Global profiling of SARS-CoV-2 specific IgG/ IgM …...2020/03/20 · The 1st global picture of the SARS-CoV-2 specific IgG/ IgM response reveals that at the convalescent phase, 100%

1

Global profiling of SARS-CoV-2 specific IgG/ IgM responses of convalescents using a

proteome microarray

He-wei Jiang1,#, Yang Li1,#, Hai-nan Zhang1,#, Wei Wang2,#, Dong Men3, Xiao Yang4, Huan Qi1, Jie

Zhou2,*, Sheng-ce Tao1,*

1Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry

of Education), Shanghai Jiao Tong University, Shanghai 200240, China

2Foshan Fourth People's Hospital, Foshan 528000, China

3State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences,

Wuhan 430071, China

4Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences,

Beijing, 100101, China

# These authors contributed equally to this work.

* corresponding should address to: Shanghai Center for Systems Biomedicine, Key Laboratory

of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai

200240, China. E-mail address: [email protected] (S.-C. Tao) or Foshan Fourth People’s

Hospital, Foshan 528000, China. [email protected] (J. Zhou)

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 (which was not certified by peer review)

The copyright holder for this preprint this version posted March 27, 2020. .https://doi.org/10.1101/2020.03.20.20039495doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

mailto:[email protected]

mailto:[email protected]

https://doi.org/10.1101/2020.03.20.20039495

http://creativecommons.org/licenses/by-nc-nd/4.0/

2

Abstract

COVID-19 is caused by SARS-CoV-2, and has become a global pandemic. There is no highly

effective medicine or vaccine, most of the patients were recovered by their own immune

response, especially the virus specific IgG and IgM responses. However, the IgG/ IgM responses

is barely known. To enable the global understanding of SARS-CoV-2 specific IgG/ IgM

responses, a SARS-CoV-2 proteome microarray with 18 out of the 28 predicted proteins was

constructed. The microarray was applied to profile the IgG/ IgM responses with 29 convalescent

sera. The results suggest that at the convalescent phase 100% of patients had IgG/ IgM responses

to SARS-CoV-2, especially to protein N, S1 but not S2. S1 purified from mammalian cell

demonstrated the highest performance to differentiate COVID-19 patients from controls. Besides

protein N and S1, significant antibody responses to ORF9b and NSP5 were also identified. In-

depth analysis showed that the level of S1 IgG positively correlate to age and the level of LDH

(lactate dehydrogenase), especially for women, while the level of S1 IgG negatively correlate to

Ly% (Lymphocyte percentage). This study presents the first whole picture of the SARS-CoV-2

specific IgG/ IgM responses, and provides insights to develop precise immuno-diagnostics,

effective treatment and vaccine.

Keywords: SARS-CoV-2; Protein microarray; Serum profiling; IgG; IgM

Highlights

⚫ A SARS-CoV-2 proteome microarray contains 18 of the 28 predicted proteins

⚫ The 1st global picture of the SARS-CoV-2 specific IgG/ IgM response reveals that at the

convalescent phase, 100% of patients have IgG/ IgM responses to protein N and S1

⚫ Significant antibody responses against ORF9b and NSP5 were identified

⚫ Protein S1 specific IgG positively correlates to age and LDH, while negatively to Ly%



https://doi.org/10.1101/2020.03.20.20039495


3

Introduction

COVID-19 is caused by coronavirus SARS-CoV-21,2. In China alone, by Mar. 18, 2020, there are

81,163 diagnosed cases with SARS-CoV-2 infection, and 3,242 death according to Chinese CDC

(http://2019ncov.chinacdc.cn/2019-nCoV/). Globally, 184,976 diagnosed cases were reported in

159 countries, areas or territories. And on Mar. 11, WHO announced COVID-19 as a global

pandemic. Aided by the high-throughput power of Next Generation Sequencing (NGS), the

causative agent of COVID, i.e., SARS-CoV-2, was successfully identified and genome

sequenced. Sequence analysis suggests that SARS-CoV-2 is most closely related to BatCoV

RaTG13 and belongs to subgenus Sarbecovirus of Betacoronavirus, together with Bat-SARS-like

coronavirus and SARS coronavirus1,2. Through the comparison with SARS-CoV and other related

coronaviruses, it is predicted that there are 28 proteins may encoded by the genome of SARS-

CoV-2, including 5 structure proteins (we split S protein to S1 and S2, and thus count as 2

proteins), 8 accessory proteins and 15 non-structural proteins3. SARS-CoV-2 may utilize the

same mechanism to enter the host cells, i.e., the high affinity binding between the receptor

binding domain (RBD) of the spike protein and angiotensin converting enzyme 2 (ACE2)4-9.

Though tremendous efforts are being poured for hunting effective therapeutic agents, i.e., small

molecules/ neutralization antibodies, and protective vaccines, unfortunately, none of them are

available at this moment, even in the near future10. By Mar. 18, 2020, 69,740 patients have been

cured in China (http://2019ncov.chinacdc.cn/2019-nCoV/). Since there is no effective anti-SARS-

CoV-2 drug and therapeutic antibody, theoretically, most of these patients are cured by

themselves, i.e., by their own immune system. It is known that for combating virus infections,

usually antibodies (IgG and IgM) play critical roles, for example, SARS-CoV11,12 and MERS-

CoV13,14. Thus, it is reasonable to argue that virus specific IgG and IgM may also significantly

contribute to the battle against SARS-CoV-2 infection. Indeed, high levels of SARS-CoV-2

specific IgG and IgM could be monitored for many of the patients15. In addition, positive results



http://2019ncov.chinacdc.cn/2019-nCoV/

http://2019ncov.chinacdc.cn/2019-nCoV/

https://doi.org/10.1101/2020.03.20.20039495


4

were observed by treating the patients with convalescent plasma collected from COVID-19

patients16,17.

However, because SARS-CoV-2 is a newly occurred pathogen, the IgG/IgM response is barely

known. There are many important questions need to be experimentally addressed: 1. What’s the

variation among different patients, especially for antibodies against nucleocapsid protein (protein

N) and spike protein (protein S)? 2. Besides nucleocapsid protein and spike protein, is there any

other viral protein that could trigger significant antibody response for at least some of the

patients? 3. Is it possible to link the intensity of the overall IgG/IgM response to the severity of

patients? and etc. It is urgently needed to answer these questions, especially in a systematic

manner. Once these questions are answered or at least touched, we may can understand the

IgG/IgM response in detail, and in turn facilitate us to develop more effective treatment,

therapeutic antibody and protective vaccine.

Traditional techniques for studying IgG/ IgM responses including ELISA18-20, and immune-

colloidal gold strip assay19,21,22. However, these techniques usually can only test one target protein

or one antibody in one reaction. Thus a powerful tool is needed that enables the studying of the

IgG/ IgM responses on a systems level. Featured by the capability of high-throughput and parallel

analysis, and miniaturized size, protein microarray may be the choice for systematic study of the

SARS-CoV-2 stimulated IgG/IgM responses. A variety of protein microarrays have already been

constructed and successfully applied for serum antibody profiling, such as the Mtb proteome

microarray23, the SARS-CoV protein microarray11, and the Dengue virus protein microarray24.

Here, we present a SARS-CoV-2 proteome microarray developed using an E.coli expression

system and SARS-CoV-2 proteins collected from several commercial sources. COVID-19

Convalescent sera were analyzed on the microarray, the first overall picture of SARS-CoV-2

specific IgG/ IgM responses was revealed.



https://doi.org/10.1101/2020.03.20.20039495


5

Results

Schematic diagram and workflow

The genome of SARS-CoV-2 is ~29.8 kb, which is predicted to encode 28 proteins3, i.e., 4

structural proteins (split S protein as S1 and S2), 8 accessory proteins and 15 non-structural

proteins (nsp) (Fig. 1A). The sequences of all these proteins and the receptor binding domain

(RBD) on S1 were codon optimized, gene synthesized and cloned to appropriate vector for

expression in E. coli. We affinity purified these proteins, meanwhile, we collected recombinant

proteins expressed from both prokaryotic and eukaryotic systems from other sources. After

quality control, these proteins were then printed on appropriate substrate slides. Convalescent sera

were collected and analyzed on the proteome microarray. The global SARS-CoV-2 specific IgG

and IgM responses were revealed.

Eighteen of the 28 predicted SARS-CoV-2 were prepared

To prepare recombinant proteins of SARS-CoV-2 for microarray fabrication, we first determined

the amino acid sequences of predicted proteins3 follow a reference genome (Genbank accession

No. MN908947.3). To obtain more precise analysis, we split protein S as S1 and S23, and also

included RBD because of its critical role during the entry of SARS-CoV-2 into the cells. The

protein sequences were subjected for codon optimization and then cloned into E.coli expression

vector (pET32a or pGEX-4T-1). The final expression library includes 31 clones (Table S1). After

several rounds of optimizations, so far, we managed to purify 17 of these proteins (Fig. S1). As

demonstrated by Western blotting with an anti-6xHis antibody and Coomassie staining, Most of

the SARS-CoV-2 proteins showed clear bands of the expected size (±10 kDa) and good purity.

Meanwhile, in order to cover the proteome of SARS-CoV-2 as completed as possible, and to take

post-translational modification (PTM), especially glycosylation into account, we also collected

recombinant SARS-CoV-2 proteins prepared using yeast cell-free system and mammalian cell



https://doi.org/10.1101/2020.03.20.20039495


6

expression system, from a variety of sources (Figure S1). Among the collected proteins, there are

several different versions of S1 and N protein (Table S1). Finally, we obtained 37 proteins of

different versions from different sources, which covers 18 out of the 28 predicted proteins of

SARS-CoV-2. These proteins are suitable for microarray construction in terms of both

concentration and purity.

Protein microarray fabrication

A total of 38 proteins along with positive and negative controls were printed on the microarray

slide (Fig. 2). Since most of the proteins were tagged with 6xHis tag, the overall quality of the

microarray were evaluated by probing with an anti-6xHis antibody. The anti-6xHis antibody results

showed that most of the proteins were nicely immobilized, and the microarray quality if fairly good.

The detailed layout of the SARS-CoV-2 proteome microarray was indicated as well (Fig. 2A). High

antibody responses were usually observed for COVID-19 patients while not in control sera (Fig.

2B). Since the Fc tag could be recognized by fluorescence labeled anti-human IgG antibody, the

ACE2-Fc generated high signals for all the tests, which could serve as control for the anti-human

IgG antibody, though the initial reason to include ACE2 on the microarray is for applications other

than serum profiling. To test the experimental reproducibility of the serum profiling using the

microarray, two COVID-19 convalescent sera were random selected. Three independent analysis

for each of these two sera were repeated on the microarray. Pearson correlation coefficients

between two repeats were 0.988 and 0.981 for IgG and IgM, respectively, and the overall

fluorescence intensity ranges of the repeated experiments were fairly close, demonstrating high

reproducibility of the microarray based serum profiling both for IgG and IgM (Fig. 2 C-E).

SARS-CoV-2 specific Serum profiles depicted by proteome microarray

To globally profile the antibody response against the SARS-CoV-2 proteins in the serum of

COVID-19 patients, we screened sera from 29 convalescent patients, along with 21 controls by the



https://doi.org/10.1101/2020.03.20.20039495


7

proteome microarray. The patients were hospitalized in Foshan Fourth hospital, China during 2020-

1-25 to 2020-2-27 with variable stay time. The information of the patients was summarized in

Table 1. Serum from each patient was collected on the day of hospital discharge when the standard

criteria were met. And there is no recurrence reported for these patients. All the samples and the

controls were probed on the proteome microarray, after data filtering and normalization, we built

the IgG and IgM profile for each serum and performed clustering analysis to generate heatmaps for

overall visualization (Fig. 3 and Fig. 4). The patients and controls are perfectly clustered for both

IgG and IgM, justifying the utility of the SARS-CoV-2 proteome microarray for the virus specific

antibody analysis of COVID-19. As expected, the N protein and S1 protein elicited high antibody

responses in almost all patients but barely in control groups, confirming the efficacy of these two

proteins for diagnosis. Interestingly, we also found that in some cases, some proteins other than N

or S1 can generate significantly higher signals compared with that of the control groups.

Strong response against S and N proteins

Since S and N protein are widely used as antigens for diagnosis of COVID-19, we next

characterized the serum antibody responses against these two proteins. For the present cohort, both

S and N proteins, except for S1-4, were proved to have excellent discrimination ability between

COVID-19 patients and controls both for IgG and IgM (Fig. 5A, B, Fig. S3A, B). The overall IgG

signal intensities were much higher than that of IgM, mainly because the sera were collected at the

convalescent stage when IgG are supposed to be dominant. It is notable that two sera from control

group have significantly higher IgG antibody response to N proteins than other controls, with one

to N-Nter and another to C-Nter (Fig. 5G), suggesting the N protein may generate a higher false

positive rate than S protein, especially S1. To investigate the consistence of signal intensities among

different sources or versions (C-term, N-term, domain or fragment) of proteins, we calculated the

Pearson correlation coefficients among S proteins (Fig. 5C) and N proteins (Fig. S2F) using data

of the convalescent sera. High correlations are observed among different concentrations of the same



https://doi.org/10.1101/2020.03.20.20039495


8

proteins, and the same protein from different sources is also of high correlation (Fig. 5D, H, Fig.

2SA, B, C, G), although N protein with high concentrations generate almost saturated signals (Fig.

S2G). Specially, for full length S1 proteins from different sources, either obtained from E.coli

(S1_T) or 293T (S1_B and S1_S) expression system, high correlations with each other were

observed (Fig. 5C, D), indicating the proteins from different sources are all have good performance

for detection. However, the background signals in control group are much lower for proteins

purified from mammalian cells, i.e., 293T (Fig. 5A), suggesting they may possess a higher septicity

and could serve as a better reagent for developing immune-diagnostics. The signals of the full

length S1 protein are highly correlated with that of S-RBD (Fig. 5E) but with much stronger signals.

In contrast, the correlation levels of S1-4 region with full-length S1 or RBD are lower (Fig. 5C,

Fig. S2D). In addition, The S1 signals are poorly correlated with S2 proteins (Fig. 5F). These data

may reflect the difference in immunogenicity for different regions of S protein, further epitope

mapping could answer this question. Similar situations are also observed for N proteins (Fig. 5H,

Fig. S2H, I). Interestingly, moderate but significant liner correlations were observed between IgG

responses of N and S1 (Fig. 5I). In addition, the correlations of IgG and IgM signals for the same

protein were low (Fig. 5J, K), this may in-part because the overall IgM signals were much lower

than that of IgG (Fig. S3) at the convalescent stage.

Antibody responses against other proteins

The proteome microarray enables us to investigate antibody responses to 18 of the 28 predicted

proteins of SARS-CoV-2, including S and N proteins. As mentioned above, some proteins other

than N and S proteins also generated high IgG signals (Fig. 3). After global analysis, we identified

6 proteins, against which high IgG responses were detected in at least one convalescent sera (Fig.

6A). Importantly, 44.8% (13/29) patients presented positive IgG antibody to ORF9b under the

threshold set based on the signals of control sera (Fig. 6B). IgG antibodies to NSP5 were positive

in 3/19 patients and positive in 1/21 control sera (Fig. 6C). To investigate if the IgG responses



https://doi.org/10.1101/2020.03.20.20039495


9

against ORF9b or NSP5 depends on the IgG responses against N or S protein, we calculated the

correlations among them. It turned out that there are no obvious correlations between the IgG to

ORF9b or NSP5 with IgG to N or S (Fig. 6D, E), suggesting these two proteins may provide

complementary information either for diagnosis or worth further study to explore the SARS-CoV-

2 specific immune response.

IgG responses are highly correlated with age, LDH and Ly%

It is known that immune response is closely related to the disease courses. To evaluate how the

relationship between the antibody response and the situation of the disease, we investigated the

correlations between IgG or IgM responses to proteins of virus with clinical characteristics. Not

surprisingly, the days after onset were correlated with the IgG response against S1 (Fig. 7A) or N

protein (date not shown), as the IgG response usually increases over time and reach the peak several

weeks after onset, which is observed by other studies25 and SARS patients26. In contrast, there is

no correlations between IgM response with days after onset. It was observed that age is also

correlated with IgG response to S1 or N proteins (Fig. 7B, Fig. S4A). Three patients with mild

symptoms indicated by red arrows show low IgG response and it is highly variable in the patients

with common symptoms. It is notable that in male patients, especially older than 40, the correlation

between age and S1 IgG is very poor (Fig. 7B). To further investigate this phenomenon, we

separately analyzed the male and female patients in different age groups. For female older than 40

years old, the S1 IgG response is significantly stronger than that either in the group of female

younger than 40 or the male counterpart (Fig. 7C). For male younger than 40, high correlation

between age and S1 or N IgG response was observed (Fig. 7D, Fig. S4C), and this correlation is

not caused by days after onset (Fig. S4B). For female, whatever the age range is, IgG response to

S1 or N is highly correlated with age (Fig. S4D, E). To exclude the influence of days after onset,

the patients with similar (18-24) days after onset were selected (Fig. S4F). High correlation is still

observed (Fig. 7E, Fig. S4G), demonstrating the correlation is independent of days after onset.



https://doi.org/10.1101/2020.03.20.20039495


10

It was found that the IgG response against S1 or N protein is highly correlated with peak lactate

dehydrogenase (LDH) level in female (Fig. S4H) but not significantly in male patients (Fig. 7E).

To evaluate whether there is correlation between peak LDH and virus specific IgG response in

female patients, the patients with 18-24 days after onset and ranging in age from 20 to 60 were

selected to form a smaller cohort, in which, no statistical correlation between age or days after onset

with peak LDH were observed (Fig. S4I). It turns out the correlation was still high and significant

both for S1 and N protein specific IgG response (Fig. 7G, Fig. S4J), demonstrating the correlation

is independent of age and days after onset. Since the LDH level could serve as an indicator of

disease severity26,27, it seems that immune system of women may be more sensitive to the virus. It

was also found that the IgG response to S1 or N protein negatively correlates with the percentage

of lymphocyte (Ly%) (Fig. 7H, I, Fig. S4K), however, this correlation might be dependent of age

(Fig. S4L).

Materials and methods

Construction of expression vectors

The protein sequences of SARS-CoV-2 were downloaded from GenBank (Accession number:

MN908947.3). According to the optimized genetic algorithm28, the amino acid sequences was

converted into E.coli codon-optimized gene sequences. Subsequently, the sequence optimized

genes were synthesized by Sangon Biotech. (Shanghai, China). The synthesized genes were

cloned into pET32a or pGEX-4T-1 and transformed into E. coli BL21 strain to construct the

transformants. Detailed information of the clones constructed in this study is given in Table S1.

Protein preparation

The recombinant proteins were expressed in E. coli BL21 by growing cells in 200 mL LB

medium to an A600 of 0.6 at 37 °C. Protein expression was induced by the addition of 0.2 mM



https://doi.org/10.1101/2020.03.20.20039495


11

isopropyl-β-d-thiogalactoside (IPTG) before incubating cells overnight at 16 °C. For the

purification of 6xHis-tagged proteins, cell pellets were re-suspended in lysis buffer containing 50

mM Tris-HCl pH 8.0, 500 mM NaCl, 20 mM imidazole (pH 8.0), then lysed by a high-pressure

cell cracker (Union-biotech, Shanghai, CHN). Cell lysates were centrifuged at 12,000 rpm for 20

mins at 4℃. Supernatants were purified with Ni2+ Sepharose beads (Senhui Microsphere

Technology, Suzhou, CHN), then washed with lysis buffer and eluted with buffer containing 50

mM Tris-HCl pH 8.0, 500 mM NaCl and 300 mM imidazole pH 8.0. For the purification of GST-

tagged proteins, cells were harvested and lysed by a high-pressure cell cracker in lysis buffer

containing 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 1 mM DTT. After centrifugation, the

supernatant was incubated with GST-Sepharose beads (Senhui Microsphere Technology, Suzhou,

CHN). The target proteins were washed with lysis buffer and eluted with 50 mM Tris-HCl, pH

8.0, 500 mM NaCl, 1 mM DTT, 40 mM glutathione. The purified proteins were analyzed by

SDS-PAGE followed by Western blotting using an anti-His antibody (Merck millipore, USA) and

Coomassie brilliant blue staining. Recombinant SARS-CoV-2 proteins were also collected from

commercial sources. Detailed information of the recombinant proteins prepared in this study is

given in Table S1.

Protein microarray fabrication

The proteins, along with negative (BSA) and positive controls (anti-Human IgG and IgM antibody),

were printed in quadruplicate on PATH substrate slide (Grace Bio-Labs, Oregon, USA) to generate

identical arrays in a 2 x 7 subarray format using Super Marathon printer (Arrayjet, UK). Protein

arrays were stored at -80°C until use.

Patients and samples

The Institutional Ethics Review Committee of Foshan Fourth Hospital, Foshan, China approved

this study, and written informed consent was obtained from each patient. COVID-19 patients were



https://doi.org/10.1101/2020.03.20.20039495


12

hospitalized and received treatment in Foshan Forth hospital during 2020-1-25 to 2020-2-27 with

variable stay time (Table 1). Serum samples were collected when the patients were discharged

from the hospital. Sera of control group from Lung cancer patients and healthy controls were

collected from Ruijin Hospital, Shanghai, China. All sera were stored at -80 ℃ until use.

Microarray based serum analysis

A 14-chamber rubber gasket was mounted onto each slide to create individual chambers for the 14

identical subarrays. The microarray was used for serum profiling as described previously29 with

minor modifications. Briefly, the arrays stored at -80°C were warmed to room temperature and then

incubated in blocking buffer (3% BSA in PBS buffer with 0.1% Tween 20) for 3 h. Serum samples

were diluted 1:200 in PBS containing 0.1% Tween 20. A total of 200 μL of diluted serum or buffer

only was incubated with each subarray overnight at 4°C. The arrays were washed with PBST and

bound autoantibodies were detected by incubating with Cy3-conjugated goat anti-human IgG and

Alexa Fluor 647-conjugated donkey anti-human IgM (Jackson ImmunoResearch, PA, USA), the

antibodies were diluted 1: 1,000 in PBST, and incubated at room temperature for 1 h. The

microarrays were then washed with PBST and dried by centrifugation at room temperature and

scanned by LuxScan 10K-A (CapitalBio Corporation, Beijing, China) with the parameters set as

95% laser power/ PMT 550 and 95% laser power/ PMT 480 for IgM and IgG, respectively. The

fluorescent intensity data was extracted by GenePix Pro 6.0 software (Molecular Devices, CA,

USA).

Statistics

Signal Intensity was defined as median of foreground subtracted by median of background for each

spot and then averaged of the quadruplicate spots for each protein. IgG and IgM data were analyzed

separately. Before processing, data from some spots, such as NSP7_0.1_T, NSP9P_K, are excluded

for probably printing contamination. Pearson correlation coefficient between two proteins or



https://doi.org/10.1101/2020.03.20.20039495


13

indicators and the corresponding p value was calculated by SPSS software under the default

parameters. Cluster analysis was performed by pheatmap package in R30.

Discussion

In order to profile the SARS-CoV-2 specific IgG/IgM responses, we have constructed a SARS-

CoV-2 proteome microarray with 18 of the 28 predicted proteins. To our knowledge, this is the

first of such. A set of 29 recovered sera were analyzed on the microarray, global IgG and IgM

profile were obtained simultaneously through a dual color strategy. Our data clearly showed that

both protein N and S1 are suitable for diagnostics, while S1 purified from mammalian cell may

possess better specificity. Significant antibody responses were identified for ORF9b and NSP5.

We showed that the level of S1 IgG positively correlate to age and the level of LDH while

negatively correlate to Ly%.

The SARS-CoV-2 proteome microarray enables not only the global profiling of virus specific

antibody responses but also providing semi-quantitative information. By adopting the dual color

strategy of microarray, we can measure IgG and IgM simultaneously. For the convalescent

COVID-19 patients tested in this study that with a median of 22 days after onset, we found that

the overall IgG response is significantly higher than that of IgM, indicating for these patients the

SARS-CoV-2 specific IgG responses are dominant at the convalescent phase, although IgM level

might reach the peak at a similar time point with that of IgG, according to some studies of SARS-

CoV31,32.

It is well known that S1 and N proteins are the dominant antigens of SARS-CoV and SARS-

CoV-2 that elicit both IgG and IgM antibodies, and antibody response against N protein is usually

stronger. However, we found for two of the control sera, strong IgG bindings were observed for

N protein, and specifically one control recognizing N protein at the N terminal while another at

the C terminal. This maybe due to the high conservation of N protein sequences across the



https://doi.org/10.1101/2020.03.20.20039495


14

coronavirus species, this indicating we should be aware of the false positive when applying N

protein for diagnosis. In contrast, S1 protein demonstrating a higher specificity. Thus an ideal

choice of developing immune-diagnostics maybe the combining of both N protein and S1 protein.

We also compared the antibody responses against a variety versions of S1, including the full

length, the RBD domain, the N terminal and the C terminal. The antibody response to RBD

region is highly correlated with that to full length protein but with weaker signals, however, the

correlations among other S1 versions are not significant, suggesting dominant epitopes that elicit

antibodies might differ among individuals. Further study of detailed epitope mapping might give

us a clear answer.

In this study, we also found the significant presence of IgG and IgM against ORF9b (13 out of

29 cases) and NSP5 (3 out of 29 cases). ORF9b is predicted as an accessory protein, exhibiting

high overall sequence similarity to SARS and SARS-like COVs ORF9b (V23I)3, and is likely to

be a lipid binding protein33. Previous studies showed that SARS ORF9b suppresses innate

immunity by targeting mitochondria34. Two previous studies have found antibody against SARS

ORF9b presented in the sera of patients recovering from SARS35,36. Our study also demonstrates

the potential of antibody against ORF9b for detection of convalescent COVID-19 patients.

COVID-19 NSP5 is also highly homologous to SARS NSP5 (96% identity, 98% similarity). Its

homologous proteins in a variety of coronaviruses have been proven to impair IFN response37-39.

Our study is the first report to provide experimental evidence to show the existence of NSP5

specific antibody in convalescents. Since NSP5 is a non-structural protein, theoretically, it should

present only in the infected cells but not in virions. So antibody against NSP5 has the potential to

be applied to distinguish between COVID-19 patients and healthy people immunized with

inactivated virus.

We have analyzed the correlations between the COVID-19 specific IgG responses with clinical

characteristics as well. It is expected that IgG responses improve over time within one or two

months after onset25,31,32 and we did observe a significant correlation between IgG signals with



https://doi.org/10.1101/2020.03.20.20039495


15

days after onset. We also found peak LDH was highly correlated with IgG response, especially

for female patients. As many studies reported, LDH tends to have a higher level in severe

COVID-19 patients and could be an indicator of severity26,27. In fact, it has been observed in

SARS patients that more severe SARS is associated with more robust serological response26,40,

similar association was confirmed in COVID-19 patients, especially for females. Interestingly, we

observed high correlation between age with IgG response in female patients and in male patients

with age less than 40 but not in older male patients, implying the humoral response against

SARS-CoV-2 may differ in gender. It is reported that severe cases are significantly more frequent

in aged patients and the mortality of male patients is higher than that of female, but the reason is

not clear. Based on our observations, we assumed that the situation might be associated with the

immune response. However, female patients, compared with male patients, may generate humoral

response more efficiently. This difference should be considered during treatment.

There are some limitations of the current SARS-CoV-2 proteome microarray. Firstly, due to

the difficulty of protein expression and purification, there are still 10 proteins missing3. We will

try to obtain these proteins through vigorous optimization or other sources, interesting finding is

anticipated in the near future for these missing proteins. Secondly, most of the proteins on the

microarray are not expressed in mammalian cells, critical post-translational modifications, such

as glycosylation is absent. It is known that there are 23 N-glycosylation sites on S protein, which

is heavily glycosylated, and the glycosylation may play critical roles in antibody- antigen

recognition5,41. We are preparing these proteins using mammalian cell systems. Once the

microarray is upgraded with proteins purified from mammalian cells, PTM specific IgG/IgM

response may could be elicited. Thirdly, only 29 samples at collected at a single time point were

analyzed. Though there are some interesting findings, we believe some of the current conclusions

could be strengthened by including more samples. Furthermore, longitudinal samples29,42

collected at different time points from the same individual after diagnosis or even after cured may



https://doi.org/10.1101/2020.03.20.20039495


16

enable us to reveal the dynamics of the SARS-CoV-2 specific IgG/IgM responses. The data may

could be further linked to the severity of COVID-19 among different patients.

The application of the SARS-CoV-2 proteome microarray is not limited to serum profiling. It

could also be explored for host-pathogen interaction43, drug/ small molecule target

identification44,45，and antibody specificity assessment46.

Through the same construction procedure, we could easily expand the microarray to a pan

human coronavirus proteome microarray by including the other two severe coronaviruses, i.e.,

SARS-CoV11,47,48 and MERS-CoV47, as well as the four known mild human coronaviruses49,50,

i.e., CoV 229E, CoV OC43, CoV HKU-1 and CoV NL63. By applying this microarray, we can

assess the immune response to coronavirus on a systems level, and the possible cross-reactivity

could be easily judged.

Taken together, we have constructed the first SARS-CoV-2 proteome microarray, this

microarray could be applied for a variety of applications, including but not limited to in-depth

IgG/ IgM response profiling. Through the analysis of convalescent sera on the microarray, we

obtained the first overall picture of SARS-CoV-2 specific IgG/ IgM profile. We believe that the

findings in this study will shed light in the development of more precise diagnostic kit, more

appropriate treatment and effective vaccine for combating the global crisis that we are facing

now.

Funding sources

This work was partially supported by National Key Research and Development Program of China

Grant (No. 2016YFA0500600), National Natural Science Foundation of China (No. 31970130,

31600672, 31670831, and 31370813).



https://doi.org/10.1101/2020.03.20.20039495


17

Acknowledgements

We thank Dr. Min Guo of Healthcode Co., Ltd. for providing affinity purified proteins. We thank

Dr. Guo-Jun Lang of Sanyou Biopharmaceuticals Co., Ltd. for provide proteins and antibodies.

We also thank Dr. Jie Wang of VACURE l Biotechnology Co.,Ltd. , Dr. Yin-Lai Li of Hangzhou

Bioeast biotech. Co.,Ltd., and Sino biological Co.,Ltd. for providing the proteins.

Author contributions

S-C. T. developed the conceptual ideas and designed the study. J. Z., W. W., D. M. and X. Y.

collected the sera samples and provided key reagents. H-W. J., Y. L, H-N. Z., H. Q. performed

the experiments, S-C.T., Y. L., and H-W. J. wrote the manuscript with suggestions from other

authors.

Declaration of conflict of interest

The authors declare no conflicts of interest.

Data availability

The SARS-CoV-2 proteome microarray data are deposited on Protein Microarray Database

(http://www.proteinmicroarray.cn) under the accession number PMDE241. Additional data

related to this paper may be requested from the authors.

References:

1. Zhou P, Yang X-L, Wang X-G, et al. A pneumonia outbreak associated with a new

coronavirus of probable bat origin. Nature 2020; 579: 270-273.

2. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in

China. Nature 2020; 579: 265-269.

3. Wu A, Peng Y, Huang B, et al. Genome composition and divergence of the novel coronavirus

(2019-nCoV) originating in China. Cell Host Microbe 2020; 27: 325-328.



https://doi.org/10.1101/2020.03.20.20039495


18

4. Ge X-Y, Li J-L, Yang X-L, et al. Isolation and characterization of a bat SARS-like

coronavirus that uses the ACE2 receptor. Nature 2013; 503: 535-538.

5. Wrapp D, Wang N, Corbett KS, et al. Cryo-EM structure of the 2019-nCoV spike in the

prefusion conformation. Science 2020; 367: 1260-1263.

6. Renhong Yan, Yuanyuan Zhang, Yaning Li, Lu Xia, Yingying Guo, Zhou Q. Structural basis

for the recognition of the SARS-CoV-2 by full-length human ACE2. Science 2020:

https://doi.org/10.1126/science.abb2762.

7. Lan J, Ge J, Yu J, et al. Crystal structure of the 2019-nCoV spike receptor-binding domain

bound with the ACE2 receptor. medRxiv 2020:

https://doi.org/10.1101/2020.1102.1119.956235.

8. Wenhui Li, Michael J. Moore, Natalya Vasilieva, et al. Angiotensin-converting enzyme 2 is a

functional receptor for the SARS coronavirus. Nature 2003; 426: 450-454.

9. Hoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 cell entry depends on ACE2

and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 2020:

https://doi.org/10.1016/j.cell.2020.1002.1052.

10. Li G, Clercq ED. Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nature

Reviews Drug Discovery 2020; 19: 149-150.

11. Heng Zhu, Shaohui Hu, Ghil Jona, et al. Severe acute respiratory syndrome diagnostics using

a coronavirus protein microarray. Proc Natl Acad Sci U S A 2006; 103: 4011-4016.

12. Zhu M. SARS immunity and vaccination. Cell Mol Immunol 2004; 1: 193-198.

13. Tang XC, Agnihothram SS, Jiao Y, et al. Identification of human neutralizing antibodies

against MERS-CoV and their role in virus adaptive evolution. Proc Natl Acad Sci U S A

2014; 111: E2018-2026.

14. Jiang L, Wang N, Zuo T, et al. Potent neutralization of MERS-CoV by human neutralizing

monoclonal antibodies to the viral spike glycoprotein. Sci Transl Med 2014; 6: 234ra259.

15. Juanjuan Zhao, Quan Yuan, Haiyan Wang, et al. Antibody responses to SARS-CoV-2 in

patients of novel coronavirus disease 2019. medRxiv 2020:

https://doi.org/10.1101/2020.1103.1102.20030189.

16. Mair-Jenkins J, Saavedra-Campos M, Baillie JK, et al. The effectiveness of convalescent

plasma and hyperimmune immunoglobulin for the treatment of severe acute respiratory

infections of viral etiology: a systematic review and exploratory meta-analysis. J Infect Dis

2015; 211: 80-90.

17. Chen L, Xiong J, Bao L, Shi Y. Convalescent plasma as a potential therapy for COVID-19.

The Lancet Infectious Diseases 2020: https://doi.org/10.1016/S1473-3099(1020)30141-

30149.



https://doi.org/10.1101/2020.03.20.20039495


19

18. Li M, Jin R, Peng Y, et al. Generation of antibodies against COVID-19 virus for development

of diagnostic tools. medRxiv 2020: https://doi.org/10.1101/2020.1102.1120.20025999.

19. Xiang J, Yan M, Li H, et al. Evaluation of enzyme-linked immunoassay and

colloidal gold-immunochromatographic assay kit for detection of novel coronavirus (SARS-

Cov-2) causing an outbreak of pneumonia (COVID-19). medRxiv 2020:

https://doi.org/10.1101/2020.1102.1127.20028787.

20. Lau SKP, Woo PCY, Wong BHL, et al. Detection of severe acute respiratory ayndrome

(SARS) coronavirus nucleocapsid protein in SARS patients by enzyme-linked

immunosorbent assay. Journal of Clinical Microbiology 2004; 42: 2884-2889.

21. Guan M, Chen HY, Foo SY, Tan YJ, Goh PY, Wee SH. Recombinant protein-based enzyme-

linked immunosorbent assay and immunochromatographic tests for detection of

immunoglobulin G antibodies to severe acute respiratory syndrome (SARS) coronavirus in

SARS patients. Clinical and Vaccine Immunology 2004; 11: 287-291.

22. Li Z, Yi Y, Luo X, et al. Development and clinical application of a rapid IgM-IgG combined

antibody test for SARS-CoV-2 infection diagnosis. Journal of Medical Virology 2020:

https://doi.org/10.1002/jmv.25727.

23. Deng J, Bi L, Zhou L, et al. Mycobacterium tuberculosis proteome microarray for global

studies of protein function and immunogenicity. Cell Rep 2014; 9: 2317-2329.

24. Qi H, Zhou H, Czajkowsky DM, et al. Rapid production of virus protein microarray using

protein microarray fabrication through gene synthesis (PAGES). Mol Cell Proteomics 2017;

16: 288-299.

25. Liu L, Liu W, Wang S, Zheng S. A preliminary study on serological assay for severe acute

respiratory syndrome coronavirus 2 (SARS-CoV-2) in 238 admitted hospital patients.

medRxiv 2020: https://doi.org/:10.1101/2020.1103.1106.20031856.

26. Lee N, Chan PK, Ip M, et al. Anti-SARS-CoV IgG response in relation to disease severity of

severe acute respiratory syndrome. J Clin Virol 2006; 35: 179-184.

27. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019

Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020; 323: 1061-1069.

28. Chutrakul C, Jeennor S, Panchanawaporn S, et al. Metabolic engineering of long chain-

polyunsaturated fatty acid biosynthetic pathway in oleaginous fungus for dihomo-gamma

linolenic acid production. J Biotechnol 2016; 218: 85-93.

29. Li Y, Li CQ, Guo SJ, et al. Longitudinal serum autoantibody repertoire profiling identifies

surgery-associated biomarkers in lung adenocarcinoma. EBioMedicine 2020; 53: 102674.

30. R K. Pheatmap: Pretty Heatmaps. 2015:

https://cran.rproject.org/web/packages/pheatmap/index.html.



https://doi.org/10.1101/2020.03.20.20039495


20

31. Li G, Chen X, Xu A. Profile of specific antibodies to the SARS-associated coronavirus. N

Engl J Med 2003; 349: 508-509.

32. Hsueh PR, Huang LM, Chen PJ, Kao CL, Yang PC. Chronological evolution of IgM, IgA,

IgG and neutralisation antibodies after infection with SARS-associated coronavirus. Clin

Microbiol Infect 2004; 10: 1062-1066.

33. Meier C, Aricescu AR, Assenberg R, et al. The crystal structure of ORF-9b, a lipid binding

protein from the SARS coronavirus. Structure 2006; 14: 1157-1165.

34. Shi CS, Qi HY, Boularan C, et al. SARS-coronavirus open reading frame-9b suppresses

innate immunity by targeting mitochondria and the MAVS/TRAF3/TRAF6 signalosome. J

Immunol 2014; 193: 3080-3089.

35. Guo JP, Petric M, Campbell W, McGeer PL. SARS corona virus peptides recognized by

antibodies in the sera of convalescent cases. Virology 2004; 324: 251-256.

36. Qiu M, Shi Y, Guo Z, et al. Antibody responses to individual proteins of SARS coronavirus

and their neutralization activities. Microbes Infect 2005; 7: 882-889.

37. Zhu X, Fang L, Wang D, et al. Porcine deltacoronavirus nsp5 inhibits interferon-beta

production through the cleavage of NEMO. Virology 2017; 502: 33-38.

38. Zhu X, Wang D, Zhou J, et al. Porcine deltacoronavirus nsp5 antagonizes Type I interferon

signaling by cleaving STAT2. J Virol 2017; 91: e00003-00017.

39. Chen S, Tian J, Li Z, et al. Feline infectious peritonitis virus nsp5 inhibits Type I interferon

production by cleaving NEMO at multiple sites. Viruses 2019; 12: 43.

40. Hsueh PR, Hsiao CH, Yeh SH, et al. Microbiologic characteristics, serologic responses, and

clinical manifestations in severe acute respiratory syndrome, Taiwan. Emerg Infect Dis 2003;

9: 1163-1167.

41. Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D. Structure, function, and

antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 2020:

https://doi.org/10.1016/j.cell.2020.1002.1058.

42. Shi Y, Wan Z, Li L, et al. Antibody responses against SARS-coronavirus and its nucleocaspid

in SARS patients. J Clin Virol 2004; 31: 66-68.

43. He X, Jiang HW, Chen H, et al. Systematic identification of mycobacterium tuberculosis

effectors reveals that BfrB suppresses innate immunity. Mol Cell Proteomics 2017; 16: 2243-

2253.

44. Zhang HN, Yang L, Ling JY, et al. Systematic identification of arsenic-binding proteins

reveals that hexokinase-2 is inhibited by arsenic. Proc Natl Acad Sci U S A 2015; 112: 15084-

15089.

45. Xu Z, Zhang H, Zhang X, et al. Interplay between the bacterial protein deacetylase CobB and

the second messenger c-di-GMP. EMBO J 2019; 38: e100948.



https://doi.org/10.1101/2020.03.20.20039495


21

46. Venkataraman A, Yang K, Irizarry J, et al. A toolbox of immunoprecipitation-grade

monoclonal antibodies to human transcription factors. Nat Methods 2018; 15: 330-338.

47. de Wit E, van Doremalen N, Falzarano D, Munster VJ. SARS and MERS: recent insights into

emerging coronaviruses. Nat Rev Microbiol 2016; 14: 523-534.

48. Stadler K, Masignani V, Eickmann M, et al. SARS--beginning to understand a new virus. Nat

Rev Microbiol 2003; 1: 209-218.

49. Corman VM, Muth D, Niemeyer D, Drosten C. Hosts and sources of endemic human

coronaviruses. Adv Virus Res 2018; 100: 163-188.

50. Reusken C, Mou H, Godeke GJ, et al. Specific serology for emerging human coronaviruses

by protein microarray. Euro Surveill 2013; 18: 20441.



https://doi.org/10.1101/2020.03.20.20039495


22

Figure Legends

Figure 1. The workflow of SARS-CoV-2 proteome microarray fabrication and serum

profiling. A) The genome of SARS-CoV-2 and the 28 predicted proteins. B) The workflow of

proteome microarray fabrication and serum profiling on the microarray.

Figure S1. The SARS-CoV-2 proteins included in this proteome microarray. The Up panel is

western blotting with an anti-6xHis antibody. The low panel is Coomassie staining. These proteins

were prepared and collected from different sources. T: Tao Lab (our laboratory); B: Hangzhou

Bioeast biotech. Co., Ltd.; K: Healthcode Co., Ltd.; S: Sanyou biopharmaceuticals Co., Ltd.; W:

VACURE l Biotechnology Co., Ltd. Y: Sino biological Co., Ltd.

Figure 2. SARS-CoV-2 proteome microarray layout and quality control. A) There are 14

identical sub-arrays on a single microarray. A microarray was incubated with an anti-6xHis

antibody to demonstrate the overall microarray quality (green). One sub-array is shown. To

facilitate labeling, this sub-array is split into 3 parts. The proteins were printed in quadruplicate.

The triangles indicate dilution titers of the same proteins. B) Representative sub-arrays probed with

sera of a COVID-19 convalescent and a healthy control. The IgG and IgM responses are shown in

green and red, respectively. C) and D) The correlations of the overall IgG and IgM signal intensities

between two repeats probed with the same serum. E) Statistics of the Pearson correlation confidents

among repeats probed with the same serum.

Figure 3. The overall SARS-CoV-2 specific IgG profiles of the 29 convalescent sera against

the proteins. Each square indicates the IgG antibody response against the protein (row) in the

serum (column). Proteins are shown with names along with concentrations (μg/mL) and sources.

Sera are shown with group information and serum number. NCP: Novel Coronavirus Patients or



https://doi.org/10.1101/2020.03.20.20039495


23

COVID-19 patients; LC：Lung Cancer; NC：Normal Control. Blank means no serum. Three repeats

were performed for serum NCP534 and NCP535. FI：Fluorescence Intensity.

Figure 4. The overall SARS-CoV-2 specific IgM profiles of the 29 convalescent sera against

the proteins. Each square indicates the IgM antibody response against the protein (row) in the

serum (column). The rest are the same as that of Figure 3.

Figure 5. IgG response to S and N proteins. A) Box plots of IgG response for S1 and S2 proteins.

Each dot indicates one serum sample either from convalescent group (green) or control group

(brown). Mean and standard deviation value for each group are indicated. The proteins labeled with

red are over expression in mammalian cell lines. P values were calculated by t.test. **, p <0.01; *,

p <0.05; n.s., not significant. B) Box plots of IgG response for N proteins. C) Pearson correlation

coefficient matrix of IgG response among different S1 and S2 proteins. D-F) Correlations of overall

IgG responses among different S1 proteins (D), S1 vs. RBD (E) and S1 vs. S2 (F). Each spot

represents one sample. G) One part of a sub-microarray showed the IgG responses of two controls,

i.e., LC169 and NC96 against N proteins, N-Cter and N-Nter indicated the C-terminal and N-

terminal of N protein, respectively. H-I) Correlations of the overall IgG responses among different

N proteins (H) and N protein vs. S protein (I). J) Statistics of the Pearson correlation coefficients

between IgG and IgM profile against a set of proteins. K) Correlations between IgG and IgM profile

against S1_0.1_W.

Figure S2. IgG response to S and N proteins. A-E）Correlations of the overall IgG responses

among different dilutions of protein S1_S (A), two RDB proteins from different sources (B), S2-1

vs. S2-2 (C), S1 vs. S1-4 (D) and S1 vs. S2 (E). F) Pearson correlation coefficient matrix of IgG

responses among N proteins. Each spot represents one sample. G-I) Correlations of the overall IgG



https://doi.org/10.1101/2020.03.20.20039495


24

responses among different dilutions of protein N_W (A), N-Nter/ N-Cter vs. N protein (H) and N-

Cter vs. N-Nter (I).

Figure S3. IgM Antibody response to S and N proteins. A) Box plots of IgM responses to S1

and S2 proteins. Each dot indicates one serum sample either from patient group (green) or control

group (brown). Mean and standard deviation for each group are indicated. The proteins labeled

with red are over-expressed in 293T. P values were calculated by t test. **, p <0.01; *, p <0.05;

n.s., not significant. B) Box plots of IgM responses to N proteins. C) Pearson correlation coefficient

matrix of IgM responses among S1 and S2 proteins of different versions from different sources.

(D-H) Correlations of the overall IgM responses among S1 proteins (D), S1 vs. RBD (E), S1 vs.

S2 proteins (F), different N proteins (G) and N vs. S proteins (H). Each spot represents one sample.

Figure 6. IgG response to other SARS-CoV-2 proteins. A) Other SARS-CoV-2 proteins that

were recognized by IgG from the convalescent sera, in comparison to that of the controls. B-C)

Anti-ORF9b IgG (B) or anti-NSP5 IgG (C) in patient and control group. Each dot represents one

serum sample. Mean and standard deviation value for each group are indicated. The dashed line

indicates cutoff value calculated as mean plus 3x standard deviation of the control group. P values

were calculated by t test. D-E) Correlations of the overall IgG responses for N or S1 protein vs.

ORF9b (D) or NSP5 (E).

Figure 7. Correlation with clinical characteristics. A) Correlations of S1 IgG responses with

Days after COVID-19 onset. Each spot indicates one COVID-19 patient. B）Correlations of S1

IgG responses with age either for female (blue) or male (pink). The red arrows indicate patients

with mild symptoms while others with common symptoms. C) S1 IgG responses in groups of

different age and gender. P values were calculated by t test. **, p <0.01; *, p <0.05; n.s., not



https://doi.org/10.1101/2020.03.20.20039495


25

significant. D-I) Correlations of S1 IgG responses with Age for male (age <40) (D), Age for female

(E), LDH for male (F), LDH for female (G), Ly% for male (H) and Ly% for female (I). Each spot

indicates one patient from the corresponding group. For E), only the patients with 18-24 days after

onset were selected. For G), only the female patients with 18-24 days after onset and ranging in

age from 20 to 60.

Figure S4. Correlation with clinical characteristics. A) Correlations of N protein specific IgG

responses with days after onset. The red arrows indicate patients with mild symptoms while others

with regular symptoms. B-L) Correlations of S1 specific IgG or N specific IgG with Days after

onset, Age, LDH or Ly% in different groups, specifically, Days after onset vs. Age for male (age

<40) (B), N protein specific IgG vs. Age for male (age <40)(C), S1 protein specific IgG vs. Age

for female (D), N protein specific IgG vs. Age for female (E), Days after onset vs. Age for female

patients with18-24 days after onset (F), N protein specific IgG vs. Age for female patients with18-

24 days after onset (G), S1 protein specific IgG vs. LDH for female; (H), Age or Days after onset

vs. LDH for female patients with 18-24 days after onset and ranging in age from 20 to 60. (I), N

protein specific IgG vs. LDH for female patients with 18-24 days after onset and ranging in age

from 20 to 60. (J), N protein specific IgG vs. Ly% for female (K) and Age vs. Ly% for female (L).

For F) and G), only the patients with 18-24 days after onset were selected.



https://doi.org/10.1101/2020.03.20.20039495


nsp1

nsp2

nsp3

nsp4

nsp5

5’ UTR 3’ UTRN

nsp12

nsp13

nsp14

nsp15

nsp16

pp1ab

pp1a

S 3a

3b

M

p6

7a

7b

8b

9borf14

E

nsp6

nsp7

nsp8

nsp9

nsp10

Predicted ORFs in SARS-CoV-2 Genome (~29.8 kb)

4 structure proteins

8 accessory proteins

15 nonstructural proteins (nsp)

S1+S2

26 proteins + S1 + S2 + RBD

Codon optimization

Gene synthesis and cloned to pET32a/pGEX-4T-1

Protein expression and purification

Collect recombinant proteins

from other sources

Protein microarray printing

Protein microarray quality control

A.

B.

Figure 1. The workflow of SARS-CoV-2 proteome microarray fabrication and serum profiling.

Profiling of SARS-CoV-2 specific serum IgG & IgM

on the microarray

Serum collection from

recovered person

27 proteins



https://doi.org/10.1101/2020.03.20.20039495


IB: His

CBB

170-130-

100-

72-

55-

40-

25-

35-

15-

170-130-100-

72-

55-

40-

25-

35-

15-

25-

35-

15-

25-

35-

25-

10-

15-

25-

10-

15-

kDa kDa kDa

IB: His

CBB

170-130-

100-

72-

55-

40-

25-

35-

170-130-100-

72-

55-

40-

25-

35-

170-130-

100-

72-55-

40-

25-

35-

170-130-

100-

72-55-

40-

25-

35-

kDa kDa kDa

170-130-

100-

170-130-

100-

Figure S1. The SARS-CoV-2 proteins included in this proteomemicroarray.

The Up panel is western blotting with an anti-6xHis antibody. The low panel isCoomassie staining. These proteins were prepared and collected fromdifferent sources. T: Tao Lab; B: Hangzhou Bioeast biotech. Co.,Ltd.; K:

Healthcode Co., Ltd.; S: Sanyou biopharmaceuticals Co.,Ltd.; W: VACURE l Biotechnology Co.,Ltd. Y: Sino biological Co.,Ltd.

72-55-

40-

35-

72-

55-

40-

35-

kDa



https://doi.org/10.1101/2020.03.20.20039495


Figure 2. SARS-COV-2 proteome microarray Layout and quality control.

0

20,000

40,000

60,000

0 30,000 60,000

Re

pe

at 2

Repeat 1

0

5,000

10,000

15,000

20,000

0 10,000 20,000

Re

pe

at 2

Repeat 1

R e p ro d u c ib ility

Lo

g 2

(Sig

na

l In

ten

sit

y)

IgG

IgM

0 .9 0

0 .9 5

1 .0 0

1 .0 51.05

1.00

0.95

0.90Corr

ela

tion c

oeff

icie

nt IgG IgM

r=0.998 r=0.987

A. B.

C.

COVID-19 Patient

Normal Control

D. E.



https://doi.org/10.1101/2020.03.20.20039495


Figure 3. The overall SARS-CoV-2 specific IgG profiles of the

29 sera against the proteins.

Log2(FI)

NC

P4

36

NC

P5

22

NC

P5

32

NC

P5

31

NC

P5

02

NC

P5

30

NC

P5

08

NC

P4

09

NC

P4

10

NC

P6

10

NC

P5

26

NC

P4

29

NC

P7

44

NC

P5

35

.2N

CP

53

5.1

NC

P5

35

.3N

CP

52

0N

CP

40

1N

CP

40

7N

CP

52

9N

CP

40

6N

CP

40

5N

CP

51

0N

CP

52

3N

CP

50

9N

CP

60

7N

CP

52

7N

CP

41

4N

CP

53

3N

CP

60

5N

CP

53

4.2

NC

P5

34

.1N

CP

53

4.3

Bla

nk.1

Bla

nk.2

LC

16

9N

C6

4N

C9

7L

C1

82

NC

96

LC

17

1N

C1

00

LC

18

4L

C1

75

LC

18

0N

C9

5N

C6

7L

C1

68

LC

17

4N

C6

6N

C9

8L

C1

77

LC

18

1N

C9

9N

C6

3N

C6

5

S-RBD_0.25_SEGFP_0.25_KEGFP_0.1_KCy5NSP4_0.1_TNSP16_0.1_KACE2-Fc_0.25_SACE2-Fc_0.5_SN-protein_0.1_SS1_0.1_SS-RBD_0.5_SS1_0.25_BS1_0.5_BN-protein_0.1_TN-protein_0.1_WN-protein_0.25_WN-Cter_0.5_KN-Cter_0.1_KN-Cter_0.25_KN-protein_0.05_KN-protein_0.1_KN-protein_0.2_KN-protein_0.25_SN-protein_0.5_WN-protein_0.5_SN-Nter_0.1_KN-Nter_0.25_KN-Nter_0.5_KS1_0.25_SS1_0.5_SS1_0.1_BeGFP_0.5_KNSP10_0.25_KNSP15_0.1_KNSP2_0.05_KE-protein_0.25_KE-protein_0.25_TORF7b_0.1_TORF6_0.1_TE-protein_0.1_KGST_0.1_H-IgGH-IgMS1_0.1_TS-RBD_0.5_YNSP5_0.1_TNSP1_0.1_TNSP16_0.1_TORF9b_0.1_TNSP5_0.25_TNSP5_0.5_TNSP8_0.25_TNSP9_0.25_TUBE2D3-HisAnti-H-IgG_0.1Anti-H-IgM_0.1S1-4_0.2_KS2-1_0.05_TS2-2_0.1_TNSP10_0.5_KNSP8_0.1_KNSP8_0.25_KNSP8_0.5_KNSP14_0.05_KACE2_0.5_SNSP14_0.1_KNSP14_0.25_KE-protein_0.5_KNSP15_0.1_TAnti-H-IgM_0.025Cy3NSP15_0.25_KNSP15_0.5_KNSP1_0.1_KNSP1_0.25_KNSP1_0.5_KNSP2_0.1_KNSP2_0.25_KNSP16_0.25_KNSP16_0.5_KAGR2-biotinAnti-H-IgG_0.025

0

5

10

15



https://doi.org/10.1101/2020.03.20.20039495


Figure 4. The overall SARS-CoV-2 specific IgM profiles of the 29sera against the proteins.

Log2(FI)

NC

P5

23

NC

P6

10

NC

P5

35

.1N

CP

53

5.2

NC

P5

35

.3N

CP

74

4N

CP

42

9N

CP

50

2N

CP

41

0N

CP

52

6N

CP

43

6N

CP

52

2N

CP

40

9N

CP

53

3N

CP

52

9N

CP

60

5N

CP

40

1N

CP

53

0N

CP

50

8N

CP

52

7N

CP

60

7N

CP

53

1N

CP

40

7N

CP

53

2N

CP

40

6N

CP

51

0N

CP

40

5N

CP

52

0N

CP

53

4.2

NC

P5

34

.1N

CP

53

4.3

NC

P4

14

NC

P5

09

Bla

nk.1

Bla

nk.2

NC

67

LC

18

1N

C6

4N

C6

6L

C1

71

LC

16

8L

C1

74

LC

16

9N

C9

5L

C1

80

NC

10

0L

C1

82

NC

65

LC

17

5L

C1

84

LC

17

7N

C9

8N

C6

3N

C9

6N

C9

7N

C9

9

NSP8_0.5_KNSP10_0.25_KNSP15_0.5_KNSP8_0.1_KNSP8_0.25_KNSP1_0.1_KNSP15_0.25_KNSP14_0.05_KNSP2_0.1_KNSP1_0.25_KNSP1_0.5_KAnti-H-IgG_0.025H-IgGAnti-H-IgG_0.1EGFP_0.1_KEGFP_0.25_KGST_0.1_eGFP_0.5_KE-protein_0.5_KNSP2_0.05_KNSP15_0.1_KCy3E-protein_0.1_KE-protein_0.25_KS-RBD_0.25_SNSP4_0.1_TNSP16_0.1_KH-IgMACE2_0.5_SACE2-Fc_0.25_SACE2-Fc_0.5_SNSP9_0.25_TUBE2D3-HisAnti-H-IgM_0.1NSP5_0.1_TNSP5_0.25_TNSP5_0.5_TE-protein_0.25_TORF7b_0.1_TORF6_0.1_TS2-1_0.05_TS2-2_0.1_TNSP8_0.25_TAnti-H-IgM_0.025NSP16_0.1_TORF9b_0.1_TNSP1_0.1_TNSP16_0.25_KNSP16_0.5_KNSP10_0.5_KNSP15_0.1_TNSP14_0.1_KNSP14_0.25_KS1_0.1_BS-RBD_0.5_SS1_0.1_SS1_0.25_BS1_0.5_BN-protein_0.1_TN-protein_0.1_SS1_0.1_TS-RBD_0.5_YS1_0.25_SS1_0.5_SN-protein_0.05_KN-protein_0.1_KN-protein_0.2_KN-protein_0.1_WN-protein_0.25_WN-protein_0.5_WN-protein_0.25_SN-protein_0.5_SN-Cter_0.1_KN-Cter_0.25_KN-Cter_0.5_KN-Nter_0.1_KN-Nter_0.25_KN-Nter_0.5_KCy5NSP2_0.25_KS1-4_0.2_KAGR2-biotin

0

5

10

15



https://doi.org/10.1101/2020.03.20.20039495


S1_0.1

_T

S1_0.1

_S

S1_0.2

5_S

S1_0.5

_S

S1_0.1

_B

S1_0.2

5_B

S1_0.5

_B

S.R

BD

_0.5

_Y

S.R

BD

_0.5

_S

S1.4

_0.2

_K

S2.1

_0.2

5_T

S2.2

_0.1

_T

S1_0.1_T

S1_0.1_S

S1_0.25_S

S1_0.5_S

S1_0.1_B

S1_0.25_B

S1_0.5_B

S-RBD_0.5_Y

S-RBD_0.5_S

S1-4_0.2_K

S2-1_0.25_T

S2-2_0.1_T

0.4

0.5

0.6

0.7

0.8

0.9

1

R e p ro d u c ib ility

Lo

g 2

(Sig

na

l In

ten

sit

y)

S1

S-R

BD S

2 N

0 .0

0 .2

0 .4

0 .6

0 .80.8

0.6

0.4

0.2

Corr

ela

tio

n c

oe

ffic

ien

t

0

IgG Vs. IgM

0

35,000

70,000

0 35,000 70,000

N_0.25_S

N_0.1_TN_0.1_WN_0.1_K

Lo

g 2

(Sig

na

l In

ten

sit

y)

S1_0.1

_T

S1_0.1

_S

S1_0.1

_B

S-R

BD

_0.1

_T

S-R

BD

_0.5

_S

S2-1

_0.0

5_T

S2-2

_0.1

_T

S1-4

_0.2

_K

0

4

8

1 2

1 6

**** **

** **

** **

**

Log

2(F

luore

scen

ce In

ten

sity)

** **** ** **

**

Log

2(F

luore

scen

ce In

ten

sity)

16

12

8

4

0

16

12

8

4

0

A. B.

0

1,000

2,000

3,000

0 5,000 10,000

S2

_0

.1_

T

S1_0.1_T

r=0.354

p=0.06

C. D. E.

F.LC169

NC96

N-C N-NG. H.

r=0.890

r=0.943

r=0.948

0

15,000

30,000

45,000

0 8,000 16,000

N_0

.1_W

S1_0.1_B

r=0.65

p<0.01

I. J. K.

0

2,000

4,000

6,000

0 10,000 20,000

IgM

IgG

S1_0.1_W

r=0.444

0

10,000

20,000

30,000

0 10,000 20,000

S1_0.1_B

S1_0.1_S

S1_0.1_T

r=0.984

r=0.928

0

6,000

12,000

0 10,000 20,000 30,000

S-R

BD

_0

.5_

S

S1_0.5_S

r=0.916

S IgG response N-protein IgG response

Figure 5. Antibody response to S and N proteins.

S1_0.1_T

S1_0.1_S

S1_0.25_S

S1_0.5_S

S1_0.1_B

S1_0.25_B

S1_0.5_B

S-RBD_0.1_Y

S-RBD_0.5_S

S1-4_0.2_K

S2-1_0.05_T

S2-2_0.1_T

S1_0.1

_T

S1_0.1

_S

S1_0.2

5_S

S1_0.5

_S

S1_0.1

_B

S1_0.2

5_B

S1_0.5

_B

S-R

BD

_0.1

_Y

S-R

BD

_0.5

_S

S1-4

_0.2

_K

S2-1

_0.0

5_T

S2-2

_0.1

_T

S1_0.1

_T

S1_0.1

_S

S1_0.2

5_S

S1_0.5

_S

S1_0.1

_B

S1_0.2

5_B

S1_0.5

_B

S.R

BD

_0.5

_Y

S.R

BD

_0.5

_S

S1.4

_0.2

_K

S2.1

_0.2

5_T

S2.2

_0.1

_T

S1_0.1_T

S1_0.1_S

S1_0.25_S

S1_0.5_S

S1_0.1_B

S1_0.25_B

S1_0.5_B

S-RBD_0.5_Y

S-RBD_0.5_S

S1-4_0.2_K

S2-1_0.25_T

S2-2_0.1_T

0.4

0.5

0.6

0.7

0.8

0.9

11

0.4

0.6

0.8

Lo

g 2

(Sig

na

l In

ten

sit

y)

N-p

rote

in_0.1

_T

N-p

rote

in_0.1

_K

N-p

rote

in_0.1

_W

N-p

rote

in_0.2

5_S

N-C

ter_

0.1

_K

N-N

ter_

0.1

_K

0

4

8

1 2

1 6



https://doi.org/10.1101/2020.03.20.20039495


0

6,000

12,000

0 10,000 20,000 30,000

S1

-4_

0.2

_K

S1_0.5_S

r=0.845

p<0.01

0

10,000

20,000

30,000

0 10,000 20,000 30,000

S1_0.1_S

S1_0.25_S

S1_0.5_S

0

4,000

8,000

12,000

0 4,000 8,000 12,000

S-R

BD

_0

.5_

S

S-RBD_0.5_Y

r=0.966

0

1,000

2,000

3,000

0 1,000 2,000

S2

-2_

0.1

_T

S2-1_0.05_T

r=0.974

0

1,000

2,000

3,000

0 5,000 10,000

S2

-2_

0.1

_T

S1_0.1_T

r=0.354

p=0.06

0

35,000

70,000

0 25,000 50,000

N_0.1_W

N_0.25_W

N_0.5_W

r=0.981

N.p

rote

in_0.1

_T

N.p

rote

in_0.1

_K

N.p

rote

in_0.2

5_K

N.p

rote

in_0.5

_K

N.p

rote

in_0.1

_W

N.p

rote

in_0.2

5_W

N.p

rote

in_0.5

_W

N.p

rote

in_0.2

5_S

N.p

rote

in_0.5

_S

N.C

ter_

0.1

_K

N.C

ter_

0.2

5_K

N.C

ter_

0.5

_K

N.N

ter_

0.1

_K

N.N

ter_

0.2

5_K

N.N

ter_

0.5

_K

N-protein_0.1_T

N-protein_0.1_K

N-protein_0.25_K

N-protein_0.5_K

N-protein_0.1_W

N-protein_0.25_W

N-protein_0.5_W

N-protein_0.25_S

N-protein_0.5_S

N-Cter_0.1_K

N-Cter_0.25_K

N-Cter_0.5_K

N-Nter_0.1_K

N-Nter_0.25_K

N-Nter_0.5_K

0.7

0.75

0.8

0.85

0.9

0.95

1

0

35,000

70,000

0 20,000 40,000 60,000

N_0.1_K

N-Cter_0.1_KN-Nter_0.1_K

r=0.893

r=0.856

0

25,000

50,000

0 40,000 80,000

N-N

ter

N-Cter

r=0.676

p<0.01

r=0.998

r=0.987

A. B. C.

D. E. F.

G. H.

Figure S2. IgG response to S and N proteins.

N_0.1_T

N_0.1_K

N_0.25_K

N_0.5_K

N_0.1_W

N_0.25_W

N_0.5_W

N_0.25_S

N_0.5_S

N-Nter_0.1_K

N-Nter_0.25_K

N-Nter_0.5_K

N-Cter_0.1_K

N-Cter_0.25_K

N-Cter_0.5_K

1

0.7

0.8

0.9

N.p

rote

in_0.1

_T

N.p

rote

in_0.1

_K

N.p

rote

in_0.2

5_K

N.p

rote

in_0.5

_K

N.p

rote

in_0.1

_W

N.p

rote

in_0.2

5_W

N.p

rote

in_0.5

_W

N.p

rote

in_0.2

5_S

N.p

rote

in_0.5

_S

N.C

ter_

0.1

_K

N.C

ter_

0.2

5_K

N.C

ter_

0.5

_K

N.N

ter_

0.1

_K

N.N

ter_

0.2

5_K

N.N

ter_

0.5

_K

N-protein_0.1_T

N-protein_0.1_K

N-protein_0.25_K

N-protein_0.5_K

N-protein_0.1_W

N-protein_0.25_W

N-protein_0.5_W

N-protein_0.25_S

N-protein_0.5_S

N-Cter_0.1_K

N-Cter_0.25_K

N-Cter_0.5_K

N-Nter_0.1_K

N-Nter_0.25_K

N-Nter_0.5_K

0.7

0.75

0.8

0.85

0.9

0.95

1

N_0.1

_T

N_0.1

_K

N_0.2

5_K

N_0.5

_K

N_0.1

_W

N_0.2

5_W

N_0.5

_W

N_0.2

5_S

N_0.5

_S

N-N

ter_

0.1

_K

N-N

ter_

0.2

5_K

N-N

ter_

0.5

_K

N-C

ter_

0.1

_K

N-C

ter_

0.2

5_K

N-C

ter_

0.5

_K

I.



https://doi.org/10.1101/2020.03.20.20039495


Lo

g 2

(Sig

na

l In

ten

sit

y)

S1_0.1

_T

S1_0.1

_S

S1_0.1

_B

S-R

BD

_0.1

_T

S-R

BD

_0.5

_S

S2-1

_0.2

5_T

S2-2

_0.1

_T

S1-4

_0.2

_K

0

4

8

1 2

1 6

**** **

** **

n.s. n.s.

*

Log

2(F

luore

scen

ce In

ten

sity)

16

12

8

4

0

A.

Lo

g 2

(Sig

na

l In

ten

sit

y)

N-p

rote

in_0.1

_T

N-p

rote

in_0.1

_K

N-p

rote

in_0.1

_W

N-p

rote

in_0.1

_S

N-C

ter_

0.1

_K

N-N

ter_

0.1

_K

0

4

8

1 2

1 6 **

**

**

**

**

**

Log

2(F

luore

scen

ce In

ten

sity) 16

12

8

4

0

B.S IgM response N-protein IgM response

Figure S3. IgM response to S and N proteins.

0

6,000

12,000

18,000

24,000

30,000

0 20,000 40,000

N_0.1_T

N_0.1_K

N_0.1_W

N_0.1_S

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 2,000 4,000 6,000 8,000

S1_0.1_S

S1_0.1_T

S1_0.1_B

r=0.817

r=0.908

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0 2,000 4,000 6,000 8,000

S-R

BD

_0

.5_

S

S1_0.5_S

r=0.825

F. G. H.

0

200

400

600

800

1,000

1,200

0 1,000 2,000 3,000 4,000

S2

-2_

0.1

_T

S1_0.1_T

S1_0.1

_T

S1_0.1

_S

S1_0.2

5_S

S1_0.5

_S

S1_0.1

_B

S1_0.2

5_B

S1_0.5

_B

S.R

BD

_0.5

_Y

S.R

BD

_0.5

_S

S1.4

_0.2

_K

S2.1

_0.2

5_T

S2.2

_0.1

_T

S1_0.1_T

S1_0.1_S

S1_0.25_S

S1_0.5_S

S1_0.1_B

S1_0.25_B

S1_0.5_B

S-RBD_0.5_Y

S-RBD_0.5_S

S1-4_0.2_K

S2-1_0.25_T

S2-2_0.1_T

0

0.2

0.4

0.6

0.8

1

r=0.327

p=0.084 r=0.998

r=0.615

r=0.988

0

6,000

12,000

18,000

24,000

30,000

0 2,000 4,000 6,000 8,000

N_0

.1_W

S_0.1_B

r=0.225

p=0.241

C. D. E.



https://doi.org/10.1101/2020.03.20.20039495


Figure 6. IgG response to other SARS-CoV-2 proteins.

Protein COVID-19 Control

ORF9b 13/29 0/21

NSP5 3/29 1/21

NSP14 1/29 0/21

NSP10 1/29 0/21

NSP8 1/29 0/21

NSP16 1/29 1/21

N S P 5

Lo

g 2

(Sig

na

l In

ten

sit

y)

769

384.2

5

0

1 ,0 0 0

2 ,0 0 0

3 ,0 0 0

4 ,0 0 0

COVID-19 Control

Anti-NSP5 IgG

4000

3000

2000

1000

0

Flu

ore

sce

nce

In

ten

sity

O R F 9 b

Lo

g 2

(Sig

na

l In

ten

sit

y)

OR

F-9

b_0.1

_T

Contr

ol

0

5 0 0

1 ,0 0 0

1 ,5 0 0

3 5 0 0

4 0 0 0

COVID-19 Control

Anti-ORF9b IgG

4000

3500

1500

1000

500

0

Flu

ore

sce

nce

In

ten

sity

A. B.p=0.006 p=0.014

0

6,000

12,000

18,000

0

12,000

24,000

36,000

48,000

0 1,000 2,000 3,000 4,000

S1

N P

rote

in

ORF9b

N-protein_0.1_W

S1_0.1_B

0

6,000

12,000

18,000

0

12,000

24,000

36,000

48,000

0 1,000 2,000 3,000 4,000

S1

N P

rote

in

NSP5

N-protein_0.1_W

S1_0.1_B

D. E.

r=0.152

p=0.432

r=-0.021

p=0.915

r=0.257

p=0.179

r=-0.207

p=0.281

C.



https://doi.org/10.1101/2020.03.20.20039495


0

4,000

8,000

12,000

200 400 600 800

S1

Ig

GPeak LDH

0

6,000

12,000

18,000

0 10 20 30 40

S1

Ig

G

Ly%

Figure 7. Correlation with clinical characteristics

0

4,000

8,000

12,000

16,000

0 20 40 60 80

S1

Ig

G

Age

0

6,000

12,000

18,000

0 10 20 30 40

S1

Ig

G

Days after onset

0

2,000

4,000

6,000

20 25 30 35 40

S1

Ig

G

Age

MaleFemaleMale (< 40 )

Male

0

6,000

12,000

18,000

0 25 50

S1

Ig

G

Ly%

Female

r=0.846

p=0.016

r=0.429

p=0.02

0

15,000

30,000

45,000

0 20 40 60 80

M

F

r=0.763

p<0.01

r=0.382

p=0.108

AgeS

1 Ig

G

r=0.86

p<0.01 r=0.227

p=0.455

r=-0.683

p=0.01r=-0.508

p=0.045

M a le a n d F a m a le

<40

≥40

<40

≥40

0

5 ,0 0 0

1 0 ,0 0 0

1 5 ,0 0 0

2 0 ,0 0 0

n.s.

*n.s. *

Male Female

S1

Ig

G

A. B. C.

D. E. F.

G. H. I.

0

6,000

12,000

200 400 600 800

S1

Ig

G

Peak LDH

r=0.79

p<0.01

Female (partial)



https://doi.org/10.1101/2020.03.20.20039495


0

10

20

30

40

50

60

0

10

20

30

40

50

60

70

200 400 600 800

Days

aft

er

onset

Age

Peak LDH

AgeDays after onset

Figure S4. Correlation with clinical information

0

10,000

20,000

30,000

40,000

50,000

60,000

0 40 80

N I

gG

Age

Mr=0.627

p<0.01

r=0.588

p=0.035

0

5

10

15

20

25

30

35

20 30 40

Da

ys a

fte

r o

nse

t

Age

0

6,000

12,000

18,000

24,000

30,000

20 30 40

N I

gG

Age

r=0.828

p=0.021

r=0.372

p=0.412

Male <40 Male <40

0

6,000

12,000

18,000

0 40 80

S1

Ig

G

Age

0

15,000

30,000

45,000

0 20 40 60 80

N I

gG

Age

r=0.751

p<0.01

r=0.627

p<0.01

Female Female

16

17

18

19

20

21

22

23

24

0 50 100

Days a

fte

r o

nse

t

Age

0

20,000

40,000

0 40 80

N I

gG

Age

r=0.629

p=0.029

r=-0.101

p=0.756

r=0.177p=0.625

r=0.559p=0.093

Female

0

20

40

60

80

0 20 40 60

Ag

e

Ly%

0

15,000

30,000

45,000

0 20 40 60

N Ig

G

Ly%

r=-0.573

p=0.02

r=-0.626

p<0.01

Female Female

A. B. C.

D. E. F.

G. H. I.

J. K. L.

Female (partial)

Female (partial)

0

6,000

12,000

18,000

200 400 600 800 1,000

S1

Ig

G

Peak LDH

Female

0

10,000

20,000

30,000

40,000

200 400 600 800

N Ig

G

Peak LDH

r=0.806

p<0.01

Female (partial)

r=0.699

p<0.01



https://doi.org/10.1101/2020.03.20.20039495


Table 1. Serum samples tested in this study.

Patient Group n=29

GenderMale 13

Female 16

Age 42.3±13.8

Severitymild cases 3

common cases 26

Days after onset 22.3±5.4

hospital stay (days) 17.9±5.7

Control group n=21

Lung cancer patients 10

GenderMale 5

Female 5

Age 55.9±7.3

sample collection data (year) 2017

Health control 11

GenderMale 6

Female 5

Age 45.1±12.9

sample collection data (year) 2017-2018



https://doi.org/10.1101/2020.03.20.20039495


Global profiling of SARS-CoV-2 specific IgG/ IgM …...2020/03/20 · The 1st global picture of the SARS-CoV-2 specific IgG/ IgM response reveals that at the convalescent phase, 100%

Documents