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SARS-CoV-2 infection generates tissue-localized immunological memory in humans

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Ending the global COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 depends on the establishment of immunological memory. SARS-CoV-2 infects the respiratory tract and induces adaptive immune responses, resulting in virus-specific T and B lymphocytes mediating viral clearance at the infection site and inhibiting viral dissemination through T cell effector functions and antibodies. It is now well-documented that mild and severe infection generates circulating virus-specific T cells and antibodies detectable in peripheral blood for up to a year or more (18). Moreover, the presence of neutralizing antibodies specific for the viral Spike (S) protein correlates with protection for SARS-CoV-2 vaccines (9, 10). However, the emergence of viral variants with potential for immune evasion (1115) and variability in vaccination rates among populations enable ongoing SARS-CoV-2 spread. An understanding of the breadth and functional potential of virus-specific T and B cell memory is needed for developing improved strategies to protect against continually evolving strains.
A major limitation in studying human immune responses is that sampling is largely confined to peripheral blood, while adaptive immune responses are generated and carry out their protective functions in a range of tissues. Memory cells are also maintained in diverse tissues—including infection sites and lymphoid organs (reviewed in (16, 17)). Virus-specific memory CD4+ and CD8+ T cells comprise heterogeneous subsets of circulating subsets and non-circulating tissue-resident memory cells (TRM) in various sites (18, 19). In mouse models, TRM in the lung mediate optimal protective responses to respiratory infection (2022), and this localized protection also involves responses in lung-associated lymph nodes (LNs) (23, 24). In humans, the majority of T cells in adults are memory cells. The subset composition of human T cell memory is specific to the tissue site; in mucosal, exocrine, and barrier sites TRM predominate, while lymphoid sites contain circulating effector and central memory (TEM, TCM) along with TRM subsets (19, 25, 26). All types of memory T cells in tissues express gene expression signatures distinct from memory T cells in peripheral blood (27), suggesting that they are differentially maintained compared to circulation. In SARS-CoV-2 infection, activated TEM and TRM were identified in the airways of severe COVID-19 (28, 29), although the distribution and functional capacity of SARS-CoV-2-specific memory T cells across tissues remain uncharacterized.
The generation of memory B cells following an infection occurs in secondary lymphoid organs (LN, spleen) and requires virus-specific follicular helper T cells (TFH) which promote B cell differentiation, survival, and somatic hypermutation in germinal centers (GC) (30). Memory B cells can persist in multiple sites, and exhibit tissue-resident phenotypes (31). In mouse respiratory infection models, resident memory B cells (BRM) in lung and lung-associated LNs can be important for protection (32). In human lymphoid and mucosal sites, memory B cells are the predominant subset, while naïve B cells prevail in circulation (33). SARS-CoV-2 infection elicits generation of S-, receptor-binding domain (RBD)-, and nucleocapsid (N)-specific memory B cells detectable in peripheral blood (1, 3, 7); however, the distribution and maintenance of SARS-CoV-2-specific memory B cells and GC B cells in tissues following natural infection have not been reported. Moreover, the relationship between human B and T cell memory in tissues is largely unexplored.
The use of physiologically healthy tissues from organ donors has enabled study of human immune cells across multiple sites (16, 34, 35). Investigating tissue immunity to SARS-CoV-2 is particularly challenging, as previously infected, but unvaccinated donors are required. Here, we present an investigation of SARS-CoV-2-specific memory T and B cell populations in lymphoid and mucosal sites of previously infected, seropositive organ donors, which we identified in bone marrow (BM), spleen, lung, and LNs up to 6 months post-infection. Lung and lung-associated LNs were the most prevalent sites for SARS-CoV-2-specific memory T and B cells, with a proportion exhibiting tissue-resident profiles. We also detected virus-specific GC B cells in lung-associated LNs along with TFH, suggesting ongoing generation of humoral immunity. Together, our results reveal local coordination of cellular and humoral memory immune responses for site-specific protective immunity.


Immunological memory is maintained by heterogeneous subsets of virus-specific T and B cells in non-lymphoid tissue sites of infection and multiple lymphoid organs. A comprehensive assessment of memory responses is therefore difficult to accomplish in humans. Here, we reveal the cellular complexity and functional diversity of SARS-CoV-2-specific memory T and B cells in lymphoid and mucosal tissues of previously infected organ donors up to 6 months post-infection (see fig. S9 for summary schematic). SARS-CoV-2-specific CD4+ T, CD8+ T, and B cells predominantly localized in the lung and lung-associated LNs and were maintained as memory cell populations. Tissue-resident T and B cells, known to participate in protection against secondary viral infections, were found most abundantly in the lung and were correlated across multiple sites. Moreover, SARS-CoV-2-specific GC B cells and TFH cells were discovered in lung-associated LNs, suggesting persisting GC responses months after resolution of infection. Together, these results indicate that the maintenance of SARS-CoV-2-specific immune memory is characterized by localized, ongoing coordination of cellular and humoral immunity within tissues.

SARS-CoV-2-specific memory T and B cells were found throughout the body and localized preferentially to lung and lung-associated LNs, providing direct evidence that those sites are key locations for establishment of immune memory after SARS-CoV-2 infection. Gut-draining LNs in some donors were also significant sites for SARS-CoV-2-specific memory T and B cells (particularly TRM and BRM), which could be due to the gut being a major site for SARS-CoV-2 replication in some cases (3, 51). The low frequency of SARS-CoV-2-specific memory T or B cells in the spleen further suggests that virus infection is generally limited to mucosal sites of entry. While a proportion of memory T and B cells in the lung exhibited phenotypic markers of tissue residency, for T cells especially, assessing tissue residency in antigen-stimulated cultures can be complicated by induction of CD69 and other proteins, and further analysis is required to definitively establish the extent of TRM formation to SARS-CoV-2. In mouse models of influenza infection, localization and tissue residence of T and B cells to the lung and lung-associated LNs is correlated with optimal protective responses (20, 23, 32). Therefore, tissue localized and resident memory T and B cells in the lung are likely important for site-specific protection and could be targets for site-specific boosting in vaccination.
SARS-CoV-2-specific memory CD4+ T cells were identified at significantly higher frequencies than CD8+ T cells across tissue sites, reflecting previous studies of peripheral blood showing that CD4+ responses are more robust than CD8+ responses months after resolution of infection (2, 52). In addition, SARS-CoV-2-specific T cells exhibited tissue-specific functional profiles, with cytotoxic proinflammatory, regulatory, and tissue repair functions variably manifested across different sites. In the lung-associated LNs, memory T cells exhibited broad proinflammatory, helper, and regulatory functional profiles. SARS-CoV-2-specific lung T cells produced higher levels of IL-10 compared to other sites, consistent with a role for T cell-derived IL-10 in regulating lung inflammation in mice (43). We previously showed in paired airway and blood samples of severe COVID-19 patients that the cytokine and chemokine profile in airway washes was distinct from that in plasma (28). Here, we further demonstrate that the functional responses of virus-specific T cells are tissue-specific – not only at the site of infection, but also across numerous lymphoid tissues. Together, these results suggest that T cells in tissues mediate responses that are functionally adapted to the tissue site, resulting in heterogeneity of immune memory stored throughout the body.

SARS-CoV-2-specific memory B cells were distributed across multiple sites. While frequencies were highest in lung and LNs, there were also significant frequencies in BM. In all sites, virus-specific memory B cells exhibited a predominantly IgG+ memory phenotype. The finding of S/RBD-specific BRM in lung and lymphoid sites was notable, as was the low frequency of IgA+ SARS-CoV-2-specific memory B cells in mucosal tissue and associate LNs.

Our results directly demonstrate ongoing, persistent GC responses in LNs following resolution of SARS-CoV-2 infection—including at least one example 6 months post-infection. Despite a report of potentially impaired GC responses in fatal COVID-19 (53), our data show coordinated TFH and GC B cells in the lung-associated LNs to non-fatal SARS-CoV-2 infection. These results provide evidence of ongoing GC reactions following resolution of infection, consistent with reports of prolonged evolution of humoral responses in peripheral blood up to 6 months following SARS-CoV-2 infection (3, 54). GC B cells were detected in donors spanning a broad age range—from 10-74 years, providing compelling evidence that the ability to establish robust GC responses to novel pathogens can be maintained with age.
These results also indicate ongoing interaction and coordination between T and B cells within LNs, which we also found related to memory populations in the lung. Significant correlations were also found between SARS-CoV-2-specific memory B and T cell populations across tissue sites, consistent with correlations between virus-specific T and B cell responses in peripheral blood of previously infected individuals (1, 7). We also identified potential inverse correlations between frequencies of virus-specific CD8+T cells in the lung and memory B cells in the lung-associated LN, suggesting that lung responses in situ can impact the magnitude or requirement for humoral responses in the associated LN. Together, these findings suggest that dynamic coordination of adaptive immune responses across the body is a feature of antiviral immunity to SARS-CoV-2.

This work has certain limitations. Namely, our study focuses on four seropositive donors across seven decades of life to provide a representative profile of tissue-specific antiviral immune responses. In addition to the challenges of obtaining live cells for immunological studies from organ donors, the findings here also depended on SARS-CoV-2 seropositive subjects who had not been vaccinated, thus limiting the size of the donor pool and the timeframe of collection (prior to December 2020). The consistency in cell type and site-specific trends and correlations across all profiled donors, as well as corroboration of larger scale blood studies, demonstrates how this project provides new insights into tissue-specific immune memory maintenance and persistence of humoral and cellular responses following SARS-CoV-2 infection.

In conclusion, we reveal here that immunological memory from SARS-CoV-2 infection is maintained as heterogeneous subsets across multiple sites, with active and preferential maintenance in lung and associated LNs, as well as site-specific functional adaptations. These findings support the development of site-specific strategies for monitoring immune memory to infections and vaccines, and for fortifying immune responses at the infection sites.


Study design

The objective of this study was to measure adaptive immune responses to SARS-CoV-2 in blood and tissues of seropositive individuals after resolution of infection. We measured the frequency of SARS-CoV-2-specific CD4+ T, CD8+ T, and B cells in seropositive organ donors compared to pre-pandemic seronegative donors to understand the maintenance of immunological memory to SARS-CoV-2 as T and B cell subsets across the body, the functional immune response in tissues, and the immune memory relationships across circulating and tissue-resident SARS-CoV-2-specific T and B cell populations.

Human samples

Human tissues were obtained from deceased organ donors at the time of organ acquisition for clinical transplantation through an approved protocol and material transfer agreement with LiveOnNY, the organ procurement organization (OPO) for the New York metropolitan area, as previously described (34, 35, 5559). Human tissues from the pediatric donor (HDL113) were obtained through arrangements with multiple OPOs across the US through the Human Atlas for Neonatal DeveLopment-immunity (HANDEL-I) program which is an extension of the coordinating center set up for nPOD (network for Pancreatic Organ Donors with Diabetes) (60). A list of donors from which tissues were used in this study is presented in Table S1. Organ donors are tested for SARS-CoV-2 infection (and confirmed as SARS-CoV-2 negative) by PCR testing of nasal swabs, tracheal aspirates, and/or bronchoalveolar lavage. A history of previous COVID-19 (D492, 498) and positive serology (D495) was provided in the donor summary and SARS-CoV-2 serology for all donors was measured in the Center for Advanced Laboratory Medicine at Columbia University Irving Medical Center. Tissues from all seropositive donors were obtained before December 2020, and all donors were free of cancer and seronegative for hepatitis B, C, and HIV. Because tissues are obtained from brain-dead organ donors, this study does not qualify as “human subjects” research, as confirmed by the Columbia University IRB.

Isolation of single-cell suspensions from tissue samples

Tissue samples were maintained in cold saline or media and transported to the laboratory within 2-4 hours of organ procurement for adult organs, and shipped to the laboratory on ice within 24 hours of procurement for pediatric donors. Tissue processing protocols were adapted from protocols previously described (34, 5559), with some recent optimizations. Briefly, mononuclear cells were isolated from the blood and BM samples by density centrifugation using Ficoll-Paque PLUS (GE, cat# 17-1440-03). Spleen was processed using mechanical dissociation, followed by pushing through 100μm filters (Fisher Scientific, cat# 50-146-1428), and Ficoll-Paque density centrifugation as above. Lung and lymph node samples were first incubated with 1mg/ml Collagenase D (Sigma, cat# 11088882001) and 0.1mg/ml DNAse (Fisher Scientific, cat# NC9709009) in Iscove’s Modified Dulbecco’s Medium (IMDM; Fisher Scientific, cat# 12-440-053) for 30 min at 37°C on a shaker followed by addition of EDTA 0.5M pH 8.0 (Fisher Scientific, cat# 15-575-020), filtration and density centrifugation as above, resulting in high yields of live leukocytes.

SARS-CoV-2 serology testing

Blood from deceased organ donors (D495, D498, HDL113) was collected and serum obtained following centrifugation using serum separating clot activator tubes (Fisher Scientific, cat# 22040546). SARS-CoV-2 serology testing for N protein was then performed by the Center for Advanced Laboratory Medicine (CALM) at Columbia University Irving Medical Center to determine previous exposure to SARS-CoV-2 for inclusion in the study.

SARS-CoV-2 ELISA titers were determined as previously described (1). Briefly, Corning 96-well half-area plates (ThermoFisher, Cat. No.3690) were coated with 1 μg/mL SARS-CoV-2 S protein, RBD protein, or N protein (SinoBiological, Cat. No. 40588-V07E) overnight at 4°C. The next day, plates were blocked with 3% milk (Skim Milk Powder ThermoFisher, Cat. No. LP0031) in PBS containing 0.05% Tween-20 (ThermoScientific, Cat. No. J260605-AP) for 2 hours at room temperature. Heat-inactivated serum (30 min at 56°C) was then added to the plates and incubated for 1.5 hours at room temperature. Plates were washed 5 times with 0.05% PBS/Tween. Secondary antibodies were diluted in 1% milk containing 0.05% Tween-20 in PBS. IgG titers were determined using anti-human IgG peroxidase antibody (Hybridoma Reagent Laboratory, Cat. No. HP6123-HRP) at 1:1000 dilution. Endpoint titers were plotted for each sample using background subtracted data. The limit of detection was defined as 1:3 for IgG.

Pseudovirus (PSV) Neutralization Assay

The PSV neutralization assays were performed as previously described (1). Briefly, 2.5×104 Vero cells (ATCC, Cat. No. CCL-81) were seeded in clear flat-bottom 96-well plates (Thermo Scientific, Cat. No. 165305) to produce a monolayer at the time of infection. Recombinant SARS-CoV-2-S-D614G pseudotyped VSV-ΔG-GFP were generated by transfecting HEK293T cells (ATCC, Cat. No. CRL-321) with plasmid phCMV3-SARS-CoV2-Spike and then infecting with VSV-ΔG-GFP. Pre-titrated rVSV-SARS-CoV-2-S-D614G was incubated with serially diluted human heat-inactivated serum at 37°C for 1-1.5 hours before addition to confluent Vero cell monolayers. Cells were incubated for 16 hours at 37°C in 5% CO2 then fixed in 4% paraformaldehyde in PBS pH 7.4 (Santa Cruz, Cat. No. sc-281692) with 10 μg/ml Hoechst (Thermo Scientific, Cat. No. 62249) and imaged using a CellInsight CX5 imager to quantify the total number of cells and infected GFP-expressing cells to determine the percentage of infection. Neutralization titers or inhibition dose 50 (ID50) were calculated using the One-Site Fit Log IC50 model in Prism 8.0 (GraphPad). As internal quality control to define the inter-assay variation, three samples were included across the PSV neutralization assays. Samples that did not reach 50% inhibition at the lowest serum dilution of 1:20 were considered as non-neutralizing.

In vitro T cell stimulations with SARS-CoV-2 peptide megapools (MPs)

Mononuclear cells from blood, BM, spleen, lung, lung-associated LNs, and gut-associated LNs of SARS-CoV-2 seropositive donors were thawed, and dead cells were removed using the EasySep Dead Cell Removal (Annexin V) Kit (STEMCELL Technologies, cat# 17899) containing 10% heat-inactivated human AB serum (Gemini, cat# 507533010) and Penicillin-Streptomycin-Glutamine (ThermoFisher Scientific, cat# 10378016) and incubated overnight at 37°C, 5% CO2. Cells were stimulated for 6 or 24 hours by the addition of SARS-CoV-2-specific CD4 and CD8 MPs (1μg/mL) (MP_S, MP_CD4_R, MP_CD8_A, MP_CD8_B) designed and synthesized as previously described. Briefly, MP_S consists of 253 15-mer peptides overlapping by 10 residues and covering the entire S protein. MP_CD4_R consists of 221 predicted HLA class II CD4+ T cell epitopes covering all proteins apart from S protein. For CD8 epitopes, MPs were synthesized based on epitope predictions for 12 most common HLA class I A and B alleles; these resulted in 628 predicted CD8+ T cell epitopes, which were separated into MP_CD8_A and MP_CD8_B (1, 2, 61). Equimolar amount of dimethyl sulfoxide (DMSO) was used as negative control. Prior to the addition of peptide MPs, cells were blocked for 15 min with 0.5μg/mL of anti-CD40 monoclonal antibody (Miltenyi Biotec, cat# 130-094-133), as previously described (62). After either 6 or 24 hours, supernatant was collected for multiplex detection of cytokines, and cells were stained for AIMs and analyzed via flow cytometry.

AIM+ antigen-specific CD4+ T cells were identified as positive following Boolean OR gating of the CD40L+OX40+, 4-1BB+OX40+, 4-1BB+CD40L+ subsets (see Fig. S1 for gating strategy). The resultant gate was used to quantify AIM+CD4+T cell frequency. AIM+ antigen-specific CD8+ T cells were identified as 4-1BB+CD25+. Antigen-specific TFH calculated as a frequency of non-naïve (NN) CD4+ T cells were gated on total CD4+ T cells excluding CD45RA+CCR7+ CD4+ T cells. Antigen-specific CD4+ and CD8+ T cells were measured as DMSO-background-subtracted data. For quantification of the frequency of total SARS-CoV-2-specific CD4+ T cells, a weighted average was taken for percentage of AIM+ CD4+ T cells identified for samples stimulated with MP_S or MP_CD4_R MPs. For quantification of the frequency of total SARS-CoV-2-specific CD8+ T cells, a weighted average was taken for percentage of AIM+ CD8+ T cells identified for samples stimulated with MP_S, MP_CD8_A, or MP_CD8_B MPs.

Flow cytometry

For flow cytometry analysis of SARS-CoV-2 antigen-reactive T cells, cells were stained in 96-well U-bottom plates protected from light using fluorochrome-conjugated antibodies (see Table S2 for antibodies in the T cell flow cytometry panel). Briefly, cells were washed with FACS-buffer (PBS with 2% heat-inactivated FBS), then resuspended with surface staining antibody cocktail for 20 min at room temperature. Surface-stained cells were fixed for 30 min at RT in fixation buffer (Tonbo, cat# TNB-0607-KIT), washed with permeabilization buffer (Tonbo, cat# TNB-0607-KIT), and washed again with FACS buffer. Flow cytometry data were collected using the 5-laser Cytek Aurora flow cytometer (Cytek Bio) and analyzed using FlowJo V 10.7 software and Prism 9.0.1. software.

For flow cytometry analysis of SARS-CoV-2-specific B cells, biotinylated protein antigens multimerized on fluorescently-labeled streptavidin were used as probes to detect antigen-specific B cells (see Table S3 for antibodies used in the B cell flow cytometry panel). Avi-tagged full-length SARS-CoV-2 S (2P-stabilized, double streptavidin-tagged) and RBD proteins were generated in-house. Biotinylation was performed using biotin protein ligase standard reaction kit (Avidity, cat# Bir500A) following the manufacturers protocol and dialyzed against PBS. Biotinylated S was mixed with streptavidin BV421 (BioLegend, cat# 405225) and streptavidin BUV737 (BD Bioscience, cat # 612775) at 20:1 ratio (~6:1 molar ratio). Biotinylated RBD was mixed with streptavidin PE-Cy7 (BioLegend, cat# 405206) and streptavidin BUV661 (BD Bioscience, cat# 612979) at 2.2:1 ratio (~4:1 molar ratio). Streptavidin PE-Cy5.5 (Thermo Fisher Scientific, cat# SA1018) was used as a decoy probe for non-specific streptavidin-binding B cells. The probes were then mixed in Brilliant Stain Buffer (BD Bioscience, Cat# 566349) containing 5μM free d-biotin (Avidity, Cat# Bir500A). Cells (~107) were prepared in U-bottom 96-well plates and stained with 50μL antigen cocktail containing 400ng S (200ng per probe), 100ng RBD (50ng per probe), and 20ng streptavidin PE-Cy5.5 at 4°C for one hour followed by staining for surface markers in Brilliant Stain Buffer at 4°C for 30 min. Dead cells were stained using LIVE/DEAD Fixable Blue Stain Kit (Thermo Fisher Scientific, Cat# L34962) in PBS at 4°C for 30 min. Cells were then fixed and permeabilized using eBioscience Intracellular Fixation & Permeabilization Buffer Set (Thermo Fisher Scientific, Cat# 88-8824-00) before staining with antibodies against transcription factors in eBioscience Permeabilization Buffer (Thermo Fisher Scientific, Cat# 00-8333-56). Samples were acquired on Cytek Aurora and analyzed using FlowJo10.7.1 (BD Bioscience). In each experiment, PBMCs from a known COVID-19 convalescent subject and an unexposed subject were included to ensure consistent sensitivity and specificity of the assay.

Multiplex detection of cytokines

Cryopreserved supernatant from in vitro T cell stimulation experiments were sent to Eve Technologies Corp. (Calgary, Alberta) for quantification of 50 total human cytokines, chemokines, and growth factors. Luminex xMAP technology was used for multiplexed quantification of 2 human cytokines in one array (perforin, granzyme B) and 48 human cytokines, chemokines, and growth factors in a separate array (sCD40L, EGF, Eotaxin, FGF-2, Flt-3 ligand, Fractalkine, G-CSF, GM-CSF, GROα, IFNα2, IFNγ, IL-1α, IL-1β, IL-1ra, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-17A, IL-17E/IL-25, IL-17F, IL-18, IL-22, IL-27, IP-10, MCP-1, MCP-3, M-CSF, MDC (CCL22), MIG, MIP-1α, MIP-1β, PDGF-AA, PDGF-AB/BB, RANTES, TGFα, TNFα, TNFβ, VEGF-A). The multiplexing analysis was performed using the Luminex 200 system with assay kits sourced by Millipore MILLIPEX (MilliporeSigma, Burlington, Massachusetts, USA) according to the manufacturer’s protocol.

Observed concentrations were calculated with the standard curve based on the fluorescence intensity of the bead population for a specific analyte. For analysis and visualization of cytokine/chemokine production by antigen-responding cells in multiple tissues sites within each individual donor, observed concentrations for each analyte were first subtracted from DMSO negative control then scaled across samples for each individual donor on a maximum absolute scale, with values ranging from -1 to 1 across all analytes, using the MaxAbsScaler features of sklearn.preprocessing function of the Python scikit-learn library (36, 63). Heatmap visualizations were generated using the Python data visualization library seaborn (64). For analysis comparing the production of analytes across donors and tissue sites, either absolute observed concentrations were used or observed concentrations for each analyte were normalized to DMSO negative control.

Correlogram plot and visualization

Correlograms were analyzed and plotted using the Pearson product moment correlation coefficient (r) between all parameter pairs from blood, lung, and lung-associated LN lymphocyte populations (see Data File 2 for raw data, R and p values). Correlograms were created with the corrplot package (v0.88) (65) running under R (v4.0.2) in RStudio (1.4.1103). Visual clustering of parameters was performed using the ‘hclust’ option of corrMatOrder. Two-sided p-values were calculated using corr.test (stats v4.0.2) and graphed (corrplot v0.88) based on *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001.

Statistical analysis

Descriptive statistics of compiled flow cytometry data and statistical testing were performed using Prism (GraphPad). Graphs were generated using Prism (GraphPad), Python matplotlib and seaborn libraries (64, 66), and RStudio corrplot package (65). Differences in means between two sample groups were compared using nonparametric test of null hypothesis Mann-Whitney U test. Pearson correlations were used to evaluate immune memory relationships. Multiple group comparisons were done using one-way ANOVA, corrected for multiple comparisons by false discovery rate (FDR) using two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli when comparing seropositive and seronegative donors. For comparing immune mediator profiles across tissue sites, statistical analyses were performed via one-way ANOVA, corrected for multiple comparisons by Tukey’s multiple comparison test. P-values below 0.05 were considered as statistically significant. For all figures, **** denotes p-value ≤ 0.0001, *** denotes p-value ≤ 0.001, ** denotes p-value ≤ 0.01, and * denotes p-value ≤ 0.05.


Funding: This work was supported by National Institutes of Health grants AI100119,AI128949 (Human Immunology Project Consortium) and AI106697 (to DLF), AI142742 (Cooperative Centers for Human Immunology) (to AS, SC), National Institutes of Health grant contract Number 75N9301900065 (DW, AS), grant K23AI141686 (TJC), and a Helmsley Charitable Trust grant (TMB, DLF). The CCTI Flow Cytometry Core was supported by NIH S10RR027050 and S10OD020056. P.D. was supported by a Cancer Research Institute (CRI) Irvington Postdoctoral Fellowship; N.L, was supported by a National Science Foundation Graduate Research Fellowship Program (NSF-GRFP); P.S. was supported by the Canadian Institutes of Health Research (CIHR) Fellowship.

Author contributions: Conceptualization: MMLP, KR, YK, DLF, SC. Methodology: MMLP, KR, YK, NIB, ZZ. Investigation: MMLP, KR, YK, NIB, ZZ. Visualization: MMLP, KR, YK. Resources: AS, AG, DW, MK, RM, KMH, EOS, MAB, TMB, TJC, BBU, NL, PAS, PD, YSL, JIG, MCB. Supervision: DLF, SC. Writing – original draft: MMLP, KR, YK, DLF, SC. Writing – review and editing: MMLP, KR, YK, DLF, SC. Project administration: DLF, SC, SBW. Funding acquisition – DLF, SC, AS.

Competing interests: A.S. is a consultant for Gritstone, Flow Pharma, Merck, Epitogenesis, Gilead, and Avalia. S.C has consulted for Avalia, Roche and GSK. L.J.I. has filed for patent protection for various aspects of T cell epitope and vaccine design work. All remaining authors declare no competing interests.

Data and materials availability: All data needed to evaluate the conclusions of the paper are available in the paper or the Supplementary Materials.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.

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