Comprehensive mapping of immune perturbations associated with severe COVID-19

Leticia Kuri-Cervantes, M Betina Pampena, Wenzhao Meng, Aaron M Rosenfeld, Caroline A G Ittner, Ariel R Weisman, Roseline S Agyekum, Divij Mathew, Amy E Baxter, Laura A Vella, Oliva Kuthuru, Sokratis A Apostolidis, Luanne Bershaw, Jeanette Dougherty, Allison R Greenplate, Ajinkya Pattekar, Justin Kim, Nicholas Han, Sigrid Gouma, Madison E Weirick, Claudia P Arevalo, Marcus J Bolton, Eileen C Goodwin, Elizabeth M Anderson, Scott E Hensley, Tiffanie K Jones, Nilam S Mangalmurti, Eline T Luning Prak, E John Wherry, Nuala J Meyer, Michael R Betts, Leticia Kuri-Cervantes, M Betina Pampena, Wenzhao Meng, Aaron M Rosenfeld, Caroline A G Ittner, Ariel R Weisman, Roseline S Agyekum, Divij Mathew, Amy E Baxter, Laura A Vella, Oliva Kuthuru, Sokratis A Apostolidis, Luanne Bershaw, Jeanette Dougherty, Allison R Greenplate, Ajinkya Pattekar, Justin Kim, Nicholas Han, Sigrid Gouma, Madison E Weirick, Claudia P Arevalo, Marcus J Bolton, Eileen C Goodwin, Elizabeth M Anderson, Scott E Hensley, Tiffanie K Jones, Nilam S Mangalmurti, Eline T Luning Prak, E John Wherry, Nuala J Meyer, Michael R Betts

Abstract

Although critical illness has been associated with SARS-CoV-2-induced hyperinflammation, the immune correlates of severe COVID-19 remain unclear. Here, we comprehensively analyzed peripheral blood immune perturbations in 42 SARS-CoV-2 infected and recovered individuals. We identified extensive induction and activation of multiple immune lineages, including T cell activation, oligoclonal plasmablast expansion, and Fc and trafficking receptor modulation on innate lymphocytes and granulocytes, that distinguished severe COVID-19 cases from healthy donors or SARS-CoV-2-recovered or moderate severity patients. We found the neutrophil to lymphocyte ratio to be a prognostic biomarker of disease severity and organ failure. Our findings demonstrate broad innate and adaptive leukocyte perturbations that distinguish dysregulated host responses in severe SARS-CoV-2 infection and warrant therapeutic investigation.

Copyright © 2020, American Association for the Advancement of Science.

Figures

Fig. 1. Atlas of immune perturbation in…
Fig. 1. Atlas of immune perturbation in severe COVID-19.
Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing immune cells subsets in healthy donors (HD, n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A) Subset frequencies were calculated within the total viable leukocyte CD45+ population. B) Dot plots for each immune cell subset in a representative HD and severe COVID-19+ individual. Gates within each plot indicate cell subset and corresponding frequency within viable CD45+ cells. Example of parent gates are shown; frequencies were calculated using the specific gating strategies shown in Fig. S2. C) Representative examples of the peripheral blood immunologic atlas of a HD and dysregulation within a severe COVID-19+ individual. T-distributed stochastic neighbor embedding (t-SNE) analysis of cell subsets gated on total viable CD45+ cells or D) PBMC (viable CD45+ cells excluding neutrophils and eosinophils) on a HD and a severe COVID-19+ individual. E) NTR calculated using flow cytometry measurements within viable CD45+ cells. F) NLR calculated using CBC counts (Fig. S1I-J). G) Spearman correlations of APACHE III score and NTR or NLR in moderate and severe COVID-19+ donors. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.
Fig. 2. Elevated frequency of plasmablasts, changes…
Fig. 2. Elevated frequency of plasmablasts, changes in B cell subsets and SARS-CoV-2-specific antibody production in COVID-19+ individuals.
Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing plasmablast and B cell subset frequencies from HD (n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A), B) Distribution and representative plots of B cell plasmablasts (defined as CD27+ CD38+ B cells) and non-plasmablast subsets defined by CD21 and CD27 expression in HD (n= 12), and moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Numbers inside the plots indicate the subset proportion of the corresponding parent population (within total B cells for plasmablasts, within non-plasmablasts for CD21/CD27 subsets). C) Frequencies of Ki-67 and CD11c in non-plasmablast B cell subsets defined in a). Analyses of CD11c are shown for 4/7 individuals with moderate COVID-19. Plots from a representative HD and severe COVID-19+ individual shown. Numbers in each plot indicate the frequency within the parent gate. D) Levels of SARS-CoV-2 spike RBD-specific IgM and IgG antibodies in serum or plasma of HD (n=12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Antibody measurements were performed by ELISA using plates coated with the receptor binding domain (RBD) from the SARS-CoV-2 spike protein. Serum and plasma samples were heat-inactivated at 56°C for 1 hour prior to testing in ELISA to inactivate virus. Antibody levels were measured as IgG and IgM arbitrary units (A.U.) based on O.D. values relative to the CR3022 monoclonal antibody (recombinant human anti-SARS-CoV-2, specifically binds to spike protein RBD). E) Spearman correlations of plasma/serum levels of SARS-CoV-2 RBD-specific IgM (top) and IgG (bottom) and days since onset of symptoms on moderate and severe COVID-19+ individuals. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.
Fig. 3
Fig. 3
Abundant antibody heavy chain sequences from severe COVID-19+ individuals have long, diverse CDR3 sequences and higher levels of somatic hypermutation. A) Clone size distribution by sequence copies. For each donor, the fraction of total sequence copies occupied by the top ten clones (yellow), clones 11-100 (grey), 101-1000 (orange) and over 1000 (blue) are shown. Total donor level clone counts are given in parentheses. B) Percentage of sequence copies occupied by the top twenty ranked clones (D20) shown for HD (n=3) and COVID-19+ individuals with moderate (n=3) and severe disease (n=7). C) Spearman correlation between the D20 value and the percentage of plasmablasts within the total B cell population. D) Examples of the overlap of top 100 copy rearrangements that overlap in at least two sequencing libraries in HD (H4), a moderate COVID-19+ (M7) and a severe COVID-19+ individual (S21). Each horizontal string is a rearrangement and each column is an independently amplified sequencing library (see Materials and Methods). Lines are heat mapped by the copy number fraction for a given replicate library. E) Clone size estimation based on sampling (presence/absence in sequence libraries). Shown are the fractions of the top 100 clones that are found in 4 or more sequencing libraries, 3 libraries, 2 libraries and 1 library. All donors had six sequencing libraries, except for M5 (four libraries). F) Fractional identity to the nearest germline VH gene sequence (1.0 = unmutated) in the top 10 copy number clones of each donor. Each symbol is a clone. G) CDR3 length distributions of the top 50 productive rearrangements in each donor. H) CDR3 lengths of the top 10 copy number clones (symbols), stratified by condition. I) CDR3 length distribution of top 50 clones in COVID-19+ donors based on whether they are found in the Adaptive database (public) or not (private). J) Distribution of CDR3 amino acid (AA) edit distances of the top 50 copy clones (productive) per donor. Clone pair counts for each edit distance are averaged across all the donors in each disease category. Differences between groups were calculated using Mann-Whitney rank-sum test. **** p<0.0001, ***p<0.001, *p<0.05.
Fig. 4. Innate immune dysregulation in severe…
Fig. 4. Innate immune dysregulation in severe COVID-19.
Multiparametric flow cytometry analyses of fresh whole blood after red blood cell lysis characterizing the expression of CD16 and HLA-DR on innate immune cells from HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. A) Proportion of CD16+ cells in monocyte, NK cell and immature granulocyte subsets. B), C), E) Median fluorescence intensity (MFI) of CD16 on neutrophil, monocyte, NK cell and immature granulocyte subsets. MFI was calculated within CD16+ cells. Representative dot plots showing CD16 expression in NK cells and immature granulocytes of a HD and a severe COVID-19 individual shown in C) and E). The numbers inside the plots indicate the percentage of CD16+ cells in the corresponding parent population. D), F) t-SNE analyses of CD16 expression (MFI) in viable CD45+ cells or immature granulocytes, respectively, on a representative HD and a severe COVID-19+ individual. G) MFI of HLA-DR on monocytes; dot plots of a representative HD and a severe COVID-19+ individual shown, with monocyte gate outlined. H) t-SNE analyses of monocyte HLA-DR expression (MFI) on a representative HD and a severe COVID-19+ individual. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test. ***p<0.001, **p<0.01, *p<0.05.
Fig. 5. Heterogeneous T cell activation in…
Fig. 5. Heterogeneous T cell activation in severe COVID-19.
Multiparametric flow cytometry analyses on fresh whole blood after red blood cell lysis characterizing immune cells subsets in HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19 individuals was performed to assess the percentage of activated memory T cells. Frequencies of CD38+, HLA-DR+CD38+, PD-1+ and Ki67+ in A) CD4+, and B) CD8+ memory T cells (excluding naïve CCR7+ CD45RA+, detailed gating strategy shown in Fig. S2). C) Spearman correlations between the frequencies of HLA-DR+CD38+ CD4+ or CD8+ memory T cells and plasmablasts in donors with moderate (orange triangles) or severe COVID-19 (dark red circles). D) Frequencies of HLA-DR+CD38+ CD8+ MAIT cells. E) Frequency of cytotoxic memory CD8+ T cells. Multiparametric flow cytometry analyses were performed on freshly isolated PBMC from HD (n=5) and severe (n=16) COVID-19+ individuals to quantify the frequency and phenotype of cytotoxic (as defined by perforin and granzyme B expression). F) CD8+ T cells, and proportion of cytotoxic CD8+ T cells expressing PD-1 and CD38. Plots for a representative HD and a severe COVID-19+ individual are shown. Numbers inside the plots indicate the frequency within the corresponding parent population. Differences between groups were calculated using Kruskal-Wallis test with Dunn’s multiple comparison post-test and Mann-Whitney rank-sum test. **** p<0.0001, ***p<0.001, **p<0.01, *p<0.05.
Fig. 6
Fig. 6
Unbiased analyses of immunophenotyping reveals selective clustering of severe COVID-19+ individuals. A) Heatmap of flow cytometric analyses of HD (n= 12), moderate (n=7), severe (n=27), and recovered (n=7) COVID-19+ individuals. Data are shown in z-score scaled values. Shape and color coding correspond to data shown in Figs. 1-6. H, HD; M, moderate COVID-19; S, severe COVID-19; R, recovered COVID-19. Stars above the symbols indicate donors who died during hospitalization. B) Principal component analysis generated using all flow cytometric data from A).

References

    1. W. Novel-Coronavirus-2019 Reports. (World Health Organization, 2020), vol. 2020.
    1. 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., Du B., Li L. J., Zeng G., Yuen K. Y., Chen R. C., Tang C. L., Wang T., Chen P. Y., Xiang J., Li S. Y., Wang J. L., Liang Z. J., Peng Y. X., Wei L., Liu Y., Hu Y. H., Peng P., Wang J. M., Liu J. Y., Chen Z., Li G., Zheng Z. J., Qiu S. Q., Luo J., Ye C. J., Zhu S. Y., Zhong N. S.; China Medical Treatment Expert Group for Covid-19 , Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 382, 1708–1720 (2020). 10.1056/NEJMoa2002032
    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J., Cao B., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020). 10.1016/S0140-6736(20)30183-5
    1. Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y., Xia J., Yu T., Zhang X., Zhang L., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 395, 507–513 (2020). 10.1016/S0140-6736(20)30211-7
    1. Ruan Q., Yang K., Wang W., Jiang L., Song J., Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 46, 846–848 (2020). 10.1007/s00134-020-05991-x
    1. Yang X., Yu Y., Xu J., Shu H., Xia J., Liu H., Wu Y., Zhang L., Yu Z., Fang M., Yu T., Wang Y., Pan S., Zou X., Yuan S., Shang Y., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir. Med. 8, 475–481 (2020). 10.1016/S2213-2600(20)30079-5
    1. Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., Wang B., Xiang H., Cheng Z., Xiong Y., Zhao Y., Li Y., Wang X., Peng Z., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 323, 1061 (2020). 10.1001/jama.2020.1585
    1. Chen G., Wu D., Guo W., Cao Y., Huang D., Wang H., Wang T., Zhang X., Chen H., Yu H., Zhang X., Zhang M., Wu S., Song J., Chen T., Han M., Li S., Luo X., Zhao J., Ning Q., Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Invest. 130, 2620–2629 (2020). 10.1172/JCI137244
    1. Wölfel R., Corman V. M., Guggemos W., Seilmaier M., Zange S., Müller M. A., Niemeyer D., Jones T. C., Vollmar P., Rothe C., Hoelscher M., Bleicker T., Brünink S., Schneider J., Ehmann R., Zwirglmaier K., Drosten C., Wendtner C., Virological assessment of hospitalized patients with COVID-2019. Nature 581, 465–469 (2020). 10.1038/s41586-020-2196-x
    1. 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., Tong S. Y. C., Lewin S. R., Kedzierska K., Breadth of concomitant immune responses prior to patient recovery: A case report of non-severe COVID-19. Nat. Med. 26, 453–455 (2020). 10.1038/s41591-020-0819-2
    1. Wilk A. J., Rustagi A., Zhao N. Q., Roque J., Martínez-Colón G. J., McKechnie J. L., Ivison G. T., Ranganath T., Vergara R., Hollis T., Simpson L. J., Grant P., Subramanian A., Rogers A. J., Blish C. A., A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. (2020). 10.1038/s41591-020-0944-y
    1. Wang W., Su B., Pang L., Qiao L., Feng Y., Ouyang Y., Guo X., Shi H., Wei F., Su X., Yin J., Jin R., Chen D., High-dimensional immune profiling by mass cytometry revealed immunosuppression and dysfunction of immunity in COVID-19 patients. Cell. Mol. Immunol. 17, 650–652 (2020). 10.1038/s41423-020-0447-2
    1. Zheng H.-Y., Zhang M., Yang C.-X., Zhang N., Wang X.-C., Yang X.-P., Dong X.-Q., Zheng Y.-T., Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell. Mol. Immunol. 17, 541–543 (2020). 10.1038/s41423-020-0401-3
    1. A. J. Wilk, A. Rustagi, N. Q. Zhao, J. Roque, G. J. Martinez-Colon, J. L. McKechnie, G. T. Ivison, T. Ranganath, R. Vergara, T. Hollis, L. J. Simpson, P. Grant, A. Subramanian, A. J. Rogers, C. A. Blish, A single-cell atlas of the peripheral immune response to severe COVID-19. medRxiv, 2020.2004.2017.20069930 (2020).
    1. Zhang J. J., Dong X., Cao Y. Y., Yuan Y. D., Yang Y. B., Yan Y. Q., Akdis C. A., Gao Y. D., Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy all.14238 (2020). 10.1111/all.14238
    1. Levitt J. E., Calfee C. S., Goldstein B. A., Vojnik R., Matthay M. A., Early acute lung injury: Criteria for identifying lung injury prior to the need for positive pressure ventilation*. Crit. Care Med. 41, 1929–1937 (2013). 10.1097/CCM.0b013e31828a3d99
    1. Ziehr D. R., Alladina J., Petri C. R., Maley J. H., Moskowitz A., Medoff B. D., Hibbert K. A., Thompson B. T., Hardin C. C., Respiratory Pathophysiology of Mechanically Ventilated Patients with COVID-19: A Cohort Study. Am. J. Respir. Crit. Care Med. 201, 1560–1564 (2020). 10.1164/rccm.202004-1163LE
    1. Ranieri V. M., Rubenfeld G. D., Thompson B. T., Ferguson N. D., Caldwell E., Fan E., Camporota L., Slutsky A. S.; ARDS Definition Task Force , Acute respiratory distress syndrome: The Berlin Definition. JAMA 307, 2526–2533 (2012).
    1. Knaus W. A., Wagner D. P., Draper E. A., Zimmerman J. E., Bergner M., Bastos P. G., Sirio C. A., Murphy D. J., Lotring T., Damiano A., Harrell F. E. Jr., The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 100, 1619–1636 (1991). 10.1378/chest.100.6.1619
    1. Merad M., Martin J. C., Pathological inflammation in patients with COVID-19: A key role for monocytes and macrophages. Nat. Rev. Immunol. 20, 355–362 (2020).
    1. Henry B. M., COVID-19, ECMO, and lymphopenia: A word of caution. Lancet Respir. Med. 8, e24 (2020). 10.1016/S2213-2600(20)30119-3
    1. Tan L., Wang Q., Zhang D., Ding J., Huang Q., Tang Y. Q., Wang Q., Miao H., Lymphopenia predicts disease severity of COVID-19: A descriptive and predictive study. Signal Transduct. Target. Ther. 5, 33 (2020). 10.1038/s41392-020-0148-4
    1. J. Liu, Y. Liu, P. Xiang, L. Pu, H. Xiong, C. Li, M. Zhang, J. Tan, Y. Xu, R. Song, M. Song, L. Wang, W. Zhang, B. Han, L. Yang, X. Wang, G. Zhou, T. Zhang, B. Li, Y. Wang, Z. Chen, X. Wang, Neutrophil-to-Lymphocyte Ratio Predicts Severe Illness Patients with 2019 Novel Coronavirus in the Early Stage. medRxiv, 2020.2002.2010.20021584 (2020).
    1. Lau D., Lan L. Y., Andrews S. F., Henry C., Rojas K. T., Neu K. E., Huang M., Huang Y., DeKosky B., Palm A. E., Ippolito G. C., Georgiou G., Wilson P. C., Low CD21 expression defines a population of recent germinal center graduates primed for plasma cell differentiation. Sci. Immunol. 2, eaai8153 (2017). 10.1126/sciimmunol.aai8153
    1. Zhao J., Yuan Q., Wang H., Liu W., Liao X., Su Y., Wang X., Yuan J., Li T., Li J., Qian S., Hong C., Wang F., Liu Y., Wang Z., He Q., Li Z., He B., Zhang T., Fu Y., Ge S., Liu L., Zhang J., Xia N., Zhang Z., Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin. Infect. Dis. ciaa344 (2020). 10.1093/cid/ciaa344
    1. Marti G. E., Rawstron A. C., Ghia P., Hillmen P., Houlston R. S., Kay N., Schleinitz T. A., Caporaso N.; International Familial CLL Consortium , Diagnostic criteria for monoclonal B-cell lymphocytosis. Br. J. Haematol. 130, 325–332 (2005). 10.1111/j.1365-2141.2005.05550.x
    1. Tabibian-Keissar H., Hazanov L., Schiby G., Rosenthal N., Rakovsky A., Michaeli M., Shahaf G. L., Pickman Y., Rosenblatt K., Melamed D., Dunn-Walters D., Mehr R., Barshack I., Aging affects B-cell antigen receptor repertoire diversity in primary and secondary lymphoid tissues. Eur. J. Immunol. 46, 480–492 (2016). 10.1002/eji.201545586
    1. DeWitt W. S., Lindau P., Snyder T. M., Sherwood A. M., Vignali M., Carlson C. S., Greenberg P. D., Duerkopp N., Emerson R. O., Robins H. S., A Public Database of Memory and Naive B-Cell Receptor Sequences. PLOS ONE 11, e0160853 (2016). 10.1371/journal.pone.0160853
    1. Kurioka A., Cosgrove C., Simoni Y., van Wilgenburg B., Geremia A., Björkander S., Sverremark-Ekström E., Thurnheer C., Günthard H. F., Khanna N., Walker L. J., Arancibia-Cárcamo C. V., Newell E. W., Willberg C. B., Klenerman P.; Swiss HIV Cohort Study; Oxford IBD Cohort Investigators , CD161 Defines a Functionally Distinct Subset of Pro-Inflammatory Natural Killer Cells. Front. Immunol. 9, 486 (2018). 10.3389/fimmu.2018.00486
    1. Poggi A., Rubartelli A., Moretta L., Zocchi M. R., Expression and function of NKRP1A molecule on human monocytes and dendritic cells. Eur. J. Immunol. 27, 2965–2970 (1997). 10.1002/eji.1830271132
    1. Goodier M. R., Lusa C., Sherratt S., Rodriguez-Galan A., Behrens R., Riley E. M., Sustained Immune Complex-Mediated Reduction in CD16 Expression after Vaccination Regulates NK Cell Function. Front. Immunol. 7, 384 (2016). 10.3389/fimmu.2016.00384
    1. Giamarellos-Bourboulis E. J., Netea M. G., Rovina N., Akinosoglou K., Antoniadou A., Antonakos N., Damoraki G., Gkavogianni T., Adami M. E., Katsaounou P., Ntaganou M., Kyriakopoulou M., Dimopoulos G., Koutsodimitropoulos I., Velissaris D., Koufargyris P., Karageorgos A., Katrini K., Lekakis V., Lupse M., Kotsaki A., Renieris G., Theodoulou D., Panou V., Koukaki E., Koulouris N., Gogos C., Koutsoukou A., Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe 27, 992–1000.e3 (2020). 10.1016/j.chom.2020.04.009
    1. McElroy A. K., Akondy R. S., Davis C. W., Ellebedy A. H., Mehta A. K., Kraft C. S., Lyon G. M., Ribner B. S., Varkey J., Sidney J., Sette A., Campbell S., Ströher U., Damon I., Nichol S. T., Spiropoulou C. F., Ahmed R., Human Ebola virus infection results in substantial immune activation. Proc. Natl. Acad. Sci. U.S.A. 112, 4719–4724 (2015). 10.1073/pnas.1502619112
    1. Wang Z., Zhu L., Nguyen T. H. O., Wan Y., Sant S., Quiñones-Parra S. M., Crawford J. C., Eltahla A. A., Rizzetto S., Bull R. A., Qiu C., Koutsakos M., Clemens E. B., Loh L., Chen T., Liu L., Cao P., Ren Y., Kedzierski L., Kotsimbos T., McCaw J. M., La Gruta N. L., Turner S. J., Cheng A. C., Luciani F., Zhang X., Doherty P. C., Thomas P. G., Xu J., Kedzierska K., Clonally diverse CD38+HLA-DR+CD8+ T cells persist during fatal H7N9 disease. Nat. Commun. 9, 824 (2018). 10.1038/s41467-018-03243-7
    1. D. Mathew, J. R. Giles, A. E. Baxter, A. R. Greenplate, J. E. Wu, C. Alanio, D. A. Oldridge, L. Kuri-Cervantes, M. B. Pampena, K. D’Andrea, S. Manne, Z. Chen, Y. J. Huang, J. P. Reilly, A. R. Weisman, C. A. G. Ittner, O. Kuthuru, J. Dougherty, K. Nzingha, N. Han, J. Kim, A. Pattekar, E. C. Goodwin, E. M. Anderson, M. E. Weirick, S. Gouma, C. P. Arevalo, M. J. Bolton, F. Chen, S. F. Lacey, S. E. Hensley, S. Apostolidis, A. C. Huang, L. A. Vella, M. R. Betts, N. J. Meyer, E. J. Wherry, Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions. bioRxiv, 2020.2005.2020.106401 (2020).
    1. Y. Jouan, A. Guillon, L. Gonzalez, Y. Perez, S. Ehrmann, M. Ferreira, T. Daix, R. Jeannet, B. Francois, P.-F. Dequin, M. Si-Tahar, T. Baranek, C. Paget, Functional alteration of innate T cells in critically ill Covid-19 patients. medRxiv, 2020.2005.2003.20089300 (2020).
    1. Seebach J. D., Morant R., Rüegg R., Seifert B., Fehr J., The diagnostic value of the neutrophil left shift in predicting inflammatory and infectious disease. Am. J. Clin. Pathol. 107, 582–591 (1997). 10.1093/ajcp/107.5.582
    1. Zuo Y., Yalavarthi S., Shi H., Gockman K., Zuo M., Madison J. A., Blair C., Weber A., Barnes B. J., Egeblad M., Woods R. J., Kanthi Y., Knight J. S., Neutrophil extracellular traps in COVID-19. JCI Insight 5, 138999 (2020). 10.1172/jci.insight.138999
    1. Victor A. R., Weigel C., Scoville S. D., Chan W. K., Chatman K., Nemer M. M., Mao C., Young K. A., Zhang J., Yu J., Freud A. G., Oakes C. C., Caligiuri M. A., Epigenetic and Posttranscriptional Regulation of CD16 Expression during Human NK Cell Development. J. Immunol. 200, 565–572 (2018). 10.4049/jimmunol.1701128
    1. Srpan K., Ambrose A., Karampatzakis A., Saeed M., Cartwright A. N. R., Guldevall K., De Matos G. D. S. C., Önfelt B., Davis D. M., Shedding of CD16 disassembles the NK cell immune synapse and boosts serial engagement of target cells. J. Cell Biol. 217, 3267–3283 (2018). 10.1083/jcb.201712085
    1. Mare T. A., Treacher D. F., Shankar-Hari M., Beale R., Lewis S. M., Chambers D. J., Brown K. A., The diagnostic and prognostic significance of monitoring blood levels of immature neutrophils in patients with systemic inflammation. Crit. Care 19, 57 (2015). 10.1186/s13054-015-0778-z
    1. Nierhaus A., Klatte S., Linssen J., Eismann N. M., Wichmann D., Hedke J., Braune S. A., Kluge S., Revisiting the white blood cell count: Immature granulocytes count as a diagnostic marker to discriminate between SIRS and sepsis—a prospective, observational study. BMC Immunol. 14, 8 (2013). 10.1186/1471-2172-14-8
    1. Balakrishnan T., Bela-Ong D. B., Toh Y. X., Flamand M., Devi S., Koh M. B., Hibberd M. L., Ooi E. E., Low J. G., Leo Y. S., Gu F., Fink K., Dengue virus activates polyreactive, natural IgG B cells after primary and secondary infection. PLOS ONE 6, e29430 (2011). 10.1371/journal.pone.0029430
    1. Wrammert J., Onlamoon N., Akondy R. S., Perng G. C., Polsrila K., Chandele A., Kwissa M., Pulendran B., Wilson P. C., Wittawatmongkol O., Yoksan S., Angkasekwinai N., Pattanapanyasat K., Chokephaibulkit K., Ahmed R., Rapid and massive virus-specific plasmablast responses during acute dengue virus infection in humans. J. Virol. 86, 2911–2918 (2012). 10.1128/JVI.06075-11
    1. M. Woodruff, R. Ramonell, K. Cashman, D. Nguyen, A. Ley, S. Kyu, A. Saini, N. Haddad, W. Chen, J. C. Howell, T. Ozturk, S. Lee, J. Estrada, A. Morrison-Porter, A. Derrico, F. Anam, H. Wu, S. Le, S. Jenks, W. Hu, F. E.-H. Lee, I. Sanz, Critically ill SARS-CoV-2 patients display lupus-like hallmarks of extrafollicular B cell activation. medRxiv, 2020.2004.2029.20083717 (2020).
    1. Martin V., Bryan Wu Y. C., Kipling D., Dunn-Walters D., Ageing of the B-cell repertoire. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140237 (2015). 10.1098/rstb.2014.0237
    1. Muggen A. F., de Jong M., Wolvers-Tettero I. L. M., Kallemeijn M. J., Teodósio C., Darzentas N., Stadhouders R., IJspeert H., van der Burg M., van IJcken W. F. J., Verhaar J. A. N., Abdulahad W. H., Brouwer E., Boots A. M. H., Hendriks R. W., van Dongen J. J. M., Langerak A. W., The presence of CLL-associated stereotypic B cell receptors in the normal BCR repertoire from healthy individuals increases with age. Immun. Ageing 16, 22 (2019). 10.1186/s12979-019-0163-x
    1. Rodriguez-Zhurbenko N., Quach T. D., Hopkins T. J., Rothstein T. L., Hernandez A. M., Human B-1 Cells and B-1 Cell Antibodies Change With Advancing Age. Front. Immunol. 10, 483 (2019). 10.3389/fimmu.2019.00483
    1. W. Wen, W. Su, H. Tang, W. Le, X. Zhang, Y. Zheng, X. Liu, L. Xie, J. Li, J. Ye, X. Cui, Y. Miao, D. Wang, J. Dong, C.-L. Xiao, W. Chen, H. Wang, Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing. medRxiv, 2020.2003.2023.20039362 (2020).
    1. Di Niro R., Lee S. J., Vander Heiden J. A., Elsner R. A., Trivedi N., Bannock J. M., Gupta N. T., Kleinstein S. H., Vigneault F., Gilbert T. J., Meffre E., McSorley S. J., Shlomchik M. J., Salmonella Infection Drives Promiscuous B Cell Activation Followed by Extrafollicular Affinity Maturation. Immunity 43, 120–131 (2015). 10.1016/j.immuni.2015.06.013
    1. Jenks S. A., Cashman K. S., Zumaquero E., Marigorta U. M., Patel A. V., Wang X., Tomar D., Woodruff M. C., Simon Z., Bugrovsky R., Blalock E. L., Scharer C. D., Tipton C. M., Wei C., Lim S. S., Petri M., Niewold T. B., Anolik J. H., Gibson G., Eun-Hyung Lee F., Boss J. M., Lund F. E., Sanz I., Distinct Effector B Cells Induced by Unregulated Toll-like Receptor 7 Contribute to Pathogenic Responses in Systemic Lupus Erythematosus. Immunity 52, 203 (2020). 10.1016/j.immuni.2019.12.005
    1. Wardemann H., Yurasov S., Schaefer A., Young J. W., Meffre E., Nussenzweig M. C., Predominant autoantibody production by early human B cell precursors. Science 301, 1374–1377 (2003). 10.1126/science.1086907
    1. Cardoso R. M., Zwick M. B., Stanfield R. L., Kunert R., Binley J. M., Katinger H., Burton D. R., Wilson I. A., Broadly neutralizing anti-HIV antibody 4E10 recognizes a helical conformation of a highly conserved fusion-associated motif in gp41. Immunity 22, 163–173 (2005). 10.1016/j.immuni.2004.12.011
    1. Fenalti G., Hampe C. S., O’connor K., Banga J. P., Mackay I. R., Rowley M. J., El-Kabbani O., Molecular characterization of a disease associated conformational epitope on GAD65 recognised by a human monoclonal antibody b96.11. Mol. Immunol. 44, 1178–1189 (2007). 10.1016/j.molimm.2006.06.025
    1. Haynes B. F., Fleming J., St Clair E. W., Katinger H., Stiegler G., Kunert R., Robinson J., Scearce R. M., Plonk K., Staats H. F., Ortel T. L., Liao H. X., Alam S. M., Cardiolipin polyspecific autoreactivity in two broadly neutralizing HIV-1 antibodies. Science 308, 1906–1908 (2005). 10.1126/science.1111781
    1. Ofek G., Tang M., Sambor A., Katinger H., Mascola J. R., Wyatt R., Kwong P. D., Structure and mechanistic analysis of the anti-human immunodeficiency virus type 1 antibody 2F5 in complex with its gp41 epitope. J. Virol. 78, 10724–10737 (2004). 10.1128/JVI.78.19.10724-10737.2004
    1. Haveri A., Smura T., Kuivanen S., Österlund P., Hepojoki J., Ikonen N., Pitkäpaasi M., Blomqvist S., Rönkkö E., Kantele A., Strandin T., Kallio-Kokko H., Mannonen L., Lappalainen M., Broas M., Jiang M., Siira L., Salminen M., Puumalainen T., Sane J., Melin M., Vapalahti O., Savolainen-Kopra C., Serological and molecular findings during SARS-CoV-2 infection: The first case study in Finland, January to February 2020. Euro Surveill. 25, 2000266 (2020). 10.2807/1560-7917.ES.2020.25.11.2000266
    1. Ni L., Ye F., Cheng M. L., Feng Y., Deng Y. Q., Zhao H., Wei P., Ge J., Gou M., Li X., Sun L., Cao T., Wang P., Zhou C., Zhang R., Liang P., Guo H., Wang X., Qin C. F., Chen F., Dong C., Detection of SARS-CoV-2-Specific Humoral and Cellular Immunity in COVID-19 Convalescent Individuals. Immunity 52, 971–977.e3 (2020). 10.1016/j.immuni.2020.04.023
    1. Okba N. M. A., Müller M. A., Li W., Wang C., GeurtsvanKessel C. H., Corman V. M., Lamers M. M., Sikkema R. S., de Bruin E., Chandler F. D., Yazdanpanah Y., Le Hingrat Q., Descamps D., Houhou-Fidouh N., Reusken C. B. E. M., Bosch B.-J., Drosten C., Koopmans M. P. G., Haagmans B. L., Severe Acute Respiratory Syndrome Coronavirus 2-Specific Antibody Responses in Coronavirus Disease Patients. Emerg. Infect. Dis. 26, 1478–1488 (2020). 10.3201/eid2607.200841
    1. Seydoux E., Homad L. J., MacCamy A. J., Parks K. R., Hurlburt N. K., Jennewein M. F., Akins N. R., Stuart A. B., Wan Y.-H., Feng J., Whaley R. E., Singh S., Boeckh M., Cohen K. W., McElrath M. J., Englund J. A., Chu H. Y., Pancera M., McGuire A. T., Stamatatos L., Analysis of a SARS-CoV-2-infected individual reveals development of potent neutralizing antibodies to distinct epitopes with limited somatic mutation. Immunity (2020). 10.1016/j.immuni.2020.06.001
    1. Amanat F., Stadlbauer D., Strohmeier S., Nguyen T. H. O., Chromikova V., McMahon M., Jiang K., Arunkumar G. A., Jurczyszak D., Polanco J., Bermudez-Gonzalez M., Kleiner G., Aydillo T., Miorin L., Fierer D. S., Lugo L. A., Kojic E. M., Stoever J., Liu S. T. H., Cunningham-Rundles C., Felgner P. L., Moran T., García-Sastre A., Caplivski D., Cheng A. C., Kedzierska K., Vapalahti O., Hepojoki J. M., Simon V., Krammer F., A serological assay to detect SARS-CoV-2 seroconversion in humans. Nat. Med. (2020). 10.1038/s41591-020-0913-5
    1. Doi H., Tanoue S., Kaplan D. E., Peripheral CD27-CD21- B-cells represent an exhausted lymphocyte population in hepatitis C cirrhosis. Clin. Immunol. 150, 184–191 (2014). 10.1016/j.clim.2013.12.001
    1. Moir S., Malaspina A., Ogwaro K. M., Donoghue E. T., Hallahan C. W., Ehler L. A., Liu S., Adelsberger J., Lapointe R., Hwu P., Baseler M., Orenstein J. M., Chun T. W., Mican J. A., Fauci A. S., HIV-1 induces phenotypic and functional perturbations of B cells in chronically infected individuals. Proc. Natl. Acad. Sci. U.S.A. 98, 10362–10367 (2001). 10.1073/pnas.181347898
    1. Sciaranghella G., Tong N., Mahan A. E., Suscovich T. J., Alter G., Decoupling activation and exhaustion of B cells in spontaneous controllers of HIV infection. AIDS 27, 175–180 (2013). 10.1097/QAD.0b013e32835bd1f0
    1. Fearon D. T., The CD19-CR2-TAPA-1 complex, CD45 and signaling by the antigen receptor of B lymphocytes. Curr. Opin. Immunol. 5, 341–348 (1993). 10.1016/0952-7915(93)90051-S
    1. Fearon D. T., Carter R. H., The CD19/CR2/TAPA-1 complex of B lymphocytes: Linking natural to acquired immunity. Annu. Rev. Immunol. 13, 127–149 (1995). 10.1146/annurev.iy.13.040195.001015
    1. Charles E. D., Brunetti C., Marukian S., Ritola K. D., Talal A. H., Marks K., Jacobson I. M., Rice C. M., Dustin L. B., Clonal B cells in patients with hepatitis C virus-associated mixed cryoglobulinemia contain an expanded anergic CD21low B-cell subset. Blood 117, 5425–5437 (2011). 10.1182/blood-2010-10-312942
    1. Illingworth J., Butler N. S., Roetynck S., Mwacharo J., Pierce S. K., Bejon P., Crompton P. D., Marsh K., Ndungu F. M., Chronic exposure to Plasmodium falciparum is associated with phenotypic evidence of B and T cell exhaustion. J. Immunol. 190, 1038–1047 (2013). 10.4049/jimmunol.1202438
    1. Thorarinsdottir K., Camponeschi A., Gjertsson I., Mårtensson I. L., CD21 -/low B cells: A Snapshot of a Unique B Cell Subset in Health and Disease. Scand. J. Immunol. 82, 254–261 (2015). 10.1111/sji.12339
    1. Visentini M., Cagliuso M., Conti V., Carbonari M., Casato M., Fiorilli M., The V(H)1-69-expressing marginal zone B cells expanded in HCV-associated mixed cryoglobulinemia display proliferative anergy irrespective of CD21(low) phenotype. Blood 118, 3440–3441, author reply 3442 (2011). 10.1182/blood-2011-05-353821
    1. Weiss G. E., Crompton P. D., Li S., Walsh L. A., Moir S., Traore B., Kayentao K., Ongoiba A., Doumbo O. K., Pierce S. K., Atypical memory B cells are greatly expanded in individuals living in a malaria-endemic area. J. Immunol. 183, 2176–2182 (2009). 10.4049/jimmunol.0901297
    1. Gies V., Schickel J. N., Jung S., Joublin A., Glauzy S., Knapp A. M., Soley A., Poindron V., Guffroy A., Choi J. Y., Gottenberg J. E., Anolik J. H., Martin T., Soulas-Sprauel P., Meffre E., Korganow A. S., Impaired TLR9 responses in B cells from patients with systemic lupus erythematosus. JCI Insight 3, e96795 (2018). 10.1172/jci.insight.96795
    1. Kahan S. M., Wherry E. J., Zajac A. J., T cell exhaustion during persistent viral infections. Virology 479-480, 180–193 (2015). 10.1016/j.virol.2014.12.033
    1. Fenwick C., Joo V., Jacquier P., Noto A., Banga R., Perreau M., Pantaleo G., T-cell exhaustion in HIV infection. Immunol. Rev. 292, 149–163 (2019). 10.1111/imr.12823
    1. Dias C. N. S., Gois B. M., Lima V. S., Guerra-Gomes I. C., Araújo J. M. G., Gomes J. A. S., Araújo D. A. M., Medeiros I. A., Azevedo F. L. A. A., Veras R. C., Janebro D. I., Amaral I. P. G. D., Keesen T. S. L., Human CD8 T-cell activation in acute and chronic chikungunya infection. Immunology 155, 499–504 (2018). 10.1111/imm.12992
    1. Ndhlovu Z. M., Kamya P., Mewalal N., Kløverpris H. N., Nkosi T., Pretorius K., Laher F., Ogunshola F., Chopera D., Shekhar K., Ghebremichael M., Ismail N., Moodley A., Malik A., Leslie A., Goulder P. J., Buus S., Chakraborty A., Dong K., Ndung’u T., Walker B. D., Magnitude and Kinetics of CD8+ T Cell Activation during Hyperacute HIV Infection Impact Viral Set Point. Immunity 43, 591–604 (2015). 10.1016/j.immuni.2015.08.012
    1. Demers K. R., Makedonas G., Buggert M., Eller M. A., Ratcliffe S. J., Goonetilleke N., Li C. K., Eller L. A., Rono K., Maganga L., Nitayaphan S., Kibuuka H., Routy J. P., Slifka M. K., Haynes B. F., McMichael A. J., Bernard N. F., Robb M. L., Betts M. R., Temporal Dynamics of CD8+ T Cell Effector Responses during Primary HIV Infection. PLOS Pathog. 12, e1005805 (2016). 10.1371/journal.ppat.1005805
    1. Agrati C., Castilletti C., Casetti R., Sacchi A., Falasca L., Turchi F., Tumino N., Bordoni V., Cimini E., Viola D., Lalle E., Bordi L., Lanini S., Martini F., Nicastri E., Petrosillo N., Puro V., Piacentini M., Di Caro A., Kobinger G. P., Zumla A., Ippolito G., Capobianchi M. R., Longitudinal characterization of dysfunctional T cell-activation during human acute Ebola infection. Cell Death Dis. 7, e2164 (2016). 10.1038/cddis.2016.55
    1. Sandalova E., Laccabue D., Boni C., Tan A. T., Fink K., Ooi E. E., Chua R., Shafaeddin Schreve B., Ferrari C., Bertoletti A., Contribution of herpesvirus specific CD8 T cells to anti-viral T cell response in humans. PLOS Pathog. 6, e1001051 (2010). 10.1371/journal.ppat.1001051
    1. Wang X., Xu W., Hu G., Xia S., Sun Z., Liu Z., Xie Y., Zhang R., Jiang S., Lu L., RETRACTED ARTICLE: SARS-CoV-2 infects T lymphocytes through its spike protein-mediated membrane fusion. Cell. Mol. Immunol. (2020).
    1. Sarzi-Puttini P., Giorgi V., Sirotti S., Marotto D., Ardizzone S., Rizzardini G., Antinori S., Galli M., COVID-19, cytokines and immunosuppression: What can we learn from severe acute respiratory syndrome? Clin. Exp. Rheumatol. 38, 337–342 (2020).
    1. Tan Y. X., Tan T. H., Lee M. J., Tham P. Y., Gunalan V., Druce J., Birch C., Catton M., Fu N. Y., Yu V. C., Tan Y. J., Induction of apoptosis by the severe acute respiratory syndrome coronavirus 7a protein is dependent on its interaction with the Bcl-XL protein. J. Virol. 81, 6346–6355 (2007). 10.1128/JVI.00090-07
    1. Yue Y., Nabar N. R., Shi C. S., Kamenyeva O., Xiao X., Hwang I. Y., Wang M., Kehrl J. H., SARS-Coronavirus Open Reading Frame-3a drives multimodal necrotic cell death. Cell Death Dis. 9, 904 (2018). 10.1038/s41419-018-0917-y
    1. Yao X. H., Li T. Y., He Z. C., Ping Y. F., Liu H. W., Yu S. C., Mou H. M., Wang L. H., Zhang H. R., Fu W. J., Luo T., Liu F., Chen C., Xiao H. L., Guo H. T., Lin S., Xiang D. F., Shi Y., Li Q. R., Huang X., Cui Y., Li X. Z., Tang W., Pan P. F., Huang X. Q., Ding Y. Q., Bian X. W., Zhonghua Bing Li Xue Za Zhi 49, E009 (2020) [A pathological report of three COVID-19 cases by minimally invasive autopsies].
    1. Barton L. M., Duval E. J., Stroberg E., Ghosh S., Mukhopadhyay S., COVID-19 Autopsies, Oklahoma, USA. Am. J. Clin. Pathol. 153, 725–733 (2020). 10.1093/ajcp/aqaa062
    1. Walker L. J., Kang Y. H., Smith M. O., Tharmalingham H., Ramamurthy N., Fleming V. M., Sahgal N., Leslie A., Oo Y., Geremia A., Scriba T. J., Hanekom W. A., Lauer G. M., Lantz O., Adams D. H., Powrie F., Barnes E., Klenerman P., Human MAIT and CD8αα cells develop from a pool of type-17 precommitted CD8+ T cells. Blood 119, 422–433 (2012). 10.1182/blood-2011-05-353789
    1. Billerbeck E., Kang Y.-H., Walker L., Lockstone H., Grafmueller S., Fleming V., Flint J., Willberg C. B., Bengsch B., Seigel B., Ramamurthy N., Zitzmann N., Barnes E. J., Thevanayagam J., Bhagwanani A., Leslie A., Oo Y. H., Kollnberger S., Bowness P., Drognitz O., Adams D. H., Blum H. E., Thimme R., Klenerman P., Analysis of CD161 expression on human CD8+ T cells defines a distinct functional subset with tissue-homing properties. Proc. Natl. Acad. Sci. U.S.A. 107, 3006–3011 (2010). 10.1073/pnas.0914839107
    1. van Wilgenburg B., Scherwitzl I., Hutchinson E. C., Leng T., Kurioka A., Kulicke C., de Lara C., Cole S., Vasanawathana S., Limpitikul W., Malasit P., Young D., Denney L., Moore M. D., Fabris P., Giordani M. T., Oo Y. H., Laidlaw S. M., Dustin L. B., Ho L. P., Thompson F. M., Ramamurthy N., Mongkolsapaya J., Willberg C. B., Screaton G. R., Klenerman P.; STOP-HCV consortium , MAIT cells are activated during human viral infections. Nat. Commun. 7, 11653 (2016). 10.1038/ncomms11653
    1. Davenport E. E., Burnham K. L., Radhakrishnan J., Humburg P., Hutton P., Mills T. C., Rautanen A., Gordon A. C., Garrard C., Hill A. V., Hinds C. J., Knight J. C., Genomic landscape of the individual host response and outcomes in sepsis: A prospective cohort study. Lancet Respir. Med. 4, 259–271 (2016). 10.1016/S2213-2600(16)00046-1
    1. Faivre V., Lukaszewicz A. C., Payen D., Downregulation of Blood Monocyte HLA-DR in ICU Patients Is Also Present in Bone Marrow Cells. PLOS ONE 11, e0164489 (2016). 10.1371/journal.pone.0164489
    1. Monneret G., Lepape A., Voirin N., Bohé J., Venet F., Debard A.-L., Thizy H., Bienvenu J., Gueyffier F., Vanhems P., Persisting low monocyte human leukocyte antigen-DR expression predicts mortality in septic shock. Intensive Care Med. 32, 1175–1183 (2006). 10.1007/s00134-006-0204-8
    1. A. R. G. Flannery, S., Dhudasia, M.B, SARS-CoV-2 Seroprevalence Among Parturient Women. Research Square, DOI: (2020).10.21203/-27402/v21201
    1. Stadlbauer D., Amanat F., Chromikova V., Jiang K., Strohmeier S., Arunkumar G. A., Tan J., Bhavsar D., Capuano C., Kirkpatrick E., Meade P., Brito R. N., Teo C., McMahon M., Simon V., Krammer F., SARS-CoV-2 Seroconversion in Humans: A Detailed Protocol for a Serological Assay, Antigen Production, and Test Setup. Curr. Protoc. Microbiol. 57, e100 (2020). 10.1002/cpmc.100
    1. Meng W., Zhang B., Schwartz G. W., Rosenfeld A. M., Ren D., Thome J. J. C., Carpenter D. J., Matsuoka N., Lerner H., Friedman A. L., Granot T., Farber D. L., Shlomchik M. J., Hershberg U., Luning Prak E. T., An atlas of B-cell clonal distribution in the human body. Nat. Biotechnol. 35, 879–884 (2017). 10.1038/nbt.3942
    1. Vander Heiden J. A., Yaari G., Uduman M., Stern J. N., O’Connor K. C., Hafler D. A., Vigneault F., Kleinstein S. H., pRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–1932 (2014). 10.1093/bioinformatics/btu138
    1. Rosenfeld A. M., Meng W., Chen D. Y., Zhang B., Granot T., Farber D. L., Hershberg U., Luning Prak E. T., Computational Evaluation of B-Cell Clone Sizes in Bulk Populations. Front. Immunol. 9, 1472 (2018). 10.3389/fimmu.2018.01472
    1. Ye J., Ma N., Madden T. L., Ostell J. M., IgBLAST: An immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res. 41 (W1), W34-40 (2013). 10.1093/nar/gkt382
    1. Rosenfeld A. M., Meng W., Luning Prak E. T., Hershberg U., ImmuneDB, a Novel Tool for the Analysis, Storage, and Dissemination of Immune Repertoire Sequencing Data. Front. Immunol. 9, 2107 (2018). 10.3389/fimmu.2018.02107
    1. Bolotin D. A., Poslavsky S., Mitrophanov I., Shugay M., Mamedov I. Z., Putintseva E. V., Chudakov D. M., MiXCR: Software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015). 10.1038/nmeth.3364
    1. Levenshtein V. I., Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707–710 (1966).

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