Integrated single-cell analysis revealed immune dynamics during Ad5-nCoV immunization.

Coronavirus disease 2019 (COVID-19), driven by SARS-CoV-2, is a severe infectious disease that has become a global health threat. Vaccines are among the most effective public health tools for combating COVID-19. Immune status is critical for evaluating the safety and response to the vaccine, however, the evolution of the immune response during immunization remains poorly understood. Single-cell RNA sequencing (scRNA-seq) represents a powerful tool for dissecting multicellular behavior and discovering therapeutic antibodies. Herein, by performing scRNA/V(D)J-seq on peripheral blood mononuclear cells from four COVID-19 vaccine trial participants longitudinally during immunization, we revealed enhanced cellular immunity with concerted and cell type-specific IFN responses as well as boosted humoral immunity with SARS-CoV-2-specific antibodies. Based on the CDR3 sequence and germline enrichment, we were able to identify several potential binding antibodies. We synthesized, expressed and tested 21 clones from the identified lineages. Among them, one monoclonal antibody (P3V6-1) exhibited relatively high affinity with the extracellular domain of Spike protein, which might be a promising therapeutic reagent for COVID-19. Overall, our findings provide insights for assessing vaccine through the novel scRNA/V(D)J-seq approach, which might facilitate the development of more potent, durable and safe prophylactic vaccines.

Particulate matter exposure exacerbates susceptibility to SARS-CoV-2 infection in humanized ACE2 mice.

The global outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as of 8 May 2021, has surpassed 150 700 000 infections and 3 279 000 deaths worldwide. Evidence indicates that SARS-CoV-2 RNA can be detected on particulate matter (PM), and COVID-19 cases are correlated with levels of air pollutants. However, the mechanisms of PM involvement in the spread of SARS-CoV-2 remain poorly understood. Here, we found that PM exposure increased the expression level of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) in several epithelial cells and increased the adsorption of the SARS-CoV-2 spike protein. Instillation of PM in a hACE2 mouse model significantly increased the expression of ACE2 and Tmprss2 and viral replication in the lungs. Furthermore, PM exacerbated the pulmonary lesions caused by SARS-CoV-2 infection in the hACE2 mice. In conclusion, our study demonstrated that PM is an epidemiological factor of COVID-19, emphasizing the necessity of wearing anti-PM masks to cope with this global pandemic.

Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China.

OBJECTIVE. Confronting the new coronavirus infection known as coronavirus disease 2019 (COVID-19) is challenging and requires excluding patients with suspected COVID-19 who actually have other diseases. The purpose of this study was to assess the clinical features and CT manifestations of COVID-19 by comparing patients with COVID-19 pneumonia with patients with non-COVID-19 pneumonia who presented at a fever observation department in Shanghai, China. MATERIALS AND METHODS. Patients were retrospectively enrolled in the study from January 19 through February 6, 2020. All patients underwent real-time reverse transcription-polymerase chain reaction (RT-PCR) testing. RESULTS. Eleven patients had RT-PCR test results that were positive for severe acute respiratory syndrome coronavirus 2, whereas 22 patients had negative results. No statistical difference in clinical features was observed (p > 0.05), with the exception of leukocyte and platelet counts (p < 0.05). The mean (± SD) interval between onset of symptoms and admission to the fever observation department was 4.40 ± 2.00 and 5.52 ± 4.00 days for patients with positive and negative RT-PCR test results, respectively. The frequency of opacifications in patients with positive results and patients with negative results, respectively, was as follows: ground-glass opacities (GGOs), 100.0% versus 90.9%; mixed GGO, 63.6% versus 72.7%; and consolidation, 54.5% versus 77.3%. In patients with positive RT-PCR results, GGOs were the most commonly observed opacification (seen in 100.0% of patients) and were predominantly located in the peripheral zone (100.0% of patients), compared with patients with negative results (31.8%) (p = 0.05). The median number of affected lung lobes and segments was higher in patients with positive RT-PCR results than in those with negative RT-PCR results (five vs 3.5 affected lobes and 15 vs nine affected segments; p < 0.05). Although the air bronchogram reticular pattern was more frequently seen in patients with positive results, centrilobular nodules were less frequently seen in patients with positive results. CONCLUSION. At the point during the COVID-19 outbreak when this study was performed, imaging patterns of multifocal, peripheral, pure GGO, mixed GGO, or consolidation with slight predominance in the lower lung and findings of more extensive GGO than consolidation on chest CT scans obtained during the first week of illness were considered findings highly suspicious of COVID-19.

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

High-dimensional single-cell analysis reveals the immune characteristics of COVID-19.

Coronavirus disease 2019 (COVID-19), driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a global pandemic in March 2020. Pathogenic T cells and inflammatory monocytes are regarded as the central drivers of the cytokine storm associated with the severity of COVID-19. In this study, we explored the characteristic peripheral cellular profiles of patients with COVID-19 in both acute and convalescent phases by single-cell mass cytometry (CyTOF). Using a combination of algorithm-guided data analyses, we identified peripheral immune cell subsets in COVID-19 and revealed CD4+ T-cell depletion, T-cell differentiation, plasma cell expansion, and the reduced antigen presentation capacity of innate immunity. Notably, COVID-19 induces a dysregulation in the balance of monocyte populations by the expansion of the monocyte subsets. Collectively, our results represent a high-dimensional, single-cell profile of the peripheral immune response to SARS-CoV-2 infection.

COVID-19 and early-stage lung cancer both featuring ground-glass opacities: a propensity score-matched study.

BACKGROUND: Radiological manifestations of coronavirus disease 2019 (COVID-19) featured ground-glass opacities (GGOs), especially in the early stage, which might create confusion in differential diagnosis with early lung cancer. We aimed to specify the radiological characteristics of COVID-19 and early lung cancer and to unveil the discrepancy between them. METHODS: One hundred and fifty-seven COVID-19 patients and 374 early lung cancer patients from four hospitals in China were retrospectively enrolled. Epidemiological, clinical, radiological, and pathological characteristics were compared between the two groups using propensity score-matched (PSM) analysis. RESULTS: COVID-19 patients had more distinct symptoms, tended to be younger (P<0.0001), male (P<0.0001), and had a higher body mass index (P=0.014). After 1:1 PSM, 121 matched pairs were identified. Regarding radiological characteristics, patients with a single lesion accounted for 17% in COVID-19 and 89% in lung cancer (P<0.0001). Most lesions were peripherally found in both groups. Lesions in COVID-19 involved more lobes (median 3.5 vs. 1; P<0.0001) and segments (median 6 vs. 1; P<0.0001) and tended to have multiple types (67%) with patchy form (54%). Early lung cancer was more likely to have a single type (92%) with oval form (66%). Also, COVID-19 and early lung cancer either had some distinctive features on computed tomography (CT) images. CONCLUSIONS: Both COVID-19 and early lung cancers showed GGOs, with similar but independent features. The imaging characteristics should be fully understood and combined with epidemiological history, pathogen detection, laboratory tests, short-term CT reexamination, and pathological results to aid differential diagnosis.

COVID-19 patients with progressive and non-progressive CT manifestations.

OBJECTIVE: To explore the clinical and radiological characteristics of COVID-19 patients with progressive and non-progressive CT manifestations. METHODS: 160 patients with COVID-19 were retrospectively included from Wenzhou and Wuhan, China. CT features including lesion position, attenuation, form and total scores (0-4) at the segment level were evaluated. Other images signs were also assessed. 65 patients were classified as progressive (group 1) and 95 as non-progressive CT (group 2) groups according to score changes between the initial and second CT. RESULTS: Symptoms onset-initial CT interval time in group 1 [5 (2, 7) days] were significantly shorter than that in group 2 [10 (8, 14) days] (P < 0.001). Group 2 had higher radiological scores, with more lobes and segments affected, and other CT signs (P < 0.05). In group 1, radiological scores, the number of lobes and segments affected as well as lesions in both peripheral and central distribution, mixed ground grass opacity and consolidation density, and patchy form increased in the second CT (P < 0.05). More reticular pattern, subpleural linear opacity and bronchial dilatation were also found (P < 0.05). CONCLUSION: Typically radiological characteristics of progressive CT patients could potentially help to predict changes and increase understanding of the natural history of COVID-19.

A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19.

OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (ß = 3.095, odds ratio (OR) = 22.088), leukocyte count (ß = - 1.495, OR = 0.224), number of segments with peripheral lesions (ß = 1.604, OR = 1.604), and crazy-paving pattern (ß = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0-1 point) - 1 × leukocyte count (0-2 points) + 1 × peripheral lesions (0-1 point) + 2 × crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19.

Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia.

OBJECTIVE: To quantify coronavirus diseases 2019 (COVID-19) pneumonia and to explore whether quantitative computer tomography (CT) could be used to assess severity on admission. MATERIALS AND METHODS: From January 17 to February 9, 2020, 38 hospitalized patients with COVID-19 pneumonia were consecutively enrolled in our hospitals. All clinical data and the chest CT on admission were retrospectively reviewed and analyzed. Firstly, a quantitative method based on multi-scale convolutional neural networks was used to assess the infected lung segments and this was compared with the semi-quantitative method. Secondly, the quantitative method was tested with laboratory results and the pneumonia severity index (PSI) by correlation analyses. Thirdly, both quantitative and semi-quantitative parameters between patients with different PSI were compared. RESULTS: Thirty cases were finally enrolled: 16 (53.33%) of them were male, and the mean age was 48 years old. The interval from onset symptoms to first chest CT scan was 8 days. The proportion of ground glass opacity (GGO), consolidation and the total lesion based on the quantitative method was positively correlated with the semi-quantitative CT score (P < 0.001 for all; rs = 0.88, 0.87, 0.90), CRP (P = 0.0278, 0.0168, 0.0078; rs = 0.40, 0.43, 0.48) and ESR (P = 0.0296, 0.0408, 0.0048; rs = 0.46, 0.44, 0.58), respectively, and was negatively correlated with the lymphocyte count (P = 0.0222, 0.0024, 0.0068; rs = -0.42, -0.53, -0.48). There was a positive correlation trend between the proportion of total infection and the pneumonia severity index (P = 0.0994; rs = 0.30) and a tendency that patients with severe COVID-19 pneumonia had higher percentage of consolidation and total infection (P = 0.0903, 0.0989). CONCLUSIONS: Quantitative CT may have potential in assessing the severity of COVID-19 pneumonia on admission.

Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China.

BACKGROUND: Little is known about COVID-19 outside Hubei. The aim of this paper was to describe the clinical characteristics and imaging manifestations of hospitalized patients with confirmed COVID-19 infection in Wenzhou, Zhejiang, China. METHODS: In this retrospective cohort study, 149 RT-PCR confirmed positive patients were consecutively enrolled from January 17th to February 10th, 2020 in three tertiary hospitals of Wenzhou. Outcomes were followed up until Feb 15th, 2020. FINDINGS: A total of 85 patients had Hubei travel/residence history, while another 49 had contact with people from Hubei and 15 had no traceable exposure history to Hubei. Fever, cough and expectoration were the most common symptoms, 14 patients had decreased oxygen saturation, 33 had leukopenia, 53 had lymphopenia, and 82 had elevated C-reactive protein. On chest computed tomography (CT), lung segments 6 and 10 were mostly involved. A total of 287 segments presented ground glass opacity, 637 presented mixed opacity and 170 presented consolidation. Lesions were more localized in the peripheral lung with a patchy form. No significant difference was found between patients with or without Hubei exposure history. Seventeen patients had normal CT on admission of these, 12 had negative findings even10 days later. INTERPRETATION: Most patients presented with a mild infection in our study. The imaging pattern of multifocal peripheral ground glass or mixed opacity with predominance in the lower lung is highly suspicious of COVID-19 in the first week of disease onset. Nevetheless, some patients can present with a normal chest finding despite testing positive for COVID-19. FUNDING: We did not receive any fundings.