Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19
Open accessPublished version of a preprint
Abstract
Respiratory immune characteristics associated with Coronavirus Disease 2019 (COVID-19) severity are currently unclear. We characterized bronchoalveolar lavage fluid immune cells from patients with varying severity of COVID-19 and from healthy people by using single-cell RNA sequencing. Proinflammatory monocyte-derived macrophages were abundant in the bronchoalveolar lavage fluid from patients with severe COVID-9. Moderate cases were characterized by the presence of highly clonally expanded CD8+ T cells. This atlas of the bronchoalveolar immune microenvironment suggests potential mechanisms underlying pathogenesis and recovery in COVID-19.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Memory B-cells are responsible for producing neutralising antibodies to prevent virus entry, while memory T-cells play a role in controlling SARS-CoV-2 infection by removing infected cells and thereby preventing more severe outcomes of COVID-19.
“Memory B-cells are responsible for producing neutralising antibodies to prevent virus entry, while memory T-cells play a role in controlling SARS-CoV-2 infection by removing infected cells and thereby preventing more severe outcomes of COVID-19 [23].”
From Effectiveness of COVID-19 vaccines against SARS-CoV-2 infection and severe outcomes in adults: a systematic review and meta-analysis of European studies published up to 22 January 2024 (2025) · cites this paper as [23]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe accumulation of memory cells at mucosal sites is critical for the control of viral pneumonia.
“The accumulation of memory cells at mucosal sites is critical for the control of viral pneumonia and their presence has been reported to be associated with less severe COVID-19 symptoms32.”
From Efficacy of an unmodified bivalent mRNA vaccine against SARS-CoV-2 variants in female small animal models (2023) · cites this paper as [32]
The paper’s T-cell discussion reports that memory T-cell responses are necessary for resolving SARS-CoV infection in mice. Its results also find more expanded CD8+ T cells with tissue-resident features in bronchoalveolar samples from moderate than severe COVID-19 cases. These passages support a role for local T cells, but do not establish that accumulation of memory cells at mucosal sites is critical for controlling viral pneumonia generally. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe presence of memory cells at mucosal sites is associated with less severe COVID-19 symptoms.
“The accumulation of memory cells at mucosal sites is critical for the control of viral pneumonia and their presence has been reported to be associated with less severe COVID-19 symptoms32.”
From Efficacy of an unmodified bivalent mRNA vaccine against SARS-CoV-2 variants in female small animal models (2023) · cites this paper as [32]
In the main text (page unavailable), the paper reports more highly expanded CD8+ T cells with tissue-resident features in bronchoalveolar lavage fluid from moderate than from severe/critical COVID-19 cases. This supports an association between a local T-cell phenotype and disease severity, but the paper describes these cells as effectors with tissue-resident features, not as confirmed memory cells, and compares severity categories rather than symptom levels. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFor SARS-CoV-2, increasing evidence indicates that T cells play a major role in the resolution of COVID-19.
“For SARS-CoV-2, increasing evidence indicates that T cells play a major role in the resolution of COVID-19,28, 29”
From Safety and immunogenicity of the SARS-CoV-2 ARCoV mRNA vaccine in Chinese adults: a randomised, double-blind, placebo-controlled, phase 1 trial (2022) · cites this paper as [28]
The Abstract and the main text report more highly expanded CD8+ T cells in moderate COVID-19 than in severe or critical cases, and suggest these findings may inform mechanisms of recovery. They do not establish that T cells play a major role in resolving COVID-19; the explicit statement about T cells being necessary for resolution concerns SARS-CoV infection in mice. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSevere COVID-19 infection is characterized by persistent lung inflammation associated with concomitant recruitment of monocyte-derived macrophages.
“Severe COVID-19 infection is characterized by persistent lung inflammation associated with concomitant recruitment of monocyte-derived macrophages.65”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [65]
The paper reports abundant proinflammatory monocyte-derived macrophages in severe COVID-19 and higher inflammatory cytokine levels in bronchoalveolar lavage fluid (Abstract; main text, page not provided). But persistently high cytokine levels were reported for only three patients with critical illness, and recruitment of monocytic cells was proposed as a possible mechanism rather than directly established as concomitant recruitment. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Two types of protective immune responses occur after vaccination.
“Two types of protective immune responses occur after vaccination.”
From Effectiveness of COVID-19 vaccines against SARS-CoV-2 infection and severe outcomes in adults: a systematic review and meta-analysis of European studies published up to 22 January 2024 (2025) · cites this paper as [23]
The paper’s discussion of T-cell responses concerns patients with COVID-19 and protection during SARS-CoV infection in mice, not responses after vaccination. Searching our copy for vaccination, protective immunity, and two types of response found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMilder disease and recovery are linked to stronger clonal expansion of CD8+ T cells in lungs and blood, but causality is uncertain.
“Milder disease and recovery have been associated with a more robust clonal expansion of CD8+ T cells in both the lungs and blood42,43, although whether this is the cause of milder disease or an effect of recovery is unclear.”
From Immunological mechanisms of vaccine-induced protection against COVID-19 in humans (2021) · cites this paper as [42]
T-cell immunity may also contribute to protection against SARS-CoV-2.
“T-cell immunity may also be involved in protection,4”
From Neutralizing Activity of BNT162b2-Elicited Serum (2021) · cites this paper as [4]
SARS-CoV-2-specific T cell responses are significantly associated with milder COVID-19 disease.
“Studies of acute and convalescent COVID-19 patients have observed that SARS-CoV-2-specific T cell responses are significantly associated with milder disease (Liao et al., 2020; Rydyznski Moderbacher et al., 2020; Sekine et al., 2020; Zhou et al., 2020b), suggesting that T cell responses may be important for control and resolution of a primary SARS-CoV-2 infection.”
From Adaptive immunity to SARS-CoV-2 and COVID-19 (2021) · cites this paper as Liao et al., 2020
Severe end-stage COVID-19 is associated with elevated neutrophils in blood and massive neutrophil numbers in lungs, but not with preferentially elevated T cell abundance in lung tissue.
“particularly observation of elevated frequencies of neutrophils (the most common cell type of the innate immune system) in blood (Kuri-Cervantes et al., 2020), and massive numbers of neutrophils in lungs, associated with severe, end-stage COVID-19 disease (Li et al., 2020b; Liao et al., 2020; Radermecker et al., 2020; Schurink et al., 2020). In contrast, end-stage disease is not generally”
From Adaptive immunity to SARS-CoV-2 and COVID-19 (2021) · cites this paper as Liao et al., 2020
The finding of more lung T cells in moderate COVID-19 cases than in severe cases is consistent with a primary association of T cells with protection, not immunopathology.
“one early study of bronchoalveolar lavage samples from COVID-19 patients found more T cells from lungs of moderate cases compared to severe cases (Liao et al., 2020), consistent with a primary association of T cells with protection, not immunopathology.”
From Adaptive immunity to SARS-CoV-2 and COVID-19 (2021) · cites this paper as Liao et al., 2020
The BALF macrophage chemokine findings support the hypothesis that T cell migration to the lungs does not account for blood lymphopenia in severe COVID-19.
“The same study also concluded that lung macrophages present in BALF of severe COVID-19 patients expressed chemokines more likely to recruit inflammatory monocytes and neutrophils, while the lung macrophages of moderate COVID-19 patients expressed higher levels of T cell-recruiting chemokines, supporting the hypothesis that T cell migration to the lungs does not account for the lymphopenia observed”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [138]
In BALF of severe COVID-19 patients, lung macrophages expressed chemokines more likely to recruit inflammatory monocytes and neutrophils.
“The same study also concluded that lung macrophages present in BALF of severe COVID-19 patients expressed chemokines more likely to recruit inflammatory monocytes and neutrophils, while the lung macrophages of moderate COVID-19 patients expressed higher levels of T cell-recruiting chemokines, supporting the hypothesis that T cell migration to the lungs does not account for the lymphopenia observed”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [138]
In bronchoalveolar lavage fluid, total CD8+ T cells with a tissue resident phenotype were greater in moderate COVID-19 than in severe COVID-19.
“One group analyzing bronchoalveolar lavage fluid (BALF) by single-cell RNA sequencing found that the total number of CD8+ T cells with a tissue resident phenotype were actually greater in patients with moderate disease compared to those with severe disease138.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [138]
In BALF of moderate COVID-19 patients, lung macrophages expressed higher levels of T cell-recruiting chemokines.
“The same study also concluded that lung macrophages present in BALF of severe COVID-19 patients expressed chemokines more likely to recruit inflammatory monocytes and neutrophils, while the lung macrophages of moderate COVID-19 patients expressed higher levels of T cell-recruiting chemokines, supporting the hypothesis that T cell migration to the lungs does not account for the lymphopenia observed”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [138]
T cell migration to the lungs probably does not completely account for the magnitude of blood lymphopenia observed in severe COVID-19.
“However, while lymphocytes do accumulate in the lungs of deceased COVID-19 patients, this migration probably does not completely account for the magnitude of the observed lymphopenia.”
From SARS-CoV-2, COVID-19 and the aging immune system (2021) · cites this paper as [138]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246 · see citing works
Self-citationA pneumonia outbreak associated with a new coronavirus of probable bat origin
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. (2020) · Nature · doi:10.1038/s41586-020-2012-7 · Cited by 21,969 · see citing works
Self-citationThe Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
Wan, Y., Shang, J., Graham, R., Baric, R. S., & Li, F. (2020) · Journal of Virology · doi:10.1128/jvi.00127-20 · Cited by 4,341 · see citing works
Reyfman, P. A., Walter, J. M., Joshi, N., Anekalla, K. R., McQuattie-Pimentel, A. C., Chiu, S., Fernandez, R., Akbarpour, M., Chen, C.-I., Ren, Z., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201712-2410oc · Cited by 1,203 · see citing works
Channappanavar, R., Fett, C., Zhao, J., Meyerholz, D. K., & Perlman, S. (2014) · Journal of Virology · doi:10.1128/jvi.01505-14 · Cited by 411 · see citing works
Zhao, J., Zhao, J., & Perlman, S. (2010) · Journal of Virology · doi:10.1128/jvi.01049-10 · Cited by 340 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Cited by
Sorted by citations
The trinity of COVID-19: immunity, inflammation and intervention
Tay, M. Z., Poh, C. M., Rénia, L., MacAry, P. A., & Ng, L. F. P. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0311-8 · Cited by 4,360 · see citing works
Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection.
Dan, J. M., Mateus, J., Kato, Y., Hastie, K. M., Yu, E. D., Faliti, C. E., Grifoni, A., Ramirez, S. I., Haupt, S., Frazier, A., et al. (2021) · Science · doi:10.1126/science.abf4063 · Cited by 2,656 · see citing works
Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages
Merad, M., & Martin, J. C. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0331-4 · Cited by 2,472 · see citing works
Adaptive immunity to SARS-CoV-2 and COVID-19
Sette, A., & Crotty, S. (2021) · Cell · doi:10.1016/j.cell.2021.01.007 · Cited by 1,860 · see citing works
Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity
Cantuti-Castelvetri, L., Ojha, R., Pedro, L. D., Djannatian, M., Franz, J., Kuivanen, S., van der Meer, F., Kallio, K., Kaya, T., Anastasina, M., et al. (2020) · Science · doi:10.1126/science.abd2985 · Cited by 1,925 · see citing works
Rydyznski Moderbacher, C., Ramirez, S. I., Dan, J. M., Grifoni, A., Hastie, K. M., Weiskopf, D., Belanger, S., Abbott, R. K., Kim, C., Choi, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.038 · Cited by 1,790 · see citing works
“Additionally, while immunological data from lungs of COVID-19 cases are limited, the data on total abundance of CD8 + T cells in COVID-19 lungs (Liao et al., 2020) are consistent with our conclusion that weaker SARS-CoV-2-specific T cell responses are associated with worse disease.”
Mathew, D., Giles, J. R., Baxter, A. E., Oldridge, D. A., Greenplate, A. R., Wu, J. E., Alanio, C., Kuri-Cervantes, L., Pampena, M. B., D’Andrea, K., et al. (2020) · Science · doi:10.1126/science.abc8511 · Cited by 1,509 · see citing works
Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment
Schulte-Schrepping, J., Reusch, N., Paclik, D., Baßler, K., Schlickeiser, S., Zhang, B., Krämer, B., Krammer, T., Brumhard, S., Bonaguro, L., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.001 · Cited by 1,461 · see citing works
Immunology of COVID-19: Current State of the Science
Vabret, N., Britton, G. J., Gruber, C., Hegde, S., Kim, J., Kuksin, M., Levantovsky, R., Malle, L., Moreira, A., Park, M. D., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.002 · Cited by 1,623 · see citing works
Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans
Arunachalam, P. S., Wimmers, F., Mok, C. K. P., Perera, R. A. P. M., Scott, M., Hagan, T., Sigal, N., Feng, Y., Bristow, L., Tak-Yin Tsang, O., et al. (2020) · Science · doi:10.1126/science.abc6261 · Cited by 1,186 · see citing works
Peng, Y., Mentzer, A. J., Liu, G., Yao, X., Yin, Z., Dong, D., Dejnirattisai, W., Rostron, T., Supasa, P., Liu, C., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0782-6 · Cited by 1,179 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
Why does COVID-19 disproportionately affect older people?
Mueller, A. L., McNamara, M. S., & Sinclair, D. A. (2020) · Aging · doi:10.18632/aging.103344 · Cited by 1,128 · see citing works
Pedersen, S. F., & Ho, Y.-C. (2020) · Journal of Clinical Investigation · doi:10.1172/jci137647 · Cited by 1,137 · see citing works
Chua, R. L., Lukassen, S., Trump, S., Hennig, B. P., Wendisch, D., Pott, F., Debnath, O., Thürmann, L., Kurth, F., Völker, M. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0602-4 · Cited by 1,035 · see citing works
Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985 · see citing works
Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19
Silvin, A., Chapuis, N., Dunsmore, G., Goubet, A.-G., Dubuisson, A., Derosa, L., Almire, C., Hénon, C., Kosmider, O., Droin, N., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.002 · Cited by 834 · see citing works
Self-citationImmunological considerations for COVID-19 vaccine strategies
Jeyanathan, M., Afkhami, S., Smaill, F., Miller, M. S., Lichty, B. D., & Xing, Z. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-00434-6 · Cited by 1,015 · see citing works
Winkler, E. S., Bailey, A. L., Kafai, N. M., Nair, S., McCune, B. T., Yu, J., Fox, J. M., Chen, R. E., Earnest, J. T., Keeler, S. P., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0778-2 · Cited by 901 · see citing works
Single-cell multi-omics analysis of the immune response in COVID-19
Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration, Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01329-2 · Cited by 742 · see citing works
COVID-19 and the human innate immune system
Schultze, J. L., & Aschenbrenner, A. C. (2021) · Cell · doi:10.1016/j.cell.2021.02.029 · Cited by 714 · see citing works
T cell responses in patients with COVID-19
Chen, Z., & John Wherry, E. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0402-6 · Cited by 797 · see citing works
Immune response, inflammation, and the clinical spectrum of COVID-19
García, L. F. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01441 · Cited by 739 · see citing works
The hypercoagulable state in COVID-19: Incidence, pathophysiology, and management
Abou-Ismail, M. Y., Diamond, A., Kapoor, S., Arafah, Y., & Nayak, L. (2020) · Thrombosis Research · doi:10.1016/j.thromres.2020.06.029 · Cited by 694 · see citing works
Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease
Mulder, K., Patel, A. A., Kong, W. T., Piot, C., Halitzki, E., Dunsmore, G., Khalilnezhad, S., Irac, S. E., Dubuisson, A., Chevrier, M., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.07.007 · Cited by 678 · see citing works
Neutralizing Activity of BNT162b2-Elicited Serum
Liu, Y., Liu, J., Xia, H., Zhang, X., Fontes-Garfias, C. R., Swanson, K. A., Cai, H., Sarkar, R., Chen, W., Cutler, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmc2102017 · Cited by 537 · see citing works
Self-citationImmunological mechanisms of vaccine-induced protection against COVID-19 in humans
Sadarangani, M., Marchant, A., & Kollmann, T. R. (2021) · Nature Reviews Immunology · doi:10.1038/s41577-021-00578-z · Cited by 560 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Acute SARS-CoV-2 Infection Impairs Dendritic Cell and T Cell Responses
Zhou, R., To, K. K.-W., Wong, Y.-C., Liu, L., Zhou, B., Li, X., Huang, H., Mo, Y., Luk, T.-Y., Lau, T. T.-K., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.07.026 · Cited by 531 · see citing works
Self-citationThe cGAS–STING pathway drives type I IFN immunopathology in COVID-19
Domizio, J. D., Gulen, M. F., Saidoune, F., Thacker, V. V., Yatim, A., Sharma, K., Nass, T., Guenova, E., Schaller, M., Conrad, C., et al. (2022) · Nature · doi:10.1038/s41586-022-04421-w · Cited by 538 · see citing works
Association of COVID-19 inflammation with activation of the C5a–C5aR1 axis
Carvelli, J., Demaria, O., Vély, F., Batista, L., Chouaki Benmansour, N., Fares, J., Carpentier, S., Thibult, M.-L., Morel, A., Remark, R., et al. (2020) · Nature · doi:10.1038/s41586-020-2600-6 · Cited by 526 · see citing works
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528 · see citing works
Immunopathology of Hyperinflammation in COVID-19
Gustine, J. N., & Jones, D. (2021) · The American Journal of Pathology · doi:10.1016/j.ajpath.2020.08.009 · Cited by 516 · see citing works
CoV-AbDab: the coronavirus antibody database
Raybould, M. I. J., Kovaltsuk, A., Marks, C., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa739 · Cited by 383 · see citing works
Mouse model of SARS-CoV-2 reveals inflammatory role of type I interferon signaling
Israelow, B., Song, E., Mao, T., Lu, P., Meir, A., Liu, F., Alfajaro, M. M., Wei, J., Dong, H., Homer, R. J., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20201241 · Cited by 415 · see citing works
Katsura, H., Sontake, V., Tata, A., Kobayashi, Y., Edwards, C. E., Heaton, B. E., Konkimalla, A., Asakura, T., Mikami, Y., Fritch, E. J., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.10.005 · Cited by 416 · see citing works
Liver macrophages in health and disease
Guilliams, M., & Scott, C. L. (2022) · Immunity · doi:10.1016/j.immuni.2022.08.002 · Cited by 395 · see citing works
SARS-CoV-2, COVID-19 and the aging immune system
Bartleson, J. M., Radenkovic, D., Covarrubias, A. J., Furman, D., Winer, D. A., & Verdin, E. (2021) · Nature Aging · doi:10.1038/s43587-021-00114-7 · Cited by 359 · see citing works
Local and systemic responses to SARS-CoV-2 infection in children and adults
Yoshida, M., Worlock, K. B., Huang, N., Lindeboom, R. G. H., Butler, C. R., Kumasaka, N., Dominguez Conde, C., Mamanova, L., Bolt, L., Richardson, L., et al. (2022) · Nature · doi:10.1038/s41586-021-04345-x · Cited by 365 · see citing works
scCODA is a Bayesian model for compositional single-cell data analysis
Büttner, M., Ostner, J., Müller, C. L., Theis, F. J., & Schubert, B. (2021) · Nature Communications · doi:10.1038/s41467-021-27150-6 · Cited by 388 · see citing works
Ravindra, N. G., Alfajaro, M. M., Gasque, V., Huston, N. C., Wan, H., Szigeti-Buck, K., Yasumoto, Y., Greaney, A. M., Habet, V., Chow, R. D., et al. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001143 · Cited by 258 · see citing works
Two distinct immunopathological profiles in autopsy lungs of COVID-19
Nienhold, R., Ciani, Y., Koelzer, V. H., Tzankov, A., Haslbauer, J. D., Menter, T., Schwab, N., Henkel, M., Frank, A., Zsikla, V., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18854-2 · Cited by 246 · see citing works
Vanderbeke, L., Van Mol, P., Van Herck, Y., De Smet, F., Humblet-Baron, S., Martinod, K., Antoranz, A., Arijs, I., Boeckx, B., Bosisio, F. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24360-w · Cited by 247 · see citing works
Temporal and spatial heterogeneity of host response to SARS-CoV-2 pulmonary infection
Desai, N., Neyaz, A., Szabolcs, A., Shih, A. R., Chen, J. H., Thapar, V., Nieman, L. T., Solovyov, A., Mehta, A., Lieb, D. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20139-7 · Cited by 245 · see citing works
Vascular Disease and Thrombosis in SARS-CoV-2-Infected Rhesus Macaques
Aid, M., Busman-Sahay, K., Vidal, S. J., Maliga, Z., Bondoc, S., Starke, C., Terry, M., Jacobson, C. A., Wrijil, L., Ducat, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.005 · Cited by 215 · see citing works
Complement activation induces excessive T cell cytotoxicity in severe COVID-19
Georg, P., Astaburuaga-García, R., Bonaguro, L., Brumhard, S., Michalick, L., Lippert, L. J., Kostevc, T., Gäbel, C., Schneider, M., Streitz, M., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.040 · Cited by 218 · see citing works
Hoang, T. N., Pino, M., Boddapati, A. K., Viox, E. G., Starke, C. E., Upadhyay, A. A., Gumber, S., Nekorchuk, M., Busman-Sahay, K., Strongin, Z., et al. (2021) · Cell · doi:10.1016/j.cell.2020.11.007 · Cited by 212 · see citing works
Leukocyte trafficking to the lungs and beyond: lessons from influenza for COVID-19
Alon, R., Sportiello, M., Kozlovski, S., Kumar, A., Reilly, E. C., Zarbock, A., Garbi, N., & Topham, D. J. (2021) · Nature Reviews Immunology · doi:10.1038/s41577-020-00470-2 · Cited by 197 · see citing works
The pulmonary pathology of COVID-19
Bösmüller, H., Matter, M., Fend, F., & Tzankov, A. (2021) · Virchows Archiv · doi:10.1007/s00428-021-03053-1 · Cited by 180 · see citing works
Tumor microbiome links cellular programs and immunity in pancreatic cancer
Ghaddar, B., Biswas, A., Harris, C., Omary, M. B., Carpizo, D. R., Blaser, M. J., & De, S. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.009 · Cited by 181 · see citing works
COVID-19 in Africa: Dampening the storm?
Mbow, M., Lell, B., Jochems, S. P., Cisse, B., Mboup, S., Dewals, B. G., Jaye, A., Dieye, A., & Yazdanbakhsh, M. (2020) · Science · doi:10.1126/science.abd3902 · Cited by 180 · see citing works
Detection of differentially abundant cell subpopulations in scRNA-seq data
Zhao, J., Jaffe, A., Li, H., Lindenbaum, O., Sefik, E., Jackson, R., Cheng, X., Flavell, R. A., & Kluger, Y. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2100293118 · Cited by 154 · see citing works
Self-citationAshary, N., Bhide, A., Chakraborty, P., Colaco, S., Mishra, A., Chhabria, K., Jolly, M. K., & Modi, D. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00783 · Cited by 121 · see citing works
Song, X., Hu, W., Yu, H., Zhao, L., Zhao, Y., Zhao, X., Xue, H., & Zhao, Y. (2023) · Cytometry Part A · doi:10.1002/cyto.a.24285 · Cited by 117 · see citing works
Magusali, N., Graham, A. C., Piers, T. M., Panichnantakul, P., Yaman, U., Shoai, M., Reynolds, R. H., Botia, J. A., Brookes, K. J., Guetta-Baranes, T., et al. (2021) · Brain · doi:10.1093/brain/awab337 · Cited by 111 · see citing works
“(C) Genetic network plot of an interferon-response-associated module detected in macrophages isolated from the bronchoalveolar fluid of patients with severe/critical and moderate COVID-19, alongside healthy individuals analysed by scRNA-seq.(66) This module shows a significant overlap with the interferon-response modu…”
BackgroundMethodRosa, B. A., Ahmed, M., Singh, D. K., Choreño-Parra, J. A., Cole, J., Jiménez-Álvarez, L. A., Rodríguez-Reyna, T. S., Singh, B., Gonzalez, O., Carrion, R., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01829-4 · Cited by 106 · see citing works
Chambers, E. S., Vukmanovic-Stejic, M., Shih, B. B., Trahair, H., Subramanian, P., Devine, O. P., Glanville, J., Gilroy, D., Rustin, M. H. A., Freeman, T. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-020-00010-6 · Cited by 96 · see citing works
Spatially restricted and ontogenically distinct hepatic macrophages are required for tissue repair
De Ponti, F. F., Bujko, A., Liu, Z., Collins, P. J., Schuermans, S., Maueroder, C., Amstelveen, S., Thoné, T., Martens, L., McKendrick, J. G., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.01.002 · Cited by 94 · see citing works
T cell reactivity to the SARS-CoV-2 Omicron variant is preserved in most but not all individuals
Naranbhai, V., Nathan, A., Kaseke, C., Berrios, C., Khatri, A., Choi, S., Getz, M. A., Tano-Menka, R., Ofoman, O., Gayton, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.03.022 · Cited by 88 · see citing works
In search of a pan-coronavirus vaccine: next-generation vaccine design and immune mechanisms
Cankat, S., Demael, M. U., & Swadling, L. (2023) · Cellular & Molecular Immunology · doi:10.1038/s41423-023-01116-8 · Cited by 84 · see citing works
T cells in COVID-19 — united in diversity
Swadling, L., & Maini, M. K. (2020) · Nature Immunology · doi:10.1038/s41590-020-0798-y · Cited by 77 · see citing works
Chen, G.-L., Li, X.-F., Dai, X.-H., Li, N., Cheng, M.-L., Huang, Z., Shen, J., Ge, Y.-H., Shen, Z.-W., Deng, Y.-Q., et al. (2022) · The Lancet Microbe · doi:10.1016/s2666-5247(21)00280-9 · Cited by 82 · see citing works
Multiscale PHATE identifies multimodal signatures of COVID-19
Kuchroo, M., Huang, J., Wong, P., Grenier, J.-C., Shung, D., Tong, A., Lucas, C., Klein, J., Burkhardt, D. B., Gigante, S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01186-x · Cited by 72 · see citing works
Cell deaths: Involvement in the pathogenesis and intervention therapy of COVID-19
Li, X., Zhang, Z., Wang, Z., Gutiérrez-Castrellón, P., & Shi, H. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01043-6 · Cited by 67 · see citing works
Self-citationLungMAP Portal Ecosystem: Systems-level Exploration of the Lung
Gaddis, N., Fortriede, J., Guo, M., Bardes, E. E., Kouril, M., Tabar, S., Burns, K., Ardini-Poleske, M. E., Loos, S., Schnell, D., et al. (2024) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2022-0165oc · Cited by 62 · see citing works
Mesenchymal stem cell immunomodulation: in pursuit of controlling COVID-19 related cytokine storm
Song, N., Wakimoto, H., Rossignoli, F., Bhere, D., Ciccocioppo, R., Chen, K.-S., Khalsa, J. K., Mastrolia, I., Samarelli, A. V., Dominici, M., et al. (2021) · Stem Cells · doi:10.1002/stem.3354 · Cited by 62 · see citing works
GM-CSF: Master regulator of the T cell-phagocyte interface during inflammation
Ingelfinger, F., De Feo, D., & Becher, B. (2021) · Seminars in Immunology · doi:10.1016/j.smim.2021.101518 · Cited by 61 · see citing works
ASGARD is a single-cell guided pipeline to aid repurposing of drugs
He, B., Xiao, Y., Liang, H., Huang, Q., Du, Y., Li, Y., Garmire, D., Sun, D., & Garmire, L. X. (2023) · Nature Communications · doi:10.1038/s41467-023-36637-3 · Cited by 57 · see citing works
Impact of COVID-19 on Alzheimer’s Disease Risk: Viewpoint for Research Action
Abate, G., Memo, M., & Uberti, D. (2020) · Healthcare · doi:10.3390/healthcare8030286 · Cited by 56 · see citing works
Review of galectin-3 inhibitors in the treatment of nonalcoholic steatohepatitis
Al Attar, A., Antaramian, A., & Noureddin, M. (2021) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2021.1894127 · Cited by 47 · see citing works
Lafon, E., Diem, G., Witting, C., Zaderer, V., Bellmann-Weiler, R. M., Reindl, M., Bauer, A., Griesmacher, A., Fux, V., Hoermann, G., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.684014 · Cited by 46 · see citing works
Pathways to Severe COVID‐19 for People with Obesity
O’Rourke, R. W., & Lumeng, C. N. (2021) · Obesity · doi:10.1002/oby.23099 · Cited by 42 · see citing works
COVID-19 as a Risk Factor for Alzheimer’s Disease
Golzari-Sorkheh, M., Weaver, D. F., & Reed, M. A. (2023) · Journal of Alzheimer's Disease · doi:10.3233/jad-220800 · Cited by 39 · see citing works
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37 · see citing works
Zhou, G., Dael, N., Verweij, S., Balafas, S., Mubarik, S., Oude Rengerink, K., Pasmooij, A. M. G., van Baarle, D., Mol, P. G. M., de Bock, G. H., et al. (2025) · European Respiratory Review · doi:10.1183/16000617.0222-2024 · Cited by 34 · see citing works
“Memory B-cells are responsible for producing neutralising antibodies to prevent virus entry, while memory T-cells play a role in controlling SARS-CoV-2 infection by removing infected cells and thereby preventing more severe outcomes of COVID-19 [23].”
Wang, J., Zhou, X., Yu, P., Yao, J., Guo, P., Xu, Q., Zhao, Y., Wang, G., Li, Q., Zhu, X., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00886-2 · Cited by 28 · see citing works
Corleis, B., Hoffmann, D., Rauch, S., Fricke, C., Roth, N., Gergen, J., Kovacikova, K., Schlottau, K., Halwe, N. J., Ulrich, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36110-1 · Cited by 22 · see citing works
Baricitinib attenuates the proinflammatory phase of COVID-19 driven by lung-infiltrating monocytes
Dobosh, B., Zandi, K., Giraldo, D. M., Goh, S. L., Musall, K., Aldeco, M., LeCher, J., Giacalone, V. D., Yang, J., Eddins, D. J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110945 · Cited by 19 · see citing works
Morrell, E. D., Holton, S. E., Lawrance, M., Orlov, M., Franklin, Z., Mitchem, M. A., DeBerg, H., Gersuk, V. H., Garay, A., Barnes, E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43223-0 · Cited by 20 · see citing works
Beyond dexamethasone, emerging immuno‐thrombotic therapies for COVID‐19
Jensen, M. P., George, M., Gilroy, D., & Sofat, R. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14540 · Cited by 17 · see citing works
Single‐Cell Insights Into Macrophage Subtypes in Pulmonary Infections
Lin, Z., Zheng, Y., Zhong, Y., & Wang, H. (2026) · Advanced Science · doi:10.1002/advs.202510758 · Cited by 13 · see citing works
de Lima Beltrão, F. E., de Almeida Beltrão, D. C., Carvalhal, G., de Lima Beltrão, F. E., de Souza Braga Filho, J., de Brito Oliveira, J., de Jesus, J. D. S., Machado, G. J. R., dos Santos Silva, H., Teixeira, H. M. P., et al. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac075 · Cited by 12 · see citing works
Wang, Z., Guo, K., Gao, P., Pu, Q., Wu, M., Li, C., & Hur, J. (2020) · arXiv · doi:10.48550/arxiv.2005.07856 · Cited by 9 · see citing works
Mansour, H. M. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102195 · Cited by 3 · see citing works
- Authors
- Liao, M., Liu, Y., Yuan, J., Wen, Y., Xu, G., Zhao, J., Cheng, L., Li, J., Wang, X., Wang, F., Liu, L., Amit, I., Zhang, S., & Zhang, Z.
- Affiliations
- Shenzhen Third People’s Hospital (CN)
- Southern University of Science and Technology (CN)
- Weizmann Institute of Science (IL)
- Fudan University (CN)
- Corresponding author
- Liu, Lei
- Amit, Ido
- Zhang, Shuye
- Zhang, Zheng
- ORCID
- Venue
- Nature Medicine
- Published
- 2020-05-12
- Volume
- 26
- Issue
- 6
- Pages
- 842-844
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3024643080
- Cited by
- 2,614
- References
- 10
- Citations by year
- 2026: 99 · 2025: 192 · 2024: 263 · 2023: 326 · 2022: 550 · 2021: 755 · 2020: 427 · 2019: 1
- FWCI
- 68.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Shenzhen Science and Technology Innovation Commission (202002073000002)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- COVID-19 pandemic, bronchoalveolar lavage fluid, inflammatory macrophages, monocyte-derived macrophages, T cell receptor repertoire, disease severity, single-cell transcriptomics, immune recovery
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Bronchoalveolar Lavage Fluid, Humans, Pneumonia, Viral, Coronavirus Infections, CD8-Positive T-Lymphocytes, Pandemics, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
