Characterisation of the dual roles of senescent-like T cells that arise during healthy and unhealthy ageing

Characterisation of the dual roles of senescent-like T cells that arise during healthy and unhealthy ageing | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Natalie E Riddell , Benny Chain , Daniel Harding , Federica M Marelli-Berg , Jonas Bystrom , Melissa Pereira Da Costa , Amaia Carrascal-Miniño , George P Keeling , Truc T Pham , Kavitha Sunassee , View ORCID Profile Rafael TM de Rosales , View ORCID Profile Samantha YA Terry , Melanie Pattrick , Caroline Sutcliffe , Anne Worthington , Gill Hood , Sarah Finer , View ORCID Profile Sian M Henson doi: https://doi.org/10.1101/2025.06.16.659752 Conor Garrod-Ketchely 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lauren A Callender 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK 2 ADC Therapeutics, Imperial College White City Campus , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johannes Schroth 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katie Littlewood 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth C Carroll 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK 3 Atlantic Technological University Sligo , Sligo, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dina Tamsan 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victoria SK Tsang 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Isabell Nessel 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natalie E Riddell 4 School of Biosciences, Faculty of Health Medical Sciences, University of Surrey , Guildford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benny Chain 5 Division of Infection & Immunity, University College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Harding 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK 6 Barts Health NHS Trust , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Federica M Marelli-Berg 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonas Bystrom 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa Pereira Da Costa 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amaia Carrascal-Miniño 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site George P Keeling 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Truc T Pham 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kavitha Sunassee 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rafael TM de Rosales 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rafael TM de Rosales Samantha YA Terry 7 School of Biomedical Engineering & Imaging Sciences, King’s College London, St Thomas’, Hospital , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samantha YA Terry Melanie Pattrick 6 Barts Health NHS Trust , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caroline Sutcliffe 6 Barts Health NHS Trust , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne Worthington 8 Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gill Hood 8 Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Finer 6 Barts Health NHS Trust , London, UK 9 Wolfson Institute of Population Health, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sian M Henson 1 William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sian M Henson For correspondence: s.henson{at}qmul.ac.uk Abstract Full Text Info/History Metrics Preview PDF Abstract Ageing is associated with significant immune changes, with unhealthy ageing characterised by chronic inflammation and immune dysregulation. Here we identify a population of CD8⁺ T EMRA cells during unhealthy ageing, which exhibit features of premature senescence and are regulated in part by TGFβ. These cells show impaired cytotoxic function and altered migratory behaviour, including an increased presence in tissues. TGFβ plays a pivotal role in modulating their phenotype by inducing CD103 expression and downregulating KLRG1, causing these cells to resemble tissue-resident memory cells. This disruption to receptor recycling leads to defective degranulation potentially altering the capacity of these cells to mount an effective immune response. Overall, these findings suggest that T EMRA cells in the context of unhealthy ageing are a pathogenic T cell subset that accumulate in tissues where they are unable to exert an effector function. Introduction For the first time in history, older individuals constitute the fastest growing demographic worldwide. Projections indicate that between 2015 and 2050, the proportion of the global population aged 60 years and older will nearly double, increasing from 12% to 22% 1 . This prolonged life expectancy, together with the increased prevalence of age-related diseases, underscores the critical importance of maintaining an effective immune system throughout ageing. A well-documented consequence of ageing is its profound impact on the T cell compartment 2 , 3 , 4 , 5 . Due to their highly proliferative nature, CD8 + T cells are particularly vulnerable to the effects of ageing and show a high degree of compartmental heterogeneity. One notable consequence is the accumulation of naïve-like memory cells, virtual memory cells (T VM ), which despite being antigen-inexperienced can exert effector functions 6 , 7 . Alongside this is the differentiation of the memory pool that leads to a population of CD8 + T cell that progressively loses functionality with age. The identification of T cell heterogeneity has relied heavily on phenotypic marker profiles, distinguishing stem cell memory cells (T SCM ), central (T CM ) and effector (T EM ) cells along with terminally differentiated effectors that re-express CD45RA (T EMRA ). This classification relies on the combinatorial expression of markers such as CD27, CD28, CCR7, CD45RA and CXCR3 8 , 9 . Senescent-like T EMRA cells, in particular adopt innate-like properties, acquiring the expression of numerous NK receptors that enable TCR-independent cytotoxicity 10 . The increased complexity of T cell populations with age has been further elucidated through advanced ‘omics’ technologies, such as single-cell cytometric and RNA-seq analysis. These approaches, like IMM-AGE and iAge, used in combination with phenotypic markers can model immune senescence providing a high-dimensional trajectory of immune ageing 11 , 12 . The accumulation of T EMRA cells is often driven by repeated antigen stimulation, particularly from chronic infections such as cytomegalovirus (CMV), resulting in memory T EMRA cells with a senescence-associated phenotype 13 , 14 . Immune senescence represents a progressively degenerative state characterised by the loss of replicative capacity or onset of replicative senescence 15 . This process is initiated by a DNA damage response (DDR) resulting from telomere shortening 16 , 17 . Critically short telomeres activate the ataxia-telangiectasia mutated (ATM) and ataxia-telangiectasia and Rad3-related (ATR) kinases, leading to the phosphorylation of histone H2AX (γH2AX) and activation of downstream effectors such as p53, p21 CIP 1 /WAF 1 and retinoblastoma protein (Rb), which collectively enforce a stable cell-cycle arrest characteristic of replicative senescence 18 . Beyond telomere erosion, other intrinsic and extrinsic stressors can also drive cellular senescence through stress-induced premature senescence (SIPS). Oxidative stress, DNA-damaging agents and oncogene activation can induce persistent DDR signalling independent of telomere attrition via activation of p16 INK4A and p53-p21 pathways 19 . While SIPS is not directly driven by telomere shortening, persistent DNA damage signalling, particularly involving γH2AX and 53BP1 foci, can contribute to telomere dysfunction, potentially linking SIPS to telomere-dependent mechanisms in certain contexts 20 . Additionally, metabolic stability plays a key role in determining susceptibility to senescence. Disruptions in metabolic homeostasis through pathways that regulate energy balance, oxidative stress, and mitochondrial integrity all accelerate the onset of senescence 21 . The activation of the metabolic sensor AMPK in senescent T cells, through DNA damage or reduced levels of ATP, heightens endogenous p38 phosphorylation which controls the inflammatory secretome associated with senescence 22 , 23 . Furthermore, the decline in mitochondrial fitness observed in CD8 + T EMRA cells increases dependency on glycolysis and elevates ROS production 24 , 25 . These dysfunctional mitochondria lead to impaired energy production and the accumulation of oxidative stress, promoting senescence-associated phenotypes. Given that senescence is sensitive to cellular metabolic states, we investigated the phenotype and function of T cells in people living with type 2 diabetes (T2D), a condition associated with premature ageing. Studying the differences between healthy and unhealthy ageing is important to understand how metabolic and inflammatory environments shape immune ageing 26 . Healthy ageing is often associated with a more balanced immune response, where homeostatic mechanisms help to maintain effective immune surveillance and reduce chronic inflammation 27 . In contrast, unhealthy ageing is characterized by metabolic dysregulation, persistent low-grade inflammation and immune cell dysfunction. Contributing to an accelerated accumulation of senescent immune cells, which may further exacerbate systemic inflammation and immune dysfunction 27 . We show here the presence of a population of CD8+ T EMRA cells driven to premature senescence by chronic inflammation rather than antigenic stimulation. Functionally, these unhealthy T EMRA cells were found to be pathogenic, exhibiting increased retention in tissues but reduced cytotoxic capacity. Understanding these differences is essential for developing targeted interventions that extend health span and improve the quality of life in ageing populations. Methods Ethics and donor recruitment Approval for our study was granted by the West London & GTAC Research Ethics Committee (20/PR/0921) and all methods were carried out in accordance with approved guidelines and regulations. Written informed consent was obtained for all participants. We recruited healthy old participants (age range: 55 – 75 years, n = 120) and people living with T2D (age range: 55 – 77 years, n = 120), identified through the Diabetes Alliance for Research in England (DARE) database. Atherosclerotic plaques (n = 4) and blood samples from participants with cardiomyopathy (Age range: 52 – 79 years, n = 43) and were approved by the Barts Cardiovascular Registry (REC 14/EE/0007). Peripheral blood was obtained using heparinised tubes and peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll hypaque (Amersham Biosciences). Flow cytometric analysis Flow cytometric analysis was performed using the following antibodies: anti-CD8 PerCP (SK1), anti-CD45RA BV605 (HI100), anti-CD45RA APC (HI1000), anti-CD27 BV421 (O323), anti-CD27 FITC (O323), anti-CD28 BV785 (CD28.2), anti-CCR7 PECy7 (G043H7) anti-CD57 AF700 (HNK-1), anti-NKG2A AF700 (S19004C), anti-NKG2D (1D11), anti-CD49d PE (9F10), anti-CD103 APC (Ber-ACT8), anti-CD69 PE (FN50), anti-perforin APC (dG9), anti-granzyme B FITC (QA16A02) and anti-CD107a APC (H4A3) from BioLegend; anti-KLRG1 PE (MAFA) from Miltenyi Biotec; and anti-NKG2C AF488 (134591) R&D Systems. Live/Dead Fixable Near-IR 775 stain (ThermoFisher). Intracellular staining was carried out using solution AB (ThermoFisher). All samples were analysed using a LSR Fortessa (BD Biosciences) and the resulting data examined using FlowJo software (BD Bioscience). Telomere length assessment by flow-FISH Telomeric DNA was quantified by the incorporation of a nucleic acid telomeric probe (CCCTAA)3 conjugated to Cy5 (TelCy5) by combining flow cytometry with fluorescence in situ hybridization (flow-FISH). PBMCs were stained for 15 min with anti-KLRG1 FITC (REA261, Miltenyi), anti-CCR7 PE (G043H7), anti-CD28 BV786 (CD28.2), anti-CD8 BV605 (SK1), anti-CD27 BV510 (O323), CD45RA BV421 (HI100) from Biolegend and Live/Dead Fixable Near-IR (775) stain (ThermoFisher), after which samples were fixed and permeabilized (Fix & Perm Cell Permeabilisation Kit, Caltag Laboratories). After washing in hybridization buffer (70% deionized formamide, 28·5 mM Tris–HCl pH 7.2, 1·4% BSA and 0·2M NaCl), cells were incubated with 0.75 μg/mL of the PNA telomeric (C3TA2)3 probe conjugated to Cy5 (Panagene). Samples were heated for 10 min at 82°C, rapidly cooled on ice, and hybridized for 1 h at room temperature in the dark. Samples were washed and analysed immediately by flow cytometry. Functional Assays Cytotoxicity was assessed using anti-CD107a APC (H4A3, Biolegend). PBMCs were stimulated with 0.5 µg/µL of anti-CD3 (OKT3) and incubated with anti-CD107a in complete RPMI-1640 media for 45 min at 37°C. 100 µM of monensin (Biolegend) was added and the cells were incubated for a further 4 h and 15 min at 37°C. The cells were washed in PBS and surface and intracellular staining was performed as described above. Where indicated 10ng/mL TGFβ1 (R&D Systems) was added to complete media and cultured with PBMCs overnight. Quantification of TGFβ Total TGFβ1 in serum samples was determined either by flow cytometry using the LegendPlex kit (Biolegend) or ELISA (Abbexa) according to the manufacturer’s instructions. To assess the amount of total TGF-β1, acid activation was performed to isolate free TGFβ1 from its latent state as described previously 28 . Cellular Senescence RT2 Profiler PCR Arrays Unstimulated CD8 + EMRA T cells from T2D and healthy age-matched control participants were isolated using MACS sorting for CD8 + T cells and then FACS sorted using anti-CD27/anti-CD45RA for EMRA isolation. RNA was extracted using the RNAeasy Micro Kit (Qiagen) and pre-amplified using the RT2 PreAMP cDNA Synthesis Kit (Qiagen) and RT2 PreAMP Pathway Primer Mix (Qiagen). The resulting gene-specific cDNA was then analysed using the Cellular Senescence RT2 Profiler PCR Arrays according to the manufacture’s instructions (Qiagen). qPCR of senescence markers RNA from MACS (Miltenyi Biotec) purified CD8+ T cells were isolated using the RNeasy kit (Qiagen) according to the manufacturer’s instructions. Transcripts were quantified using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) and the SsoAdvanced Universal SYBR Green Supermix (Bio-Rd Laboratories) according to the manufacturer’s instructions. The following primers were purchased from IDT. TGFBR1 F: GCTGTATTGCAGACTTAGGACTG; TGFBR1 R: TTTTTGTTCCCACTCTGTGGTT; TGFBR2 F: AAGATGACCGCTCTGACATCA; TGFBR2 R: CTTATAGACCTCAGCAAAGCGAC; TGFBR3 F: TGGGGTCTCCAGACTGTTTTT; TGFBR3 R: CTGCTCCATACTCTTTTCGGG; p53 F: GCC AAG TCT GTG ACT TGC ACG; p53 R: TGT GGA ATC AAC CCA CAG CTG, p21 F: GGC AGA CCA GCA TGA CAG ATT TC; p21 R: CGG ATT AGG GCT TCC TCT TGG, p16 F: CAA GAT CAC GCA AAA ACC TCT G, p16 R: CGA CCC TAT ACA CGT TGA ACT G, BActin F: CAC CAT TGG CAA TGA GCG GTT C; BActin R: AGG TCT TTG CGG ATG TCC ACG T. TCR repertoire analysis Unstimulated CD8 + EMRA T cells from 3 T2D and 3 healthy age-matched participants were isolated using CD8 MACS positive selection followed by FACS sorting using anti-CD27/anti-CD45RA for EMRA isolation. The resulting EMRA T cells were used to create a TCR library according to previously published protocols 29 . The data was analysed using Decombinator V3 and V5, a software package which allows for fast, efficient gene assignment in T cell receptor sequences using a finite state machine 30 . The resulting output was analysed further using R. Diversity indexes, such as Gini-coefficient and Shannon’s entropy were analysed after bootstrapping. 89 Zirconium radiolabelling and PET imaging [ 89 Zr]Zr(oxalate) (Perkin Elmer) was chelated with oxine forming [ 89 Zr]Zr(oxinate) 4 as previously described 31 . CD8+ T cells were isolated by positive selection using the MACS system (MiltenyiBiotec) according to the manufacturer’s instructions and were mixed with [ 89 Zr]Zr(oxinate) 4 with a volume ratio not lower than 30:1 (cells: [ 89 Zr]Zr(oxinate) 4 ). Radioactivity was counted using a Wallac Wizard gamma counter to determine cell-associated radioactivity and the percentage of injected activity (%IA) present in cells was then calculated. 89 Zr-labelled CD8+ T cells were injected intravenously into female Nod scid gamma (NSG, NOD.Cg- Prkdc scid IL2rg tm1Wjl /SzJ) mice aged 6 weeks, average body weight of 20.7 ± 1.5 g (Charles River) at a concentration of 3×10 6 cell/animal under anaesthesia (1.5-2.5% isoflurane in oxygen). Female NSG mice were used in this study as they were found to better support engraftment of human immune cells 32 . Animal experiments were approved by the UK Home Office under The Animals (Scientific Procedures) Act (1986), with local approval from King’s College London Animal Welfare and Ethics Review Body. All procedures complied with relevant guidelines and regulations and have been reported in accordance with the ARRIVE guidelines. Three hours post-injection, mice were re-anaesthetised and placed in a preclinical nanoPET/CT scanner (Mediso) where anaesthesia was maintained and the bed was heated to maintain a normal body temperature. Two hours of PET acquisition (1:5 coincidence mode; 5 ns coincidence time window) were followed by CT. PET-CT was repeated at t = 24 and 72 h. PET/CT images were reconstructed using a Monte Carlo-based full-3D iterative algorithm (Tera-Tomo, 400-600 keV energy window, 1-3 coincidence mode, 4 iterations and subsets) at a voxel size of (0.4 × 0.4 × 0.4) mm 3 and corrected for attenuation, scatter, and decay. Images were co-registered and analysed using VivoQuant v.3.0 (InVicro LLC) capturing maximum intensity projection (MIP) and transverse plane images. If required, background, not associated with anatomy, was manually removed. Spherical VOIs were placed on organs based on the CT image when the spleen tissue was not visible. %IA was calculated to assess overall injected activity in each animal and %IA/mL to determine injected activity distribution and concentration in selected organs. Mice from imaging studies were used for biodistribution studies at 72 h post injection. After culling, organs were dissected, weighed, and γ-counted together with standards prepared from a sample of injected material. The percentage of injected activity per gram injected dose per gram (%IA/g) of tissue was calculated. Plaque digestion Atrial plaques were processed within 2 h of surgery, and digested with 300U/ml Collagenase Type IV (Sigma) and 300U DNase I (Sigma) for 30 min at 37°C. The released cells were strained first through a 100µm then a 40µm cell strainer. CD3+ T cells were isolated by MACS positive selection (MiltenyiBiotec) according to the manufacturer’s instructions. The resulting T cells were then phenotyped by flow cytometry. Statistical analysis GraphPad Prism was used to perform statistical analysis. Statistical significance was evaluated using a Wilcoxon matched-pairs signed rank test or ANOVA followed by Tukey multi-comparison test. Diversity within T cell repertoire data was assessed by calculating the Shannon Diversity Index and dispersion with the Gini index. Both indices were computed using R and compared across conditions using Wilcoxon rank-sum test. Graphs show SD. Differences were considered significant when P was <0.05. Stars signify the following: *p<0.05, **p<0.01, ***p<0.005, ****p<0.001. Code Availability No new code was used to generate the data. TCR analysis was performed using the Decombinator package 33 , while the Circos package 34 was used to generate the Circos plots. Results Heterogeneity of CD8 + T EMRA cells in healthy and unhealthy ageing CD8 + T cell subsets are classified based on their function, location and expression of surface makers, with T cell differentiation often being defined based on CD45RA and CCR7 expression 22 , 35 . Using these markers CD8 + T cells can be divided into naïve (T N ; CD45RA + CCR7 + ), central memory (T CM ; CD 45RA - CCR7 + ), effector memory (T EM ; CD45RA - CCR7 - ) and effector memory CD45RA re-expressing cells (T EMRA ; CD45RA + CCR7 - ) 36 . We initially employed these markers to quantify terminally differentiated T cells in older adults (>55 years) experiencing unhealthy ageing, with T2D serving as a model of premature ageing (Supplementary Figure 1a). In line with previous reports we found a higher percentage of CD45RA/CCR7 defined T EMRA cells in those ageing unhealthy ( Figure 1A ). However, these markers fail to capture the heterogeneity within the T EMRA subset. Therefore, we further defined differentiation using the co-stimulatory receptors CD27 and CD28, which resulted in three populations increasing in differentiation from CD27 + CD28 + (E1) CD27 + CD28 - (E2) through to CD27 - CD28 - (E3) cells. Incorporating these markers revealed that individuals experiencing unhealthy ageing exhibited a significantly higher proportion of highly differentiated E3 cells ( Figure 1B ). Download figure Open in new tab Figure 1. CD8 + T EMRA cells in unhealthy ageing are highly differentiated. (A) Flow cytometry plot and graph showing CD45RA/CCR7 defined CD8 + T cell populations. (B) Flow cytometry plot and graph showing CD27/CD28 expression in the CD45RA + CCR7 - T EMRA population. (C) Histogram and graph showing telomere length in CD45RA/CCR7 defined T EMRA cells. (D) Chord diagrams displaying the frequencies and clonalities of the TCR from T EMRA cells. (E) Graphs showing Shannon diversity index and Gini-coefficient values for CD8 + T EMRA populations. All data compares individuals with and without T2D, was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05, **p<0.01 and ****p<0.001. To confirm that the increase in highly differentiated T EMRA cells was not driven by high glucose levels, we repeated our analysis on individuals with dilated cardiomyopathy (DCM), comparing those with and without T2D. We observed that individuals with cardiomyopathy also had a greater amount of CD45RA/CCR7 defined T EMRA cells, with no significant difference between the T2D and non-T2D groups (Supplementary Figure 1B). Furthermore, these T EMRA cells from unhealthily aged individuals contained a higher proportion of highly differentiated E3 cells (Supplementary Figure 1C). A functional assessment of differentiation further confirmed the highly differentiated nature of T EMRA cells in individuals experiencing unhealthy ageing. Telomere length analysis revealed significant telomere shortening in these cells, indicating increased replicative history and potential senescence ( Figure 1C ). Additionally, TCR sequencing in individuals with T2D compared to healthy older adults demonstrated a shift towards a more oligoclonal repertoire with unhealthy ageing ( Figure 1D , Supplementary Figure 1D), characterised by reduced diversity and the disproportionate expansion of a few dominant T cell clones ( Figure 1E ). These findings further support the advanced differentiation state of T EMRA cells in this context. Interestingly, despite their highly differentiated phenotype anti-CD3 stimulated T EMRA cells from individuals experiencing unhealthy ageing begin to lose markers traditionally associated with T cell senescence, such as KLRG1 and NK receptors, suggesting a divergence from the expected characteristics of highly differentiated CD8 + T cells. KLRG1 an inhibitory receptor commonly associated with replicative senescence and reduced proliferative capacity in T cells was found to be significantly lower in T EMRA cells undergoing unhealthy ageing ( Figure 2A ). Similarly, both the activatory NKG2D and inhibitory NKG2A receptors were also reduced ( Figure 2B ). Notably, this decreased was not due to receptor degradation but rather to a failure in the recycling process. Evident when analysing the ratio of internal to surface-expressed KLRG1, which revealed a high proportion of intracellular KLRG1 within T EMRA cells from indiviudals with T2D ( Figure 2C ). This phenomenon was consistent across individuals with cardiomyopathy, further supporting the finding (Supplementary Figure 1E). Interestingly, KLRG1 is a marker commonly used to define tissue-resident memory T cells (T RM ), and T EMRA cells from both health or unhealthy ageing expressed lower levels of CD103 and CD69 compared to the overall CD8 + T cell population. However, CD8 + T EMRA cells from individuals experiencing unhealthy ageing, showed higher CD103 and CD69 expression than those from healthy individuals indicating a more tissue-resident phenotype (Supplementary Figure 1F). These findings suggest that in the context of unhealthy ageing, T EMRA cells undergo an altered regulatory process that may affect their function and responsiveness to external stimuli. Download figure Open in new tab Figure 2. CD8 + T EMRA cells in unhealthy ageing internalise phenotypic markers associated with senescence. (A) Histogram and graph showing expression of KLRG1 in CD45RA/CCR7 defined CD8 + T EMRA cells. (B) Flow cytometry plots and graphs showing expression of NKG2D and NKG2A in CD45RA/CCR7 defined CD8 + T EMRA cells. (C) Ratio of internal: external expression of KLRG1 in T EMRA cells. (D) Graph showing senescence-associated genes in T EMRA cells that were up or down-regulated > 2-fold using an RT 2 profiler PCR array, n=3 for each group. Genes with a fold change ≥ 2 were considered significant. All data compares individuals with and without T2D, was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05 and **p<0.01. The altered phenotype of T EMRA cells suggests a different pathway driving differentiation in unhealthy ageing. This shift may involve distinct inducers of senescence, such as chronic inflammation and was investigated using a Cellular Senescence RT2 Profiler Array. Unlike classical senescence pathways that rely heavily on p53, our data indicate that CD8 + T EMRA cells from people with T2D arise through a p21-dependent pathway independent of p53 ( Figure 2D ). Additionally, one of the most striking observations from the senescence array was the enhanced expression of TERT , the catalytic subunit of telomerase. TERT was significantly upregulated in T EMRA cells from individuals with unhealthy ageing ( Figure 2D ). This increase in TERT suggests that T EMRA cells may be relying on non-telomere-dependent mechanisms to sustain cell viability and function under stress, potentially compensating for the observed telomeric attrition. TGFβ1 contributes to the formation of T EMRA cells in unhealthy ageing The data presented so far highlight the presence of a CD8 + T EMRA population with reduced KLRG1 expression in unhealthy ageing, where these cells undergo premature senescence rather than the conventional pathway of replicative senescence. Given our previous findings that sera from individuals with T2D contains elevated levels of inflammatory mediators capable of inducing senescence 37 , we investigated whether the inflammatory secretome associated with unhealthy ageing could be driving the emergence of these KLRG1-low T EMRA cells. In particular, we focused on TGFβ1, which is known to induce senescence in a p53-independent but p21-dependent manner 38 . Indeed, TGFβ1 levels were significantly higher in individuals experiencing unhealthy ageing ( Figure 3A , Supplementary Figure 2A), and its presence correlated with the accumulation of T EMRA cells (Supplementary Figure 2B). Furthermore, T EMRA cells from individuals undergoing unhealthy ageing exhibited higher expression of TGFβ receptor 3 (TGFβR3) (Supplementary Figure 3C). In addition to the increased expression of TGFβ1 in T2D-associated T EMRA cells, the RT2 Profiler Array also revealed an upregulation of TGFβ1i1 (transforming growth factor beta 1 induced transcript 1), which enhances TGFβ1 signalling through the inhibition of Smad7 39 (Supplementary Figure 3D). Interestingly, a significant proportion of participants were taking statins, which are known to reduce TGFβ1 levels 40 , 41 . When we stratified the data from individuals without T2D, where there was a more even split between those taking and not taking statins, we found that those on statins had a lower proportion of T EMRA cells (Supplementary Figure 2E). However, while statin use was associated with a reduction in serum TGFβ1 levels in healthy individuals, this effect was not observed in individuals with T2D, (Supplementary Figure 2F), suggesting that the inflammatory environment in T2D may override the TGFβ1-lowering effects of statins. Download figure Open in new tab Figure 3. TGFβ1 regulates the phenotype of CD8 + T EMRA cells in unhealthy ageing. (A) Serum TGFβ1 levels measured by ELISA in individuals with and without T2D. (B) Flow cytometry plot and graph showing CD45RA/CCR7 defined CD8 + T cell populations with and without 10ng/mL TGFβ1. (C) Histogram and graph showing expression of KLRG1 in T EMRA cells with and without TGFβ1. (D) Flow cytometry plots and graphs showing expression of NKG2D and NKG2A in CD45RA/CCR7 defined CD8 + T EMRA cells with and without TGFβ1. (E) Ratio of internal: external expression of KLRG1 in T EMRA cells with and without TGFβ1. (F) Relative gene expression of p53, p21 and p16 using RT-PCR in T EMRA cells with and without TGFβ1. All data was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05, **p<0.01 and ****p<0.001. To determine whether TGFβ1 could induce a premature T EMRA phenotype, we incubated CD8 + T cells with 10 ng/ml TGFβ1 and assessed their differentiation. TGFβ1 treatment led to a significant increase in T EMRA cells ( Figure 3B ), accompanied by phenotypic changes associated with unhealthy ageing, including the reduced expression of KLRG1 expression ( Figure 3C ) as well as lower levels of both NKG2D and NKG2A ( Figure 3D ). Incubation with TGFβ1 also increased the ratio of intracellular to extracellular KLRG1 levels ( Figure 3E ). Additionally, TGFβ1 led to an upregulation of p21 but not p53 ( Figure 3F ), reinforcing the idea that TGFβ1-induced senescence in T EMRA cells occurs through a p53-independent, p21-dependent pathway. These findings suggest that TGFβ1-driven signalling plays a critical role in shaping the premature senescence phenotype of T EMRA cells in unhealthy ageing. T EMRA cells in unhealthy ageing are functionally impaired TGFβ can influence adhesion by modulating the expression of cell adhesion molecules and by influencing extracellular matrix (ECM) synthesis and remodelling 42 . To determine whether elevated TGFβ1 levels contribute to altered trafficking and tissue residency of T EMRA cells in unhealthy ageing, we investigated its impact on migration patterns and integrin expression. We isolated CD8+ T cells from both healthy older adults and individuals with T2D and radiolabelled them with zirconium-89 ([⁸⁹Zr]Zr-(oxinate)₄). These ⁸⁹Zr-labelled CD8 + T cells were then injected into NOD scid gamma (NSG) mice at an average activity of 24 ± 18 kBq, and their movement was tracked using PET imaging. PET scans revealed a significantly greater accumulation of ⁸⁹Zr-labelled CD8 + T cells from individuals with T2D ( Figure 4A ). Download figure Open in new tab Figure 4. CD8 + T EMRA cells in unhealthy ageing show enhanced adhesion in tissues. (A) Representative images of NSG mice injected intravenously with 89 Zr-labelled 3 × 10 6 CD8 + T cells from individuals with and without T2D. Mice were subjected to whole-body preclinical PET at 3 h, 24 h and 72 h. (B) Ex vivo biodistribution of 89 Zr-labelled CD8+ T cells isolated from individuals with and without T2D 72 h after CD8 + T cell administration. (C) Graphs showing the image-based analysis of percentage injected activity (%IA) and injected activity/mL, calculated for the whole-body and spleen tissue VOIs of 2 mice per group at 24 and 72 h after the administration of radioactive CD8 + T cells. (D) Graph showing adhesion-related genes in T EMRA cells that were up-regulated > 2-fold using an RT 2 profiler PCR array. Genes with a fold change ≥ 2 were considered significant. Data compares individuals with and without T2D, n = 3 for each group. (E) Histogram and graph showing expression of CD49d in T EMRA cells from individuals with and without T2D. Data was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05 and ****p<0.001. Biodistribution analysis showed that the highest concentration of ⁸⁹Zr-labelled CD8 + T cells was found in the spleen, with a significantly higher accumulation of CD8 + T cells from individuals with T2D compared to those from healthy older individuals ( Figure 4B ). To assess whether this increased accumulation resulted from differences in migration speed, we conducted serial PET scans and calculated the percentage injected activity (%IA) over time ( Figure 4C ). This analysis confirmed that while most of the radioactivity was retained within the whole-body volume of interest (VOI), there was no evidence of increased migration kinetics of CD8 + T cells from individuals with T2D. Instead, the higher accumulation in the spleen suggests altered retention or preferential homing of these cells influenced by the inflammatory environment in the context of unhealthy ageing. This shift in trafficking dynamics may be linked to collagen remodelling, a process regulated by TGFβ. We show that CD8 + T EMRA cells from individuals with unhealthy ageing exhibited increased expression of collagen genes, COL1A1 and COL3A1, ( Figure 4D ) and the integrin CD49d ( Figure 4E ), indicating a shift toward enhanced tissue adhesion and retention. The enhanced tissue accumulation of T EMRA cells in unhealthy ageing is pathogenic, as evidenced by their increased presence in atherosclerotic plaques ( Figure 5A ), where they exhibit a premature senescent phenotype characterised by reduced KLRG1 expression ( Figure 5B ). Despite showing similar levels of the cytotoxic granules perforin and granzyme B ( Figure 5C ), T EMRA cells from individuals with unhealthy ageing exhibited significantly reduced CD107a expression ( Figure 5D ), indicating a defect in degranulation rather than granule production. This impaired degranulation was found to be TGFβ1-mediated, as incubation of T EMRA cells with TGFβ1 from healthy individuals induced a similar decrease in CD107a expression (Supplementary Figure 2G). Furthermore, TGFβ induced signalling has been shown to suppress CD69 expression on numerous immune cells 43 . Consistent with this, we observed a downregulation of CD69 expression on T EMRA cells from individuals with unhealthy ageing ( Figure 5E ). This reduction in CD69 may contribute to an altered activation state of T EMRA cells, potentially promoting their retention in secondary lymphoid organs. As a result, this dysregulated activation may impair their ability to respond effectively to external stimuli, ultimately decreasing their activation and overall immune function. Download figure Open in new tab Figure 5. CD8 + T EMRA cells in unhealthy ageing are pathogenic. (A) Atherosclerotic plaques and data showing the amount of CD8 + CD45RA/CCR7 defined T EMRA cells contained in them compared to peripheral blood, n=4. (B) Graph showing KLRG1 expression in the T EMRA subset comparing peripheral blood and plaque. (C) Flow cytometry plot and graph showing granzyme B and perforin expression in the CD45RA + CD27 - T EMRA population in individuals with T2D, dilated cardiomyopathy compared to those without disease. (D) Flow cytometry plots and graphs showing expression of CD107a and (E) CD69 in individuals with and without T2D. Data was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05. Collectively, these data demonstrate that in unhealthy ageing CD8 + T EMRA cells undergo significant changes in both phenotype and function, rendering them less capable of carrying out effector functions. This highlights the immune dysfunction that characterises unhealthy ageing and underscores the impact of altered T cell behaviour on immune responses in age-related diseases. Discussion Ageing is accompanied by significant changes in immune function, but the trajectory of these changes differs between healthy and unhealthy ageing. In healthy ageing, the immune system undergoes gradual adaptations while maintaining sufficient responsiveness to infections and immune challenges. In contrast, unhealthy ageing is characterized by excessive immune dysregulation, including chronic inflammation and metabolic stress 26 . Here, we identify a population of CD8⁺ T EMRA cells in unhealthy ageing that downregulate conventional phenotypic markers of T cell senescence and instead exhibit features of premature senescence, regulated in part by TGFβ. These T EMRA cells in unhealthy ageing undergo functional changes, becoming more tissue-resident like, yet display an impaired ability to degranulate, potentially compromising their effector function. An important mechanism for preserving immune protection in older individuals is the enhanced expression of NK receptors on CD8⁺ T EMRA cells, which help maintain their cytotoxic capacity and compensate for the decline in T cell repertoire diversity 44 . However, we identify a subset of CD8⁺ T EMRA cells in unhealthy ageing that lose NK receptor expression, leading to a corresponding loss of cytotoxicity. Interestingly, our findings contrast with a study that reported an age-related increase in granzyme K (GZMK)-expressing CD8⁺ effectors 45 . However, this study was conducted in healthy, lean older adults, suggesting that its conclusions may not fully reflect the immune alterations seen in unhealthy ageing. Despite the loss of NK receptors, CD8⁺ T EMRA cells in unhealthy ageing were found to be highly differentiated both phenotypically and functionally, exhibiting shorter telomeres and a more oligoclonal repertoire than T EMRA cells from individuals undergoing healthier ageing. A decline in TCR diversity is typically driven by homeostatic expansion of the peripheral T cell pool following lymphopenia, often in response to IL-7 and IL-15 46 . However, homeostatic expansion can also be triggered by self-antigens, contributing to the development of autoimmunity 47 . Indeed, we show here that T EMRA cells from individuals with T2D exhibited a higher frequency of publicly expanded TCR sequences, particularly those utilising TRAV1-2. This TCR bias gives the repertoire a germline-like quality, characterised by low diversity, high generation probability and frequent publicness. Such features are commonly associated with TCRs recognising conserved microbial antigens, suggesting that chronic exposure to microbial or microbiota-derived antigens may shape the TCR repertoire 48 . The enrichment of public TCRs also raises the possibility of shared, antigen-driven immune responses that could contribute to the development or perpetuation of inflammation in unhealthy ageing. Interestingly, telomerase reverse transcriptase (TERT) the catalytic subunit of the enzyme telomerase was found to be higher in CD8 + T EMRA cells during unhealthy ageing, despite these cells having shorter telomers. This suggests that TERT may play an extra-telomeric role in this context 49 . Telomere-independent activities of TERT have been shown to influence many essential cellular processes, such as gene expression, signalling pathways, mitochondrial function and cell survival 50 , 51 , 52 . Additionally, TERT can interact with the NF-κB pathway contributing to inflammation 53 , which may further support the inflammatory secretome produced by the CD8⁺ T EMRA cells during unhealthy ageing 37 . Our study suggests that CD8 + T EMRA cells are generated through TGFβ1 stimulation in a p21 dependent manner. TGFβ signalling is important both for immunity, where it controls the differentiation of CD8 + T cells, as well as regulating senescence 54 . The role of TGFβ in senescence has been well documented; being involved in cell proliferation, cell cycle regulation, the production of reactive oxygen species (ROS), DNA damage repair, telomere regulation, unfolded protein response (UPR), and autophagy 55 . Importantly, TGFβ is a potent inducer of premature senescence activating the Smad pathway responsible for upregulating p21 56 . It is also secreted as part of the inflammatory senescence-associated secretome, which perpetuates senescence and age-related pathologies through autocrine and paracrine signalling. Here, we show that T EMRA cells from unhealthy ageing express higher levels of all three TGFβ receptors together with increased amounts of TGFβ1i1. TGFβ1i1 functions as a transcription cofactor that along with Smad7 regulates p21 expression 57 . Notably, we found that TGFβ1 is a key driver of the altered T EMRA phenotype in unhealthy ageing, particularly through the internalisation of NK receptors. This aligns with previous findings demonstrating that TGFβ signalling in both mouse and human CD8⁺ T cells downregulated KLRG1 expression 58 . Additionally, TGFβ may contribute to the observed oligoclonality in T EMRA cells, as it promotes IL-7-dependent survival of low affinity T cells 59 . Together, these findings reinforce the link between TGFβ-driven inflammation and premature senescence in unhealthy ageing. TGFβ not only shapes the phenotypic and functional properties of CD8⁺ TEMRA cells but also modulates their migratory behaviour by influencing adhesion molecule expression. Indeed, we show that CD8⁺ TEMRA cells from individuals experiencing unhealthy ageing accumulate in tissues at a higher frequency, suggesting a shift toward a more tissue-restricted phenotype. This may be driven by TGFβ-induced upregulation of CD103, a heterodimeric transmembrane complex that binds to E-cadherin facilitating tissue retention 60 . Interestingly, E-cadherin also serves as a ligand for KLRG1, creating a competitive interaction between these two molecules. While KLRG1 engagement inhibits effector T cell function, CD103 binding to E-cadherin enhances cell-cell interactions 61 . This acquisition of CD103 aligns T EMRA cells during unhealthy ageing closely with tissue-resident memory T cells. T RM cells are generated from KLRG1 lo memory precursors, which are either KLRG1 - effectors or KLRG1 + effectors that have lost KLRG1 expression (ExKLRG1). However, CD8⁺ T EMRA cells during unhealthy ageing are unlikely to be ExKLRG1-derived T RM cells, as these normally retain high cytotoxic and proliferative capacity 62 . Instead, these T EMRA cells exhibit diminished effector function and cytotoxicity, suggesting an alternative differentiation pathway influenced by the inflammatory environment of unhealthy ageing. We propose that TGFβ plays a central role in driving this phenotype by inducing CD103 expression while simultaneously downregulating KLRG1, a process documented to be dynamically regulated by intrinsic and extrinsic signals 62 . Collectively, these findings support the notion that CD8⁺ TEMRA cells formed during unhealthy ageing represent a pathogenic subset that contribute to immune suppression. In addition to their altered migratory and adhesion properties, CD8 + T EMRA cells during unhealthy ageing exhibit a loss of cytotoxic potential, further compromising their ability to mount effective immune responses. Recent work has demonstrated that TGFβ suppresses NK cell function, and that knockout of the common signalling mediator SMAD4 resulted in NK cells with enhanced cytotoxic actitity 63 . CD8⁺ T EMRA cells in unhealthy ageing show defective degranulation. Given that we demonstrate a failure in receptor recycling in this context, it is possible that the inflammatory environment may regulates endocytosis through cytoskeletal remodelling or SNARE-mediated vesicle trafficking 64 . TGFβ has been shown to inhibit NKG2D-mediated cytotoxicity without altering the expression of perforin or Fas ligand in NK cell 65 . This occurs via multiple mechanisms, including downregulation of NKG2D transcripts, increased maturation of miR-1245, which binds the 3’-UTR of NKG2D to repress its expression, or reducing DAP10 levels, a key adaptor required for NKG2D surface stability 66 . Within the setting of unhealthy ageing, these disruptions in cytotoxic function contribute to the accumulation of dysfunctional T EMRA cells with altered capacity to provide effective local immune surveillance. In recent years, the experimental elimination of senescent cells has gained a lot of attention, as these studies show that the clearance of senescent cells increases healthy lifespan 67 , 68 . Senescent cells can be recognised and eliminated by many different immune cell types including CD8 + T cells facilitated by their NK receptor expression 10 . However, the appearance of a population of CD8 + T EMRA cells which have reduced NK receptor expression and poor cytotoxic capability in unhealthy ageing may lead to incomplete elimination of senescent cells with age. It would be interesting to assess whether removing inflammation alone would be sufficient to restore CD8⁺ T EMRA cells to their previous state or whether prolonged exposure to inflammation induces irreversible changes. However, even if inflammation removal allowed these cells to revert, achieving this clinically remains challenging, as the outcomes of anti-inflammatory therapies have been largely inconclusive 69 . For instance, while anti-TNFα therapy has shown benefits in mouse models of insulin resistance and T2D 70 , its therapeutic effects in humans have been limited 71 , 72 . Moreover, anti-TNFα treatment for rheumatoid arthritis is associated with an increased risk of serious infections 73 . Furthermore, we show here that statins can reduce TGFβ levels in healthy ageing, but fail to do so in unhealthy ageing, suggesting that chronic inflammation alters TGFβ regulation in a way that resists conventional intervention. Consequently, a more targeted approach is needed to successfully manipulate CD8⁺ T EMRA cells in unhealthy ageing. Achieving this will require a deeper understanding of the molecular mechanisms driving their generation and persistence in this context. Author Contributions CKG, LAC, SMH wrote the manuscript. CKG, LAC, JS, KL, ECC, DT, VSKT, IN, NER, BC, JB, MPDC ACM, GPK, TTP, KS, RTMdR, SYAT, SMH designed and performed the experiments, as well as analysing the data and reviewed the manuscript. DH, FMB, CS, AW, GH, SF provided samples and reviewed the manuscript. Conflict of interest LAC is currently employed by ADC Therapeutics. All work was undertaken while LAC was at QMUL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary Figure Legends Download figure Open in new tab Supplementary Figure 1. Phenotypic characterisation of CD8 + T cell subsets in unhealthy ageing. (A) Dot plots showing gating strategy. (B) Graph showing CD45RA/CCR7 defined CD8 + T cell populations and (C) CD27/CD28 expression in the CD45RA + CCR7 - TEMRA population in individuals with dilated cardiomyopathy (DCM) with and without T2D compared to those without disease. (D) The proportion of the TCR repertoire with public sequences in people with and without T2D. (E) The proportion of T EMRA cells that express KLRG1 in individuals with dilated cardiomyopathy with and without T2D compared to those without disease. (F) Flow cytometry plots and graph showing the proportion of CD103/CD69 defined T RM cells in the total CD8 + population compared to the TEMRA subset. Data was analysed using ANOVA followed by Tukey multi-comparison test or a Wilcoxon matched-pairs signed rank test and expressed as mean ± SD. *p<0.05, **p<0.01, ****p<0.001. Download figure Open in new tab Supplementary Figure 2. The role of TGFβ on CD8 + T EMRA cells in unhealthy ageing. (A) Serum TGFβ levels measured by Legend plex in individuals with and without T2D. (B) Correlation between serum TGFβ levels and the proportion of CD45RA/CD27 defined CD8 + T EMRA cells. A line of best fit was generated using simple linear regression analysis. The coefficient of determination (R² = 0.34, p = 0.0067) is shown to indicate goodness of fit. (C) Relative gene expression of TGFβ receptors using RT-PCR in T EMRA cells from individuals with and without T2D. (D) Graph showing the upregulation of TGFβ and TGFβI|I genes in T EMRA cells using an RT2 profiler PCR array. Genes with a fold change ≥ 2 were considered significant. Data compares individuals with and without T2D, n = 3 for each group. (E) Graph showing the proportion of CD45RA/CD27 defined T EMRA cells in individuals taking statins compared to those who do not. (F) Serum TGFβ levels measured by Legend plex split into individuals with and without T2D and whether they take statins. (G) Flow cytometry plot and graph showing expression of CD107a in CD45RA/CCR7 defined T EMRA cells with and without 10 ng/mL TGFβ1. All data was analysed using a Wilcoxon matched-pairs signed rank test and is expressed as mean ± SD. *p<0.05 and **p<0.01. Acknowledgments This work was supported by the British Heart Foundation (FS/15/69/32043, LAC), the Academy of Medical Science (SBF001\1013, ECC, SMH), Barts Charity (MGU0536, CGK, SMH and G-002143 JS, SMH), Diabetes UK (19/0006057, JB, SYAT, SMH) and the BBSRC (BB/X009610/1, VT, SMH). DH is supported by the British Heart Foundation (BHF) Clinical Research Training Fellowship (FS/CRTF/20/24058) and FMB is supported by the BHF (CH/15/2/32064, RG/20/8/34995 and AA/18/5/34222). We thank the CRUK Flow Cytometry Core Service at Barts Cancer Institute (Core Award C16420/A18066). Additionally, this work was funded in part by the EPSRC programme for next generation molecular imaging and therapy with radionuclides (EP/S032789/1), the Wellcome/EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z), a Wellcome Trust Multiuser Equipment Grant (212885/Z/18/Z). The nanoPET/CT scanner at KCL was funded by an equipment grant from the Wellcome Trust (WT 084052/Z/07/Z). Finally, we thank the research participants for their commitment to this study and their generous donation of blood samples. Funder Information Declared British Heart Foundation, https://ror.org/02wdwnk04 , FS/15/69/32043 , FS/CRTF/20/24058 , CH/15/2/32064 , RG/20/8/34995 , AA/18/5/34222 Academy of Medical Sciences, https://ror.org/00c489v88 , SBF001\1013 Barts Charity , MGU0536 , G-002143 Biotechnology and Biological Sciences Research Council, https://ror.org/00cwqg982 , BB/X009610/1 Wellcome Trust , WT 212885/Z/18/Z , WT 084052/Z/07/Z , WT 203148/Z/16/Z Engineering and Physical Sciences Research Council , EP/S032789/1 References 1. ↵ World Health, O. World report on ageing and health . World Health Organization : Geneva , 2015 . 2. ↵ Callender , L.A. et al. Human CD8(+) EMRA T cells display a senescence-associated secretory phenotype regulated by p38 MAPK . Aging Cell 17 ( 2018 ). 3. ↵ Zhang , H. , Weyand , C.M. & Goronzy , J.J . Hallmarks of the aging T-cell system . 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Share Characterisation of the dual roles of senescent-like T cells that arise during healthy and unhealthy ageing Conor Garrod-Ketchely , Lauren A Callender , Johannes Schroth , Katie Littlewood , Elizabeth C Carroll , Dina Tamsan , Victoria SK Tsang , Isabell Nessel , Natalie E Riddell , Benny Chain , Daniel Harding , Federica M Marelli-Berg , Jonas Bystrom , Melissa Pereira Da Costa , Amaia Carrascal-Miniño , George P Keeling , Truc T Pham , Kavitha Sunassee , Rafael TM de Rosales , Samantha YA Terry , Melanie Pattrick , Caroline Sutcliffe , Anne Worthington , Gill Hood , Sarah Finer , Sian M Henson bioRxiv 2025.06.16.659752; doi: https://doi.org/10.1101/2025.06.16.659752 Share This Article: Copy Citation Tools Characterisation of the dual roles of senescent-like T cells that arise during healthy and unhealthy ageing Conor Garrod-Ketchely , Lauren A Callender , Johannes Schroth , Katie Littlewood , Elizabeth C Carroll , Dina Tamsan , Victoria SK Tsang , Isabell Nessel , Natalie E Riddell , Benny Chain , Daniel Harding , Federica M Marelli-Berg , Jonas Bystrom , Melissa Pereira Da Costa , Amaia Carrascal-Miniño , George P Keeling , Truc T Pham , Kavitha Sunassee , Rafael TM de Rosales , Samantha YA Terry , Melanie Pattrick , Caroline Sutcliffe , Anne Worthington , Gill Hood , Sarah Finer , Sian M Henson bioRxiv 2025.06.16.659752; doi: https://doi.org/10.1101/2025.06.16.659752 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Immunology Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17650) Bioengineering (13871) Bioinformatics (41881) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22476) Immunology (17713) Microbiology (40331) Molecular Biology (17148) Neuroscience (88473) Paleontology (666) Pathology (2827) Pharmacology and Toxicology (4816) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)