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])

.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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

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https://doi.org/10.1101/2020.07.23.217703

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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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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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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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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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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



https://doi.org/10.1101/2020.07.23.217703


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).



http://bigd.big.ac.cn/gsa-human

https://doi.org/10.1101/2020.07.23.217703


29



https://doi.org/10.1101/2020.07.23.217703


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



https://doi.org/10.1101/2020.07.23.217703


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


p=0.0016



https://doi.org/10.1101/2020.07.23.217703


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

Fol

dCha

nge

( Sev

ere

/ HD

)

others


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



https://doi.org/10.1101/2020.07.23.217703


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

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


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






https://doi.org/10.1101/2020.07.23.217703


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-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-GZMK

CD8+ Effector-GZMK

CD4+ Effector-GNLY

CD8+ Effector-GNLY

Treg

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

QQ

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


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


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


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




https://doi.org/10.1101/2020.07.23.217703


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

Fol

dCha

nge

( Sev

ere

/ HD

)


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




https://doi.org/10.1101/2020.07.23.217703


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


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

~~



https://doi.org/10.1101/2020.07.23.217703


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



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

https://doi.org/10.1101/2020.07.23.217703


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