Background Rheumatoid arthritis (RA) has been shown to pathologically modify the human lung environment. Individuals with RA have been shown to have higher incidence of respiratory infections and worse resultant patient outcomes. Objective We investigated whether single-cell transcriptional signals within sputum distinguished healthy lungs from those of patients with RA before and after infection. Methods Sputum samples were collected at both baseline (study enrollment) and 1 month following respiratory infection. Expectorated sputum was fixed at time of sampling and sequenced via a 10x Flex single-cell RNA sequencing protocol. Cells were clustered via transcriptomic signal, and cell types were identified via canonic markers. Differentially expressed genes within cell types between disease state and timepoints were grouped into coexpressed gene modules, and their relative expression and putative functions were described. Results A total of 5 female donors (2 healthy and 3 with RA) were included. A mean of 5,773 cells per donor were captured, resulting in a total of 23,094 high-quality cells included in this study. The samples comprised 5 major cell types: 2 distinct macrophage populations, a neutrophil population, and minor populations of both B and T cells. There were no statistically significant differences in proportion of cell types between samples from healthy donors and those from patients with RA or between baseline and postinfection timepoints. However, gene modules of significantly differentially expressed genes between groups revealed transcriptional differences between groups that were associated with neutrophil function, including NETosis and inflammatory responses. Conclusion Immune cell proportions in donors with RA and in healthy donors are similar both before and after infection. However, transcriptional differences within lung neutrophils persist up to 30 days following respiratory infection.

A recent clinical study tested the effects of two different monoclonal antibodies (mAbs) (siltuximab, anti-IL6; tocilizumab, anti-IL6R) on the fate and function of T-cells in people with type 1 diabetes. While both mAbs affect the response of T-cells to stimulation, they have very different, sometimes opposing mechanisms. Here, we use mass-spectrometry based proteomics to analyze longitudinal serum samples (baseline and two weeks post-treatment) from 20 clinical trial participants to examine the effects of siltuximab and tocilizumab on extracellular vesicles. To accomplish this, serum samples were enriched for extracellular vesicles with Mag-Net and analyzed by LC-MS/MS to identify significantly differentially abundant protein groups and pathways. Proteome analysis confirmed highly reproducible measurements across multiple draw dates. In total, we quantified >3300 protein groups of which 46 protein groups had significantly altered abundance after mAb treatment. Tocilizumab altered pathways associated with proteostasis (neddylation) and pre-notch transcription and translation. Siltuximab altered FCGR activation pathway members. In addition, quantitation of the monoclonal antibody therapies themselves enabled the measurement of the correlation between drug amounts and impacted proteins. Taken together, this work demonstrates the utility of the Mag-Net method to evaluate the impacts of therapeutic interventions on serum extracellular vesicles.

Ulcerative colitis (UC) is characterized by epithelial barrier dysfunction and dysregulated mucosal immune responses; however, the mechanisms driving disease onset remain poorly defined. Autoantibodies against the epithelial-restricted integrin αvβ6 are a highly specific biomarker of UC that can precede clinical diagnosis by up to 10 years. Because αvβ6 activates TGFβ at epithelial surfaces, we hypothesized that UC-associated αvβ6 autoantibodies inhibit mucosal TGFβ activation and disrupt epithelial homeostasis. We showed that αvβ6 autoantibodies were enriched in UC and that IgG from autoantibody-positive individuals inhibited αvβ6-dependent activation of TGFβ. αvβ6 blockade dampened TGFβ signaling and altered differentiation-associated gene programs in human intestinal epithelial cells. In mice, deletion of αv caused expansion of inflammation-associated goblet cells in the colon and changes in intestinal immune cells. Using a novel mouse model, we showed that αvβ6-specific autoantibody disrupted epithelial-immune crosstalk and increased susceptibility to DSS colitis. Together, these findings establish anti-αvβ6 autoantibodies as active inhibitors of epithelial TGFβ signaling, constituting a de facto anti-cytokine response, rather than passive biomarkers. By linking preclinical seropositivity to impaired epithelial signaling and heightened susceptibility to colitis, this work identifies epithelial αvβ6-dependent TGFβ activation as a pathway that may be leveraged to modify disease risk or limit disease severity.

Abstract Introduction Single-cell chromatin accessibility (scATAC-seq) profiles genome-wide regulatory elements that shape immune cell identity and function, but its interpretation is currently limited by low cell type resolution and small reference datasets. Existing datasets annotate fewer than 20 immune cell types and are too coarse to resolve heterogeneity and characterize cell type-specific gene regulatory programs and functions. Here, we present a large-scale scATAC-seq resource that substantially improves immune cell annotation and regulatory inference. Methods By integrating matched-donor scRNA-seq and scATAC-seq data from human peripheral blood mononuclear cells (PBMCs) with trimodal TEA-seq (single-cell ATAC, RNA, and surface protein), we classified 36 immune cell types, including 4 myeloid, 6 B cell, 5 NK cell, 6 CD4 T cell, and 15 CD8 T cell subtypes. Cell frequencies from published scRNA-seq and new scATAC-seq labels were highly correlated (median ρ = 0.84). Labels were applied to our longitudinal multi-modal dataset of 206 samples spanning over 3 million PBMCs from 78 healthy human donors. Results We used these annotations to define baseline epigenetic states, age-associated differences, and epigenetic changes following influenza vaccination. Our analysis revealed extensive sets of differentially accessible tiles and enriched transcription factor motifs that define cell type-specific regulatory identities. Linking these chromatin regions and transcription factors to differentially expressed target genes enabled the construction of gene regulatory circuits associated with cell type, aging, and vaccination. Additionally, we trained a classification model for high resolution cell type labeling and doublet detection in new scATAC-seq datasets. Conclusion Together, this multi-modal atlas and associated cell type-labeling model provide an unprecedented reference for immune cell gene regulatory circuits and a valuable resource for exploring the epigenome of human immune cells. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)

IntroductionThe glutamic acid decarboxylase autoantibody (GADA) test is a widely used marker to differentiate type 1 diabetes and type 2 diabetes in adults. We conducted a systematic review to estimate the sensitivity and specificity of the presence of GADA for T1D in adults with newly diagnosed diabetes.MethodsWe conducted a PubMed and Embase search for studies that report both the GADA result and type 1 versus type 2 diabetes in adults diagnosed with diabetes. We calculated the sensitivity and specificity for each study.ResultsWe identified 19 studies involving 11,760 patients from diverse geographic settings. Across these studies, the sensitivity of GADA for identifying adult-onset type 1 diabetes varied widely (range: 0.27 to 0.83), with a pooled estimate of 0.53 (95% CI: 0.46–0.60). In contrast, specificity was consistently high, with a pooled estimate of 0.93 (95% CI: 0.89–0.96). The positive and negative likelihood ratios were 7.3 (95% CI 4.8 to 11.3) and 0.51 (0.44 to 0.58), respectively.DiscussionOur review demonstrates that GADA has high specificity and moderate sensitivity for identifying adult-onset type 1 diabetes. As a limitation, factors such as assay choice and cut-off values, as well as heterogeneity of both the type 1 and type 2 diabetes groups with regard to unmeasured genetic influences may contribute to the variability in antibody prevalence between studies. Our pooled likelihood ratios for GADA results might be useful for developing clinical and algorithmic tools to distinguish adult-onset type 1 diabetes from type 2 diabetes.

Introduction and Objective: T cells play a primary role in the autoimmune-driven loss of insulin-producing beta cells in Type 1 diabetes (T1D). Levels of T stem cell memory (TSCM) cells, which can continually give rise to effector T cells, are elevated in T1D subjects, including in disease-relevant antigen-specific cells. We hypothesized that TSCM may indicate or contribute to active disease, and tested if circulating TSCM levels were associated with natural progression or response to immunotherapy in subjects with T1D stages 1, 2, and 3. Methods: Cytometry data from multiple TrialNet (KQ1, TN18, TN19) and ITN (EXTEND) studies in at-risk individuals and new onset T1D were re-analyzed to gate for CD4 and CD8 TSCM cells at baseline and later time points. We used linear and generalized additive models to test for associations between baseline TSCM frequencies or changes in TSCM levels and disease progression or treatment response, as measured by transitions from stage 1/2 to 2/3 and metabolic outcomes including C-peptide quantitative response (QR) in stage 3 disease. Models included age, sex, BMI, and number of T1D autoantibodies as covariates. Results: TSCM levels were stable within individuals across several years in the absence of immune-modifying treatments but were strongly perturbed by some immunotherapies. Baseline CD8 TSCM levels were marginally associated with C-peptide loss in placebo-treated stage 3 T1D in an age-dependent manner, but this was not supported in an independent validation dataset. We did not find evidence of CD4 or CD8 TSCM levels associating with treatment response, or with progression in other stages of T1D. TSCM levels did correlate with age and BMI, but their inclusion as covariates did not affect associations with disease variation. Conclusion: Circulating TSCM levels do not clearly associate with disease progression or treatment response in T1D stage 1, 2, and 3, suggesting elevated levels of TSCM may not be linked to disease pathogenesis. Disclosure M. Dufort: None. J. Chen: None. A. Long: Advisory Panel; Current; Diamyd. Consultant; Current; Sanofi. Research Support; Ended; Novo Nordisk. Consultant; Current; SAB Biotherapeutics, Inc. C. Speake: Consultant; Ended; GentiBio. Advisory Panel; Ended; Sanofi. Consultant; Current; Cour Pharmaceuticals. Research Support; Ended; Cour Pharmaceuticals. K.S. Cerosaletti: Research Support; Current; Cour Pharmaceuticals, GentiBio, Inc, InduPro Therapeutics. Consultant; Ended; Mozart Therapeutics. Other - Research collaboration supported by Breakthrough T1D grant funding; Ended; Base5 (Benthic) Genomics. Research Support; Ended; IM Therapeutics. Funding American Diabetes Association (11-22-IBSPM-17)

OBJECTIVE Hyperglycemia during acute pancreatitis (HDAP) likely reflects both stress hormone responses and pancreatic islet injury, distinguishing it from typical stress-induced hyperglycemia. The aim of this study was to determine the prevalence of HDAP and its prognostic significance for early-onset diabetes following acute pancreatitis (AP). RESEARCH DESIGN AND METHODS Diabetes Related to Acute Pancreatitis and Its Mechanisms (DREAM) is a prospective multicenter study examining the development of diabetes following AP. This analysis included 395 participants without prior diabetes with an AP episode, focusing on their glucose levels during the event. Two definitions of HDAP were examined: peak glucose >140 mg/dL (HDAP140) and >200 mg/dL (HDAP200). Outpatient glycemic status after recovery (median: 111 days post-AP) was evaluated using fasting glucose, oral glucose tolerance test, and HbA(1c). RESULTS HDAP140 and HDAP200 were present in 37.5% and 7.1% of participants, respectively. Age, race, etiology, and AP severity were significant predictors of HDAP140. Among participants with HDAP140, 14.8% developed early-onset diabetes after AP recovery vs. 1.2% in those without (P = 0.0001). In those with HDAP200, 42.9% developed early-onset diabetes vs. 3.5% in those without (P = 0.0001). The absence of HDAP140 and HDAP200 was associated with negative predictive values of 99% and 97%, respectively, for diabetes. CONCLUSIONS HDAP can be common in individuals without diabetes and is associated with early-onset diabetes following AP. Individuals without HDAP have a low risk of diabetes short term, while those with HDAP200 are at high risk. Monitoring glycemia during AP can identify individuals best suited for early targeted postdischarge care.

BACKGROUND:Plasma proinsulin concentrations are used to investigate hypoglycemia. They have also been proposed as a marker of β-cell function, particularly as a ratio with C-peptide. Immunoassays remain the primary method for measuring proinsulin despite potential limitations. Mass spectrometry-based assays have been described, but are semiquantitative or rely on nano-flow liquid chromatography. We aimed to develop a liquid chromatography-tandem mass spectrometry assay (LC-MS/MS) at typical clinical laboratory flow rates to quantify proinsulin and its partially processed forms in human plasma and ensure accuracy over time with distributable well-characterized calibrators. METHODS:Sample preparation consists of protein precipitation, Glu-C digestion, peptide immunoaffinity enrichment, and LC-MS/MS analysis of 2 surrogate peptides: RGFFYTPKTRREAE spans the cleavage site for des-31,32-proinsulin and GSLQKRGIVE spans the site for des-64,65-proinsulin. A purified protein calibration material was characterized (HPLC and amino acid analysis) and used to value-assign a matrix-matched single-point calibration material. RESULTS:Within-batch and between-batch imprecision were ≤12.2% and ≤16.4%, respectively. The assay was linear from 0.14 to 53.5 pM and 1.1 to 91.6 pM, with a lower limit of the measuring interval of 2.8 and 8.8 pM for RGFFYTPKTRREAE and GSLQKRGIVE, respectively. The concentration of des-31,32-proinsulin increased more during childhood than intact proinsulin. Participants with type 1 diabetes-associated autoantibodies had higher proinsulin-to-C-peptide ratios. Method comparison with 2 commercial immunoassays revealed variable cross-reactivity with insulin. CONCLUSION:The validated assay is robust and will be a useful tool for advancing studies of β-cell function. A detailed standard operating procedure, well-characterized calibration material, and monoclonal antibodies are available for adoption in other laboratories.

Macrophage activation syndrome (MAS) is driven by a hyperinflammatory response characterized by aberrant activation of lymphocytes and phagocytes. While monocytes and macrophages are thought to be important in MAS pathogenesis, their role remains poorly understood. We used bulk and single-cell RNA sequencing (RNA-Seq) on sorted monocytes from children with MAS and healthy controls to identify transcriptional changes during MAS. We defined a MAS signature in classical monocytes that correlated with ferritin and was elevated in monocytes from systemic lupus erythematosus and COVID-19 patients. We also identified a subset of classical monocytes with high levels of interferon-stimulated genes (ISGs) that expanded during MAS. Surprisingly, the transcriptional signature of these cells was driven by type I IFNs, rather than IFNγ. Consistent with this finding, we detected increased levels of circulating IFNβ during MAS, suggesting that IFNβ plays an unrecognized role in driving MAS monocyte responses. We also identified a MAS-associated CD8+ T cell population with a distinctive transcriptional signature. We used cell-cell communication algorithms to predict increased immunoregulatory interactions between monocytes and T cells during MAS. Together, these results provide new evidence for a role for type I IFN during MAS and identify a unique CD8+ T cell population that may contribute to MAS pathophysiology.

Although individuals with Down syndrome (DS) remain highly vulnerable to severe infections, vaccination remains underutilized. Here we review, specific to people with DS, the safety and efficacy of vaccination, drivers of susceptibility to infection, and existing and emerging opportunities to improve vaccine response. We find that vaccines are generally safe and immunogenic in individuals with DS, although continued research is essential to improve vaccine efficacy and health outcomes.

Dietary patterns have been associated with altered risk of diabetes mellitus (DM) in the general population. These patterns can estimate habitual intake of food groups associated with decreased or increased (e.g., greater intake of red meats) risk of DM. This analysis aimed to examine the underexplored associations of four dietary patterns in patients presenting with an acute pancreatitis (AP) diagnosis based on the presence of pre-existing DM. Study participants were selected from the Diabetes RElated to Acute Pancreatitis and its Mechanisms (DREAM) study, an ongoing, multicenter study of adults (18–75 years) with AP in the US. This is a cross-sectional analysis of baseline data collected using the VioScreen computer-administered Food Frequency Questionnaire. We examined four dietary patterns: the Alternate Mediterranean Diet (AMED), the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND), the Healthy Eating Index-2020 (HEI-2020), and the Alternate Healthy Eating Index-2010 (AHEI-2010). Multivariable logistic regression was used to estimate the odds ratios (ORs) and 95

Diurnal Variation of Islet Autoantibody Titers in Established Type 1 Diabetes Suggests Restricted-Time Sampling Improves Aab Measurement and Detection Diurnal islet autoantibody (Aab) variation in type 1 diabetes (T1D) remains poorly understood and could hinder efforts to develop, test and refine new therapies. We sought to describe the extent and pattern of diurnal variation in islet autoantibodies and, having found significant variation, translate to the clinical setting. We conducted two studies in human subjects with established T1D: (1) a prospective study (n = 10) of the range of islet autoantibody and immunoglobulin daily variation within humans and (2) an independent retrospective, cross-sectional study (n = 705) of the effect of time of sample collection on Aab titer and detection in clinical settings. We found that some individuals have wide Aab variations during the day and which can exceed expected levels of inter-assay variation. For some Aab, this variation followed a circadian pattern. We also found that, in clinical settings, time-restricted sampling can lead to increased IA-2A and ZnT8A detection within specific age-groups. We conclude that time-restriction can potentially improve the use of Radiobinding-measured Aabs as biomarkers for the development and monitoring of disease-modifying therapies and in developing islet transplantation strategies in established T1D. Investigation of diurnal Aab variation and time restriction is needed in early-stage disease. .

Aims/hypothesis:Type 1 diabetes is a complex autoimmune disorder in which autoreactive CD4⁺ and CD8⁺ T cells destroy pancreatic beta-cells, resulting in insulin deficiency and hyperglycemia. Although genetic susceptibility, particularly certain HLA alleles, contributes to disease risk, not all genetically predisposed individuals develop Type 1 diabetes. Screening first degree relatives (FDRs) for islet autoantibodies (GAD65, IAA, IA-2, ZnT8) helps detect autoimmune activity. However, these serum markers arise only after T-helper cell activation, limiting early intervention opportunities. Since protein antigen recognition by B cells requires T-helper cell assistance through linked recognition, T cell activation precedes B cell activation and autoantibody production. Activation of these T cells leads to shedding of the immune-regulatory (activation) surface protein LAG-3 (Lymphocyte Activation Gene-3 or CD223), generating its soluble form, sLAG-3, that is detectable in circulation. We hypothesized that sLAG-3 may serve as an early biomarker of autoimmune activity preceding islet autoantibody development in type 1 diabetes. Methods:Plasma sLAG-3 levels were measured longitudinally in female diabetes-prone NOD mice and analyzed in relation to islet antigen-specific CD4⁺ T cell expansion and diabetes onset. To mechanistically link autoreactive T cell activation to sLAG-3 release. Naive autoreactive C6.6.9 TCR-transgenic (TCR-Tg) CD4⁺ T cells were adoptively transferred into NOD.SCID mice and longitudinal assessment for plasma sLAG-3, beta-cell antigen specific CD4⁺ T cell tetramer profiles, and circulating insulin ( Ins2 ) mRNA to determine ongoing beta-cell stress. In parallel, sLAG-3 levels were analyzed from different human cohorts, including FDRs of individuals with type 1 diabetes, using cross-sectional and longitudinal approaches. Results:In murine models, elevated sLAG-3 correlated with expansion of islet-specific CD4⁺ T cells that preceded hyperglycemia and diabetes onset. In the adoptive transfer model, early increases in sLAG-3 and circulating Ins2 mRNA marked immune activation and emerging beta-cell stress prior to overt diabetes. In our human cohorts, sLAG-3 was detectable in autoantibody-negative and single-autoantibody-positive FDRs, with higher levels observed in progressors compared to non-progressors, and associated with high-risk HLA genotypes. Conclusions/interpretation:These findings identify sLAG-3 as a candidate biomarker of early T cell activation in type 1 diabetes that may precede islet autoantibody development. Integration of sLAG-3 with antigen-specific T cell and beta-cell stress markers could improve early risk stratification and inform preventive strategies before substantial loss of beta-cell. Prospective longitudinal studies aligned to seroconversion are required to validate sLAG-3 as a surrogate marker of early disease activity. Research in context:What is already known about this subject?: Before the clinical onset of hyperglycemia, type 1 diabetes is characterized by a prolonged preclinical phase in which autoreactive B and T cells mediate progressive beta-cell destruction.Current risk stratification strategies rely mainly on genetic susceptibility (genomic DNA) and the detection of islet autoantibodies in plasma/serum.Islet autoantibodies arise only after CD4⁺ T cell activation and therefore do not capture the earliest stages of immune dysregulation.Consequently, biomarkers that directly reflect early pathogenic T cell activity prior to, or independent of, seroconversion remain limited and insufficiently validated.What is the key question?: Can plasma sLAG-3 levels, beta-cell antigen-specific CD4⁺ T cell tetramer expression, and circulating Ins2 mRNA serve as very early biomarkers of autoimmune activity in type 1 diabetes and serve to better inform risk stratification, thereby informing preventive intervention strategies for the clinician? What are the new findings?: sLAG-3 increases transiently during early antigen-specific CD4⁺ T cell activation stage, precedes hyperglycemia in mouse models, and is elevated in autoantibody-negative and single-autoantibody-positive first-degree relatives who later progress to type 1 diabetes. sLAG-3 was associated with beta-cell antigen-specific CD4⁺ T cell expansion, assessment of stress induced beta cell Ins2 mRNA release and high-risk HLA genotypes, indicating early autoimmune activation rather than established disease. How might this impact clinical practice in the foreseeable future?: These findings support sLAG-3 as a candidate early biomarker of T cell activation, before or at the earliest stages of islet autoantibody development in some at-risk individuals. Integration of plasma sLAG-3 with beta-cell antigen specific CD4⁺ T cell profiling and insulin mRNA measurements could complement current autoantibody-based screening, improve risk stratification, and enable earlier preventive interventions to preserve beta-cell function in patients at-risk for type 1 diabetes.

The Allen Institute for Immunology was founded in 2018 to perform deep, longitudinal profiling of the human immune system in health and disease. We established partnerships to profile healthy adults and children as well as patients at risk for rheumatoid arthritis, with inflammatory bowel disease, with multiple myeloma diagnosis, under treatment for melanoma, and with COVID-19. We sampled the same subjects longitudinally for up to two years, then performed immune profiling using scRNA-seq, 4 high-dimensional flow cytometry panels, plasma or serum proteomics, and clinical lab tests. In total, we profiled >2,300 samples from >450 subjects, including >55 million cells profiled by scRNA-seq to date. To process, analyze, and distribute this data, we developed the Human Immune System Explorer (HISE) platform, a flexible, scalable, cloud-based framework to enable storage, interactive analysis, visualization, and generation of Certificates of Reproducibility that enable inspection and replay of any step of an analysis workflow. We joined our robust, large-scale analytical platform to public-facing visualization tools, scientific context, and data releases, starting with the Human Immune Health Atlas: an expertly annotated dataset of > 1.3 million PBMCs from 108 healthy donors from 11 to 65 years of age. We invite immunologists to explore this resource and our expanding library of immunology data, insights, and tools at https://explore.allenimmunology.org/. Computational and Systems Immunology (COMP)

Understanding the variability of immune cell composition and responsiveness in health is critical to define changes that predict and explain immune-associated diseases like autoimmunity and cancer. Here, we comprehensively phenotyped a cohort of 100 healthy adults aged 25 to 35 and 55 to 65 years who were longitudinally followed for 10 visits over 2 years. Using mass cytometry, we identified four stable immunotypes derived from cell populations that remained stable within, but differed between, individuals. We characterized these immunotypes using whole-blood RNA sequencing, Olink proteomic profiling, and whole-blood ex vivo stimulation. Although cytomegalovirus (CMV) seropositivity, age, and sex are known to influence the immune landscape, the four immunotypes were not solely determined by these factors. A CMV-dominant immunotype exhibited exaggerated traditional markers of CMV positivity but also features unrelated to CMV positivity, including lower numbers of B cells and B cell-related transcripts. Immunotype was strongly associated with response to ex vivo stimulation with lipopolysaccharide (LPS) but not serological response to influenza vaccination, suggesting that these immunotypes are most relevant in understanding variations in innate immune responsiveness among healthy individuals. Last, we identified an immunotype comprising young females with unusually high LPS responsiveness, mature neutrophil frequency, and increased inflammatory markers. Overall, our findings establish that healthy individuals can exhibit one of four shared immunotypes, defined by adaptive and innate cell populations, that are stable over time, influence the response to innate signals, associate with clinical markers of inflammation, and shed light on overall immune health.

The generation and maintenance of immunity is a dynamic process that is dependent on age1-3. Here, to better understand its progression, we profiled peripheral immunity in more than 300 healthy adults (25 to 90 years of age) using single-cell RNA sequencing, proteomics and flow cytometry, following 96 adults longitudinally across 2 years with seasonal influenza vaccination. The resulting resource generated a single-cell RNA-sequencing dataset of more than 16 million peripheral blood mononuclear cells with 71 immune cell subsets from our Human Immune Health Atlas and enabled us to interrogate how immune cell composition and states shift with age, chronic viral infection and vaccination. From these data, we demonstrate robust, non-linear transcriptional reprogramming in T cell subsets with age that is not driven by systemic inflammation or chronic cytomegalovirus infection. This age-related reprogramming led to a functional T helper 2 (TH2) cell bias in memory T cells that is linked to dysregulated B cell responses against highly boosted antigens in influenza vaccines. Collectively, this study reveals unique features of the immune ageing process that occur prior to advanced age and provides novel targets for age-related immune modulation. We provide interactive tools for exploring this extensive human immune health resource at https://apps.allenimmunology.org/aifi/insights/dynamics-imm-health-age/ .