f). Meanwhile, the CD4+ Effector-
GZMK, CD8+ Effector-GZMK and NKT CD160 clusters showed higher exhaustion
scores than those of the other subsets. Within these highly exhausted subsets, healthy
donors all had the lowest exhaustion scores, while severe patients showed the most
exhausted state (Fig. 4d, e and Supplementary Fig. 4f), in agreement with previous
functional studies which examined CD8+ T cells from severe patients and found highly
exhausted status and functional impairment23.
To further investigate differential transcriptomic changes in T cells after SARS-
CoV-2 infection, we compared the expression profiles of effector T cells (excluding
CD4+ Naive, CD4+ Memory, CD8+ Naive and NKT Naive clusters) between the
Moderate or Severe and HD conditions. We observed that DEGs up-regulated in
COVID-19 patients were involved in processes including interferon responses,
cytokine production, cell killing, leukocyte cell-cell adhesion and cytoskeleton
organization (Fig. 4f, g and Supplementary Fig. 4i). In addition, using an apoptosis and
migration scoring system, we observed that T cells in severe patients likely underwent
migration and apoptosis (Fig. 4h, i and Supplementary Fig. 4g, h). Significant activation
of cell death and migration pathways in the PBMCs of severe patients suggests that cell
death and lymphocytes migration may be associated with lymphopenia, a common
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phenomenon observed in severe COVID-19 patients18, 19, 24.
Clonal expansion in T cells and preferred usage of V(D)J genes in COVID-19
patients
Next, to gain insight into the clonal relationship among individual T cells and usage
of V(D)J genes across four conditions, we reconstructed TCR sequences from the TCR
sequencing. Briefly, there were more than 70% of cells in all subsets with matched TCR
information except for the three NKT subsets (Fig. 5a, b). First, compared to the HDs,
clonal expansion was obvious in COVID-19 patients and patients in convalescence (Fig.
5c-e). The extent of clonal expansion in the Moderate and Conv conditions was higher
than that of the Severe condition. Meanwhile, large clonal expansions (clonal size >
100) were absent in the Severe condition (Fig. 5e), indicating that severe patients might
lack efficient clonal expansion of effector T cells. We observed different degrees of
clonal expansion among T cell subsets (Fig. 5c, d). Effector T cell subsets CD4+
Effector-GNLY, CD8+ Effector-GZMK and CD8+ Effector-GNLY showed high
proportions of clonal cells (Fig. 5a, d and Supplementary Fig. 5a) and contained high
proportions of inter-cluster clonal cells (Fig. 5f), suggesting that effector T cells
underwent dynamic state transitions (Fig. 5a, f).
To study the dynamics and genes preference of TCRs in COVID-19 patients and
HDs, we compared the usage of V(D)J genes across four conditions (Fig. 5g-i and
Supplementary Fig. 5b). The top 10 complementarity determining region 3 (CDR3)
sequences were different across four conditions (Fig. 5h). The Moderate and Conv
conditions shared some CDR3 sequences because four samples from these conditions
were paired. The usage percentage of the top 10 CDR3 sequences in the HD condition
was lower and more balanced compared to those of the other three conditions. Of note,
we discovered a different usage of V(D)J genes with decreased diversity in COVID-19
patients, which was more pronounced in TRA genes (Fig. 5i). We also identified over-
representation of TRAJ39 and TRAJ43 in severe patients compared to moderate and
Conv patients (Fig. 5g). The preferred TRBJ gene in severe patients was TRBJ1-1,
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whereas TRBJ2-1 was preferred in moderate and Conv patients (Fig. 5i). The selective
usage of V(D)J genes indicates that different immunodominant epitopes may drive the
molecular composition of T cell responses and may be associated with SARS-CoV-2
specific infection.
Features of B cell subsets in COVID-19 patients
To trace the dynamic changes of different B subtypes, we sub-clustered B cells into
six subsets according to the expression and distribution of canonical B cell markers (Fig.
6a, b and Supplementary Fig. 6a). We identified one naive B subset (Naive B;
MS4A1+IGHD+), one memory B subset (Memory B; MS4A1+CD27+), one intermediate
transition memory B subset (Intermediate Memory B; IGHD+CD27+), one germinal
center B subset (Germinal Center B; MS4A1+NEIL1+) and two plasma subsets, Plasma
B (Plasma B; MZB1+CD38+) and dividing plasma B (Dividing Plasma B;
MZB1+CD38+MKI67+).
Notably, the proportions of active state B subsets, including Germinal Center B,
Plasma B and Dividing Plasma B subsets, increased in COVID-19 patients in
comparison with those of HDs. In contrast, the proportion of Memory B cells decreased
in COVID-19 patients compared to that of HDs (Fig. 6c-e).
To further investigate differential transcriptomic changes in B cells after SARS-
CoV-2 infection, we compared the expression profiles of B/Plasma cells of the
Moderate or Severe condition to those of the HD condition. DEGs that were most
significantly enriched in COVID-19 patients were involved in genes associated with
the interferon response (Fig 6f, g and Supplementary Fig. 6c). Moreover, DEGs in
severe patients were associated with protein synthesis, maturation and transport related
biological processes (Supplementary Fig. 6b). These results reveal the transcriptomic
features of B cell subsets in COVID-19 patients.
Expanded B cells and specific rearrangements of V(D)J genes in severe patients
We also reconstructed B cell receptor (BCR) sequences from BCR sequencing and
analyzed the state of B cell-receptor (BCR) clonal expansion. Briefly, the detection
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percentage of BCRs was more than 75% in each cluster (Fig. 7a, b). We found that B
cells from severe patients showed obvious clonal expansions (Fig. 7c and
Supplementary Fig. 6d) than other three conditions, indicating that B cell activity and
humoral immune responses were strongly activated in severe patients, reminiscent of
previous observation that higher antibody titers are associated with worse clinical
outcomes25-27. This raises the concern that, pathogen-directed antibodies can promote
disease pathology, resulting in antibody-dependent enhancement (ADE) similar to that
observed in SARS28.
Next, we evaluated the distribution of IgA, IgD, IgG and IgM (IgE not detected) in
each patient at the Moderate, Severe and Conv conditions, respectively. In most patients,
IgM was the predominant immunoglobulin (Fig. 7d, e). Compared to HDs, the
abundance of IgG increased in COVID-19 patients, while that of IgM levels decreased.
In convalescent patients, levels of IgG and IgM returned to levels similar to those of
HDs.
To study biased V(D)J rearrangements of the BCR, we compared the usage of
V(D)J genes across four conditions (Fig. 7f, g and Supplementary Fig. 6e). We found
more specific V(D)J usage in the Severe condition compared with the other three
conditions, indicating that B cells might have undergone unique and specific V(D)J
rearrangements in severe patients (Fig. 7g). We also discovered comprehensive usage
of IGHJ4 in all HDs and patients (Fig. 7f), but the paired IGHV genes of IGHJ4 were
different in severe patients compared with patients in the other three conditions (Fig.
7g). We observed over-representation of IGHV3-7 in severe patients (Fig. 7f). Moreover,
the top two paired V-J frequencies in severe patients were IGHV3-7/IGHJ4 and IGKV3-
15/IGKJ3 (Fig. 7g). Taken together, increased B cell clonality and skewed usage of the
IGHV and IGKJ genes in severe patients, suggests that SARS-CoV-2 infection is
associated with V(D)J rearrangements in B cells of the host. Notably, selective usage
of dominant IGV genes, especially IGHV3-7 and IGKV3-15 in severe patients, may
facilitate the design of vaccines.
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Discussion
COVID-19 is usually considered as an acute self-limited viral disease29, although
it remains unknown whether SARS-CoV-2 infection can lead to chronic disease in
asymptomatic carriers. Host immune response against acute SARS-CoV-2 infection not
only plays an antiviral role, but also leads to simultaneous pathogenic injury of organs
and tissues, especially in the lungs of COVID-19 patients, which determines the disease
severity, progression and outcome. Studies have reported the characteristics of innate
and adaptive immune responses15, 18, 24, which have helped us understand the potential
pathogenesis of SARS-CoV-2 infection. However, it is difficult to obtain an integrated
scenario of the cellular and molecular immune responses upon SARS-CoV-2 infection.
To address this issue, here we have profiled the immunological response landscape in
COVID-19 patients at single cell resolution, which illustrates the dynamic nature of
cellular responses during disease progression and reveals the critical factors responsible
for antiviral immunity and pathogenesis in moderate and severe patients.
Our study provides an unbiased visualization of the immunological hallmarks for
COVID-19 patients. First, COVID-19 patients showed a concerted and strong
interferon alpha response, an overall acute inflammatory response, and an enhanced
migration ability, which peaked in patients with severe disease in most major cell types
in the PBMCs. Second, broad immune activation was observed in COVID-19 patients,
evidenced by increased proportions of activated T, Pro T and plasma B cells, and
decreased proportions of naive T and Mono DC compartments. Third, the proportions
of active state T clusters were significantly higher in COVID-19 patients and with a
preferential enrichment of effector T cell subsets, such as CD4+ Effector-GNLY, CD8+
Effector-GNLY and NKT CD160 cells in moderate patients and NKT CD56 subset in
severe patients. T cells showed higher cytotoxicity and more robust expansion in
moderate patients, whereas higher exhaustion levels and less specific clonal expansion
were seen in severe patients. Fourth, B cells experienced unique and specific V(D)J
rearrangements in severe patients, indicated by an increase of B cell clonality and a
skewed use of the IGHV and IGKJ genes. Finally, though most of the clinical
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parameters recovered to normal range in patients at the early phase of convalescence,
the state of the immune system was not fully restored, exemplified by the ratios of naive
T and Treg subsets. A long-term follow-up study is needed to investigate how long it
takes to achieve full immune recovery in patients with COVID-19.
An effective anti-viral immune response in moderate patients was characterized by
moderate and broad activation of innate immune signals as well as expansion of highly
cytotoxic effector T cell subsets. The expanded Effector T clusters, including CD4+
Effector-GNLY, CD8+ Effector-GNLY, NKT CD56 and NKT CD160, share features of
high expression of NKG7, GZMA. GZMB, GZMH and GNLY, and may promote rapid
resolution of SARS-CoV-2 infection through their direct cytotoxicity. The CD4+
Effector-GNLY cluster resembles classical CD4+ cytotoxic T cells30. CD4+ cytotoxic T
cells with MHC class II-restricted cytotoxic activity play an important role in viral
infections31, autoimmune diseases32, and malignancies33. Further, it is of great interest
to identify immune factors that may predict or prevent progression to severe illness.
Notably, the expansion of NKT CD160 cluster in moderate patients is almost absent in
the Severe condition. The NKT CD160 cluster refers to a previously described γδ NKT
or Vδ1 T cell subset that shows phenotypical and functional similarity to traditional NK
cells34. Moreover, FCGR3A was also among the enriched genes in the NKT CD160
cluster, suggesting that it may mediate the antibody dependent cell-mediated
cytotoxicity. Furthermore, Vδ1 T cells are implicated in immune responses to viral
infection, particularly cytomegalovirus35, 36, Epstein Barr virus37, and Vδ1 T cells can
also recognize a broad range of cancer cells38, 39. As in COVID-19, the preferential
expansion of NKT CD160 cells might promote rapid control of the disease through
direct cytotoxicity as well as mediating the antibody dependent cell-mediated
cytotoxicity effect. Mechanisms underlying the expansion and function of NKT CD160
cells in COVID-19 warrant future studies. It will be valuable to investigate whether
Vδ1 T cells could be used in adoptive cellular therapies to curb overt COVID-19
associated tissue and organ damages.
The immunopathogenesis of disease deterioration in severe patients was
characterized by a deranged interferon response, profound immune exhaustion with
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15
skewed TCR repertoire and broad T cell expansion. Importantly, previous studies on
SARS-CoV showed that the virus can harness multiple mechanisms to antagonize
interferon responses in host cells12. Based on the results of in vitro cell infection, SARS-
CoV-2 was recognized as a weak inducer of type I interferons40, 41. However, here we
observed strong interferon alpha responses in almost all cells types in the PBMCs from
severe patients in comparison to moderate patients. Considering that the virus burden
peaked soon after disease onset and then decreased gradually7, 8, it seems that the
systematic interferon alpha signal activation in severe patients might be induced by
factors other than the virus alone. Over-activation of interferon pathways may
contribute to immune dysfunction and immune injury in severe COVID-19 patients.
Particularly, interferon-α2b nebulization was widely applied in SARS-CoV-2 infection,
which was developed from treating MERS and SARS42. The use of interferon for
treatment may need a careful reconsideration and reexamination, especially in severe
COVID-19 cases.
There are several limitations in this study. For example, it was very difficult to
obtain the immune cells in bronchoalveolar lavage fluid due to biosafety reasons during
the outbreak of COVID-19 when we performed this study. Also, the sample size is
comparatively small. Therefore, future studies with longitudinal samples from more
COVID-19 patients may help to determine the cause-and-effect relationships between
immune characteristic of different cell types and disease outcome.
Taken together, this integrated, multi-cellular description in our study lays the
foundation for future characterization of the complex, dynamic immune responses to
SARS-CoV-2 infection. The transcriptomic data, coupled with detailed TCR- and BCR-
based lineage information, can serve as a rich resource for deeper understanding of
peripheral lymphocytes in COVID-19 patients and pave the way for rationally designed
therapies as well as development of SARS-CoV-2-specific vaccines.
Acknowledgements
We thank the study participants who provided blood samples, and Chun-Bao Zhou, Jin-
Hong Yuan for flow cytometry analysis. Jun Hou provided guidance for safely handling
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samples from COVID-19 patients. This work was supported by the National Key
Research and Development Program of China (grant no. 2020YFC0841900 and
2020YFC0844000) to F-S. W. from the Ministry of Science and Technology of China.
Author contributions
F-S.W. and F.B. conceived and designed the study; J-Y.Z., X.F., J-W.S., X-P.D. and C.Z.
performed the experiments; X-M.W. and X.X. led the bioinformatic analyses; X-M.W.,
X.X., J-Y.Z., X.F., J-W.S., C.Z., F.B. and F-S.W. wrote the manuscript; J-L.F., P.X., S-
Y.W., L.S., Z.X., L.H., and T-J.J. took care of patients and provided the clinical
information; R-N.X., M.S., Y.Z., A.Z. and M.M. edited the manuscript and provided
comments and feedback. All authors read and approved the final manuscript.
Competing interests
The authors declare no competing interests.
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Figure Legends Fig. 1 | Study design and single-cell transcriptional profiling of PBMCs from healthy donors and COVID-19 patients. a, A schematic showing the overall study design. Single-cell RNA sequencing was applied to peripheral blood mononuclear cells across four conditions, and the output data were used for TCR and BCR profiling and expression analyses. b, Timeline of the course of disease for 13 SARS-CoV-2 infected patients enrolled in our study. RT-qPCR indicates polymerase chain reaction test for SARS-CoV-2 nucleic acids. RT-qPCR positive indicate nasopharyngeal or sputum samples positive for SARS-CoV-2 nucleic acids. The color bars in the most left representing conditions with the same color in (a). c, Cellular populations identified. The UMAP projection of 122,542 single cells from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples, showing the formation of 14 clusters with the respective labels. Each dot corresponds to a single cell, colored according to cell types. d, Canonical cell markers were used to label clusters by cell identity as represented in the UMAP plot. Colored according to expression levels and legend labeled in log scale. e, Violin plots showing the expression distribution of selected canonical cell markers in the 14 clusters. Row representing selected marker genes and column representing clusters with the same color in (c). Fig. 2 | Differences of cell compositions across disease conditions. a, UMAP projection of the HD, Moderate, Severe and Conv condition. Each dot corresponds to a single cell, colored according to cell type. b, Average proportion of each cell type derived from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. c, The top dot plot shows the sum of the absolute counts of lymphocytes and monocytes in the PBMCs of each sample. The bottom bar plot shows the cell compositions at a single sample level. d, Condition preference of each cluster. Y axis: average percent of samples across four conditions. Conditions are shown in different colors. Each bar plot represents one cell cluster. Error bars represent ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. e, Box plots of the expression levels of two GO biological process terms across clusters derived from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. Conditions are shown in different colors. Horizontal lines represent median values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. All differences with P < 0.01 are indicated. **P < 0.001; ***P < 0.0001; two-sided unpaired Dunn’s (Bonferroni) test.
Fig. 3 | Characterization of innate immune cells in individuals across four conditions. a, UMAP projection of monocytes (39,276) and NK cells (7,479). Each dot corresponds to a single cell, colored according to cell type. b, UMAP projection of the HD, Moderate, Severe and Conv conditions. c, Scatter plot showing DEGs in the monocytes of moderate (n=7) or severe patients (n=4) in comparison with those of HDs (n=5). Each red dot denotes an individual gene with Benjamini-Hochberg adjusted P value (two-sided unpaired Mann-Whitney U test) ≤ 0.01 and average log2 fold change ≥ 0.5 for the Moderate/HD and Severe/HD comparisons. Example genes are labeled with a gene name. d, Gene enrichment analyses of the DEGs colored in red in (c). GO terms are labeled with name and id, and sorted by -log10 (P) value. A darker color indicates a smaller p-value. The top 20 enriched GO terms are shown. Interesting terms are
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labeled in red color. e, Similar to (c), but for NK cells. f, Similar to (d), but for NK cells. g, Box plots of the expression levels of two GO biological process terms in monocytes and NK cells across clusters derived from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. Conditions are shown in different colors. Horizontal lines represent median values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. All differences with P < 0.01 are indicated. **P < 0.001; ***P < 0.0001; two-sided unpaired Dunn’s (Bonferroni) test.
Fig. 4 | Immunological features of T cell subsets. a, UMAP projection of 55,655 T cells. Each dot corresponds to a single cell, colored according to cell type. b, Violin plots showing the expression distribution of canonical cell markers in 12 T cell subsets. c, Condition preference of each cluster. Error bars represent ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. d, Box plots of the cytotoxicity and exhaustion scores across different clusters and conditions. The squares in the X axis indicate subsets corresponding to subsets in (a). Horizontal lines represent median values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. All differences with P < 0.01 are indicated. **P < 0.001; ***P < 0.0001; two-sided unpaired Dunn’s (Bonferroni) test. e, Dot plot showing the expression levels of well-defined cytotoxic and exhaustion-related genes in NKT CD160 cells across four conditions. f, DEGs of moderate (n=7) or severe patients (n=4) in comparison with HDs (n=5). Each red dot denotes an individual gene with Benjamini-Hochberg adjusted P value (two-sided unpaired Mann-Whitney U test) ≤ 0.01 and average log2 fold change ≥ 0.5 in the Moderate/HD and Severe/HD comparisons. Example genes are labeled with the gene name. g, Gene enrichment analyses of DEGs colored in red in (f). Interesting GO terms are labeled in red. h, Box plots of the median cell scores for each cluster for two GO biological process terms across HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. Horizontal lines represent median values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. All differences with P < 0.05 are indicated; two-sided paired Mann-Whitney U test. i, The expression levels of two GO biological process terms across clusters derived from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. Horizontal lines represent median values, with whiskers extending to the farthest data point within a maximum of 1.5 × interquartile range. All differences with P < 0.01 are indicated. **P < 0.001; ***P < 0.0001; two-sided unpaired Dunn’s (Bonferroni) test.
Fig. 5 | Expanded TCR clones and selective usage of V(D)J genes. a, UMAP of T cells derived from PBMCs. Clusters are denoted by color labeled with inferred cell types (top left), TCR detection (top right), selected TCR clones belonging to the same clusters (bottom left) and different clusters (bottom right). b, Bar plots showing the percentage of TCR detection in each T cell cluster. c, The association between the number of T cell clones and the number of cells per clonotype. The dashed line separates non-clonal and clonal cells. LOESS fitting is labeled as the solid line showing negative correlation between the two axes. d, The distribution of the clone state of T cells in each cluster. e, The clonal status percentage of T cells (left) and percentage of different levels of clonal T cells (right) across four conditions. f, Comparison
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between the fraction of clonal cells in each subset (X-axis) and percentage of cells with TCRs shared across clusters (Y-axis). g, Usage of some TRAV (top left), TRAJ (bottom left), TRBV (top right) and TRBJ (bottom right) genes across four conditions. Error bars represent ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. h, The top ten CDR3 usages are shown. Each bar is colored by condition identity. Shared CDR3 sequences are in a red font. i, TRA/B rearrangements differences across four conditions. The colors indicate the usage percentage of specific V-J gene pairs.
Fig. 6 | Immunological features of B cell subsets. a, UMAP projection of 11,377 B cells. Each dot corresponds to a single cell, colored according to cell type. b, Canonical cell markers were used to label clusters by cell identity as represented in the UMAP plot. Colored according to expression level and legend labeled in log scale. c, Average proportion of each B cell subtype derived from HD (n=5), Moderate (n=7), Severe (n=4) and Conv (n=6) samples. d, Bar plot showing B cell compositions at the single sample level. e, Condition preference of each cluster. Y axis: average percentage of samples across four conditions. Conditions are shown in different colors. Each bar plot represents one cell cluster. Error bars represent ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. f, Scatter plot showing DEGs in moderate (n=7) or severe (n=4) patients in comparison with those of HDs (n=5). Each red dot denotes an individual gene with Benjamini-Hochberg adjusted P value (two-sided unpaired Mann-Whitney U test) ≤ 0.01 and average log2 fold change ≥ 0.5 in the Moderate/HD and Severe/HD comparisons. Example genes are labeled with the gene name. g, Gene enrichment analyses of DEGs colored in red in (f). GO terms are labeled with name and id and sorted by -log10 (P) values. A darker color indicates a smaller p-value. The top 20 enriched GO terms are shown. Interesting terms are labeled in red. Fig. 7 | Expanded BCR clones and selective usage of V(D)J genes. a, UMAP of B cells derived from PBMCs. Clusters are denoted by color and labeled with inferred cell types (left). The UMAP of B cells is also colored based on BCR detection (right). b, Bar plot showing the percentage of BCR detection in each B cell cluster. c, Bar plot showing the distribution of the clone state of B cells in each cluster. d, Bar plot showing the percentages of IGHA, IGHD, IGHG and IGHM in each condition, with error bars representing ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. e, Bar plots showing the percentages of IGHA, IGHD, IGHG and IGHM at the single sample level. f, Usage of some IGHV (top left), IGHJ (top right), IGKV (middle left), IGKJ (middle fight), IGLV (bottom left) and IGLJ (bottom right) genes across the HD, Moderate, Severe and Conv conditions. Conditions are shown in different colors. Error bars represent ± s.e.m. for five healthy donors and 13 patients. All differences with P < 0.05 are indicated; two-sided unpaired Mann-Whitney U test. g, Heatmaps showing IGH/K/L rearrangements differences across the HD, Moderate, Severe and Conv conditions. The colors indicate the usage percentages of specific V-J gene pairs.
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Methods
Patient cohort and clinical characteristics
Thirteen COVID-19 patients were admitted at the Fifth Medical Center of PLA
General Hospital and enrolled in the study from January 23 to February 15, 2020. The
category of three clinical groups (Moderate, Severe and Convalescent (Conv)) were
based on Guidelines for diagnosis and treatment of Corona Virus Disease 2019 issued
by the National Health Commission of China (7th edition)
(http://www.chinacdc.cn/jkzt/crb/zl/szkb_11803/jszl_11815/202003/t20200305_2141
42.html). Moderate group included non-pneumonia and mild pneumonia cases. Severe
group included severe cases who met one of the following criteria: (1) Respiratory
distress, respiratory rate ≥30 breaths/min; (2) Pulse oxygen saturation (SpO2) ≤93%
without inhalation of oxygen-support at quiet resting state; (3) Arterial partial pressure
of oxygen (PaO2) / oxygen concentration (FiO2) ≤300 mmHg; (4) CT image shows
there is more than 50% increase of lung infiltrating change within 24 to 48 hours.
Critically ill cases who were grouped in the Severe condition in this study generally
required mechanical ventilation and exhibited respiratory failure, septic shock, and/or
multiple organ dysfunction/failure that required monitoring and treatment in the ICU.
One case in Severe group died during the study period. The patients in convalescent
group met the discharge criteria as follows: afebrile for more than 3 days, resolution of
respiratory symptoms, substantial improvement of chest CT images, and 2 consecutive
negative reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tests
for viral RNA in respiratory tract swab samples obtained at least 24 hours apart. This
study was approved by the ethics committee of the hospital and written informed
consents or the telephone call permissions were obtained from each patient or their
guardian in the very difficult conditions of early COVID-19 pandemic.
The clinical data and disease course of the 13 patients are shown in Table 1 and
Fig. 1b, respectively. Blood sampling for single cell RNA sequencing was usually
performed at the time of admission or discharge. CT images for one moderate and one
severe case exhibited bilateral ground-glass opacities (Supplementary Fig. 1).
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Quantitative reverse transcription polymerase chain reaction
The throat swab, sputum from the upper respiratory tract and blood were collected
from patients at various time-points after hospitalization. Sample collection,
processing, and laboratory testing complied with WHO guidance. Viral RNA was
extracted from samples using the QIAamp RNA Viral Kit (Qiagen, Heiden, Germany)
according to the manufacturer's instructions. SARS-CoV-2 infected patients were
confirmed using a RT-qPCR kit (TaKaRa, Dalian, China) as recommended by the
China CDC.
Preparation of single-cell suspensions
Peripheral venous blood samples were obtained on admission of 13 COVID-19
patients and five healthy donors within 24 hours, placed into vacutainer tubes, and
centrifuged at 400 × g for 5 min at 4 °C. The time of sampling relative to the onset of
symptoms was recorded. Plasma samples were collected and stored at -80 °C until use.
For each sample, cell viability exceeded 90%.
Droplet-based single cell sequencing
Using a Single Cell 5′ Library and Gel Bead Kit (10X Genomics, 1000006) and
Chromium Single Cell A Chip Kit (10X Genomics, 120236), the cell suspension (300-
600 living cells per microliter as determined by Count Star) was loaded onto a
Chromium single cell controller (10X Genomics) to generate single-cell gel beads in
the emulsion (GEMs) according to the manufacturer’s protocol. Briefly, single cells
were suspended in PBS containing 0.04% BSA. Approximately 10,000 cells were
added to each channel, and approximately 6,000 target cells were recovered. Captured
cells were lysed and the released RNA was barcoded through reverse transcription in
individual GEMs. Reverse transcription was performed on a S1000TM Touch Thermal
Cycler (Bio Rad) at 53 °C for 45 min, followed by 85 °C for 5 min, and a hold at 4 °C.
cDNA was generated and amplified, after which quality was assessed using an Agilent
4200 (performed by CapitalBio Technology, Beijing). According to the manufacturer’s
introduction, single-cell RNA-seq libraries were constructed using a Single Cell 5′
Library and Gel Bead Kit, Single Cell V(D)J Enrichment Kit, Human T Cell (1000005)
and a Single Cell V(D)J Enrichment Kit, Human B Cell (1000016). The libraries were
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25
sequenced using an Illumina Novaseq6000 sequencer with a paired end 150 bp (PE150)
reading strategy (performed by CapitalBio Technology, Beijing).
Single cell RNA-seq data processing
Raw gene expression matrices were generated for each sample by the Cell Ranger
(Version 3.0.2) Pipeline coupled with human reference version GRCh38. The output
filtered gene expression matrices were analyzed by R software (Version 3.5.3) with the
Seurat43 package (Version 3.0.0). In brief, genes expressed at a proportion > 0.1% of
the data and cells with > 200 genes detected were selected for further analyses. Low-
quality cells were removed if they met the following criteria: 1) < 800 UMIs; 2) < 500
genes; or 3) > 10% UMIs derived from the mitochondrial genome. After removal of
low-quality cells, the gene expression matrices were normalized by the NormalizeData
function, and 2000 features with high cell-to-cell variation were calculated using the
FindVariableFeatures function. To reduce dimensionality of the datasets, the RunPCA
function was conducted with default parameters on linear-transformation scaled data
generated by the ScaleData function. Next, the ElbowPlot, DimHeatmap and
JackStrawPlot functions were used to identify the true dimensionality of each dataset,
as recommended by the Seurat developers. Finally, we clustered cells using the
FindNeighbors and FindClusters functions, and performed non-linear dimensional
reduction with the RunUMAP function with default settings. All details regarding the
Seurat analyses performed in this work can be found in the website tutorial
(https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html).
Multiple dataset integration
To compare cell types and proportions across four conditions, we employed the
integration methods described at (https://satijalab.org/seurat/v3.0/integration.html)44.
The Seurat package (Version 3.0.0) was used to assemble multiple distinct scRNA-seq
datasets into an integrated and unbatched dataset. In brief, we identified 2000 features
with high cell-to-cell variation as described above. Second, we identified “anchors”
between individual datasets with the FindIntegrationAnchors function and inputted
these “anchors” into the IntegrateData function to create a “batch-corrected”
expression matrix of all cells, which allowed cells from different datasets to be
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26
integrated and analyzed together.
Sub-clustering of B cells and T cells
B cells and plasma cells were extracted from PBMCs. Next, these major cell types
were integrated for further sub-clustering. After integration, genes were scaled to unit
variance. Scaling, PCA and clustering were performed as described above. Naive and
Activated T cells in PBMCs were also extracted and sub-clustered using the procedure
used for B cells.
Cell type annotation and cluster markers identification
After non-linear dimensional reduction and projection of all cells into two-
dimensional space by UMAP, cells clustered together according to common features.
The FindAllMarkers function in Seurat was used to find markers for each of the
identified clusters. Clusters were then classified and annotated based on expressions of
canonical markers of particular cell types. Clusters expressing two or more canonical
cell-type markers were classified as doublet cells and excluded from further analysis.
Differential expression genes (DEGs) identification and functional enrichment
Differential gene expression testing was performed using the FindMarkers
function in Seurat with parameter "test.use=wilcox" by default, and the Benjamini-
Hochberg method was used to estimate the false discovery rate (FDR). DEGs were
filtered using a minimum log2(fold change) of 0.5 and a maximum FDR value of 0.01.
Enrichment analysis for the functions of the DEGs was conducted using the Metascape
webtool (www.metascape.org). Gene sets were derived from the GO Biological Process
Ontology.
Defining cell state scores
We used cell scores to evaluate the degree to which individual cells expressed a
certain pre-defined expression gene set45-47. The cell scores were initially based on the
average expression of the genes from the pre-defined gene set in the respective cell. For
a given cell i and a gene set j (Gj), the cell score SCj (i) quantifying the relative
expression of Gj in cell i as the average relative expression (Er) of the genes in Gj
compared to the average relative expression of a control gene set (Gjcont): SCj (i) =
average[Er(Gj ,i)] – average[Er(Gjcont,i)]. The control gene set was randomly selected
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The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
27
based on aggregate expression levels bins which yield a comparable distribution of
expression levels and over size to that of the considered gene set. The AddModuleScore
function in Seurat was used to implement the method with default settings. We used
RESPONSE TO INTERFERON ALPHA (GO:0035455), RESPONSE TO
INTERFERON BETA (GO:0035456), ACUTE INFLAMMATORY RESPONSE
(GO:0002526), APOPTOTIC SIGNALING PATHWAY (GO:0097190),
LEUKOCYTE MIGRATION (GO:0050900), 4 well-defined naive markers (CCR7,
TCF7, LEF1 and SELL), 12 cytotoxicity associated genes (PRF1, IFNG, GNLY, NKG7,
GZMB, GZMA, GZMH, KLRK1, KLRB1, KLRD1, CTSW and CST7) and 6 well-defined
exhaustion markers (LAG3, TIGIT, PDCD1, CTLA4, HAVCR2 and TOX) to define the
interferon alpha/beta response, inflammatory response, apoptosis, migration, naive
state, cytotoxicity and exhaustion score, respectively.
TCR and BCR V(D)J sequencing and analysis
Full-length TCR/BCR V(D)J segments were enriched from amplified cDNA from
5′ libraries via PCR amplification using a Chromium Single-Cell V(D)J Enrichment kit
according to the manufacturer’s protocol (10X Genomics). Demultiplexing, gene
quantification and TCR/BCR clonotype assignment were performed using Cell Ranger
(Version 3.0.2) vdj pipeline with GRCh38 as reference. In brief, a TCR/BCR diversity
metric, containing clonotype frequency and barcode information, was obtained. For the
TCR, only cells with at least one productive TCR alpha chain (TRA) and one productive
TCR beta chain (TRB) were kept for further analysis. Each unique TRA(s)-TRB(s) pair
was defined as a clonotype. For the BCR, only cells with at least one productive heavy
chain (IGH) and one productive light chain (IGK or IGL) were kept for further analysis.
Each unique IGH(s)-IGK/IGL(s) pair was defined as a clonotype. If one clonotype was
present in at least two cells, cells harboring this clonotype were considered clonal, and
the number of cells with such pairs indicated the degree of clonality of the clonotype.
Using barcode information, T cells with prevalent TCR clonotypes and B cells with
prevalent BCR clonotypes were projected on UMAP plots.
Plasma cytokines detection
Plasma levels of IFN-γ, IL-6, IL-18, MCP-1, MIP-1α and IP-10 were evaluated by
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28
using an Aimplex kit (Beijing Quantobio, China) following manufacturer’s instructions.
Type I IFN, represented by IFN-β, was detected by ELISA kit (Cat. EK1236, Multi
Sciences, China) according to the manufacture’s protocols. All samples were performed
in duplicate.
Statistics
The statistical tools, methods and threshold for each analysis are explicitly
described with the results or detailed in the Figure Legends or Methods sections.
Data Availability
The raw sequence data reported in this paper have been deposited in the Genome
Sequence Archive of the BIG Data Center, Beijing Institute of Genomics (BIG),
Chinese Academy of Sciences, under accession number HRA000150 and are publicly
accessible at http://bigd.big.ac.cn/gsa-human. The raw source data are available from
the corresponding author upon reasonable request.
Code Availability
Experimental protocols and data analyses pipeline used in our work are following
the 10X Genomics and Seurat official website. The analyses steps, functions and
parameters used are described in details in Methods section. Custom scripts for
analyzing data are available upon reasonable request.
References 43. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36, 411-420 (2018). 44. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888-1902 e21 (2019). 45. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189-196 (2016). 46. Venteicher, A.S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355 (2017). 47. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611-1624 e24 (2017).
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
29
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
Patients (n = 13)
Severe
Moderate
PBMC
TCR / BCR Profiling
Expression Analyses
10X scRNA-seq
a
b
UMAP_1
UM
AP_
2
-15 -10 -5 5 100
0
5
10
-10
-5
Naive TActivated T
MAITT
Pro TNKBPlasmaCD14+ MonoCD16+ MonoMono DCspDCPlateletHSC
c
d
Naive
TAc
tivat
ed T
MAI
T TPr
o T NK B
Plas
ma
CD14
+ M
ono
CD16
+ M
ono
Mon
o DC
spD
CPl
atele
tHS
CCD3D
CD3ECD3G
CCR7TCF7LEF1GZMKGZMAPRF1GNLYSLC4A10TRAV1-2TRGV9TRDV2MKI67PCNA
KLRF1TYROBP
MS4A1CD79A
MZB1LYZ
FCGR3ACD14
PPBP
CD1CFCER1A
CLEC4C
GP9
GATA2CYTL1
LILRA4
0
5
10
-10
-5
100-10
CD3D
0
5
10
-10
-5
100-10
SLC4A10 TRDV2
100-10
0
5
10
-10
-5
100-10
0
5
10
-10
-5
MKI67
4
0123
4
0123
4
0123
012
CYTL1
0
5
10
-10
-5
100-10
0123
PPBP
0
5
10
-10
-5
100-10
4
02
6
KLRF1
0
5
10
-10
-5
100-10
0123
0
5
10
-10
-5
100-10
CD79A
4
0123
0
5
10
-10
-5
100-10
CD1C
0123
100-10
0
5
10
-10
-5
LILRA4
0123
100-10
0
5
10
-10
-5
FCGR3A
4
0123
CD14
4
0123
100-10
0
5
10
-10
-5
UMAP_1
UM
AP_
2
HD(n = 5)
(n = 4)
(n = 7)
(n = 6)Conv
e0 5 10 15 20 25 30 35 40
Onset of symptom (days)
P01
P06
P07
P05
P10
S01
S02
S03
S08
S09
S10
S11
S12
S13
S14
S15
S16
S17
S05
S04
S07
S06
P11
P08
P09
P12
P03
P04
P02
Onset Admission Sampling Discharge RT-qPCR + Death
P13
RT-qPCR -
Seve
reC
onv
Mod
erat
e
Figure 1
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The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
HDa
b c
d
e
Moderate Severe ConvHD
Mod
erat
eSe
vere
Conv
0%
25%
50%
75%
100%
Perc
enta
geA
bsol
ute
Cou
nts
( 10^
9 / L
)
0%
25%
50%
75%
100%
Perc
enta
geU
MA
P_2
UMAP_1HD
01HD
02HD
03HD
04HD
05 P01
P02
P03
P04
P05
P06
P07
P08
P09
P10
P11
P01-
2
P12
P02-
2
P13
P03-
2P0
4-2
Naive TActivated TMAIT
TPro TNKBPlasmaCD14+ MonoCD16+ MonoMono DCspDCPlateletHSC
0.5
1.5
2.5
3.5
Resp
onse
To
Inte
rfero
n Al
pha
2.5
0.0
2.5
5.0
Acut
e In
flam
mat
ory
Resp
onse
Naive T
Activated TMAIT T
Pro T NK BPlasma
CD14+ Mono
CD16+ Mono
Mono DCspDC
PlateletHSC
2.5
0.0
2.5
******
******
******
******
******
******
************
***
*********
*********
********* ******
******
******
***
******
***
******
***
*********
******
******
***
*********
***
***
*********
*** ***
********
*** ******
******
******
***********
*********
*********
******
******
******
******
******
*****
*****
*********
******
******
***
*********
*********
*********
*****
p=0.018 p=0.0025
p=0.0025
p=0.0045
p=0.0070
p=0.0013 p=0.0061
p=0.024p=0.0025
p=0.016p=0.016
p=0.0061
p=0.0087p=0.0095
p=0.0051p=0.016
p=0.042p=0.024
p=0.024p=0.016p=0.030
p=0.038
Mono DCs pDC Platelet HSC
NK B
Plasma CD14+ Mono CD16+ Mono
Naive T Activated T MAIT T Pro T
2
4
6
8
0.0
2.5
5.0
7.5
2
4
6
0
20
40
60
80
0.0
0.1
0.2
0.3
2
4
6
0
5
10
15
0
2
4
6
25
50
75
0
5
10
15
0.0
0.5
1.0
0
20
40
60
0
5
10
15
20
0
1
2
3Perc
enta
ge
Figure 2
ConditionHDModerateSevereConv
ConditionHDModerateSevereConv
p=0.0016
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The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
0 10 20 30 40-log10(P)
GO:0006968: cellular defense responseGO:0002576: platelet degranulationGO:1903706: regulation of hemopoiesisGO:0001909: leukocyte mediated cytotoxicityGO:0051092: positive regulation of NF-kappaB transcription factor activityGO:0052547: regulation of peptidase activityGO:0032480: negative regulation of type I interferon productionGO:0060700: regulation of ribonuclease activityGO:0002449: lymphocyte mediated immunityGO:0002768: immune response-regulating cell surface receptor signaling pathwayGO:0032479: regulation of type I interferon productionGO:0045824: negative regulation of innate immune responseGO:0001817: regulation of cytokine productionGO:0009617: response to bacteriumGO:0002274: myeloid leukocyte activationGO:0002479: antigen processing and presentation of exogenous
peptide antigen via MHC class I, TAP-dependentGO:0035455: response to interferon-alphaGO:0045088: regulation of innate immune responseGO:0034341: response to interferon-gamma
GO:0060337: type I interferon signaling pathway
0
0
-22
2
4
4
log2 FoldChange ( Moderate / HD )
log2
Fol
dCha
nge
( Sev
ere
/ HD
)
LY6E
IFI27
IFITM1
ISG15 IFI6IFITM3
IFI44LTNFSF10CLU
FCGR1A
LILRB1LILRB4
CD300A
SP100
CD14CD36
CCL5STAT1
CST7
CTSDBST2
CTSA
others
log2FC >=0.5 & adjust P-value <=0.01
0-1
0
1
-1
-22
2
1
3
log2 FoldChange ( Moderate / HD )
log2
Fol
dCha
nge
( Sev
ere
/ HD
)
others
log2FC >=0.5 & adjust P-value <=0.01
0 10 20 30 40-log10(P)
GO:0019985: translesion synthesisGO:0035821: modification of morphology or physiology of other organismGO:0002347: response to tumor cellGO:0051098: regulation of bindingGO:0061025: membrane fusionGO:0052548: regulation of endopeptidase activityGO:0042098: T cell proliferationGO:0001959: regulation of cytokine-mediated signaling pathwayGO:0001818: negative regulation of cytokine productionGO:0032479: regulation of type I interferon productionGO:0060700: regulation of ribonuclease activityGO:0001909: leukocyte mediated cytotoxicityGO:0051092: positive regulation of NF-kappaB transcription factor activityGO:0043299: leukocyte degranulationGO:0035455: response to interferon-alphaGO:0001817: regulation of cytokine productionGO:0002253: activation of immune responseGO:0045088: regulation of innate immune responseGO:0034341: response to interferon-gammaGO:0034340: response to type I interferon
IFI6ISG15
IFI44LXAF1
MX1
HLA-A
IRF7LY6E
HLA-DPB1
KLRD1FCGR3A
KLRF1GZMH
CTSW
CD53HLA-DRB1
CD48
IFI27 PRF1IFITM1CX3CR1
CTSC
UMAP_1
HD Moderate
Severe Conv
a
c
e f
g
d
b
UM
AP_
2
-15 -10 -5 5 100UMAP_1
-15 -10 -5 5 100 -15 -10 -5 5 100
0
5
10
-10
-5
UM
AP_
2
0
5
10
-10
-5
0
5
10
-10
-5
NKother cells
CD14+ MonoCD16+ MonoMono DCspDC
Condition HD Moderate Severe Conv
GO
Bio
logi
cal P
roce
sses
Ter
ms
GO
Bio
logi
cal P
roce
sses
Ter
ms
CD14+ Mono
CD16+ Mono
Mono DCspDC
0.05
0.00
0.05
0.10
0.00
0.05
0.10
0.00
0.05
0.10
0.15
0.0
0.1
0.2
Apo
ptos
is
CD14+ Mono
CD16+ Mono
Mono DCspDC
0.00
0.05
0.10
0.15p=0.0014
p=0.0015
0.05
0.10
0.15
0.05
0.10
0.15
0.20
0.0
0.1
0.2
0.3
Mig
ratio
n
NK
0.05
0.00
0.05
0.10
0.15
NK0.00
0.05
0.10
0.15
0.20***
*********
******
******
*********
******
******
*** ***
******
******** ***
*********
******0.15
***
******
******
*** **
******
******
******** ****** ******
Figure 3
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
CD3E
CD4
CD8A
CCR7
SELL
TCF7
LEF1LTB
UMAP_1
a
cd
e
f g
h i
bCD4+ Naive
CD8+ Naive
CD4+ MemoryCD4+ Effector MemoryCD4+ Effector-GZMK
CD8+ Effector-GZMK
CD4+ Effector-GNLY
CD8+ Effector-GNLY
Treg
NKT NaiveNKT CD56NKT CD160
-5
-5
0
5
0 5 10
UM
AP_
2
S100A4
GPR183
CD69
GZMK
GZMA
GZMB
GZMH
GNLY
NKG7
FOXP3
IL2RA
TIGIT
HAVCR2
CTLA4
LAG3
PDCD1
TOX
FCGR3A
KIR3DL2
TYROBP
NCAM1
CD160
0
-2
-2
-1
-1
0
0
2
2
1
1
10 20 30 40-log10(P)
log2 FoldChange ( Moderate / HD )
log2
Fol
dCha
nge
( Sev
ere
/ HD
)
GO:0051493: regulation of cytoskeleton organizationGO:0032479: regulation of type I interferon productionGO:0051100: negative regulation of bindingGO:0007163: establishment or maintenance of cell polarityGO:0070486: leukocyte aggregationGO:0001959: regulation of cytokine-mediated signaling pathwayGO:0019835: cytolysisGO:0035821: modification of morphology or physiology of other organismGO:0060700: regulation of ribonuclease activityGO:0034109: homotypic cell-cell adhesionGO:0009617: response to bacteriumGO:0007159: leukocyte cell-cell adhesionGO:0001906: cell killingGO:0045055: regulated exocytosisGO:0001817: regulation of cytokine productionGO:0002253: activation of immune responseGO:0045088: regulation of innate immune responseGO:0035456: response to interferon-betaGO:0034341: response to interferon-gamma
GO:0034340: response to type I interferon
ISG15
KLRF1ISG20
IFI6
IFI44LLY6E
IRF7PRF1
GZMH
IL2RG
CTSW
CD53
NKG7
log2FC >=0.5 & adjust P-value <=0.01
OAS1
GZMBCX3CR1
GZMA
ITGB7CCL4
FCGR3AMYOM2
KLRD1
others GO
Bio
logi
cal P
roce
sses
Ter
ms
**** ***
***************
******Ex
haus
tion
0.0
0.51.0
1.5
***
******C
ytot
oxic
ity
1
01
2******
***
***
*********
***
******
***
***
******
******
***
***
******
************
***
0.05
0.10
HDM
oder
ate
Conv
Mig
ratio
n
0.00
0.03
0.06
0.09p=0.014
p=0.00049
p=0.0034
p=0.0020
p=0.0020
p=0.00098
p=0.00049
p=0.00049
p=0.0049p=0.00098
p=0.00098
HDM
oder
ate
Seve
reSe
vere
Conv
Apo
ptos
is
0.0
0.1p=0.0027
p=0.0033
p=0.0032
p=0.0085
p=0.0078
p=0.0017
p=0.0067
0.00
0.05
0.10
0.15
Mig
ratio
nA
popt
osis
CD4+ Naive
CD4+ Memory
CD4+ Effector-GZMK
C D4+ Effector-GNLY
Treg
CD8+ Naive
NKT Naive
NKT CD56
NKT CD160
***
******
***
***
*********
******
*********
********
***** ***
******** *********
**
***
*********
************
************
*** ******
********* *****
***********
*****
*********
************
******
******
******
*********
*********
*********
******
************
***
*********
CD8+ Effector-GZMK
CD8+ Effector-GNLY
CCR7
SELL
LEF1
TCF7
PRF1
IFNG
GNLY
NKG7
GZM
BGZ
MA
GZM
HKL
RK1
KLRB
1KL
RD1
CTSW
CST7
FCGR
3ALA
G3TI
GIT
PDCD
1CT
LA4
HAVC
R2 TOX
0
20
40
60
80
100
-2
-1
0
1
2Percentage ExpressionCONV
Severe
Moderate
HD
CD4+ Effector Memory
p=0.0087p=0.0025 p=0.018
p=0.0025
p=0.018p=0.015
p=0.011p=0.032 p=0.018
p=0.042
p=0.0092p=0.0073
p=0.0051
p=0.0032 p=0.0022
p=0.017p=0.0025 p=0.016
p=0.038
p=0.016
0.0
0.5
1.0
1.5
2.0
0
10
20
30
0.0
2.5
5.0
7.5
10.0
0
3
6
9
0
10
20
30
0
20
40
60
80
0
10
20
30
0
2
4
6
0.0
0.5
1.0
0
10
20
30
40
50
0
5
10
15
20
25
0
10
20
30
CD4+ Naive CD4+ Memory CD4+ Effector Memory CD4+ Effector-GZMK
CD8+ Naive CD8+ Effector-GZMKCD4+ Effector-GNLY Treg
CD8+ Effector-GNLY NKT Naive NKT CD56 NKT CD160
Perc
enta
ge
Figure 4
ConditionHDModerateSevereConv
ConditionHDModerateSevereConv
ConditionHDModerateSevereConv
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
TCRNo TCR
OthersClonetype1Clonetype2Clonetype3Clonetype4Clonetype5
a b
0
25
50
75
100
NKT Naive
NKT CD56
NKT CD160
TCR
Per
cent
age
0
5
10
15
0 2 4 6log2(number of cells per clonotype)
log2
(num
ber o
f clo
noty
pes)
c
d HD Moderate Severe Conv
0
10
20
30
Dis
trib
utio
n of
Clo
ne S
tatu
s (%
)
NonVDJNoClonalClonal
HD
Moderate
Severe
Conv
unique2 56 1011 2021 3031 5051 100>100
HD Moderate
Severe Conv
e f
0
25
50
75
100
0 25 50 75 100Clonal cells in each cluster(%)
Shar
ed c
lona
l cel
ls in
eac
h cl
uste
r(%
)
g
UMAP_1
UM
AP_
2
CD4+ Naive
CD8+ Naive
CD4+ Memory
CD4+ Effector Memory
CD4+ Effector-GZMK
CD8+ Effector-GZMK
CD4+ Effector-GNLY
CD8+ Effector-GNLY
Treg
NKT Naive
NKT CD56
NKT CD160
CD4+ Naive
CD8+ Naive
CD4+ MemoryCD4+ Effector MemoryCD4+ Effector-GZMK
CD8+ Effector-GZMK
CD4+ Effector-GNLY
CD8+ Effector-GNLY
Treg
CD4+ Naive
CD8+ Naive
CD4+ Memory
CD4+ Effector Memory
CD4+ Effector-GZMK
CD8+ Effector-GZMK
CD4+ Effector-GNLY
CD8+ Effector-GNLY
Treg
HD Moderate Severe Conv
HD Moderate Severe Conv
h i
0.00.51.01.5
CAL
SAFL
ESSG
SAR
QLT
FC
ILLS
TSG
GG
SNYK
LTF
CAV
NTL
RAG
GG
ADG
LTF
CAV
KKLS
FC
AASR
VGG
GN
TPLV
FC
ATVP
NTG
FQKL
VFC
AVER
SLLS
NFG
NEK
LTF
CAV
ND
MN
RD
DKI
IFC
ATEA
NFG
NEK
LTF
CAM
RVN
YGQ
NFV
FC
AVW
NYG
QN
FVF
CAM
NAG
NN
RKL
IWC
GSY
TDKL
IFC
AASH
DAG
GG
ADG
LTF
CAL
LNTN
AGKS
TFC
ALSG
SSN
DYK
LSF
CLL
GSN
FGN
EKLT
FC
AAEY
STLT
FC
AEKH
TNAG
KSTF
CAT
EYQ
TGAN
NLF
FC
VVIS
YNN
ND
MR
FC
ALSP
GG
KLIF
CAY
RSR
EYG
NKL
VFC
AGG
QG
GSE
KLVF
CAA
SIG
NFG
NEK
LTF
CVP
KGG
SQG
NLI
FC
AVR
NAG
NM
LTF
CAV
RSA
GN
MLT
FC
AETL
NTG
FQKL
VFC
AASL
RR
GAQ
KLVF
CG
SYTD
KLIF
CAL
SGSS
NDY
KLSF
CAL
LNTN
AGKS
TFC
AVR
DG
QKL
LFC
AAEY
STLT
FC
AAH
NSG
GG
ADG
LTF
CAV
LGG
GKL
IFC
VVSE
SGG
YQKV
TFC
AMR
EGYW
DD
KIIF
CAV
ENYG
GSQ
GN
LIF
TRA
Per
cent
age
0.00.51.01.52.0
CAI
SEG
ERR
GG
ETQ
YFC
ASSS
WD
RD
NSP
LHF
CAS
SVAP
DPY
NEQ
FFC
ASSL
VAKE
QYF
CAS
SNTV
NQ
PQH
FC
ASSI
VFG
TGVG
EQFF
CAT
SDEQ
EAN
EQFF
CAS
SYG
PYN
EQFF
CAS
SLVS
SSYN
EQFF
CAS
SLIP
PGLA
GVG
TYEQ
YFC
ASSR
NN
EQFF
CAS
SQG
FQR
GTN
EQYF
CAS
STD
TQYF
CSA
RVTG
TTYN
EQFF
CAS
SPG
LAG
DN
EQFF
CAS
GKL
GG
AEQ
FFC
ASSF
GPA
DTQ
YFC
SAR
GQ
GG
TGEL
FFC
ASSR
LAAY
NEQ
FFC
ASEP
WVL
SSYE
QYF
CAS
SVEG
SSYN
EQFF
CAT
QD
GM
NTE
AFF
CAS
SIVE
PGEQ
YFC
ASG
YGG
AYYG
YTF
CAS
SPQ
RN
TEAF
FC
ASSR
PGYE
QYF
CAS
KGVN
TEAF
FC
ASR
PDLG
GYE
QYF
CAS
SGSN
SPLH
FC
ASSH
GG
SEQ
FFC
ASSQ
GFQ
RG
TNEQ
YFC
SARV
TGTT
YNEQ
FFC
ASSG
GTS
GES
SYN
EQFF
CAS
SPG
L AG
DN
EQFF
CAS
SLSG
GR
ETQ
YFC
ASSQ
GW
PYEQ
YFC
ASSW
RAD
TQYF
CAS
STPR
GSG
DTY
EQYF
CSA
ARD
SYSS
YEQ
YFC
ASSS
RYN
EQFFTR
B P
erce
ntag
e
HD
TRAV1
2TR
AV122
TRAV12
3TR
AV16TR
AV20TR
AV23DV6TR
AV29DV5TR
AV30TR
AV35TR
AV382DV8
TRAV40
TRAV41
TRAV8
2TR
AV92
TRBV10
1TR
BV103
TRBV12
4TR
BV19TR
BV2TR
BV201
TRBV27
TRBV28
TRBV3
1TR
BV41
TRBV4
3TR
BV51
TRBV5
4TR
BV66
TRBV7
2TR
BV79
TRBV9
TRAJ11TRAJ20TRAJ23TRAJ26TRAJ27TRAJ33TRAJ34TRAJ38TRAJ39TRAJ43TRAJ45TRAJ47TRAJ48TRAJ57TRBJ1 1TRBJ1 2TRBJ1 5TRBJ2 1TRBJ2 3TRBJ2 5TRBJ2 7
0
0.5
1
1.5
2
2.5
Moderate
TRAV1
2TR
AV122
TRAV12
3TR
AV16TR
AV20TR
AV23DV6TR
AV29DV5TR
AV30TR
AV35TR
AV382DV8
TRAV40
TRAV41
TRAV8
2TR
AV92
TRBV10
1TR
BV103
TRBV12
4TR
BV19TR
BV2TR
BV201
TRBV27
TRBV28
TRBV3
1TR
BV41
TRBV4
3TR
BV51
TRBV5
4TR
BV66
TRBV7
2TR
BV79
TRBV9
TRAJ11TRAJ20TRAJ23TRAJ26TRAJ27TRAJ33TRAJ34TRAJ38TRAJ39TRAJ43TRAJ45TRAJ47TRAJ48TRAJ57TRBJ1 1TRBJ1 2TRBJ1 5TRBJ2 1TRBJ2 3TRBJ2 5TRBJ2 7
0
1
2
3
4
Conv
TRA V1
2TR
AV122
TRAV12
3TR
AV16TR
AV20TR
AV23DV6TR
AV29DV5TR
AV30TR
AV35TR
AV382DV8
TRAV40
TRAV41
TRAV8
2TR
AV92
TRBV10
1TR
BV103
TRBV12
4TR
BV19TR
BV2TR
BV201
TRBV27
TRBV28
TRBV3
1TR
BV41
TRBV4
3TR
BV51
TRBV5
4TR
BV66
TRBV7
2TR
BV79
TRBV9
TRAJ11TRAJ20TRAJ23TRAJ26TRAJ27TRAJ33TRAJ34TRAJ38TRAJ39TRAJ43TRAJ45TRAJ47TRAJ48TRAJ57TRBJ1 1TRBJ1 2TRBJ1 5TRBJ2 1TRBJ2 3TRBJ2 5TRBJ2 7
0
1
2
3
4Severe
TRAV1
2TR
AV122
TRAV12
3TR
AV16TR
AV20TR
AV23DV6TR
AV29DV5TR
AV30TR
AV35TR
AV382DV8
TRAV40
TRAV41
TRAV8
2TR
AV92
TRBV10
1TR
BV103
TRBV12
4TR
BV19TR
BV2TR
BV201
TRBV27
TRBV28
TRBV3
1TR
BV41
TRBV4
3TR
BV51
TRBV5
4TR
BV66
TRBV7
2TR
BV79
TRBV9
TRAJ11TRAJ20TRAJ23TRAJ26TRAJ27TRAJ33TRAJ34TRAJ38TRAJ39TRAJ43TRAJ45TRAJ47TRAJ48TRAJ57TRBJ1 1TRBJ1 2TRBJ1 5TRBJ2 1TRBJ2 3TRBJ2 5TRBJ2 7
0
0.5
1
1.5
2
2.5
3
3.5
Clone State
CD4+
Effe
ctor-G
ZMK
Figure 5
CD4+
Naiv
eCD
4+ M
emor
y
CD4+
Effe
ctor-G
ZMK
Treg
CD8+
Naiv
e
CD4+
Effe
ctor-G
NLY
CD8+
Effe
ctor-G
ZMK
CD8+
Effe
ctor-G
NLY
CD4+
Effe
ctor-G
ZMK
CD4+
Naiv
eCD
4+ M
emor
y
CD4+
Effe
ctor-G
ZMK
Treg
CD8+
Naiv
e
CD4+
Effe
ctor-G
NLY
CD8+
Effe
ctor-G
ZMK
CD8+
Effe
ctor-G
NLY
CD4+
Effe
ctor-G
ZMK
CD4+
Naiv
eCD
4+ M
emor
y
CD4+
Effe
ctor-G
ZMK
Treg
CD8+
Naiv
e
CD4+
Effe
ctor-G
NLY
CD8+
Effe
ctor-G
ZMK
CD8+
Effe
ctor-G
NLY
CD4+
Effe
ctor-G
ZMK
CD4+
Naiv
eCD
4+ M
emor
y
CD4+
Effe
ctor-G
ZMK
Treg
CD8+
Naiv
e
CD4+
Effe
ctor-G
NLY
CD8+
Effe
ctor-G
ZMK
CD8+
Effe
ctor-G
NLY
0
5
10
15
TRBJ11
TRBJ21
TRBJ25
TRBJ27
TRBV101
TRBV113
TRBV123
TRBV19
TRBV201
TRBV241
TRBV27
TRBV28
TRBV51
TRBV64
TRBV72
TRBV78
TRBV9
TRAV12
TRAV122
TRAV123
TRAV131
TRAV14DV4
TRAV16
TRAV20
TRAV23DV6
TRAV261
TRAV29DV5
TRAV30
TRAV35
TRAV382D
V8
TRAV4
TRAV40
TRAV41
TRAV82
TRAV92
Perc
enta
gePe
rcen
tage
Perc
enta
gePe
rcen
tage
TRAJ11
TRAJ17
TRAJ20
TRAJ23
TRAJ26
TRAJ27
TRAJ29
TRAJ33
TRAJ34
TRAJ37
TRAJ38
TRAJ39
TRAJ43
TRAJ44
TRAJ45
TRAJ47
TRAJ48
TRAJ57
p=0.024
p=0.017p=0.024
p=0.042p=0.038
p=0.017p=0.024
p=0.024
p=0.024
p=0.044p=0.048
p=0.042p=0.042
p=0.019p=0.017
p=0.024
p=0.033
p=0.017
0
5
10
0
5
10
15
0
10
20
30
ConditionHDModerateSevereConv
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
UMAP_1
UM
AP_
2
a
-10 -5 50
0
4
8
-4
Naive B
Dividing Plasma BPlasma B
Memory B
Intermediate Memory B
Germinal Center B
b
c
0%
25%
50%
75%
100%
Perc
enta
ge
d
e
f g
GO
Bio
logi
cal P
roce
sses
Ter
ms
0%
25%
50%
75%
100%
Perc
enta
ge
Naive B
Dividing Plasma BPlasma BMemory BIntermediate Memory BGerminal Center B
HD01
HD02
HD03
HD04
HD05 P01
P02
P03
P04
P05
P06
P07
P08
P09
P10
P11
P01-
2
P12
P02-
2
P13
P03-
2P0
4-2
HDM
oder
ate
Seve
re
Conv
4
0
4
8
01234
CD79A
012345
MS4A1
01234
MZB1
0123
CD38
01234
IGHD
0
1
2
3
CD27
0
1
2
3
NEIL1
0.00.51.01.52.02.5
MKI67
UMAP_1
UM
AP_
2
10 5 0 5 10 5 0 5 10 5 0 5 10 5 0 5
4
0
4
8
0 10 20 30 40-log10(P)
GO:0000302: response to reactive oxygen speciesGO:0061025: membrane fusionGO:0044766: multi-organism transportGO:0035821: modification of morphology or physiology of other organismGO:0032647: regulation of interferon-alpha productionGO:0001895: retina homeostasisGO:0050688: regulation of defense response to virusGO:1903706: regulation of hemopoiesisGO:0000209: protein polyubiquitinationGO:0060700: regulation of ribonuclease activityGO:0009617: response to bacteriumGO:0032479: regulation of type I interferon productionGO:0002757: immune response-activating signal transductionGO:0002479: peptide antigen via MHC class I, TAP-dependent
antigen processing and presentation of exogenous GO:0045088: regulation of innate immune responseGO:0035456: response to interferon-betaGO:0034341: response to interferon-gamma
GO:0034340: response to type I interferon
-2
-2.5
0
0
2
2.5
5
log2 FoldChange ( Moderate / HD )
log2
Fol
dCha
nge
( Sev
ere
/ HD
)
log2FC >=0.5 & adjust P-value <=0.01
others
ISG15ISG20
IFITM1
IFI44L
IRF7
IFI6MX1
HLA-A
MZB1
JCHAIN
ZBP1
XAF1
IGKC IGKV4-1
IGLV3-21
OAS1
IGLL5LY6E
TNFSF10
HLA-FHLA-B
Perc
enta
ge
Naive B Germinal Center B Intermediate Memory B Memory B Dividing Plasma BPlasma B
p=0.048 p=0.010
p=0.010p=0.016
p=0.0054p=0.018
p=0.012
0
5
10
15
20
0
10
20
30
40
0
5
10
15
20
0
20
40
60
80
01020304050
0
20
40
Figure 6
ConditionHDModerateSevereConv
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
aFigure 7
b
c
0
25
50
75
100
Plas
ma
B
Divid
ing
Plas
ma
B
BC
R P
erce
ntag
e
BCRNo BCR
0
10
20
30
40
50
NonVDJNoClonalClonal
HD Moderate Severe Conv
Dis
trib
utio
n of
Clo
ne S
tatu
s(%
)
Naive BDividing Plasma B
Plasma B
Memory B
Intermediate Memory B
Germinal Center B
UMAP_1
UM
AP_
2
d
HD
IGH
V118
IGH
V12
IGH
V13
IGH
V146
IGH
V169
2IG
HV2
5IG
HV2
70IG
HV3
11IG
HV3
15IG
HV3
21IG
HV3
23IG
HV3
30IG
HV3
33IG
HV3
43IG
HV3
48IG
HV3
7IG
HV3
74IG
HV4
31IG
HV4
34IG
HV4
39IG
HV4
59IG
HV4
61IG
HV5
51IG
KV112
IGKV1
33IG
KV15
IGKV1D
39IG
KV2D28
IGKV2D
29IG
KV311
IGKV3
15IG
KV320
IGKV4
1IG
LV140
IGLV1
44IG
LV147
IGL V1
51IG
LV211
IGLV2
14IG
LV223
IGL V3
1IG
LV319
IGLV3
21IG
LV325
IGLV4
69
IGHJ3
IGHJ4
IGHJ5
IGHJ6
IGKJ1
IGKJ2
IGKJ3
IGKJ4
IGKJ5
IGLJ1
IGLJ2
IGLJ3
0
0.5
1
1.5
2
2.5
3
3.5Moderate
IGH
V118
IGH
V12
IGH
V13
IGH
V146
IGH
V169
2IG
HV2
5IG
HV2
70IG
HV3
11IG
HV3
15IG
HV3
21IG
HV3
23IG
HV3
30IG
HV3
33IG
HV3
43IG
HV3
48IG
HV3
7IG
HV3
74IG
HV4
31IG
HV4
34IG
HV4
39IG
HV4
59IG
HV4
61IG
HV5
51IG
KV112
IGKV1
33IG
KV15
IGKV1D
39IG
KV2D28
IGKV2D
29IG
KV311
IGKV3
15IG
KV320
IGKV4
1IG
LV140
IGLV1
44IG
LV147
IGLV1
51IG
LV211
IGLV2
14IG
LV223
IGLV3
1IG
LV319
IGLV3
21IG
LV325
IGLV4
69
IGHJ3
IGHJ4
IGHJ5
IGHJ6
IGKJ1
IGKJ2
IGKJ3
IGKJ4
IGKJ5
IGLJ1
IGLJ2
IGLJ3
0
0.5
1
1.5
2
2.5
Severe
IGH
V118
IGH
V12
IGH
V13
IGH
V146
IGH
V169
2IG
HV2
5IG
HV2
70IG
HV3
11IG
HV3
15IG
HV3
21IG
HV3
23IG
HV3
30IG
HV3
33IG
HV3
43IG
HV3
48IG
HV3
7IG
HV3
74IG
HV4
31IG
HV4
34IG
HV4
39IG
HV4
59IG
HV4
61IG
HV5
51IG
KV112
IGKV1
33IG
KV15
IGKV1D
39IG
KV2D28
IGKV2D
29IG
KV311
IGKV3
15IG
KV320
IGKV4
1IG
LV140
IGLV1
44IG
LV147
IGLV1
51IG
LV211
IGLV2
14IG
LV223
IGLV3
1IG
LV319
IGL V3
21IG
LV325
IGLV4
69
IGHJ3
IGHJ4
IGHJ5
IGHJ6
IGKJ1
IGKJ2
IGKJ3
IGKJ4
IGKJ5
IGLJ1
IGLJ2
IGLJ3
0
1
2
3
4
Conv
IGH
V118
IGH
V12
IGH
V13
IGH
V146
IGH
V169
2IG
HV2
5IG
HV2
70IG
HV3
11IG
HV3
15IG
HV3
21IG
HV3
23IG
HV3
30IG
HV3
33IG
HV3
43IG
HV3
48IG
HV3
7IG
HV3
74IG
HV4
31IG
HV4
34IG
HV4
39IG
HV4
59IG
HV4
61IG
HV5
51IG
KV112
IGKV1
33IG
KV15
IGKV1D
39IG
KV2D28
IGKV2D
29IG
KV311
IGKV3
15IG
KV320
IGKV4
1IG
LV140
IGL V1
44IG
L V147
IGLV1
51IG
LV211
IGLV2
14IG
LV223
IGLV3
1IG
LV319
IGLV3
21IG
LV325
IGL V4
69
IGHJ3
IGHJ4
IGHJ5
IGHJ6
IGKJ1
IGKJ2
IGKJ3
IGKJ4
IGKJ5
IGLJ1
IGLJ2
IGLJ3
0
0.5
1
1.5
2
2.5
3
e
f
0%
25%
50%
75%
100%
IGHA
Perc
enta
ge
IGHDIGHGIGHM
HD01
HD02
HD03
HD04
HD05 P01
P02
P03
P04
P05
P06
P07
P08
P09
P10
P11
P01-
2
P12
P02-
2
P13
P03-
2P0
4-2
g
Clone State
Naive
B
Germ
inal
Cent
er B
Inte
rmed
iate
Mem
ory B
Mem
ory
B
Plas
ma
BDi
viding
Plas
ma
B
Naive
BGe
rmina
l Ce
nter
BIn
term
ediat
e
Mem
ory B
Mem
ory
B
Plas
ma
BDi
viding
Plas
ma
B
Naive
BGe
rmina
l Ce
nter
BIn
term
ediat
e
Mem
ory B
Mem
ory
B
Plas
ma
BDi
viding
Plas
ma
B
Naive
BGe
rmina
l Ce
nter
BIn
term
ediat
e
Mem
ory B
Mem
ory
B
Plas
ma
BDi
viding
Plas
ma
B
Naive
BGe
rmina
l Ce
nter
BIn
term
ediat
e
Mem
ory B
Mem
ory
B
IGHA
IGHD
IGHG
IGHM
Perc
enta
ge ConditionHDModerateSevereConv
0
20
40
60
80 p=0.022
p=0.010p=0.030p=0.0061
p=0.0095p=0.016
p=0.018 p=0.038 p=0.048
p=0.048
p=0.030p=0.0087
p=0.030
p=0.048
p=0.042 p=0.017p=0.038
p=0.019p=0.042p=0.050 p=0.0025
p=0.0043
p=0.042
p=0.018
0
5
10
15
2040
IGHV1
18
IGHV1
2
IGHV1
3
IGHV1
692
IGHV2
70
IGHV3
11
IGHV3
15
IGHV3
21
IGHV3
23
IGHV3
30
IGHV3
33
IGHV3
43
IGHV3
7
IGHV3
73
IGHV3
74
IGHV4
31
IGHV4
34
IGHV4
39
IGHV4
4
IGHV5
51
IGHV6
10
20
40
60
IGHJ3
IGHJ4
IGHJ5
IGHJ6
Perc
enta
ge
Perc
enta
gePe
rcen
tage
Perc
enta
gePe
rcen
tage
Perc
enta
ge
IGKV1
12
IGKV1
16
IGKV1
17
IGKV1
27
IGKV1
33
IGKV1
5
IGKV1
8
IGKV1D
39
IGKV2
24
IGKV2D
28
IGKV2D
29
IGKV3
11
IGKV3
15
IGKV3
20
IGKV4
1IG
KJ1IG
KJ2IG
KJ3IG
KJ4IG
KJ5
IGLJ
1IG
LJ2
IGLJ
3
IGLV
140
IGLV
144
IGL V1
47
IGLV
151
IGLV
211
IGLV
214
IGLV
223
IGLV
31
IGLV
321
IGLV
325
IGLV
469
0
10
20
30
0
20
40
60
80
HDModerateSevereConv
Condition
0
5
10
15
20
0
5
10
15
20
25
40~~
IGLV
319
~~
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted July 24, 2020. ; https://doi.org/10.1101/2020.07.23.217703doi: bioRxiv preprint
Normal RangeSampleID S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22PatientID P03 P04 P02 P01 P06 P07 P05 P10 P11 P08 P09 P03-2 P04-2 P02-2 P01-2 P12 P13 HD01 HD02 HD03 HD04 HD05CharateristicsAge, years 41 15 55 36 60 37 35 50 78 40 79 41 15 55 36 43 48 29 58 44 33 35Sex F M F M F M M F M M F F M F M M M M M M M M
Comorbidity No No No No No No No No hypertension, diabetes diabetes hypertension No No No No No No NA NA NA NA NA
Sign and symptom fever,cough fever No fever,
cough
fever,cough,expectoration, short ofbreath
fever, sorethroat,myalgia orfatigue
fever,cough,expectoration, myalgiaor fatigue
fever, cough,expectoration, myalgia orfatigue
fever,cough,myalgia orfatigue
fever, cough,expectoration, myalgia orfatigue
fever, cough,expectoration, myalgia orfatigue
No No No No No No NA NA NA NA NA
Onset of symptom to sampling, days 10 4 6 5 7 7 8 15 10 4 5 27 21 19 9 11 26 NA NA NA NA NAOnset of symptom to hospitaladmission, days 9 3 5 4 6 6 7 11 9 3 4 9 3 5 4 2 15 NA NA NA NA NA
Pulmonary inflammation No No No Unilateral Bilateral Unilateral No Bilateral Bilateral Bilateral Bilateral No No No No No No No No No No NoMechanical ventilation No No No No No No No No No No Yes No No No No No No No No No No NoICU care No No No No No No No Yes Yes Yes Yes No No No No No No No No No No NoSeptic shock No No No No No No No No No No Yes No No No No No No No No No No NoMulti-organ failure No No No No No No No Yes No No Yes No No No No No No No No No No NoRespiratory parameterDyspnea No No No No yes No No Yes Yes Yes Yes No No No No No No No No No No NoRespiratory rate (per min) 16-24 19 20 22 18 23 20 20 30 26 26 25 18 19 20 20 22 18 NA NA NA NA NAFiO2 (%) 21 21 21 8 33 21 21 35 45 21 70 21 21 21 21 21 21 NA NA NA NA NAPaO2 (mmHg) 80-100 NA NA 73 NA 144 85 NA 81 102 62 59 NA NA 88 NA NA NA NA NA NA NA NASpO2 (%) 95-100 100 99 98 100 99 96 99 99 97 91 92 99 100 98 99 98 100 NA NA NA NA NAPaO2/FiO2 ratio 400-500 NA NA 348 NA 436 405 NA 231 227 295 84 NA NA 419 NA NA NA NA NA NA NA NABlood routineLeucocytes count, × 10⁹/L 3.97-9.15 7.36 6.35 3.71 4.64 5.17 4.53 5.48 5.51 3.69 5.4 4.1 7.07 5.87 3.17 5.01 4.36 5.54 4.95 7.27 6.81 6.24 8.2Neutrophils count, × 10⁹/L 2.0-7.0 4.31 2.72 1.8 2.84 2.93 1.72 3.75 4.8 3.03 3.91 2.51 4.42 2.9 1.46 2.35 2.52 3.25 2.53 3.89 4.38 3.63 5.27Lymphocytes count, × 10⁹/L 0.80-4.00 2.43 2.93 1.53 1.3 1.68 2.24 1.17 0.52 0.41 0.98 1.23 2.05 2.42 1.25 1.98 1.23 1.41 1.89 2.15 1.87 1.64 2.01Platelets count, × 10⁹/L 85.0-303.0 189 168 157 113 185 173 170 252 162 134 128 232 191 212 100 267 189 171 269 209 249 300Haemoglobin, g/L 131.0-172.0 132 150 140 160 148 151 162 114 112 158 148 124 142 120 147 149 149 150 149 154 162 161Coagulation functionActivated partial thromboplastin time, s 23.0-42.0 25.8 32.6 33.5 79.7 30.4 36.2 34.2 18.5 64.5 33 33.8 NA NA NA NA NA 33.2 NA NA NA NA NAProthrombin time, s 10.20-14.30 12.1 13.6 11.8 12.2 10.9 12.8 11.6 11.5 18.1 11.4 11.6 NA NA NA NA NA 11.9 NA NA NA NA NAD-dimer, µg/L 0.00-0.55 0.24 0.28 0.43 0.24 0.62 NA 0.24 2.31 4.89 0.13 0.19 NA NA NA NA 0.55 0.14 NA NA NA NA NABlood biochemistryAlbumin, g/L 35.0–55.0 40 43 38 44 42 43 45 30 31 40 36 38 42 NA NA 33 38 46 42 44 48 47Alanine aminotransferase, U/L 5.0-40.0 107 65 14 32 16 23 26 76 34 12 14 50 28 NA NA 25 21 15 26 20 12 40Aspartate aminotransferase, U/L 8.0–40.0 81 60 28 30 24 27 25 18 50 18 23 26 39 NA NA 23 15 23 20 15 16 35Total bilirubin, μmol/L 3.40-20.5 11 14.7 7 11.6 18.9 23.1 22.4 27.7 13.1 11.3 17.5 6.1 10.3 NA NA 14.8 16.6 12 6.6 12.2 9.8 14.6Serum creatinine, μmol/L 62.0-115.0 69 83 71 85 68 84 68 68 81 82 68 58 83 NA NA 64 71 96 96 81 99 73Lactate dehydrogenase, U/L 109.0–245.0 213 516 212 162 247 197 256 232 305 180 156 128 147 NA NA 225 90 NA NA NA NA NAInterleukin-6, pg/mL 0.0-7.0 2.01 2.87 25.1 10.23 7.87 0.77 11.66 1.17 275.19 44.03 42.16 16.33 8.75 12.53 0.77 2.87 16.33 NA NA NA NA NAC-reactive protein, mg/L 0.068-8.20 1.2 25.5 7 4.9 32 21.9 33.9 3.65 42.95 36.4 37 2.07 NA 1.75 2.1 19.5 0.41 NA NA NA NA NAProcalcitonin, ng/mL 0.0-0.5 0.029 0.063 0.034 0.058 0.031 0.039 0.043 0.079 0.533 0.049 0.037 0.034 0.041 <0.02 0.073 0.042 0.035 NA NA NA NA NAErythrocyte sedimentation rate, mm/h 0.0-15.0 8 13 28 11 45 8 39 97 53 6 10 31 NA 37 NA 64 2 NA NA NA NA NASerum ferritin, ng/mL 30.0-400.0 30.83 339.1 194.6 432.3 339.5 396.7 556.1 818.3 854.4 323.3 430.7 42.32 383.4 199.5 310.2 587.9 363.4 NA NA NA NA NA
Table 1 | Characteristics and laboratory findings of healthy donors and enrolled patients infected with SARS-CoV-2 in the study.
All clinical data are from the sampling day. COVID-19: coronavirus disease 2019
Moderate Severe Conv HD
.C
C-B
Y-N
C-N
D 4.0 International license
made available under a
(which w
as not certified by peer review) is the author/funder, w
ho has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprint
this version posted July 24, 2020. ;
https://doi.org/10.1101/2020.07.23.217703doi:
bioRxiv preprint