Impaired homeostasis of T follicular helper cells in elderly patients with COVID-19

doi:10.21203/rs.3.rs-3629075/v1

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Impaired homeostasis of T follicular helper cells in elderly patients with COVID-19

https://doi.org/10.21203/rs.3.rs-3629075/v1

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Background

In December 2022, the discontinuation of China’s dynamic zero policy resulted in a nationwide surge of Corona Virus Disease 2019 (COVID-19)-related cases and hospitalizations. We sought to probe the immune profile of the elderly with COVID-19 and explore the feasibility of a certain cell population as biomarker for risk stratification, which may provide foundation for the diagnosis and treatment of upcoming COVID-19 wave this winter. Our study recruited 57 elderly patients with acute COVID-19 and 27 convalescent donors. SARS-CoV-2-specific adaptive immunity including binding antibodies, neutralizing antibodies and T-cell responses were assessed across the COVID-19 severity spectrum.

Results

Patients with acute illness underwent impaired CD4⁺ T homeostasis, preferential loss of follicular helper T cell (Tfh) subsets including Tfh-em, Tfh-cm, Tfh1, Tfh2, Tfh17 and T follicular regulatory cells (TFR), which correlated with antibody production through different pathways, were observed. Severity of acute respiratory distress syndrome correlated with the degree of Tfh deficiency, which may act as biomarkers for risk stratification of elderly patients with COVID-19. Moreover, vaccination ameliorated Tfh and TFR deficiency and helped to promote NAb production.

Conclusion

The elderly had gone through severity-dependent CD4⁺-biased lymphopenia post SARS-CoV-2 infection, and vaccination contributed to ameliorate prognosis of them via alleviating the impairment degree of Tfh subsets.

COVID-19

Elderly patients

Immunosenescence

Lymphopenia

Risk Stratification

Vaccination

The United Nations Decade of Healthy Aging (2021–2030) began in the shadow of Corona Virus Disease 2019 (COVID-19) pandemic (1). The decade is particularly relevant for China, which is experiencing rapid changes in its population demographics. During the last few months of 2022, China had implemented drastic changes in its COVID-19 maintenance policies, such as the termination of mass testing, strict quarantine, and mobility restrictions, leading to the first nationwide wave of COVID-19-related cases and hospitalizations in the following months. Overwhelming evidence has shown that aging is a significant risk factor for critical COVID-19, death, and other adverse health outcomes (2, 3). In China, individuals aged ≥ 65 years accounted for 91% of the 59,938 COVID-19-related deaths between December 2022 and January 2023 (4).

Immunosenescence, known as age-related immune function decline and dysregulation, contributes to heightened vulnerability to severe COVID-19 and poor prognosis. The main hematological biomarker associated with severe COVID-19 is lymphopenia, which mostly occurs in the elderly characterized by immunosenescence (5). In-depth investigations have revealed that T cell clonal expansion is telomere length (TL)-dependent. Individuals in their twenties with average hematopoietic cell TL (HCTL) have maximal T cell clonal expansion capacity, whereas individuals aged ≥ 60 years experienced > 90% decline. The collapse of T cell expansion capacity coincides with a steep increase of COVID-19-related mortality (6). Nevertheless, an adequate T cell response is essential for anti-viral responses and immune memory. Increasing evidence on the protective role of T cell immunity in preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and limiting COVID-19 severity has been reported, lymphopenia is considered a major indicator of COVID-19 severity (7, 8). Further elucidation of T cell subsets susceptible to immunosenescence and the relationship between immunokinetics and illness status in the elderly with COVID-19, remains to be performed.

To date, studies on immune profiling have usually focused on either individuals with moderate, severe COVID-19, or recovered individuals, however, it is important to assess adaptive immunity across COVID-19 severity spectrum in a coordinated manner to gain insights on the associated immunophenotypes and potential immunopathogenesis. Moreover, despite the great progress regarding increasing vaccination coverage, the World Health Organization (WHO) still highlights the inadequate vaccine coverage among people aged ≥ 60 years. The impact of inadequate vaccination, combined with COVID-19 policy changes, on older adults in China is not fully understood.

Study participants and design

We enrolled 57 patients (aged ≥ 60 years), including eight DPs, with positive SARS-CoV-2 PCR test results. Elderly with a prior positive SARS-CoV-2 PCR test and confirmed to be convalescent by negative PCR test were recruited as CDs ( aged ≥ 60 years). Individuals with current malignant diseases or undergoing immunomodulatory treatments were excluded. Clinical data of the patients were extracted from electronic medical records and CDs completed a survey on baseline characteristics and symptoms.

The grouping criteria of severe and moderate illnesses vary slightly according to different reports, howbeit there is uniformity regarding the reference SpO2% and PaO2/FiO2, that is, utilizing 94% and 300 mm Hg as cutoff indicators for severity definition. Other criteria, such as respiratory rates, degree of ventilation, radiography scan images and certain pathological scores or indices (9) can aid comprehensive evaluation of patient’ s status. In this study, we defined “severe” as individuals with SpO2 < 94% on room air at sea level and PaO2/FiO2 < 300 mm Hg (10). Patients with inconsistent SpO2% and PaO2/FiO2 levels (e.g., SpO2 > 94% while PaO2/FiO2 < 300 mm Hg, vice versa), if invasive ventilation was given, the patients would be classified as severe (11). The remainder were classified as MPs. WHO Ordinal Scale was adopted to rank the degree of assisted ventilation (12). APACHE II scoring was based on data collected in the first 24h of intensive care or general inpatient unit admission (13).

Blood sample collection and processing

Blood samples were collected in duplicate for peripheral blood mononuclear cell (PBMC) separation and serum acquisition, respectively. PBMCs were isolated from heparinized whole blood via standard density gradient centrifugation using Ficoll-Paque Plus (GE Healthcare, CAT# 17-1440-02) according to the manufacturer’s instructions (400 g, 20°C, 20 min). The PBMCs were thereafter aliquoted and cryopreserved at − 80°C until further use. Serum were obtained by centrifugation (10,000 g, 20°C, 20 min) after clotting at 4°C over 12 h, and stored at -80°C for further use. Peripheral blood and throat swab specimens were obtained from 57 patients 1–21 days after symptom onset (only one patient was sampled at 26 day PSO). The CDs were sampled > 42 days PSO to guarantee rehabilitation.

S1 protein production

The truncated S1 subunits of ancestral (GenBank: USS79549.1) and BA.5 strain (GenBank: UWU41566.1) were cloned into the pET28-His vector to generate pET28a-WT-S1 and pET28a-BA.5-S1, respectively. The recombinant plasmids were subsequently transformed into E. coli BL21 strain (DE3) and shook until 0.6–0.8 OD600 was achieved. The bacteria were then induced with 0.2mM isopropyl-β-D-thiogalactoside (IPTG) for 14h at 16°C, harvested, lysed with buffer A (50 mM Tris-HCl, 50 mM NaCl, pH 8.0) and centrifuged at 10,000 g for 30 min. Pellets were dissolved in buffer A containing 8M urea at 37°C for 2 h and centrifuged at 10,000 g for 30 min. The supernatant was loaded onto His-Ni-NAT resin (Smart-lifesciences, CAT# SA004025) and eluted with elution buffer (8M urea, 50 mM Tris-HCl, 50 mM NaCl and 300 mM imidazole; pH 9.0). Each purified protein underwent dialysis at 4°C for 6 h in buffer A with 5% glycerol to remove urea and imidazole. The concentration and purity of proteins were confirmed using Bicinchoninic Acid Assay (Thermo Fisher Scientific, CAT# 23225) and SDS-PAGE, respectively, before use.

Enzyme-linked immunosorbent assay (ELISA)

S1-specific total IgG and IgG1, IgG2, IgG3 and IgG4 subclasses were detected using ELISA. Briefly, ancestral or BA.5-S1 protein (100 ng/well) was coated at 4°C for at least 16 h. Sera were diluted 1:100, 1:300, 1:100, 1:100, 1:100 to determine the titer of total IgG, IgG1, IgG2, IgG3 and IgG4, respectively. Two-fold serial dilution of positive antiserum was performed in each plate to construct standard curves. After blocking with 5% skim milk, the diluted sera were incubated on S1-coating plates at 37°C for 2 h. The plates were then incubated with HRP-conjugated anti-human IgG (1:30,000, Abcam, CAT# ab6759), IgG1 (1:50,000, Sino Biological, CAT# 10702-MM01T), IgG2 (1:3,300, Abcam, CAT# ab99779) and IgG3 (1:2500, SouthernBiotech, CAT# 9210-05), IgG4 (1:6,600, Abnova, CAT# MAB22588) for 1 hour at 37°C followed by the addition of TMB substrate (NCM Biotech, CAT# M30500). Titers of the specimens were calculated using standard curves as previously reported (14).

Peptide pool

The S1 peptide pool contained 158 peptides, 15-mer overlapping by 11, spanning the full-length S1 sequence of BA.5 strain (Genscript Biotech).

Flow cytometry

Frozen PBMCs were thawed and washed carefully. Cells were then resuspended in complete RPMI containing sodium pyruvate, L-glutamine (Hyclone, CAT# SH30809.01), 10% fetal bovine serum (FBS) (EallBio, CAT# 03.U16001DA), 1 × penicillin–streptomycin (NCM Biotech, CAT# C100C5) and 0.05 mg/ml DNaseI (Solabio, CAT# D8071). Each specimen was divided into two parts. For in vitro activation assay, 1–5×10⁶ cells were placed in 96-well plates and stimulated with a final concentration of 2 ug/ml peptide pools for 12 h at 37°C. GolgiPlug protein transport inhibitor (BD Bioscience, CAT# 555029) was added 6 h post peptide pool incubation. Cell Stimulation Cocktail (PMA and ionomycin, Invitrogen, CAT# 00-4970-03) was used as positive control and DMSO were used as negative control. After overnight incubation, cells were washed with FACS buffer (PBS containing 2% FBS and 2 mM EDTA), Fc-receptor blocking Abs and LIVE/DEAD Fixable Aqua Dead Cell Stain (Invitrogen, CAT# L34962) were incubated for 15 min at 25°C. For surface staining, cells were incubated with an extracellular cocktail of BUV496-CD3, APC-Cy7-CD4, Percp-Cy5.5-HLA-DR, Alexa Fluor 700-CD8a, BV605-CD69, APC-CD137, BV510-CD40L and BV421-CD38 for 30 min at 25°C. For intracellular staining, cells were fixed and permeabilized with BD fixation/permeabilization solution (BD Biosciences, CAT# 555028) prior to staining with FITC-Granzyme B, PE-IFN-γ and PE-Cy7-IL-4.

The remaining part of each PBMC specimen was washed in FACS buffer and stained with chemokine cocktail containing APC-CXCR5, BV605-CXCR3, FITC-CCR7 and PE-CCR6 for 15 min at 37℃. Cells were then incubated with Fc block Ab and LIVE/DEAD stain for 30 min at 25 ℃, followed by extracellular cocktail of BUV496-CD3, APC-Cy7-CD4, Alexa Fluor 700-CD8a, PE-Cy7-PD-1, PercP-Cy5.5-CD127, BV421-CD45RA, BV510-CD45RO and PE-Dazzle594-CD25 for 30 min at 25 ℃. All samples were fixed with 2% PFA at 4℃ for 30 min before acquisition and detected on BD LSRFortessa (BD Bioscience).

SARS-CoV-2 pseudovirus neutralization assay

The SARS-CoV-2 pseudotype virus was generated through co-transfection of 293T cells with 2 plasmids, pNL4-3.Luc.R-E and pCAGGS-S-CΔ19, carrying human immunodeficiency virus type 1 backbone and optimized SARS-CoV-2 Spike gene of ancestral, BA.5 or CH.1.1 strains (GenBank: WDB04369.1), respectively. Supernatants were yielded 48 h post-transfection. To determine neutralization activity, Huh7 cells were seeded in 96-well plates at a density of 2.5 × 10⁴ cells per well. Heat-inactivated sera were three-fold serially diluted and mixed with equal volumes of pseudoviruses. The mixture was incubated at 37°C for 1 h before transferring to Huh7 cells. Relative luciferase activity (RLA) was measured after 48 h using SteadyGlo Luciferase Assay System (Promega, CAT# E2550). The 50% pseudovirus neutralization (pVNT50) titer was calculated as the reciprocal of the serum dilution at which RLA was reduced by 50% compared to the RLA of virus control wells infected with pseudovirus in the absence of serum.

RNA extraction and phylogenetic analysis

RNA was extracted from the throat swab specimens using a sputum RNA extraction Kit (BIOG, CAT# 51033) according to the manufacturer’s instructions. Reverse transcription and PCR were performed using the Hifair Ⅲ 1st Strand cDNA Synthesis SuperMix kit (Yeasen, CAT# 11141ES10) and 2 × Phanta Max Master Mix kit (Vazyme, CAT# P515), respectively. Approximately 1500 bp fragments of each specimen were obtained using a Gel Extraction kit (Omega, CAT# D2500-02) and Sanger sequencing were performed. Primary primers from conserved region of Wuhan strain (NC_045512) were as follows:

WuHan-850-F: 5‘-tcttgttttattgccactagtctc-3’;

WuHan-850-R, 5‘-gtctacagcatctgtaatggttc-3’;

WuHan-1600-F, 5‘-atagaagttatttgactcctggtg-3’;

WuHan-1600-R, 5‘-acacctgtgcctgttaaaccat-3’.

We retrieved 76 reference SARS-CoV-2 Spike sequences from the NCBI database, including the ancestral hCoV-19/Wuhan strain (NC_045512.2) and the bat coronavirus strain (MN996532). Spike sequences of Omicron, Alpha, Beta, Gamma, Delta, Epsilon, Eta, Iota, Kappa, Lambda, Mu, Theta, Zeta variants, and AV.1, B.1.1.318, B.1.617.3, C.1.2, C.36.3 lineages were downloaded for comprehensive comparison. Overall, 134 fragments were used to create cladogram. Throat swab sequences were aligned using the MEGA 7.0 ClustalW. Before establishment, substitution saturation was measured using DAMBE software (Iss < Iss.c, p < 0.05) and the overall Genetic Distance was computed to confirm the feasibility of Neighbor Joining (NJ) algorithm. A neighbor-joining (NJ) phylogenetic tree was built with bootstrap replication of 1,000 cycles to assess statistical robustness. Maximum Likelihood (ML) algorithm were also performed using T92 + G (Model/Method + Rates among Sites) model.

Statistical analysis

All Statistical analyses were performed using the GraphPad software (version 8.4.2). Cytometry data gating and tSNE clustering were carried out using Flowjo software (version 10). Spearman correlation matrices were proformed using R studio (version 4) with the ggplot2 packages or Chiplot (https://www.chiplot.online/). Phylogenetic analyses were performed utilizing MEGA (version 7.0) and DAMBE (version 5).

Lymphopenia and altered CD4⁺T cell profile

From December 2022 to February 2023, 57 patients (mean age of 73 years) diagnosed with SARS-CoV-2 infection were admitted to the Physical and Rehabilitation Department of Tianjin Medical University General Hospital. The severity of acute respiratory distress syndrome (ARDS) in the elderly was age-dependent, and hypoxia indices SpO2% and PaO2/FiO2 correlated negatively with age, whereas the acute physiologic assessment and chronic health evaluation (APACHE) score and assisted ventilation grade correlated positively with age (Fig. 1A-E). Overall, eight patients died (described as deceased patients, DPs) at 5–23 days post symptom onset (PSO). The DPs had gone through severe hypoxemia (mean SpO2 of 81% and PaO2/FiO2 of 89 mm Hg), and five out of eight DPs underwent invasive ventilation. The cohort details are summarized in Table 1.

Table 1

**Demographics and clinical characteristics of all donors.**
Characteristic	MPs (n = 22)	SPs (n = 27)	DPs (n = 8)	CDs (n = 27)	P value
Demographic
Age, median (SD), y	69 (8.5)	76 (9.0)	78 (7.9)	73 (5.6)	.0404^* .0343^#
Sex, No. (%)
Male	16 (72.7)	15 (55.6)	6 (75.0)	16 (59.3)
Female	6 (27.3)	12 (44.4)	2 (25.0)	11 (40.7)
Comorbidity, No. (%)
Cardiovascular diseases	15 (68.2)	19 (70.4)	3 (37.5)	22 (81.5)
Endocrine system disease	10 (45.5)	7 (25.9)	4 (50.0)	12 (44.4)
Nervous system disease	8 (36.4)	8 (29.6)	3 (37.5)	13 (48.1)
Other chronic comorbidity	3 (13.6)	5 (18.5)	4 (50.0)	3 (11.1)
Oxygen saturation, median (SD), %	95 (0.4)	88 (6.9)	81 (5.0)	NA	< .0001^* < .0001^# .0048^&
PaO2/FiO2, median (SD), mmHg	331 (58.9)	159 (56.2)	89 (19.8)	NA	< .0001^* < .0001^# .0104^&
Maximum oxygen treatment, No. (%)
No oxygen therapy	3 (13.6)	0 (0.0)	0 (0.0)	NA
Mask/nasal prongs	18 (81.8)	19 (70.4)	3 (37.5)	NA
Noninvasive/high-flow ventilation	1 (4.5)	2 (7.4)	0 (0.0)	NA
Invasive ventilation	0 (0.0)	6 (22.2)	5 (62.5)	NA
Hospitalized status, No. (%)
ICU	4 (18.2)	11 (40.7)	7 (87.5)	NA
Non-ICU	18 (81.8)	16 (59.3)	1 (12.5)	NA
Duration of hospitalization	34 (18.5)^a	54 (44.5)^a	14 (7.1)^b	NA
APACHE II score	15 (2.1)	27 (6.8)	31 (5.7)	NA	< .0001^* < .0001^# .0005^&
Positive Nucleic acid qPCR at sampling, No. (%)	22 (100.0)	27 (100.0)	8 (100.0)	0 (0.0)

NA, non-available; MPs: Moderate patients; SPs: Severe patients; DPs: Deceased patients; CDs: Convalescent donors; Data were analyzed by One-way-ANOVA with Tukey correction or Kruskal-Wallis test according to the distribution of data. P values < 0.05 were denoted.

* indicate the statistical differences between MPs and SPs;

# indicate the statistical differences between MPs and DPs;

& indicate the statistical differences between SPs and DPs;

^a Mean time from symptom onset to date of discharge; ^b Mean time from symptom onset to date of death;

Elderly patients with acute COVID-19 had CD3⁺ T deficiency, which was ameliorated after recovery (Fig. 1F). Kinetics of CD4⁺ T revealed consistency with CD3⁺ whereas CD8⁺ didn’t, indicating that altered CD4⁺ T magnitude, rather than CD8⁺, contributed to lymphopenia, especially in severe (SPs) and DPs (Fig. 1G-H). SARS-CoV-2-specific CD8⁺ effector T cells increase rapidly as early as 1 day PSO, accompanied by expression of potent cytotoxic molecules, such as gamma interferon (IFNγ), granzyme B (GZMB), perforin, and CD107a, CD8⁺ memory T (TM) cells still retain similar expression profiles as effector compartments (15). Accordingly, indiscriminate and sustained secretion of GZMB by CD8⁺T cells was observed in all individuals, including fully recovered convalescent donors (CDs) (Fig. 1I ), representing sustained cytotoxicity sensitization of CD8⁺ T cells after COVID-19 in the elderly (16).

We classified memory TM cells into naive (TN: CD45RA⁺CCR7⁺), effector memory (TEM: CD45RA^–CCR7^–), central memory (TCM: CD45RA^–CCR7⁺) and effector memory-expressing CD45RA (TEMRA: CD45RA⁺CCR7⁻) subsets (Fig. 1J-K). The DPs exhibited feeble CD4⁺TEM responses but significantly higher TN proportion than moderate patients (MPs) and SPs, indicating a dysfunctional CD4⁺ TM profile in DPs during acute COVID-19 (Fig. 1L-M). We considered that if patients’ severity were relatively under control (such as MPs and SPs), COVID-19 contributed to the activation of effector T immunity and promoted the acquirement of SARS-CoV-2-specific central memory imprints. Following recovery, their TM profile changed and resembled the pattern of CDs, which preserved a high proportion of TCM for combating antigen re-encounter in the future (Fig. 1N). For critically ill patients (DPs), however, poor physiological condition and severe hypoxemia led to inferior T cell activation, feeble memory counterparts preservation and poor prognosis.

Moreover, CD8⁺TEM and CD8⁺TEMRA dominated CD8⁺TM as previously reported (17). CD8⁺TEM also abated in DPs (Supplementary Fig. 1A).

Activation signatures of T cells in geriatric

Spike-reactive activation profile of T cells are heterogeneous. In our study, CD38 and HLA-DR were uniformly co-expressed irrespective of disease severity whereas CD69⁺CD137⁺CD4⁺ were not. All patients, including fully recovered CDs, had elevated CD38⁺HLA-DR⁺CD4⁺ level compared with healthy donors (four healthy donors, HDs, with a mean age of 45 years), whereas CD69⁺CD137⁺CD4⁺ were induced in MPs and SPs but not in DPs or CDs (Fig. 2A-B). Actually, CD69⁺CD137⁺CD4⁺ correlated negatively with age and positively with SPO2 and PaO2/FiO2 (Fig. 2C-E). However, no correlation was observed between CD38⁺HLA-DR⁺CD4⁺ and COVID-19 severity. Further dissection revealed that HLA-DR⁺CD4⁺ displayed slightly discordant trend with CD38⁺CD4⁺, the percentage of HLA-DR⁺CD4⁺ increased mildly whereas CD38⁺CD4⁺ decreased as the disease progressed (Fig. 2F-G). Spearman correlation analyses showed that SARS-CoV-2-specific CD38⁺CD4⁺ correlated positively with SpO2% and PaO2/FiO2, and negatively with the APACHE and WHO Ordinal scale scores, whereas HLA-DR⁺CD4⁺ exhibited opposite trends with weaker degree of correlation (Fig. 2H-K).

Regarding the corresponding CD8 compartments, CD38⁺HLA-DR⁺CD8⁺ remained higher in all patients and CDs compared with HDs (Supplementary Fig. 1B). Statistically, CD69⁺CD137⁺CD8⁺ remained similar among all groups, with certain severe or convalescent individuals raised its expression level (Supplementary Fig. 1C). Neither of the co-expression clusters correlated with the aforementioned clinical features. Briefly, we deemed that co-expression of CD38 and HLA-DR, regardless of CD4⁺ or CD8⁺, were inclined to act as general T cell activation indicator, and CD69⁺CD137⁺CD4⁺ were more likely to be indicators of severity-dependent T cell activation, especially in the scenario of CD4 perturbation.

For SARS-CoV-2-specific cytokine, IL-4 was almost undetectable in all individuals, whereas IFN-γ⁺CD4⁺ was induced in all patients regardless of disease severity compared with HDs (Supplementary Fig. 1D). IFN-γ⁺CD8⁺ peaked in SPs and DPs (Supplementary Fig. 1E). IFN-γ⁺CD4⁺ production was sustained to a certain degree and IFN-γ⁺CD8⁺ restored to baseline level in CDs, indicating that recovered individuals tend to preserve evidence of virus-specific CD4⁺ T cell responses than CD8⁺ (18).

Severity-dependent impairment of Tfh subsets and TFR homeostasis

Tfh cells support high affinity and long-term antibody responses. Though usually located in secondary lymphoid organs, T cells bearing features of Tfh cells can also be identified in peripheral blood (circulating Tfh, cTfh) (19). He et al elucidated that within human circulating CXCR5⁺CD4⁺ T cells, CCR7^loPD-1^hi subset had a partial effector phenotype (Tfh-em), whereas CCR7^hi PD-1^lo cells displayed central-memory like phenotype (Tfh-cm) (20). Upon antigen re-exposure in vivo, Tfh-em take effect in early phase with a transient increase around 7 days, while Tfh-cm subset normally peak at around 21 days and may persist into memory phase (21). In our study, as the disease progressed, total Tfh decreased, which is similar to the trend observed for CD4⁺T cells. HDs and CDs retained baseline level of it (Fig. 3A). In detail, Tfh-cm outnumbered Tfh-em regardless of the state of illness, and showed complementary trend with Tfh-em, signifying a “conversion” (20) but “quantitatively conserved” relationship between the two clusters (Fig. 3B). The “quantitative gap” between Tfh-cm and Tfh-em represented “conversion ratio”. HDs and CDs exhibited low conversation ratio due to inactivated or recovered Tfh responses, respectively (Fig. 3C). For patients, MPs and SPs had relative higher Tfh-em: Tfh-cm ratio (Supplementary Fig. 2A), whereas DPs induced minimal Tfh-cm-Tfh-em conversion, indicating inferior Tfh-cm mobilization and Tfh-em activation (Fig. 3C). Percentages of Tfh-cm and Tfh-em in CD4 also wanned as the disease progressed (Fig. 3D-E).

We also classified Tfh into Tfh1 (CXCR3⁺CCR6⁻), Tfh2 (CXCR3⁻CCR6⁻) and Tfh17 (CXCR3⁻CCR6⁺) subsets. All three subsets are supportive of antibody production with Tfh17 and Tfh2 being superior contributor, owing to their naive B cells helper function, whereas Tfh1 cells merely support memory B cells but not naive B cells (22). Within CXCR5⁺ populatuion, the enrichment of Th2 phenotype was the most significant (Supplementary Fig. 2B). Both Tfh1 and Tfh2 revealed downtrend as the COVID-19 aggravated, MPs exhibited significantly higher enrichment level of these two subsets compared with DPs and CDs. Tfh17 declined to practically undetectable level in DPs with no statistical differences among DPs, MPs and SPs, but MPs and SPs showed advantages over CDs (Fig. 3F-G). Generally, the most and least enriched Tfh subsets were observed in MPs and DPs, respectively. TFR cells are thought to share phenotypic characteristics with Tfh and conventional Foxp3⁺ regulatory T cells (Treg) yet are distinct from either. The precise role of TFR in regulating germinal center (GC) and Ab production remains controversial. Similar to the Tfh subsets, TFR percentages in CD45RA⁻or CD4⁺ population both wanned as the COVID-19 progressed, with DPs merely triggered baseline level of TFR (Fig. 3H).Trivial changes were found in other subsets such as Tregs (Fig. 3I). Briefly, the elderly underwent severity-dependent defects in Tfh subsets and TFR during acute COVID-19.

Spearman correlation exhibited that Tfh-cm was negatively correlated with age and WHO ordinal scale score, while correlated positively with SpO2%. Nevertheless, Tfh-em didn’t show correlation with any of the aforementioned indicators. We speculated that Tfh-em is characterized by rapid maturation in the context of antigen exposure, while Tfh-cm is more like a resting but relative sustained status (20), which may comprehensively represent patients’ status. Among Tfh1, Tfh2 and Tfh17, only Tfh2 correlated with age, SpO2% and PaO2/FiO2 (Fig. 3J).

Ab responses and their correlation with TfH subsets

Antibodies (Abs) are key components of effective immune responses against viruses. Specificity or affinity of antibody responses can be fine-tuned via somatic hypermutation and affinity-based positive selection in germinal centers (23). In our study, the magnitude of IgG and IgG subclass profile were similar among all groups, with ancestral S1-specific binding antibodies (BAbs) being slightly lower than corresponding BA.5 BAbs. IgG1 and IgG3 dominated IgG while IgG2 and IgG4 were relative rare, which were consistent with previous reports (Fig. 4A-B) (24, 25).

For neutralizing Abs (NAbs), BA.5 and ancestral-NAbs were robust whereas CH.1.1-NAbs were relative feeble (Fig. 4C). Moreover, IgG was correlated with the corresponding NAbs (Fig. 4D). These results support the fact that BA.5 variant dominated the epidemic wave at the end of 2023 in Tianjin, China (Supplementary Fig. 3). COVID-19 severity influenced the magnitude of NAb titers, which was robust in MPs whereas wanned in SPs and DPs.

Spearman correlation analyses manifested that Tfh-em correlated positively but weakly with ancestral and BA.5-NAbs, while Tfh-cm correlated moderately with the two NAbs (Fig. 4E). These phenomena may be attributed to the “temporal match” between Tfh-cm kinetics and NAb profiles. Serial blood collection from nine of 57 elderly patients (five MPs and four SPs) was performed at < 7, 7–14 and 14–28 day PSO. In this small cohort, we observed that NAbs production was time-dependent, as they surged after 7 day and peaked at approximately 14 day PSO (Fig. 4F). Unlike Tfh-em, which differentiates in the early phase after antigen exposure (< 7 day PSO), Tfh-cm represents the majority of CXCR5⁺ T cells under resting conditions, which peaked at approximately 7–14 day PSO in our study (Fig. 4G). When subdividing all individuals into seven time phases, NAb peaked at approximately 12–15 day PSO and maintained at high level afterwards (Fig. 4H). Tfh-cm increased gradually after COVID-19 onset and peaked at around 8–11 day PSO, conforming to the temporal fluctuation of NAbs to a certain extent (Fig. 4I). Compared with Tfh-em, NAbs production pattern is slightly hysteretic but sustained, similar to that of Tfh-cm.

Tfh1, Tfh2, Tfh17 and TFR all correlated positively with ancestral and BA.5-NAb but not CH.1.1 (Fig. 4E). Recent study unveiled that TFR curtailed cytotoxic TFH responses, which express high levels of granzyme B akin to CD8⁺ cytotoxic T cell and have the potential of killing B cells in the GC (26). In our study, we did not detect cytotoxic Tfh subsets, however, we found that as the severity of COVID-19 increased, TFR decline was accompanied by a slight increase of CD4⁺GZMB, which correlated negatively with NAb titers (Fig. 4E). These results may be indicative of the relationship between TFR and NAb, that is, TFR deficiency underlay the insufficient restraint of cytotoxic Tfh, which consequently dampened GC B response and led to NAb generation impediment. However, the conclusion has not been confirmed.

Vaccination contributed to Tfh subsets recovery and NAbs elevation

We divided 55 out of 57 patients with available vaccination records into age- and sex-matched groups according to SARS-CoV-2 vaccination history. The cohort details are summarized in Table 2. Increased CD4⁺ rather than CD8⁺ T may have facilitated the minor elevation of CD3⁺ T among vaccinees (Fig. 5A). We wanted to investigate which CD4⁺ subsets are responsible for the raise of CD4⁺T. No differences of activated CD4⁺T cells or TM subpopulations were found between non-vaccinated individuals and vaccinees (Fig. 5B-C). Further investigation revealed that total Tfh, Tfh-cm and Tfh-em, increased synchronously in vaccinated individuals (Fig. 5D). Percentage of Tfh2 raised significantly, Tfh1 and Tfh17 also presented upward trend although the differences were not statistically significant (Fig. 5E). TFR percentage of CD45RA⁻ was elevated, accompanied by a mild reduction of GZMB⁺CD4⁺ cells (Fig. 5F). Treg frequency was not affected by vaccination (Fig. 5G).

BAbs remained similar between groups whereas ancestral and BA.5-NAbs were significantly higher in the vaccinated individuals (Fig. 5H-I).We also divided CDs into non-vaccinated and vaccinee cohorts, and found that BAbs remained unchanged, whereas ancestral and BA.5-NAbs were significantly boosted by vaccination (Fig. 5J-K).

Taken together, vaccination attenuated Th imbalance by increasing percentages of Tfh-em, Tfh-cm, Tfh2, TFR, and augmented the magnitude of ancestral and BA.5 NAbs. NAbs, CD4⁺ T cells, and CD8⁺ T cells all have protective roles against SARS-CoV-2 infection, high degree of synergy and redundancy between the three branches of adaptive immunity bring about diverse pathways to robust immunity (15). Hence, albeit the direct relationship between vaccination history and illness status still needs further investigation, it is worth mentioning that vaccination restored the impairment of Tfh homeostasis and promoted NAbs production, which potentially improve the prognosis among the elderly.

Table 2

Demographics and clinical characteristics of non-vaccinated individuals and Vaccinees
Characteristic	Non-vaccinated (n = 15)	Vaccinated (n = 40)	P value
Demographic
Age, median (SD), y	78 (9.8)	73 (8.3)	NS
Sex, No. (%)
Male	8 (53.3)	25 (69.4)	NS
Female	7 (46.7)	11 (30.6)	NS
Comorbidity, No. (%)
Cardiovascular diseases	11 (73.3)	23 (63.9)	NS
Endocrine system disease	7 (46.7)	10 (27.8)
Nervous system disease	5 (33.3)	11 (30.6)
Other chronic comorbidity	1 (6.7)	9 (25.0)
Oxygen saturation, median (SD), %	90 (5.8)	89 (7.7)	NS
PaO2/FiO2, median (SD), mmHg	190 (89.0)	213 (109.0)	NS
Maximum oxygen treatment, No. (%)
No oxygen therapy	0 (0.0)	1 (2.8)	NS
Mask / nasal prongs	12 (80.0)	23 (63.9)
Noninvasive/ high-flow ventilation	1 (6.7)	3 (8.3)
Invasive ventilation	2 (13.3)	9 (25.0)
Hospitalized status, No. (%)
ICU	5 (33.3)	16 (44.4)	NS
Non-ICU	10 (66.7)	20 (55.6)	NS
Duration of hospitalization	48 (58.4)	44 (26.2)	NS
APACHE II score^a	25 (6.8)	28 (7.6)	NS
Positive Nucleic acid qPCR at sampling, No. (%)	15 (100.0)	36 (100.0)	NS
NS, no significance;
Quantitative parameters were analyzed using t test or non-parametric Mann-Whitney tests according to data distribution; categorical variables were analyzed using Chi-square test or Fisher's exact test.

Coordinated SARS-CoV-2-specific adaptive immune responses are associated with mild COVID-19, indicating the protective roles of CD4⁺ and CD8⁺ T cells in patients with COVID-19 (27). In this study, we made a “head to head” comparison of patients in different states of illness and recovered individuals, and demonstrated severity- or age-dependent impairment of CD4⁺ homeostasis whereas CD8⁺ profiles had not been altered evidently. CD4⁺ T cell pool is functionally and phenotypically heterogeneous. Accumulating evidence has demonstrated that SARS-CoV-2-specific CD4⁺ T cells are strongly associated with lessened COVID-19 severity (27), and that the rapid induction of SARS-CoV-2-specific CD4⁺ T cells in the acute phase correlates with mild disease and accelerated viral clearance (28). In contrast, a strikingly extended absence of SARS-CoV-2-specific CD4⁺ T cells is associated with severe or fatal COVID-19 (27–29).

According to our observations, the severity-dependent decline of Tfh subsets seemed responsible for the CD4 disturbance. Specifically, frequencies of Tfh-em and Tfh-cm displayed downtrend as the COVID-19 progressed, with deceased individuals exhibited critical impairment of the two clusters. The proportion of Tfh-em and Tfh-cm to CXCR5⁺ population displayed a classical immunokinetics pattern of Tfh hierarchy: Quantitatively, Tfh-cm stood out as the preponderant circulating Tfh cells, representing a “quiescent and stand-by” state of Tfh, which were previously described as relatively unpolarized cells at an early stage of effector differentiation, lacking the expression of activation markers and costimulatory molecules (30–33). However, the resting Tfh-cm hold the capability of converting into effector-like phenotypes and CCR7^loPD-1^hi Tfh-em cells have been recognized as the bona fide Tfh counterpart to foster antibody production against antigen exposure or re-encounter (20, 34). In our study, Tfh-cm, but not Tfh-em, correlated significantly with patients’ age and other clinical characteristics including hypoxemia degree and NAb levels. Although Tfh-em correlated weakly with NAbs, no correlation was observed between it and other clinical characteristics. We deem that Tfh-cm is more likely to serve as a reliable biomarker for comprehensively evaluating the severity status of patients with COVID-19. Moreover, these results emphasize the importance of Tfh-cm in antibody promotion, as previously reported that CXCR5⁺ TCM efficiently induces plasma cell differentiation and Ab secretion (35). On the other hand, EM-like Tfh cells are particularly sensitive to antigen exposure, which decreased significantly without antigen stimulation and reinvigorated evidently following antigen re-encounter. Tfh-cm, however, is normally maintain at high level over several months after vaccination, suggesting that Tfh-em and Tfh-cm have the potential of acting as key biomarkers to evaluate the effectiveness and longevity of COVID-19-specific responses, respectively (36). Taken together, Tfh-cm and Tfh-em were all susceptible to immunosenescence after COVID-19, in the context of Tfh perturbance, inherent traits of the two clusters, and the temporal coincidence between Tfh-cm and NAb, make Tfh-cm a more suitable biomarker to denote patients’ status and NAb magnitude.

Another key observation of our study was that the frequency of TFR decreased as COVID-19 worsened. Yeonseok et al. demonstrated that TFR generated from Foxp3⁺ natural Treg precursors and this GC-specific Treg cells suppress the differentiation of GC B cells in follicles in vivo (37). Michelle et al. interpreted the so-called “GC inhibitory” function of TFR as a “GC regulation” propertiy, suggested that TFR cells are specialized in controlling GC reaction by limiting the scale of Tfh response and inhibiting selection of non-antigen-specific B cells including those carrying self-reactive receptors, to guarantee the dominance of antigen-specific clones in GC response and formation of long-lived plasma cells and memory B cells (38). Moreover, the Tfh-limiting effect of TFR is also reflected in restraining cytotoxic Tfh, which were significantly triggered in hospitalized COVID-19 patients and correlated negatively with SARS-CoV-2- specific Ab levels (39). Overall, based on our results, we speculated that Tfh subsets deficiency undermined Ab production whereas TFR decline led to uncontrolled cytotoxic Th cell elevation, which conspired to NAbs insufficiency in patients with poor condition.

Vaccination is the primary defense against SARS-CoV-2 infection, particularly among older adults with chronic comorbidities (40). By July/August 2022, 92.3% of the Chinese population aged ≥ 60 years had received at least one COVID-19 vaccination (41). According to our study, vaccination contributed to the alleviation of CD4⁺ disturbance, which was primarily manifested as the reinvigoration of Tfh-cm, Tfh-em, Tfh2 and TFR. Universally, immunosenescence in the elderly is considered to affect vaccine performance, howbeit we considered that the relationship between immunosenescence and vaccine potency are interdependent rather than unilateral, that is, vaccination can reverse the immunophenotypic imbalance of older adults. Due to the small sample size of non-vaccinated individuals, we did not subdivide the patients at different severity status into non-vaccinated and vaccinated groups. Hence, the influence of vaccination in MPs, SPs, and DPs can not be elaborated. Furthermore, the relationship between vaccination history and prognosis requires further investigation.

Our study has some limitations. First, more CD4⁺ subsets should be included in the flow cytometry assay for an in-depth exploration of CD4⁺ T cell profile. Moreover, the existing cell population we detected could be refined, for instance, activated or SARS-CoV-2-specific Tfh and TFR could be measured by addition of activation markers or cytokines, which may supplement our existing conclusion. Second, we illustrated the relationship between age (or severity) and immunophenotype to a certain degree, but the direct correlation between immunophenotype and prognosis remains to be investigated.

Taken together, through a comprehensive assessment of adaptive immunity across COVID-19 severity spectrum, we systematically interpretated the immunodynamic changes in the elderly following SARS-CoV-2 infection. As COVID-19 aggravated, the extent of Th deficiency worsened progressively. Attenuation of Tfh subsets and TFR, which promoted NAb production and suppressed cytotoxic Tfh, respectively, jointly contributed to the wanned NAb magnitude. Moreover, CD4 subset imbalance was reversed to certain extents through vaccination. Regarding global aging, our observations provide evidence of the significance of vaccination among the geriatric. Moreover, Changes like neutrophil-to-lymphocyte or urea-to-creatinine ratio have been identified as stand-alone parameters for risk stratification of COVID-19 (42). Our results thus inspired us to interrogate whether the immunophenotypic changes in Tfh subsets and/or TFR can potentially be used as senescence-aging related biomarkers to distinguish severity in the elderly, which may provide reference for COVID-19 diagnosis and treatment.

China has seen drastic changes in the COVID-19 maintaining policies. People older than 60 years are at greater risk of poor COVID-19 outcomes due to immunosenescence, comorbidities and/or inadequate vaccine coverage. Immune profile analyses of geriatric across the spectrum of COVID-19 severity manifested severity-dependent impairment of Tfh subsets and TFR. SARS-CoV-2 vaccination helped to regain the CD4⁺ homeostasis. Our results suggest that Tfh subsets, which can be boosted through vaccination, may act as biomarkers to forecast NAb level and patient outcome.

HDs

healthy donors

MPs

moderate patients

SPs

severe patients

DPs

deceased patients

CDs

convalescent donors

PSO

post symptom onset

Ethics approval and consent to participate

This study was approved by The Medical Ethics Committee of Tianjin Medical University General Hospital (IRB2023-YX-019-01) and was conducted in accordance with the Declaration of Helsinki, International Ethical Guidelines for Biomedical Research Involving Human Subjects and local regulatory requirements. Informed consent was obtained from all enrolled individuals

Consent for publication

Not applicable

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files

Competing interest

The authors declare that they have no competing interests

Funding

This research was supported by the National Natural Science Foundation of China (32070926, 82241065) to D.Z.

Authors' contributions

D.Z., C.W., M.X., and Y.W. conceived the project and designed the experiments; Y.W., Q.W., F.H., and M.X. performed the experiments. Y.W., F.H., D.Z. and X.L., contributed to data analyses. N.Q., L.W., L.S., W.D., Y.L. and X.P. assisted experiments performance, J.H., C.H., G.Y., C.P., and Z.H. assisted sample collection and information acquisition. Y.W. drafted the manuscript. D.Z. edited the manuscript.

Acknowledgement

We are grateful to the person who volunteered for this study, to all participants of the dedicated medical team.

Amuthavalli Thiyagarajan J, Mikton C, Harwood RH, Gichu M, Gaigbe-Togbe V, Jhamba T et al. The UN Decade of healthy ageing: strengthening measurement for monitoring health and wellbeing of older people. Age Ageing. 2022;51(7).
Chen Y, Klein SL, Garibaldi BT, Li H, Wu C, Osevala NM, et al. Aging in COVID-19: Vulnerability, immunity and intervention. Ageing Res Rev. 2021;65:101205.
Zhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, et al. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021;76(2):533–50.
The Lancet Healthy L, After. COVID-19: what next for older people in China? The Lancet Healthy Longevity. 2023;4(2).
Napoli C, Benincasa G, Criscuolo C, Faenza M, Liberato C, Rusciano M. Immune reactivity during COVID-19: Implications for treatment. Immunol Lett. 2021;231:28–34.
Anderson JJ, Susser E, Arbeev KG, Yashin AI, Levy D, Verhulst S, et al. Telomere-length dependent T-cell clonal expansion: A model linking ageing to COVID-19 T-cell lymphopenia and mortality. EBioMedicine. 2022;78:103978.
Walter LO, Cardoso CC, Santos-Pirath ÍM, Costa HZ, Gartner R, Werle I, et al. T cell maturation is significantly affected by SARS‐CoV‐2 infection. Immunology. 2023;169(3):358–68.
Gao Yd, Ding M, Dong X, Zhang Jj, Kursat Azkur A, Azkur D, et al. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy. 2020;76(2):428–55.
Sattler A, Angermair S, Stockmann H, Heim KM, Khadzhynov D, Treskatsch S, et al. SARS-CoV-2-specific T cell responses and correlations with COVID-19 patient predisposition. J Clin Invest. 2020;130(12):6477–89.
Balachandran H, Phetsouphanh C, Agapiou D, Adhikari A, Rodrigo C, Hammoud M, et al. Maintenance of broad neutralizing antibodies and memory B cells 1 year post-infection is predicted by SARS-CoV-2-specific CD4 + T cell responses. Cell Rep. 2022;38(6):110345.
Wang Y, Zhang D, Du G, Du R, Zhao J, Jin Y, et al. Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. Lancet. 2020;395(10236):1569–78.
Bacher P, Rosati E, Esser D, Martini GR, Saggau C, Schiminsky E, et al. Low-Avidity CD4(+) T Cell Responses to SARS-CoV-2 in Unexposed Individuals and Humans with Severe COVID-19. Immunity. 2020;53(6):1258–71e5.
Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE et al. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. 2020;369(6508).
Pradhan SK, Kamble NM, Pillai AS, Gaikwad SS, Khulape SA, Reddy MR, et al. Recombinant nucleocapsid protein based single serum dilution ELISA for the detection of antibodies to infectious bronchitis virus in poultry. J Virol Methods. 2014;209:1–6.
Sette A, Crotty S. Adaptive immunity to SARS-CoV-2 and COVID-19. Cell. 2021;184(4):861–80.
Ramljak D, Vukoja M, Curlin M, Vukojevic K, Barbaric M, Glamoclija U et al. Early Response of CD8 + T Cells in COVID-19 Patients. J Personalized Med. 2021;11(12).
Adamo S, Michler J, Zurbuchen Y, Cervia C, Taeschler P, Raeber ME, et al. Signature of long-lived memory CD8 + T cells in acute SARS-CoV-2 infection. Nature. 2021;602(7895):148–55.
Grifoni A, Weiskopf D, Ramirez SI, Mateus J, Dan JM, Moderbacher CR, et al. Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell. 2020;181(7):1489–501e15.
Walker LSK. The link between circulating follicular helper T cells and autoimmunity. Nat Rev Immunol. 2022;22(9):567–75.
He J, Tsai Louis M, Leong Yew A, Hu X, Ma Cindy S, Chevalier N, et al. Circulating Precursor CCR7loPD-1hi CXCR5 + CD4 + T Cells Indicate Tfh Cell Activity and Promote Antibody Responses upon Antigen Reexposure. Immunity. 2013;39(4):770–81.
Song W, Craft J. T follicular helper cell heterogeneity: Time, space, and function. Immunol Rev. 2019;288(1):85–96.
Gao X, Luo K, Wang D, Wei Y, Yao Y, Deng J et al. T follicular helper 17 (Tfh17) cells are superior for immunological memory maintenance. eLife. 2023;12.
Victora GD, Nussenzweig MC. Germinal centers. Annu Rev Immunol. 2012;30:429–57.
Tejedor Vaquero S, de Campos-Mata L, Ramada JM, Díaz P, Navarro-Barriuso J, Ribas-Llaurado C et al. The mRNA-1273 Vaccine Induces Cross-Variant Antibody Responses to SARS-CoV-2 With Distinct Profiles in Individuals With or Without Pre-Existing Immunity. Front Immunol. 2021;12.
Farkash I, Feferman T, Cohen-Saban N, Avraham Y, Morgenstern D, Mayuni G et al. Anti-SARS-CoV-2 antibodies elicited by COVID-19 mRNA vaccine exhibit a unique glycosylation pattern. Cell Rep. 2021;37(11).
Xie MM, Fang S, Chen Q, Liu H, Wan J, Dent AL. Follicular regulatory T cells inhibit the development of granzyme B–expressing follicular helper T cells. JCI Insight. 2019;4(16).
Rydyznski Moderbacher C, Ramirez SI, Dan JM, Grifoni A, Hastie KM, Weiskopf D, et al. Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity. Cell. 2020;183(4):996–1012e19.
Tan AT, Linster M, Tan CW, Le Bert N, Chia WN, Kunasegaran K et al. Early induction of functional SARS-CoV-2-specific T cells associates with rapid viral clearance and mild disease in COVID-19 patients. Cell Rep. 2021;34(6).
Braun J, Loyal L, Frentsch M, Wendisch D, Georg P, Kurth F, et al. SARS-CoV-2-reactive T cells in healthy donors and patients with COVID-19. Nature. 2020;587(7833):270–4.
Schaerli P, Willimann K, Lang AB, Lipp M, Loetscher P, Moser B. CXC chemokine receptor 5 expression defines follicular homing T cells with B cell helper function. J Exp Med. 2000;192(11):1553–62.
Kim CH, Rott LS, Clark-Lewis I, Campbell DJ, Wu L, Butcher EC. Subspecialization of CXCR5 + T cells: B helper activity is focused in a germinal center-localized subset of CXCR5 + T cells. J Exp Med. 2001;193(12):1373–81.
Rivino L, Messi M, Jarrossay D, Lanzavecchia A, Sallusto F, Geginat J. Chemokine receptor expression identifies Pre-T helper (Th)1, Pre-Th2, and nonpolarized cells among human CD4 + central memory T cells. J Exp Med. 2004;200(6):725–35.
Breitfeld D, Ohl L, Kremmer E, Ellwart J, Sallusto F, Lipp M, et al. Follicular B helper T cells express CXC chemokine receptor 5, localize to B cell follicles, and support immunoglobulin production. J Exp Med. 2000;192(11):1545–52.
Gong F, Dai Y, Zheng T, Cheng L, Zhao D, Wang H, et al. Peripheral CD4 + T cell subsets and antibody response in COVID-19 convalescent individuals. J Clin Invest. 2020;130(12):6588–99.
Chevalier N, Jarrossay D, Ho E, Avery DT, Ma CS, Yu D, et al. CXCR5 Expressing Human Central Memory CD4 T Cells and Their Relevance for Humoral Immune Responses. J Immunol. 2011;186(10):5556–68.
Zhou P, Cao C, Ji T, Zheng T, Dai Y, Liu M et al. Longitudinal analysis of memory Tfh cells and antibody response following CoronaVac vaccination. JCI Insight. 2023;8(15).
Chung Y, Tanaka S, Chu F, Nurieva RI, Martinez GJ, Rawal S, et al. Follicular regulatory T cells expressing Foxp3 and Bcl-6 suppress germinal center reactions. Nat Med. 2011;17(8):983–8.
Linterman MA, Pierson W, Lee SK, Kallies A, Kawamoto S, Rayner TF, et al. Foxp3 + follicular regulatory T cells control the germinal center response. Nat Med. 2011;17(8):975–82.
Meckiff BJ, Ramírez-Suástegui C, Fajardo V, Chee SJ, Kusnadi A, Simon H, et al. Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4 + T Cells in COVID-19. Cell. 2020;183(5):1340–53e16.
Dhama K, Patel SK, Natesan S, Vora KS, Iqbal Yatoo M, Tiwari R, et al. COVID-19 in the elderly people and advances in vaccination approaches. Hum Vaccines Immunotherapeutics. 2020;16(12):2938–43.
Wang G, Yao Y, Wang Y, Gong J, Meng Q, Wang H, et al. Determinants of COVID-19 vaccination status and hesitancy among older adults in China. Nat Med. 2023;29(3):623–31.
Lynch SM, Guo G, Gibson DS, Bjourson AJ, Rai TS. Role of Senescence and Aging in SARS-CoV-2 Infection and COVID-19 Disease. Cells. 2021;10(12).

No competing interests reported.

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Impaired homeostasis of T follicular helper cells in elderly patients with COVID-19

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

Background

Methods

Study participants and design

Blood sample collection and processing

S1 protein production

Enzyme-linked immunosorbent assay (ELISA)

Peptide pool

Flow cytometry

SARS-CoV-2 pseudovirus neutralization assay

RNA extraction and phylogenetic analysis

Statistical analysis

Results

Lymphopenia and altered CD4⁺T cell profile

Activation signatures of T cells in geriatric

Severity-dependent impairment of Tfh subsets and TFR homeostasis

Ab responses and their correlation with TfH subsets

Vaccination contributed to Tfh subsets recovery and NAbs elevation

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Impaired homeostasis of T follicular helper cells in elderly patients with COVID-19

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

Background

Methods

Study participants and design

Blood sample collection and processing

S1 protein production

Enzyme-linked immunosorbent assay (ELISA)

Peptide pool

Flow cytometry

SARS-CoV-2 pseudovirus neutralization assay

RNA extraction and phylogenetic analysis

Statistical analysis

Results

Lymphopenia and altered CD4+T cell profile

Activation signatures of T cells in geriatric

Severity-dependent impairment of Tfh subsets and TFR homeostasis

Ab responses and their correlation with TfH subsets

Vaccination contributed to Tfh subsets recovery and NAbs elevation

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Lymphopenia and altered CD4⁺T cell profile