Distinct immune response landscapes of COVID-19 and influenza patients with peripheral mononuclear single-cell sequencing

The coronavirus disease 2019 (COVID-19) pandemic represents an ongoing global public health threat, but there is relatively little information available on its key characteristics than other infectious diseases.

This article outlines the single-cell transcriptional landscape of longitudinally collected peripheral blood mononuclear cells (PBMCs) in COVID-19- and influenza A virus (IAV)-infected patients.

A notable increase of plasma cells was observed in both COVID-19 and IAV patients. This was noted along an XIAP associated factor 1 (XAF1)-, tumor necrosis factor (TNF)- and FAS- induced T cell apoptosis in COVID-19 patients.

Detailed analysis highlighted clear signaling pathways activated in COVID-19 (STAT1 and IRF3) versus IAV (STAT3 and NFkB) patients, as well as considerable variation in the expression of key factors.

These factors include an increase of interleukin (IL)6R and IL6ST expression in COVID-19 patients, as well as a comparable increase in IL-6 concentrations versus IAV patients. These findings support clinical observations of increased proinflammatory cytokines in COVID-19 patients.

Due to this, it is prudent to explore the landscape of PBMCs to reveal these distinct immune response pathways in COVID-19 and IAV patients.

The novel coronavirus (CoV) labeled severe acute respiratory syndrome (SARS)-CoV-2 acted as the foundation of a pandemic-level infection: the coronavirus disease 2019 (COVID-19).

This disease spread rapidly and on a global scale, prompting the World Health Organization (WHO) to declare this a severe public health emergency of international concern (PHEIC) (Wang et al., 2020a).

Over 200 countries and territories reported COVID-19 infections by June 30, 2020, amounting to 10,185,374 confirmed cases and 503,862 deaths according to the Centers for Disease Control and Prevention (CDC), the European Centre for Disease Prevention and Control (ECDC) and the WHO.

A number of other frequently encountered respiratory viruses have been noted and studied. These include influenza A virus (IAV), influenza B virus, CoV, parainfluenza virus, respiratory syncytial virus and adenovirus.

CoV is part of the virus family Coronaviridea - an enveloped, non-segmented, single-stranded positive-sense RNA virus (Zhao et al., 2017).

Much like Middle East respiratory syndrome (MERS)-CoV and SARS-CoV, SARS-CoV-2 is a b-coronavirus. It is believed that these viruses originated in bats (Zhou et al., 2020b).

There is evidence to suggest that the two of the key mediators for SARS-CoV-2 host cell entry are the cellular receptor angiotensin-converting enzyme 2 (ACE2) and the serine protease for SARS-CoV entry, transmembrane serine protease 2 (TMPRSS2) (Li et al., 2003, Wang et al., 2020b).

It has also been demonstrated that ACE2 is expressed in the respiratory system as well as in an array of organs, tissues and cell types (Xu et al., 2020a). This key finding highlights the viral infection’s potential to quickly spread throughout the body as the disease progresses.

SARS-CoV initially emerged in China in 2002–2003, while the initial reports of MERS- CoV occurred in Saudi Arabia in 2012.

The WHO reported mortality rates for SARS-CoV and MERS-CoV as approximately 10% (8,098 cases and 774 deaths) and 34.4% (2,494 cases and 858 deaths), respectively (Wu et al., 2020).

Data collected by the WHO (up to March 24, 2020) confirmed that 4.9% of SARS-CoV-2 cases were fatal (823,626 cases and 40,598 deaths) - a lower mortality rate than that of MERS-CoV and SARS-CoV (Liu et al., 2017).

Influenza symptoms related to annual flu seasons exhibit a notable degree of similarity to the respiratory diseases caused by CoVs.

Annual estimates and data from 2010–2011 to 2015–2016 influenza seasons highlight the burden of seasonal influenza in the United States.

Influenza viruses are reported to have led to an estimated 9,200,000–35,600,000 illnesses and 139,000–708,000 hospitalizations. These have also resulted in 4,000–20,000 deaths from pneumonia and influenza along with 12,000–56,000 deaths from respiratory and other circulatory symptoms - a mortality rate of 0.04%–0.83% (Rolfes et al., 2018).

A number of SARS-CoV-2 infection diagnostics exist, including pneumonia detection via computed tomography (CT) scans and viral RNA detection via throat swab samples that can be extracted and tested using a combination of real-time RT-PCR and SARS-CoV-2-specific primers and probes).

This RT-PCR approach can also be used to test secretions acquired from peripheral blood, feces or the lower respiratory tract.

Patients may present mild symptoms such as cough, myalgia or fatigue, fever and sputum production. There is also the potential for infected individuals to be asymptomatic. These mild symptoms may infrequently include intestinal signs and symptoms (Huang et al., 2020).

It may be possible to detect other symptoms via routine blood examination following a diagnosis of COVID-19.

For example, research at the Jinyintan Hospital in Wuhan, China, has revealed that neutrophils in 38% of COVID-19 patients have been found to be above the normal range, while hemoglobin in 51% COVID-19 patients has been found to be below the normal range.

Lymphocyte levels were also found to decrease in 35% of patients (Chen et al., 2020), highlighting the possibility of dysfunctional cell-mediated immunity in patients with COVID-19.

Both acute respiratory distress syndrome (ARDS) and virally driven hyperinflammation represent further significant causes of mortality (Huang et al., 2014).

There have been cases where proinflammatory cytokine or chemokine responses have triggered viral sepsis and devastating systemic inflammation.

This can in turn lead to cytokine storm syndromes (CSSs) which include acute inflammatory-induced lung injury, as well as the development of ARDS, pneumonitis and respiratory failure, resulting in hemodynamic instability, shock, multiple organ dysfunction and even death.

Increased inter-leukin-6 (IL-6) concentrations and ferritin have been noted in line with illness deterioration. This has been reported in non-survivors and compared with survivors within a subgroup of patients with COVID-19 (Zhou et al., 2020a).

Recent studies found that during the acute phase of the disease, a group of proinflammatory cytokines was upregulated in lung injury (Murray score) in the most severe patients. These cytokines can therefore be employed as biomarkers to better predict the severity of disease following SARS-CoV-2 infection (Liu et al., 2020).

There is a growing repository of clinical data relating to blood cell indices, but there remains a lack of clarification around underlying molecular mechanisms.

This article explores the transcriptome dynamics of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and evaluates these in comparison with profiles in IAV patients and control, healthy donors.

The study presented here investigates the landscape and features of these infections through the integration of single-cell RNA sequencing (scRNA-seq) with clinical symptoms.

It was observed in the clinic that there was an increased proportion of plasma cells coupled with reduction of lymphocytes – a reduction that analyses suggest was caused XAF1-, tumor necrosis factor (TNF)-, and Fas-induced apoptosis.

Expression of IL6R and IL6ST was upregulated in COVID-19 patients, which synergistically promotes increased proinflammatory cytokines during pathogenesis. This was different from that noted in IAV patients.

The study also revealed that a number of interferon (IFN)-stimulated genes (ISGs; including ISG15, IFI44, IFI44L, and RSAD2) were upregulated in PBMCs from COVID-19 patients, improving immune modulatory and antiviral functions following viral infection.

Results

Single-cell transcriptional landscape of PBMCs from COVID-19 and IAV patients

To investigate the pathogenesis and mechanism of SARS-CoV-2 infections in COVID-19 patients, blood samples were collected from a number of sources (Figure 1A; Table S1):

  • Three healthy controls
  • Two IAV-infected patients
  • A total of five COVID-19 patients, including four patients (COV-1 to COV-4) with uncomplicated disease courses and one patient (COV-5) that later progressed to severe disease

Seasonal IAV-infected patients were selected as controls due to the fact that both SARS-CoV-2 and IAV are contagious RNA viruses, and both of these viruses trigger respiratory tract infection.

COVID-19 exhibits distinct clinical signatures compared to IAV infections, however. These signatures include high rates of morbidity and mortality.

The aforementioned COVID-19 patients were enrolled in the study within 5–10 days of the onset of symptoms. The infection was confirmed via positive nucleic acid testing results, and the day of initial PBMC collection was termed Day 1.

Table S1 lists the time points corresponding to disease onset and sample collection. PBMCs were also acquired at a number of subsequent time points (Figure 1A).

Once low quality cells had been filtered out, it was possible to acquire transcriptome datasets from 46,022 cells with an average of 2,000 cells per participant per time point (Figure 1B). Unsupervised clustering was performed to reveal immune cell populations in COVID-19, obtaining a total of 15 cell populations (Figure 1C).

A range of immunocytes were identified based on the expression of classic cell-type markers. These included T cells, B cells, monocytes, stem cells, natural killer (NK) cells, DCs and megakaryocytes (Figure 1C).

This allowed five populations to be annotated as T cells, including a series of naive T cells (CD3+CCR7+GZMA), cytotoxic CD8+ T cells (CD3+CD8+GZMA+), mucosal associated invariant T cells (MAIT cells; CD3+SLC4A10+), activated CD4+ T cells (CD3+CD4+IL7R+) and cycling T cells (CD3+MKI67+).

A total of four populations were then annotated as B cells, including naive B cells (MS4A1+IGHG1), plasma cells (MZB1+IGHG1+), memory B cells (MS4A1+IGHG1+) and cycling plasma cells (MZB1+IGHG1+MKI67+).

Two populations were annotated as NK cells (KLRF1+), while one population was annotated as DCs (CD1C+LYZ+) and monocytes (LYZ+CD68+) (Figure 1D and Figure S1A).

The majority of clusters were comprised of cells from numerous patients, suggesting the presence of common immune traits among patients.

It was also noted that PBMC samples from patients did not express ACE2 and TMPRSS2 receptors and that these did not exhibit viral reads – factors that would suggest that SARS-CoV-2 does not infect PBMCs (Figure S1B).

Single-Cell Gene Expression Profiling of Immune Cells Derived from PBMCs of the Participants. (A) Schematic outline of the study design. 10 subjects, including three healthy donors, five COVID-19 patients, and two IAV-infected patients were included in this study. (B) Bar plot shows the log10 transformed cell number of each sample for every donor at different time points. Blue represents three healthy donors, orange represents two IAV-infected patients, and five COVID-19 patients are displayed using five different colors. (C) The clustering result of 46,022 cells from ten donors. Each point represents one single cell, colored according to cell type. Mega., Megakaryocytes. (D) Expression levels of cell typing genes in cell type clusters. CD3G indicates T cells, KLRF1 and XCL1 indicate NKs, MS4A1 indicates B cells, IGHG1 and MZB1 indicate plasma cells, CD68 indicates monocytes, LYZ indicates DCs, MKI67 and TOP2A indicate cycling T cells, GZMA indicates cytotoxic CD8+ T cells and NKs, and PPBP indicates megakaryocytes. See also Figure S1 and Table S1.

Figure 1. Single-Cell Gene Expression Profiling of Immune Cells Derived from PBMCs of the Participants. (A) Schematic outline of the study design. 10 subjects, including three healthy donors, five COVID-19 patients, and two IAV-infected patients were included in this study. (B) Bar plot shows the log10 transformed cell number of each sample for every donor at different time points. Blue represents three healthy donors, orange represents two IAV-infected patients, and five COVID-19 patients are displayed using five different colors. (C) The clustering result of 46,022 cells from ten donors. Each point represents one single cell, colored according to cell type. Mega., Megakaryocytes. (D) Expression levels of cell typing genes in cell type clusters. CD3G indicates T cells, KLRF1 and XCL1 indicate NKs, MS4A1 indicates B cells, IGHG1 and MZB1 indicate plasma cells, CD68 indicates monocytes, LYZ indicates DCs, MKI67 and TOP2A indicate cycling T cells, GZMA indicates cytotoxic CD8+ T cells and NKs, and PPBP indicates megakaryocytes. See also Figure S1 and Table S1. Image Credit: Nexcelom Bioscience LLC

Increased plasma cells in PBMCs from COVID-19 and IAV patients

General patterns of PBMC populations were found to be comparable across patients (Figure 2A, Table S2).

It was also determined that the proportion of plasma cells and cycling plasma cells showed a considerable increase in both COVID-19- and IAV-infected patients (Figures 2B and S2) with no notable difference between these infection types.

There is a possibility that increased plasma cells could also induce protective neutralizing antibodies, helping to prevent viruses from infecting cells.

As anticipated, an investigation into the functions of upregulated genes in B cells of COVID-19 patients versus healthy donors revealed that protein complex assembly and protein-transport-related pathways were particularly enriched, potentially due to the synthesis of a significant number of proteins during this process (Figure 2C).

B-cell-activation-related pathways were also found to be enriched (Figure 2C) with representative genes, including PRDM1, XBP1 and IRF4 (Figure 2D).

Both the identity and function of plasma cells are influenced by the transcription factors PRDM1, XBP1 and IRF4 (Ochiai et al., 2013; Shaffer et al., 2004). PRDM1 is key to determining and shaping the secretory arm of mature B cell differentiation as well as encouraging immunoglobulin (Ig) synthesis.

XBP1 is a positively acting transcription factor in the CREB-ATF family, expressed in significant amounts within plasma cells. This factor is key to increasing protein synthesis in plasma cells (Shaffer et al., 2004).

IRF4 helps to regulate immunoglobulin class-switch recombination, and its presence in sustained and higher concentrations is recognized as helping to promote plasma cell generation (Ochiai et al., 2013).

After an increase in plasma cells and the expression of relevant transcription factors, an elevated expression of CD2AP on activated CD4+ T cells was also observed, compared with the healthy controls (Figure 2E).

The adaptor molecule CD2AP in CD4+ T cells is responsible for modulating the differentiation of follicular helper T cells and enhancing protective antibody responses as part of viral infection (Raju et al., 2018).

Beyond its role in supporting plasma cell function, TNFSF14 expression was also found to increase in activated CD4+ T cells and cytotoxic CD8+ T cells – further factors promoting T cell activation and T cell recruitment to tissues from peripheral blood.

KDM5A is responsible for encoding an H3K4me3 demethylase required for both NK cell and T cell activation. This is upregulated in NK cells and cytotoxic CD8+ T cells of COVID-19 (Figure 2E).

This combination of results has shown that elevated plasma cells and increased activation of T cells and NK cells in COVID-19 patients could potentially contribute to defense against the virus.

Dynamic Composition and Functional Changes in Immune Cells during SARS-CoV-2 Infection. (A) The cell-type frequency in each sample. Bars are colored by cell types. (B) Differences in plasma and cycling plasma proportion among samples from healthy donors (Ctrl) (n = 3), COVID-19 patients (COV) (n = 16), and IAV patients (IAV) (n = 4). Student’s t test was applied to test the significance of the  difference. *p < 0.05, **p < 0.01, ***p < 0.001. (C) Enriched GO terms for upregulated genes in COVID-19 patients compared to healthy controls in B cells. (D) The differential expression levels of B cell activation-related genes PRDM1, XBP1, and IRF4 between healthy donors (Ctrl) and COVID-19 patients (COV) in plasma cells. (E) The expression levels of T cell activation related genes in activated CD4+ T cells, cytotoxic CD8+ T cells, and NKs in samples from healthy donors and COVID-19 patients. In the upper panel, the color of each dot represents expression levels of the gene, while the dot size represents the fraction of cells expressing the gene in the specific cell type. In the lower panel, the difference between healthy donors (Ctrl) (n = 3) and COVID-19 patients (COV) (n = 16) were tested using the Student’s t test. *p < 0.05, **p < 0.01, ***p < 0.001. See also Figure S2 and Table S2.

Figure 2. Dynamic Composition and Functional Changes in Immune Cells during SARS-CoV-2 Infection. (A) The cell-type frequency in each sample. Bars are colored by cell types. (B) Differences in plasma and cycling plasma proportion among samples from healthy donors (Ctrl) (n = 3), COVID-19 patients (COV) (n = 16), and IAV patients (IAV) (n = 4). Student’s t test was applied to test the significance of the  difference. *p < 0.05, **p < 0.01, ***p < 0.001. (C) Enriched GO terms for upregulated genes in COVID-19 patients compared to healthy controls in B cells. (D) The differential expression levels of B cell activation-related genes PRDM1, XBP1, and IRF4 between healthy donors (Ctrl) and COVID-19 patients (COV) in plasma cells. (E) The expression levels of T cell activation related genes in activated CD4+ T cells, cytotoxic CD8+ T cells, and NKs in samples from healthy donors and COVID-19 patients. In the upper panel, the color of each dot represents expression levels of the gene, while the dot size represents the fraction of cells expressing the gene in the specific cell type. In the lower panel, the difference between healthy donors (Ctrl) (n = 3) and COVID-19 patients (COV) (n = 16) were tested using the Student’s t test. *p < 0.05, **p < 0.01, ***p < 0.001. See also Figure S2 and Table S2. Image Credit: Nexcelom Bioscience LLC

IFN response and lymphocyte apoptosis in COVID-19 patients

A number of Gene Ontology (GO) analyses were conducted to gain insight into the functions of a range of cell subsets between COVID-19 patients and healthy controls.

Genes in the group referred to as ‘‘response to type I IFN signaling’’ were enriched in T, B and NK cell subsets of D1 and D4, but not D16 samples (Figures 3A and Figure S3A). This was done in line with the concept that the IFN response is central to ensuring the effective response triggered by viral infection.

Genes in the group referred to as ‘‘defense response to virus signaling’’ were also enriched in T, B and NK cells of all five COVID-19 patients on D1 and D4, but not D16 (Figure 3A and Figure S3A). This suggested a continuing immune response against SARS-CoV-2 virus.

Endoplasm- and protein-unfolding-related pathways were also enriched in B cells at all three time points (D1, D4 and D16) (Figure 3A and Figure S3A).

It was hypothesized that this was due to a higher proportion of plasma cells in B cell clusters, resulting in the need for high demand of protein synthesis during antibody production.

Further investigation is required into the role of other signaling pathways, including ‘‘regulation of chromosome organization’’ and ‘‘DNA conformation change’’, which were also upregulated during SARS-CoV-2 infection.

Next, differentially expressed genes (DEGs) in these transcriptomic profiles were compared between COVID-19 patients and healthy controls.

ISGs – the key to early viral control (Schoggins and Rice, 2011) – were successfully identified in patients infected with SARS-CoV-2 on D1 and D4 (Figure 3B and Figure S3B). This was deemed to be in line with enrichment for ‘‘response to type I IFN signaling’’ pathways in the GO analysis (Figure 3A and Figure S3A).

These ISGs included ISG15, IFI44L, MX1 and X-linked inhibitor of apoptosis (XIAP)-associated factor 1 (XAF1) – all of which were found to be upregulated in T, B, NK and DC cell subsets (Figure 3B and Figure S3B).

The expression of these genes was also found to be significantly higher in COVID-19 patients than healthy controls at the bulk level (Figure 3C).

Transcription dynamics of these genes were examined during the disease process by dividing the disease processes of COVID-19 patients from symptom onset to discharge into four distinct stages (Table S3).

Six time-dependent expression patterns were identified (Figure 3D) and their biological significance (Figure 3E) was investigated.

Cluster 3 was found to contain 158 genes that exhibited decreased expression levels over time.

These genes’ functions were considerably enriched in biological processes linked to IFN responses, signifying that the transcriptional regulation of these genes is dynamic and that these have been activated at early time points and deactivated at late time points (Figure 3E).

Cluster 1 was found to contain 38 genes with expression levels that were elevated from Stage 2. GO enrichment analysis revealed that these cells’ functions were significantly enriched in translation and protein-synthesis-related pathways.

These findings were consistent with the timing of antibody production (Thevarajan et al., 2020) because a significant amount of protein synthesis takes place throughout this process (Figure 3E).

The severe patient (COV-5) exhibited a stronger response to IFNa on SARS-CoV-2 infection compared to the mild patients.

Expression of ISG15, IFI44L, MX1 and XAF1 were also found to be higher at earlier time points of disease progression before individually decreasing at later phases (Figure S3C). This behavior clearly shows their dynamic responses to interferons.

Ubiquitin-like proteins, such as ISG15, IFI44L and MX1, have specific roles in the antiviral response (Perng and Lenschow, 2018). XAF1 is involved in pro-apoptotic responses and there is evidence to suggest that this forms a positive feedback loop with IRF-1, driving apoptosis under stress (Jeong et al., 2018).

It was also noted that TP53-mediated apoptosis was enhanced by XAF1 via post-translational modification (Zou et al., 2012). The expression of genes linked to XAF1-mediated apoptosis was therefore analyzed. This included IRF1, TP53, BCL2L11 and CASP3 (Figure S3D).

Expression of IRF1, TP53 and CASP3 was found to increase in T, B and NK cell subsets in COVID-19 patients versus controls, while BCL2L11 was found to exhibit different patterns in different cell subsets.

An examination into the expression of genes in other apoptosis-linked pathways, including TNF and FAS pathways (Elmore, 2007), was also conducted in both COVID-19 patients and healthy controls (Figure 3F and Figure S3E).

It was also noted that the expression of TNFSF10 (TRAIL) and its receptor TNFRSF10A increased in T cells from COVID-19 patients in comparison to the healthy controls. Other TNF path members were also relatively upregulated in COVID-19 patients, for example, TNFRSF1B.

In terms of the FAS path, expression of FAS, FASLG and FADD was found to be upregulated in T cells of COVID-19 patients, though only slightly (Figure 3F).

In NK and B cell subsets, TNFSF10 and FADD were particularly increased in COVID-19 patients. Other genes increased somewhat, while FAS in B cells and TRADD in NK cells were marginally decreased (Figure S3E).

This combination of findings highlighted that upregulated genes relevant to the XAF1, TNF and FAS pathways could potentially result in increased T cell apoptosis in COVID-19 patients.

Analysis of IFN Response- and Apoptosis-Associated Genes in COVID-19 Patients. (A) The top 20 enriched biological processes by GO analysis in day 1 samples from COVID-19 patients compared to healthy controls in different cell populations. Dot color indicates the statistical significance of the enrichment (p), and dot size represents gene ratio annotated to each term. (B) The differentially expressed genes in day 1 samples from COVID-19 patients compared to healthy controls in different cell subsets. Red dots represent genes upregulated in COVID-19 patients (adjusted p < 0.01 and fold Change (FC) ≥ 2), while blue dots represent downregulated genes in COVID-19 patients (adjusted p < 0.01 and FC ≤ 0.5). Genes with log2(FC) ≥ 1.5 were labeled by gene symbols. (C) The gene expression of ISG15, IFI44L, MX1, and XAF1 in healthy donors (Ctrl) (n = 3) and COVID-19 patients (COV) (n = 16). Student’s t test was applied to test the significance of the difference. *p < 0.05, **p < 0.01, ***p < 0.001. (D) Genes clustered by their expression pattern along the progression of the disease by the mfuzz R package. (E) The top 10 enriched biological processes in each cluster of genes as revealed by GO analysis. (F) The difference in expression levels of apoptosis-associated genes between COVID-19 patients (COV) (n = 5) and healthy controls (Ctrl) (n = 3) in T cells. Student’s t test was applied. *p < 0.05, **p < 0.01, ***p < 0.001. See also Figure S3 and Table S3.

Figure 3. Analysis of IFN Response- and Apoptosis-Associated Genes in COVID-19 Patients. (A) The top 20 enriched biological processes by GO analysis in day 1 samples from COVID-19 patients compared to healthy controls in different cell populations. Dot color indicates the statistical significance of the enrichment (p), and dot size represents gene ratio annotated to each term. (B) The differentially expressed genes in day 1 samples from COVID-19 patients compared to healthy controls in different cell subsets. Red dots represent genes upregulated in COVID-19 patients (adjusted p < 0.01 and fold Change (FC) 2), while blue dots represent downregulated genes in COVID-19 patients (adjusted p < 0.01 and FC ≤ 0.5). Genes with log2(FC) 1.5 were labeled by gene symbols. (C) The gene expression of ISG15, IFI44L, MX1, and XAF1 in healthy donors (Ctrl) (n = 3) and COVID-19 patients (COV) (n = 16). Student’s t test was applied to test the significance of the difference. *p < 0.05, **p < 0.01, ***p < 0.001. (D) Genes clustered by their expression pattern along the progression of the disease by the mfuzz R package. (E) The top 10 enriched biological processes in each cluster of genes as revealed by GO analysis. (F) The difference in expression levels of apoptosis-associated genes between COVID-19 patients (COV) (n = 5) and healthy controls (Ctrl) (n = 3) in T cells. Student’s t test was applied. *p < 0.05, **p < 0.01, ***p < 0.001. See also Figure S3 and Table S3. Image Credit: Nexcelom Bioscience LLC

Immune molecular signatures of COVID-19 patients compared to IAV patients

The next stage of the study looked to identify immune molecular signatures linked to COVID-19 and IAV infection.

This was done via a comparison of the expression of cytokines, cytokine receptors and transcription factors in T cell subsets, evaluating these within NK cells, and DCs among COVID-19 patients, IAV patients and healthy controls (Figure 4A and Figure S4A).

Gene clustering patterns suggested that upregulated genes in COVID-19 patients primarily encode proinflammatory cytokines, cytokine receptors and IFN-responsive transcription factors. In IAV patients, both proinflammatory transcription factors and virus-interacting host factors would appear to be highly expressed.

A number of key transcription factors impact the host immune response - STAT3, NFKB1 and REL were found to be upregulated in a range of cell types from IAV patients (Figure 4A and Figure S4A).

NFKB1 and REL are responsible for encoding active subunits of NF-kB heterodimer. This is one of the characteristic transcription factors activated by IAV infection (Ludwig and Planz, 2008).

Activation of NF-kB is important in ensuring proper regulation of the proinflammatory innate and adaptive immune responses. Existing research has illustrated that IL-6 and IL-10 are highly increased in severe IAV patients (Yu et al., 2011). Both are known activators of STAT3 signaling.

Expression of STAT3 was found to be elevated in IAV patients versus both COVID-19 patients and healthy controls. These appeared to correlate with time post-admission. RUNX3 expression was also upregulated in activated CD4+ T cells of IAV patients versus other groups (Figure 4B).

It has been theorized that RUNX3 induction is a central step in CD4+ T cells’ acquisition of cytotoxic activity. A further study highlighted that RUNX3 induced via IAV infection throughout the NF-kB pathway encouraged apoptosis in airway epithelial cells (Gan et al., 2015).

Further investigation is required into the role of RUNX3 in the T-cell-mediated immune response. A number of pro-viral host factors that contribute to viral infection, replication and immune evasion were also found to be upregulated in IAV patients (Figures 4A and S4A) (Shapira et al., 2009). These included CHD1, BCLAF1 and PHF3.

To investigate the response of these genes’ changes over time, the disease processes of IAV patients were divided into two stages (Table S3).

No gene exhibiting statistically significant time-dependent regulation was found, but some genes did exhibit a trend towards increased or decreased expression (Figure S4B and Figure S4C).

STAT1 – a significant transcription factor activated in response to interferon - was found to be upregulated in activated CD4+ T cells, cytotoxic CD8+ T cells, naive T cells and DCs in COVID-19 patients (Figures 4A and S4A).

A number of proinflammatory factors were found to be elevated in activated CD4+ T cells, cytotoxic CD8+ T cells, MAIT cells and NK cells (Figures S4A and S4E). These included TNF and TNFSF14, implying an enhanced effector function and the potential for memory cell development (Desai et al., 2018).

It was not possible to detect the expression of IL6 in PBMCs from any patient or healthy control.

Plasma concentrations of IL-6 were measured during COVID-19 patients’ hospitalization and following discharge from the hospital (Figure S4D).

Plasma IL-6 were found to be above the typical range (0.0–7.0 pg/mL) (Chen et al., 2020) in two of five COVID-19 patients at the point of admission to the hospital, reducing to normal levels throughout hospitalization and remaining stable following recovery.

A rising phase of IL-6 was noted in the majority of COVID-19 patients, suggesting the presence of an active inflammatory response. This is also observed in IAV patients (Yu et al., 2011).

The patient exhibiting severe symptoms presented with a significantly higher level of IL-6 when hospitalized, taking longer to return to normal plasma IL-6 levels. This can therefore be understood as corresponding with disease severity.

IL-6 exercises its function via its binding to IL-6R. The IL-6-IL-6R complex binds to GP130 (IL-6 receptor subunit beta (IL6ST)). This common signal-transducing chain is shared by a number of related cytokines (Mihara et al., 2012).

Expression of IL6R was found to be elevated in activated CD4+ T cells, naive T cells and DCs of COVID-19 patients versus IAV patients and healthy controls (Figure 4C and Figure S4A). Expression of IL6ST was found to be upregulated in numerous cell types from both COVID-19 patients and IAV patients versus healthy controls.

There is evidence to suggest that IL-6R is typically shed from activated T cells’ membranes, binding the soluble form of IL-6 and acting in trans on cells expressing IL6ST.

IL-6 trans-signaling via IL6ST could therefore contribute to the proinflammatory properties of IL-6 (Wolf et al., 2014), highlighting the importance of IL6ST upregulation noted in this.

Clinical observations have revealed that plasma concentrations of inflammatory cytokines (for example, IFN-α2, IL-7, IL-17, and IL-10) were higher in COVID-19 patients compared to healthy adults (Huang et al., 2020).

A comparison was conducted of their expression levels and corresponding receptors in activated CD4+ T cells, cytotoxic CD8+ T cells, MAIT cells and NK cells; this was done across the range of COVID-19 patients, IAV patients and healthy controls (Figure S4E).

IFNAR1 is α chain of the interferon α/β receptor. Its upregulation was found to be significant in these cell types in COVID-19 patients versus healthy controls.

It was also noted that receptor subunits for IL-7, IL-17 and IL-27 were also significantly elevated in activated CD4+ T cells of COVID-19 patients versus other groups. This suggests that CD4+ T cells could be involved in the major inflammatory response to cytokines.

Temporal changes were also examined in their expression (Figure S4B and Figure S4C).

A number of IFN-responsive transcription factor genes (for example, STAT1 and IRF3) typically exhibit decreased expression levels over time, but the limited number of patients meant that none of these patterns were statistically significant.

This question can be addressed via future studies with more patients and additional time points.

Hallmarks of COVID-19 Compared to IAV (Revealed by Single-Cell Analysis of Cytokines, Cytokine Receptors, and Transcription Factors). (A) The relative expression level (Z score) of key cytokines, cytokine receptors, and transcription factors among COVID-19 patients, IAV patients, and healthy controls in the activated CD4+ T cells population. (B) The expression level of four representative genes highly expressed in activated CD4+ T cells of IAV patients. Upper panel: the color of each dot in the dot plot indicates expression level of the gene; dot size represents the fraction of cells expressing the gene in activated CD4+ T cells population. Lower panel: difference in gene expression among samples from COVID-19 patients (COV) (n = 16), IAV patients (IAV) (n = 4), and healthy donors (Ctrl) (n = 3). Each dot in the boxplot represents the average expression level of a gene in the activated CD4+ T cells population in one sample. Student’s t test was applied to test the significance of the difference. *p < 0.05, **p < 0.01, ***p < 0.001. (C) Similar to (B), showing four representative genes highly expressed in the activated CD4+ T cells of COVID-19 patients.

Figure 4. Hallmarks of COVID-19 Compared to IAV (Revealed by Single-Cell Analysis of Cytokines, Cytokine Receptors, and Transcription Factors). (A) The relative expression level (Z score) of key cytokines, cytokine receptors, and transcription factors among COVID-19 patients, IAV patients, and healthy controls in the activated CD4+ T cells population. (B) The expression level of four representative genes highly expressed in activated CD4+ T cells of IAV patients. Upper panel: the color of each dot in the dot plot indicates expression level of the gene; dot size represents the fraction of cells expressing the gene in activated CD4+ T cells population. Lower panel: difference in gene expression among samples from COVID-19 patients (COV) (n = 16), IAV patients (IAV) (n = 4), and healthy donors (Ctrl) (n = 3). Each dot in the boxplot represents the average expression level of a gene in the activated CD4+ T cells population in one sample. Student’s t test was applied to test the significance of the difference. *p < 0.05, **p < 0.01, ***p < 0.001. (C) Similar to (B), showing four representative genes highly expressed in the activated CD4+ T cells of COVID-19 patients. See also Figure S4. Image Credit: Nexcelom Bioscience LLC

Discussion

There is a continuous threat to human health from both emerging and re-emerging viruses (Gao, 2018). The recent SARS-CoV-2 virus infection results in severe pulmonary disease and associated complications, ultimately leading to significant morbidities and mortalities.

There is not currently an optimal treatment or effective drug able to effectively treat this fatal lung disease, and limited understanding of the host immune response to SARS-CoV-2 infection has led to challenges in designing vital novel therapeutics.

In the study presented here, scRNA-seq was performed on PBMCs from COVID-19 patients. SARS-CoV-2 infection was found to have minimal impact on the composition of immune cells in PBMCs.

Upon investigation of the immune cell clusters, the percentage of plasma cells was found to increase substantially among all five COVID-19 patients compared with healthy controls.

It is possible that this increase in plasma cells may produce various protective neutralizing antibodies, including B-cell-derived antibodies that may be key to preventing death from acute respiratory tract infections and offering ongoing protection from future infection-induced illness and/or death.

There is also potential in the use of blood plasma containing protective neutralizing antibodies from recovered patients in the treatment of severely affected patients.

IFNs may be induced by viral infection to exert antiviral functions and to help balance virus control and immune pathology throughout this process.

Plasma IFNγ and TNF-α levels were higher in SARS-CoV-2-infected ICU patients (Huang et al., 2020) and both IFNα 2 and IFNγ have been strongly linked to lung injury in COVID-19 patients (Liu et al., 2020).

IFNγ and granulocyte-macrophage colony-stimulating factor (GM-CSF) coexpressing Th1 cells were only found to be present in intensive care unit (ICU) patients infected by SARS-CoV-2 compared to healthy controls (Zhou et al., 2020c).

The group labeled ‘‘IFN-I response’’ was consistently enriched in different PBMC subsets of COVID-19 patients, and the expression of ISGs (for example, ISG15, IFI44L, MX1 and XAF1) was notably upregulated in these patients versus three healthy controls.

These findings suggested that robust IFN antiviral functions may be triggered (Perng and Lenschow, 2018).

Severe COVID-19 patients also showed a stronger response to IFNs and virus infection when compared to mild and healthy patients, suggesting that interferon response intensity may indicate the severity of COVID-19 disease as well as distinguishing COVID-19 patients from healthy people.

IFN response dynamics also suggest individual differences, highlighting the criticality of the timing of IFN therapy against SARS-CoV-2 infection.

Results from this study suggested that upregulated XAF1 expression may be involved in increased T cell apoptosis in COVID-19 patients, working in conjunction with various other genes, including IRF1, TP53, BCL2L11 and CASP3 (Jeong et al., 2018; Zou et al., 2012).

A further study found that TP53 expression was consistently found to increase in COVID-19 patients (Xiong et al., 2020). As well as XAF1-induced apoptosis, the extrinsic pathway of apoptosis – for example, TNF-α / TNFR1 and FAS/FASL path (Elmore, 2007) - were involved in various cell subtypes in COVID-19 patients.

Increased plasma TNF-a was also reported in severe cases of COVID-19 (Chen et al., 2020; Huang et al., 2020), while TNF expression was found to be upregulated in PBMCs of COVID-19 patients.

The study presented here highlighted a noteworthy correlation between increased TNF-α  secretion and TNF-α -induced apoptosis in COVID-19 patients (Rath and Aggarwal, 1999).

Both IFN and XAF1 may be induced by TNF-α , the latter functioning as an alternate pathway for TNF-α -induced apoptosis (Straszewski-Chavez et al., 2007). Another study highlighted that XAF1 may potentially collaborate with TNFSF10 (TRAIL) to promote Dengue virus-induced apoptosis (Zhang et al., 2019).

These considerations led the study presented here to theorize that upregulated genes relevant to XAF1, TNF and FAS pathways could potentially lead to increased T cell apoptosis in COVID-19 patients.

The expression of cytokines, cytokines receptors and transcription factors analyzed are essential factors in immune responses to viral infection. This analysis allowed the pinpointing of specific gene expression patterns in COVID-19 patients that were distinct from IAV patients.

Upregulation of cytokine receptors was found to be in line with increased serum cytokine levels, potentially facilitating enhanced cytokine-mediated inflammatory responses.

The expression of most cytokines was not detected in PBMCs, potentially indicating that serum cytokines primarily arise from the infection site, for example, the lower respiratory tract in COVID-19 cases.

Plasma concentrations of IL-6 were found to be above normal levels in most COVID-19 patients, in good agreement with other studies (Chen et al., 2020).

Elevated IL-6 has also been reported in IAV patients (Yu et al., 2011) – a key immune signature in patients with acute respiratory stress syndrome (Wang et al., 2020c) associated with mortality resulting from cytokine release syndrome.

Treatments blocking IL-6 or IL-6R have been previously approved for patients presenting with pneumonia and elevated IL-6, while a limited study has highlighted the efficacy of Tocilizumab (a monoclonal antibody against IL-6R) in easing clinical symptoms (Xu et al., 2020b).

Data from the study presented here demonstrates that the expression of IL6R and IL6ST is upregulated in patients with COVID-19. It is theorized that increased expression of IL6R and IL6ST may synergize with elevated IL- 6 to induce a robust inflammatory response. Patients may therefore benefit from IL-6 or IL-6R antagonist treatments.

Limitations of the study

A number of key limitations should be noted when interpreting the results of this study.

Due to the limited number of patients examined - particularly for IAV (two patients) – the differences noted between COVID-19 and IAV during infection should be further validated via larger clinical trials and/or additional studies.

This study focuses on the single-cell transcriptome of PBMCs in blood. Combining this data with data from lesion sites – for example, the lung – would enable more systematic analysis and more comprehensive conclusions.

The study presented here visualized the dynamic landscape of immune responses throughout the disease process in COVID-19 patients. This was compared with that of IAV patients at the single-cell transcriptome level.

Results represent immune molecular and cellular signatures throughout the clinical process of COVID-19 while suggesting routes to both vital diagnostic biomarkers and therapeutic targets for this novel disease.

References

  1. Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420.
  2. Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395, 507–513.
  3. Desai, P., Tahiliani, V., Hutchinson, T.E., Dastmalchi, F., Stanfield, J., Abboud, G., Thomas, P.G., Ware, C.F., Song, J., Croft, M., and Salek-Ardakani, S. (2018). The TNF superfamily molecule LIGHT promotes the generation of circulating and lung-resident memory CD8 T cells following an acute respiratory virus infection. J. Immunol. 200, 2894–2904.
  4. Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21.
  5. Elmore, S. (2007). Apoptosis: a review of programmed cell death. Toxicol. Pathol. 35, 495–516.
  6. Gan, H., Hao, Q., Idell, S., and Tang, H. (2015). Transcription factor Runx3 is induced by influenza A virus and double-strand RNA and mediates airway epithelial cell apoptosis. Sci. Rep. 5, 17916.
  7. Gao, G.F. (2018). From ‘‘A’’IV to ‘‘Z’’IKV: attacks from emerging and re-emerging pathogens. Cell 172, 1157–1159.
  8. Huang, F., Guo, J., Zou, Z., Liu, J., Cao, B., Zhang, S., Li, H., Wang, W., Sheng, M., Liu, S., et al. (2014). Angiotensin II plasma levels are linked to disease severity and predict fatal outcomes in H7N9-infected patients. Nat. Commun. 5, 3595.
  9. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506.
  10. Jeong, S.I., Kim, J.W., Ko, K.P., Ryu, B.K., Lee, M.G., Kim, H.J., and Chi, S.G. (2018). XAF1 forms a positive feedback loop with IRF-1 to drive apoptotic stress response and suppress tumorigenesis. Cell Death Dis. 9, 806.
  11. Kumar, L., and Futschik, M.E. (2007). Mfuzz: A software package for soft clustering of microarray data. Bioinformation 2, 5–7.
  12. Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359.
  13. Li, W., Moore, M.J., Vasilieva, N., Sui, J., Wong, S.K., Berne, M.A., Somasundaran, M., Sullivan, J.L., Luzuriaga, K., Greenough, T.C., et al. (2003). Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 426, 450–454.
  14. Liu, C., Wu, T., Fan, F., Liu, Y., Wu, L., Junkin, M., Wang, Z., Yu, Y., Wang, W., Wei, W., et al. (2019). A portable and cost-effective microfluidic system for massively parallel single cell transcriptome profiling. bioRxiv. https://doi.org/ 10.1101/818450.
  15. Liu, Y., Zhang, C., Huang, F., Yang, Y., Wang, F., Yuan, J., Zhang, Z., Qin, Y., Li, X., Zhao, D., et al. (2020). Elevated plasma levels of selective cytokines in COVID-19 patients reflect viral load and lung injury. Natl. Sci. Rev. 7, 1003–1011.
  16. Liu, W.J., Zhao, M., Liu, K., Xu, K., Wong, G., Tan, W., and Gao, G.F. (2017). T-cell immunity of SARS-CoV: Implications for vaccine development against MERS-CoV. Antiviral Res. 137, 82–92.
  17. Ludwig, S., and Planz, O. (2008). Influenza viruses and the NF-kappaB signaling pathway - towards a novel concept of antiviral therapy. Biol. Chem. 389, 1307–1312.
  18. Mihara, M., Hashizume, M., Yoshida, H., Suzuki, M., and Shiina, M. (2012). IL-6/IL-6 receptor system and its role in physiological and pathological conditions. Clin. Sci. (Lond.) 122, 143–159.
  19. Ochiai, K., Maienschein-Cline, M., Simonetti, G., Chen, J., Rosenthal, R., Brink, R., Chong, A.S., Klein, U., Dinner, A.R., Singh, H., and Sciammas, R. (2013). Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4. Immunity 38, 918–929.
  20. Perng, Y.-C., and Lenschow, D.J. (2018). ISG15 in antiviral immunity and beyond. Nat. Rev. Microbiol. 16, 423–439.
  21. Raju, S., Kometani, K., Kurosaki, T., Shaw, A.S., and Egawa, T. (2018). The adaptor molecule CD2AP in CD4 T cells modulates differentiation of follicular helper T cells during chronic LCMV infection. PLoS Pathog. 14, e1007053.
  22. Rath, P.C., and Aggarwal, B.B. (1999). TNF-induced signaling in apoptosis. J. Clin. Immunol. 19, 350–364.
  23. Rolfes, M.A., Foppa, I.M., Garg, S., Flannery, B., Brammer, L., Singleton, J.A., Burns, E., Jernigan, D., Olsen, S.J., Bresee, J., and Reed, C. (2018). Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respir. Viruses 12, 132–137.
  24. Schoggins, J.W., and Rice, C.M. (2011). Interferon-stimulated genes and their antiviral effector functions. Curr. Opin. Virol. 1, 519–525.
  25. Shaffer, A.L., Shapiro-Shelef, M., Iwakoshi, N.N., Lee, A.H., Qian, S.B., Zhao, H., Yu, X., Yang, L., Tan, B.K., Rosenwald, A., et al. (2004). XBP1, downstream of Blimp-1, expands the secretory apparatus and other organelles, and increases protein synthesis in plasma cell differentiation. Immunity 21, 81–93.
  26. Shapira, S.D., Gat-Viks, I., Shum, B.O., Dricot, A., de Grace, M.M., Wu, L., Gupta, P.B., Hao, T., Silver, S.J., Root, D.E., et al. (2009). A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139, 1255–1267.
  27. Straszewski-Chavez, S.L., Visintin, I.P., Karassina, N., Los, G., Liston, P., Halaban, R., Fadiel, A., and Mor, G. (2007). XAF1 mediates tumor necrosis factor-alpha-induced apoptosis and X-linked inhibitor of apoptosis cleavage by acting through the mitochondrial pathway. J. Biol. Chem. 282, 13059–13072.
  28. Thevarajan, I., Nguyen, T.H.O., Koutsakos, M., Druce, J., Caly, L., van de Sandt, C.E., Jia, X., Nicholson, S., Catton, M., Cowie, B., et al. (2020). Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19. Nat. Med. 26, 453–455.
  29. Wang, C., Horby, P.W., Hayden, F.G., and Gao, G.F. (2020a). A novel coronavirus outbreak of global health concern. Lancet 395, 470–473.
  30. Wang, Q., Zhang, Y., Wu, L., Niu, S., Song, C., Zhang, Z., Lu, G., Qiao, C., Hu, Y., Yuen, K.-Y., et al. (2020b). Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2. Cell 181, 894–904.
  31. Wang, Z., Yang, B., Li, Q., Wen, L., and Zhang, R. (2020c). Clinical Features of 69 Cases with Coronavirus Disease 2019 in Wuhan, China. Clin. Infect. Dis. 71, 769–777.
  32. Wolf, J., Rose-John, S., and Garbers, C. (2014). Interleukin-6 and its receptors: a highly regulated and dynamic system. Cytokine 70, 11–20.
  33. Wu, J.T., Leung, K., and Leung, G.M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395, 689–697.
  34. Xiong, Y., Liu, Y., Cao, L., Wang, D., Guo, M., Jiang, A., Guo, D., Hu, W., Yang, J., Tang, Z., et al. (2020). Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients. Emerg. Microbes Infect. 9, 761–770.
  35. Xu, H., Zhong, L., Deng, J., Peng, J., Dan, H., Zeng, X., Li, T., and Chen, Q. (2020a). High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. Int. J. Oral Sci. 12, 8.
  36. Xu, X., Han, M., Li, T., Sun, W., Wang, D., Fu, B., Zhou, Y., Zheng, X., Yang, Y., Li, X., et al. (2020b). Effective treatment of severe COVID-19 patients with to- cilizumab. Proc. Natl. Acad. Sci. USA 117, 10970–10975.
  37. Yu, X., Zhang, X., Zhao, B., Wang, J., Zhu, Z., Teng, Z., Shao, J., Shen, J., Gao, Y., Yuan, Z., and Wu, F. (2011). Intensive cytokine induction in pandemic H1N1 influenza virus infection accompanied by robust production of IL-10 and IL-6. PLoS ONE 6, e28680.
  38. Yu, G., Wang, L.G., and He, Q.Y. (2015). ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383.
  39. Zhang, F., Chen, D., Yang, W., Duan, S., and Chen, Y. (2019). Combined effects of XAF1 and TRAIL on the apoptosis of lung adenocarcinoma cells. Exp. Ther. Med. 17, 4663–4669.
  40. Zhao, M., Zhang, H., Liu, K., Gao, G.F., and Liu, W.J. (2017). Human T-cell immunity against the emerging and re-emerging viruses. Sci. China Life Sci. 60, 1307–1316.
  41. Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020a). Clinical course and risk factors for mortality of adult inpa- tients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, 1054–1062.
  42. Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., et al. (2020b). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273.
  43. Zhou, Y., Fu, B., Zheng, X., Wang, D., Zhao, C., Qi, Y., Sun, R., Tian, Z., Xu, X., and Wei, H. (2020c). Pathogenic T cells and inflammatory monocytes incite inflammatory storm in severe COVID-19 patients. Natl. Sci. Rev. 7, 998–1002.
  44. Zou, B., Chim, C.S., Pang, R., Zeng, H., Dai, Y., Zhang, R., Lam, C.S., Tan, V.P., Hung, I.F., Lan, H.Y., and Wong, B.C. (2012). XIAP-associated factor 1 (XAF1), a novel target of p53, enhances p53-mediated apoptosis via post-translational modification. Mol. Carcinog. 51, 422–432.

Acknowledgments

Produced from materials originally authored by Linnan Zhu, Penghui Yang, Yingze Zhao, Zhenkun Zhuang, Zhifeng Wang, Rui Song, Jie Zhang, Chuanyu Liu, Qianqian Gao, Qumiao Xu, Xiaoyu Wei, Hai-Xi Sun, Beiwei Ye, Yanan Wu, Ning Zhang, Guanglin Lei, Lingxiang Yu, Jin Yan, Guanghao Diao, Fanping Meng, Changqing Bai, Panyong Mao, Yeya Yu, Mingyue Wang, Yue Yuan,1 Qiuting Deng, Ziyi Li, Yunting Huang, Guohai Hu, Yang Liu, Xiaoqian Wang, Ziqian Xu, Peipei Liu, Yuhai Bi, Yi Shi, Shaogeng Zhang, Zhihai Chen, Jian Wang, Xun Xu, Guizhen Wu, Fu-Sheng Wang, George F. Gao, Longqi Liu, and William J. Liu.

About Nexcelom Bioscience

Nexcelom Bioscience is a developer and marketer of image cytometry products for cell analysis in life science and biomedical research. Products range from cell viability counters (Cellometer) to high throughput microwell image cytometry workstations (Celigo), used in thousands of research laboratories in academic institutes, and pharmaceutical and biotech companies. The company contributes to the life science industry through innovation and expertise in the science of cell counting.

The product family includes instruments, consumables and reagents. Nexcelom customers engage in a wide variety of research, such as cancer research, immunology, stem cell research, and neuroscience. Nexcelom offers different Cellometer models to count and analyze cell lines and primary cells, through bright field and fluorescence imaging modes. In addition, Celigo is a powerful high image quality, high throughput image cytometry system for adherent and suspension cells in microwell plates.

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