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Possibilities of using T-cell biophysical biomarkers of ageing

Published online by Cambridge University Press:  16 September 2022

Blanca González-Bermúdez
Affiliation:
Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain Department of Materials Science, E.T.S.I. de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain Instituto de Investigación Sanitaria Hospital Clínico San Carlos, IdISSC, Madrid, Spain
Aldo Abarca-Ortega
Affiliation:
Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain Department of Materials Science, E.T.S.I. de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain Departamento de Ingeniería Mecánica, Universidad de Santiago de Chile, Santiago, Chile
Mónica González-Sánchez
Affiliation:
Department of Genetics, Physiology and Microbiology, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, E-28040 Madrid, Spain
Mónica De la Fuente
Affiliation:
Department of Genetics, Physiology and Microbiology, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, E-28040 Madrid, Spain
Gustavo R. Plaza*
Affiliation:
Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain Department of Materials Science, E.T.S.I. de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain Instituto de Investigación Sanitaria Hospital Clínico San Carlos, IdISSC, Madrid, Spain
*
Author for correspondence: Gustavo R. Plaza, E-mail: gustavo.plaza@upm.es
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Abstract

Ageing is interrelated with the development of immunosenescence. This article focuses on one of the cell sets of the adaptive immune system, T cells, and provides a review of the known changes in T cells associated with ageing. Such fundamental changes affect both cell molecular content and internal ordering. However, acquiring a complete description of the changes at these levels would require extensive measurements of parameters and, furthermore, important fine details of the internal ordering that may be difficult to detect. Therefore, an alternative approach for the characterisation of cells consists of the performance of physical measurements of the whole cell, such as deformability measurements or migration measurements: the physical parameters, complementing the commonly used chemical biomarkers, may contribute to a better understanding of the evolution of T-cell states during ageing. Mechanical measurements, among other biophysical measurements, have the advantage of their relative simplicity: one single parameter agglutinates the complex effects of the variety of changes that gradually appear in cells during ageing.

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Ageing and the decline of the immune system during lifespan are two interrelated processes, leading to the development of deleterious immune responses. There is abundance of evidence that the survival of an organism is largely dependent on a well-functioning immune system, such as the fact that elderly people are more prone to a variety of diseases, including failure to clear infected cells, senescent cells and malignant transformed cells, as a consequence of age-associated immunological changes (Ref. Reference Minciullo1). This phenomenon is broadly referred to as immunosenescence, which is the sum of changes affecting the functional decline of immune parameters observed in all mammals studied so far (Ref. Reference Aw, Silva and Palmer2). In this article we focus on one of the cell sets of the adaptive immune system, T cells, reviewing biophysical markers of ageing.

Why to measure biophysical features of T cells

Surface molecules known as clusters of differentiation (CDs) are the most widely used biomarkers of T-cell status. To date, nearly 400 clusters of differentiation have been named using CD nomenclature, ranging from CD1 to CD372 (Ref. Reference Clark3). The expression of these proteins and other chemical biomarkers are the main markers currently available for T cells. For instance, key markers that have been used to identify senescent T cells are loss of CD28, which enables quick interactions during immunological synapses by binding with ligands present at antigen-presenting cells, such as CD80 of the B7 family, and increased expression of CD57 (Ref. Reference Rodriguez4). Biochemical changes observed in old adult T cells include an increase in proinflammatory cytokines and decrease in telomere length (Ref. Reference Rodriguez4). Nevertheless, although molecule properties and molecule content are chief markers of T-cell state, part of the effects produced by T cells and many of the changes they suffer when performing immune tasks may be labelled with the adjective physical, since these effects and changes are associated with geometry, movement, deformation, forces, etc.

Reasonably, the physical features and changes depend on the biochemical characteristics of the cells, that is, molecular characteristics and content, and also on how the molecules are ordered. This internal ordering may be included among the physical properties of the cells. A complete description of the T-cell state would include both molecule content and internal ordering. Examples of internal-ordering features are densities of molecules in different parts of the cell, sizes of organelles and other geometrical aspects, such as the thickness of the cytoskeletal cortex. However, important fine details of the internal ordering may be difficult to detect by such geometrical analyses. Furthermore, these fine details may be associated with specific molecules (e.g. proteins in the cytoskeleton, in focal adhesions or in the nuclear lamina) and a comprehensive characterisation of the cells based on biochemical (quantification of the content of particular molecules) and internal-ordering (size and distribution of organelles and cell structures) parameters would require an extremely large number of parameters. Therefore, an alternative approach for the biochemical–biophysical characterisation of cells consists of performing physical measurements for the whole cell, such as deformability measurements or migration-dynamics measurements: the physical parameters, complementing the commonly used chemical biomarkers, may help to better understanding the evolution of T-cell state during ageing.

The importance of physical features of T cells, and their direct relationship with cell function, is evident when considering their stages of differentiation, migration, proliferation and defensive actions. These routes are represented schematically in Figure 1. The figure shows a brief synthesis of the variety of states of differentiation of T cells. Various studies have proposed that, at least in the case of CD8+ T cells, T-cell differentiation proceeds in a progressive, irreversible manner, from naive T cell, to memory stem T cell, central memory T cell, effector memory T cell and finally effector T cell (Refs Reference Restifo and Gattinoni5, Reference Crompton6). After recovery from an infection, long-lived memory T cells remain in the body indefinitely, being able to generate a rapid response in the case of a secondary infection (Refs Reference Flynn7, Reference Woodland and Kohlmeier8). For these cells, the factors that determine tissue residence or lymph-vessel migration are not well known (Ref. Reference Teijeira9). Section ‘Direct relationship between biophysical features and T-cell function’ is devoted to the relationships between biophysical properties and T-cell function. Later in this article, the relationships between ageing-related biophysical changes in T cells and their function are analysed in Section ‘Enhancing the biochemical picture: biophysical principles of T-cell ageing’.

Fig. 1. Scheme of the main steps of differentiation, migration and proliferation of T cells. After being selected in the thymus, naive T cells are present in the lymphatic and the circulatory systems. Following activation in a lymphatic node by encountering an antigen presenting cell, naive T cells become effector T cells and some of them migrate and act of the infected tissue. The long-lived memory T cells may be found in tissues (tissue resident memory T cells) and in the lymphatic and circulatory systems (central memory T cells).

Direct relationship between biophysical features and T-cell function

The term deformability is commonly used referring to the ability of cells to undergo passive deformations. Here the adjective ‘passive’ is used for cell deformation produced by external forces, whereas the adjective ‘active’ is used for the deformation that the cell undergoes when the contribution of cellular molecular motors is required. Non-muscle myosin, acting as a molecular motor, is of chief importance for migrating cells. The deformability of cells is one aspect of their mechanical properties, and it is important in the many occasions in which they undergo both passive and active deformation. Firstly, in lymph vessels, T cells enter and actively migrate within afferent lymphatic capillaries, being this course affected by inflammation-induced expression of cell surface proteins, but are passively transported in contractile collecting lymphatic vessels (Ref. Reference Teijeira9) and in blood vessels, needing to reach large deformations (Ref. Reference Fung10). In addition to the lumen of the vessels, the active transmigration through the wall of high endothelial venules and migration in tissues and lymph nodes depend also on a sufficient deformability. The deformability is important for both CD4+ and CD8+ T cells since both subsets of cells undergo these same processes. However, the fact that CD4+ T cells outnumber CD8+ T cells in peripheral blood and in lymph vessels (Ref. Reference Jackson11) suggests that deformability and motility could be particularly important for the first subset. The memory CD8+ T cells remain mainly immotile as tissue-resident cells (Ref. Reference Jackson11).

Apart from vessels and healthy tissues, the ability of T cells to migrate in tumours is also of chief importance. T cells penetrate only in the so-called hot tumours, but not in the rest of tumours, known as cold tumours. Hot tumours exhibit an initial immune response that is dampened by upregulation of immune checkpoints or increased numbers of suppressive immune cells (Ref. Reference van der Woude12). It is usually assumed that the ability of T cells, particularly effector CD8+ T cells, to reach and migrate into tumours is basically determined by signalling molecules expressed by a variety of cells (Refs Reference van der Woude12, Reference Nicolas-Boluda and Donnadieu13), and therefore the biophysical features of the T cells are, a priori, of minor importance in regard of the ability to migrate into cold tumours.

The geometrical characteristics, in particular the T-cell size, are of importance considering the suitability for transportation in narrow vessels and migration. The diameter of human capillaries is in the range of 5–10 μm (Ref. Reference Fung10), pore size for endothelial migration of T cells is around 5 μm (Ref. Reference Carman and Springer14) and the average diameter of suspended lymphocytes is approximately 7 μm (Ref. Reference Guillou15). T cells pass through narrow capillaries undergoing large deformations that is assumed to take place at constant volume while increasing their surface, and therefore being the folds of membrane surface reservoirs of critical importance (Ref. Reference Guillou15).

The deformability of cells depends, evidently, on their internal components. In a recent work, the authors proved that the relative size of the nucleus is under certain conditions the most important internal-ordering feature regarding the T-cell deformability (Ref. Reference González-Bermúdez16). That work shows that, for memory CD4+ T cells from mice of similar age, differences in the content of three cytoskeletal proteins – actin, myosin, vimentin – were of very minor importance compared with differences in the relative size of the nucleus.

Measurement of biophysical parameters

The deformability of T cells may be quantified by measuring mechanical parameters. Although the cells are complex and nonhomogeneous, the simplest method is to assess the apparent values of mechanical parameters approaching the cell as if it was a homogeneous material (Refs Reference Plaza17, Reference González-Bermúdez, Guinea and Plaza18). In particular, assuming linear elasticity for sufficiently small deformations, it is possible to assess the apparent Young's elastic modulus, E (a constant that measures the stiffness of the material). A more accurate characterisation of the cell consists of evaluating parameters when time-dependency is considered (viscous models). For the large deformations undergone by the cell, for instance when it flows passively in a capillary, it is possible to assess its apparent viscosity, μ (Ref. Reference Plaza17). There is a variety of techniques to measure mechanical parameters, including micropipette aspiration, atomic force microscopy, optical tweezers (microscopic beads are optically manipulated to produce deformations and measure forces) and microfluidics-based techniques. The micropipette aspiration technique allows easy evaluating the overall deformability of the cell, by aspirating the cell with a microcapillary (Ref. Reference González-Bermúdez, Guinea and Plaza18). Atomic force microscopy generally requires the immobilisation of a cell on a transparent substrate and the use of an inverted microscope to position a cantilever with a probing tip, to indent the cell and measure – typically local – mechanical parameters (Ref. Reference Haase and Pelling19). In the last few decades the use of microfluidic devices has become important (Ref. Reference Urbanska20). In this case, one possibility is to make the fluid with suspended cells to pass through microchannels that display a reduction in their cross section, generating a concentration of the flow and therefore a cell deformation, which can be by contact with the walls of the constrictions or without contact, deforming only by the action of the shear flow.

The cell deformability is key not only when the cells undergo passive deformation but also when they deform actively and produce forces, in particular when they migrate in lymphatic vessels or in tissues. The forces produced by T cells when they interact with other cells may be measured for instance by using microcapillaries (Ref. Reference González-Bermúdez, Guinea and Plaza18). Basic migration experiments may be performed on two-dimensional substrates. The forces exerted by cells during two-dimensional migration may be measured by techniques such as traction force microscopy (forces exerted by cells on a flexible substrate are quantified by observing the displacements of submicrometric beads embedded in the substrate) (Refs Reference Munevar, Wang and Dembo21, Reference Sabass22, Reference López-Fagundo23) or by the use of micropillars (flexible micrometric pillars are used to assess forces exerted by cells) (Ref. Reference Coppola24). Additionally, kinematic parameters of the migration process may be computed by time-lapse microscopy (a series of images is used to analyse the movement of cells). More realistic experiments, replicating better the physiological environment, may be performed by using microchannels and gels (Ref. Reference Hervas-Raluy25).

Approach to the study of age-related changes in T-cells

During ageing, the most evident changes associated with the T-cell population have been described as hallmarks of ageing (see Section ‘Collateral biochemical damage as driver of T-cell ageing’). Most of the changes typically identified in T cells can be labelled as biochemical or functional. In such a description, biophysical changes are not explicitly mentioned, likely because it is assumed that they are an effect of biochemical changes and, in any case, that they are already included in the functional changes. In these three categories (biochemical, functional, mechanical), there are various possible biomarkers to be measured with a variety of available experimental techniques, as explained in the previous section.

The deleterious changes in molecule content predictably affect the properties of the components and organelles and the internal ordering in the cells. Plausibly, biophysical measurements may reflect conveniently these deleterious changes. Mechanical measurements, or other biophysical measurements, have the advantage of a relative simplicity: one single parameter agglutinates the complex effects of the variety of changes introduced in the cells by ageing.

Figure 2 schematically shows a list of types of features studied in T cells to analyse the effects of ageing. A representative list of studies is shown in Table 1. It is important to remark that a reduced number of longitudinal studies have been performed in comparison with cross-sectional studies. This relative lack of longitudinal studies should prompt new works aiming to describe the evolution of the biophysical properties of the immune system (Ref. Reference González-Bermúdez26).

Fig. 2. Main examples of biochemical, functional and biophysical features studied for T cells in research works in which the effect of the age of the donor is considered. The figure is based on Table 1.

Table 1. Representative list of the relatively limited number of works studying T-cell features for various ages of the donors

As shown in Table 1, the study of age-related biophysical changes is very limited. The most widely used parameters are biochemical parameters. In this sense, protein expression and genetic changes in T cells have been extensively studied, focusing on changes relevant for the immune function. The reduction of haematopoietic stem cells and the thymic involution result in a reduced number of circulating naive T cells, and increased frequencies of well-differentiated memory CD28− T cells with limited proliferative potential, that is, loss of naive and central memory phenotype with age at the expense of phenotypically distinct CD8+ effector T cells, being CD4+ T cells more resistant to changes with ageing than CD8 T cells (Ref. Reference Czesnikiewicz-Guzik29). The CD4+/CD8+ ratio decreases with age, and an inverted ratio is associated with short-term mortality (Ref. Reference Wikby34), while reaching an advanced age (over 100 years) was linked to maintaining a high CD4/CD8 ratio (Ref. Reference Strindhall30). Also, it has been proposed that a quantification of biological age of the immune system may be performed by using measurements of intracellular signalling responses to cytokines (Ref. Reference Alpert27). These functional responses decrease with age, significantly from the middle age (between 40 and 60 years). Survival was found to be more significantly associated with this quantification than with DNA methylation (Ref. Reference Alpert27).

Migration studies are extensively performed to characterise functionality of T cells (see Table 1). Young individuals show higher T-cell migration ability compared with old individuals (Ref. Reference González-Bermúdez26). As a second example of functional feature, the CD4+ T and CD8+ T cell response to influenza has also been studied comparing young and old individuals, finding increased levels of many proinflammatory cytokines in old individuals, including interleukin (IL)-6 (Ref. Reference Deng33).

Internal ordering is related firstly to material distribution in the cytoplasm and nucleus. For instance, a cross-sectional study of how ageing affects the content of F-actin in T cells found that the basal F-actin content was significantly higher in lymphocytes from old individuals when compared with young individuals (Ref. Reference González-Bermúdez26). In that work, the higher content of F-actin in T cells from old donors was assumed to imply a higher stiffness of the cells. Confirming this assumption, in a recent longitudinal study with mice, it has been shown that the measured stiffness of T cells grows, on average, during ageing (Ref. Reference González-Bermúdez26). Regarding mechanical characterisation, it has been studied with special emphasis on the change in stiffness that they may present as an effect of immune responses. The stiffness of T lymphocytes has been studied for resting, activated and apoptotic cells, being significantly stiffer the activated cells and more flexible the apoptotic cells, likely because of structural degradation (Ref. Reference Hu39). With some similarities to the cell damage produced by ageing, the effects derived from X-ray radiation, a process prior to blood transfusion in many immunocompromised patients, have also been studied in lymphocytes, by measuring mechanical and rheological properties, observing a significant increase in the relaxation characteristic time of those irradiated at 25 Gy compared with native lymphocytes (Ref. Reference Thomas37).

Age-related changes in immune-cell functionality have been studied by quantifying T-cell subset fractions and other relevant features. Such studies established that lymphocyte proliferative responses to mitogens are decreased in old humans and experimental mammals (Refs Reference Hallgren42, Reference Martínez de Toda43). Cell fraction studies have shown that, apart from thymic involution, ageing results in a biased output of haematopoietic stem cells towards the myeloid lineage at the expense of lymphoid cells (Ref. Reference Keenan and Allan44). RNA sequencing, assay for transposase-accessible chromatin sequencing and flow cytometry measurements of peripheral-blood mononuclear cells for men and for women showed a progressive reduction of the fraction of adaptive-immune cells and also epigenetic changes, in both cases being detected earlier and more intensely for men (Ref. Reference Márquez45). Particularly, age-related changes in inflammatory genes/pathways were most significant in men, suggesting an accelerated inflamm-ageing signal (see below) in this sex. Previous studies had established different effects of ageing on CD4+ T cell and CD8+ T cell subset fractions (Ref. Reference Weinberger46).

The interplay between ageing and structural, biophysical and functional properties of innate and adaptive immune cells is still poorly understood, partly because of the difficulty of experimental manipulation of cells without perturbations (e.g. activation), whereas performing in vivo biophysical measurements is challenging by the short life of some cell types such as neutrophils, which has precluded the biomolecular, biophysical and functional analysis of specific populations (Ref. Reference Adrover, Nicolás-Ávila and Hidalgo47). Previous studies with neutrophils reported age-related alterations of F-actin assembly, morphology, deformability and chemotactic migration potential upon stimulation with pro-inflammatory compounds (Ref. Reference Tanji-Matsuba48); these changes have been associated with a decline in neutrophils' ability to extravasate during inflammation (Ref. Reference Adrover, Nicolás-Ávila and Hidalgo47). As red blood cells (RBCs) age, their morphology shifts and their intracellular density increases (Refs Reference Luten49, Reference Piomelli and Seaman50); these changes have been often attributed to membrane loss, and are associated with RBC stiffening, leading to functional failure in squeezing through inter-endothelial slits (Ref. Reference Salbaş51). In macrophages, ageing-impaired F-actin polymerisation was found to reduce alveolar phagocytosis in a mouse model (Ref. Reference Li52); these macrophage-dependent changes and the accumulated apoptotic debris promote immune dysfunctions that occur with advancing age and chronic inflammation (Ref. Reference Kim53).

Overall, the experimental evidence suggest that the biophysical analysis of immune cells may provide a useful framework to better understand the repertoire of changes promoted by ageing, although the primary causes of these changes is still under debate. In the next section, we examine the proposed explanations of the factors that may drive T-cell ageing.

Explaining T-cell ageing and relationship with biophysical features: theories and models

Several theories purport to explain the ultimate causes of ageing at the molecular, cellular, organ and system levels, and how they give rise to late-life immunosenescence; however, given the multifactorial nature of ageing, involving genetics and environmental factors in a 1:3 estimated ratio, this problem is still open and has led to the emergence of various plausible models (Ref. Reference Alonso-Fernandez and De la Fuente54).

Ageing theorists divide the theories that have contributed most significantly to the field into two categories: stochastic (non-adaptive) and non-stochastic (adaptive) theories. Advocates of the stochastic theories sustain that ageing is caused by diverse forms of molecular damage that accumulate with age and lead to the late-life functional decline (Ref. Reference Gladyshev55). Observations of various types of stochastic age-related cellular damage form the basis of the damage-based theories of ageing. On the other hand, the question of whether ageing follows a predetermined sequence of events (a developmental programme) has also been debated for decades (Ref. Reference Gems and de Magalhães56). Although most aspects of programmatic theories of ageing do not retain their significance today (Ref. Reference Alonso-Fernandez and De la Fuente54), some programmatic features of ageing have been recently subject to revision by Gems and Magalhães because of experimental results from rodents and apes showing an overlap between patterns encoded in the genome and developmental processes (Ref. Reference Gems and de Magalhães56). A paradigmatic shift between the stochastic versus non-stochastic dichotomy was carried by the hallmarks of ageing scheme (Ref. Reference López-Otín57), focused on how several modes of ageing (primary, secondary or antagonistic and tertiary or integrative causes) might be interrelated, borrowing the idea from a previously published paper on the hallmarks of cancer. The hallmarks of ageing scheme has created a novel perspective on how ageing occurs (it is the most cited article in the field of ageing), although some authors criticise that it explicitly avoids explaining the basic interactions between these hallmarks (Ref. Reference Gems and de Magalhães56).

The hallmarks of ageing were initially drawn from non-vertebrate animal models that lack sophisticated adaptive immunity, so accordingly the ultimate causes of ageing would precede the evolution of T cells, and hence lymphocytes would only play homoeostatic functions unrelated to the ageing process. However, as detailed in the previous section, there is ample experimental evidence in mice that T cells undergo age-related changes of biochemical, biophysical and functional nature. Furthermore, studies of centenarians and supercentenarians suggest that people who reach extreme ages maintain more ‘youthful’ T cell profiles (Refs Reference Ligotti58, Reference Hashimoto59). These lines of evidence suggest that T-cell ageing is linked to longevity and immunosenescence (Ref. Reference Mittelbrunn and Kroemer60). This section provides a brief description of theories of various biochemical processes at work in T-cell ageing, and outlines a biophysical model that considers not only biochemical but also biomechanical aspects at work in T-cell ageing, drawing on recent mechanobiological evidence.

Collateral biochemical damage as driver of T-cell ageing

The problem of revealing order out of the ‘many–many’ relationships between T-cell features and ageing has been addressed by several damage-based theories of ageing. The theories described here put different series of events at the center of T-cell ageing, namely inflammageing, genetic and epigenetic alterations, and cellular senescence.

Ageing and the oxidative-inflammatory stress

Ageing is accompanied by events that involve chronic sterile low-grade inflammation, a phenomenon named as inflammageing by Franceschi et al. in 2006 (Ref. Reference Franceschi61). Inflammageing has its foundation in the observation that organisms are under sustained exposure to various stressor agents over extended time periods. Accordingly, for maintaining life, stressors must be reduced by regulatory mechanisms of the immune systems. Consistent with inflammageing, changes in both the innate and adaptive immune systems occur with age, which are responsible for age-related breakdown of immune regulation, and ultimately contribute to frailty, morbidity and mortality (Ref. Reference Franceschi61). Regarding the events of the innate immune system that promote inflammageing, data show that there is an overstimulation of innate immune cells, as reflected by an increase in pro-inflammatory markers with age, such as c-reactive protein, IL-18, tumour necrosis factor-α and IL-6, produced by dendritic cells, macrophages and other types of cells (Ref. Reference Fagiolo62). In concert, inflammation signals induce the recruitment and activation of adaptive immune cells. In this way, T-cell-producing cytokines can initiate further inflammatory responses, for example via Th1 cells, which are able to activate macrophages, both through cell contact and interferon-γ secretion (Ref. Reference Monney63). Furthermore, inflammageing has been related to age-associated changes in gut microbiota that can lead to inflammation, thereby accelerating the rate of ageing (Refs Reference Ginaldi64, Reference De Martinis65).

Considering the phenomenon of inflammageing, it seems reasonable to presume that understanding the nature of ageing requires identifying the causes of inflammageing, considered a key hallmark of the T-cell ageing process (Ref. Reference Mittelbrunn and Kroemer60). Based upon the global reduction in the capacity of immune cells to cope with a variety of stressors and inflammageing, the oxidative-inflammageing (oxi-inflamm-ageing) theory proposes links for dysfunctional events leading to ageing (Ref. Reference De la Fuente and Miquel66). The oxi-inflamm-ageing theory unifies the ageing theory of oxidation and the age-related changes in immune cells with age.

According to the ageing theory of oxidation, the ageing process is a result of accumulated random molecular damage caused by the high reactivity of free radicals and reactive oxygen species (ROS) produced in cells, as a result of the necessary use of oxygen, which lead to mitochondrial dysfunction in various ways (e.g. free radicals can generate a cellular material rich in lipids and proteins called lipofuscin, which older adults have more of when compared with younger adults). This non-degradable material decreases lysosomal function, which in turns impacts already damaged mitochondria (Ref. Reference Brunk and Terman67)). In order to reduce oxygen toxicity, cells count with a variety of antioxidant mechanisms that prevent the formation of ROS or neutralise them once they are produced (e.g. reduced glutathione, capable of neutralising of peroxides (Ref. Reference Brunk and Terman67)). However, these systems are insufficient to fully eradicate the damage: when the ROS level exceeds the antioxidant level, an oxidative stress state appears resulting in cell dysfunction.

This oxi-inflamm-ageing theory developed by De la Fuente et al. propose that ageing is linked with the excess of chronic oxidative stress, which especially affects cells of the homoeostatic systems (nervous, endocrine, immune) (Ref. Reference Alvarado68). As a consequence, these regulatory systems lose their ability to preserve their redox state, which leads to a loss of function compromising homoeostasis (Ref. Reference De la Fuente and Miquel66). In this theory, a key role is attributed to the immune system, as the deregulation of immune cell responses, increased by oxidative stress, leads to an increase in the production of proinflammatory cytokines. This increase produces a low-grade chronic inflammatory state that contributes to the generation of ROS, thus producing a vicious cycle of oxidation–inflammation–oxidation (Ref. Reference Brunk and Terman67). Oxidative and inflammatory compounds are continuously required by the immune system cells to perform defense functions. If the immune system is dysregulated, it can activate nuclear factor-κB (NF-κB), which in turn has been related to many chronic inflammatory disease states: when a certain level of NF-κB activation is reached, the expression of genes that programme the production of inflammatory compounds is activated, contributing to the aforementioned cyclical process. In this way, both oxidative stress and inflammatory stress, by impairing physiological homoeostasis, would cause oxi-inflamm-ageing.

The oxi-inflamm-ageing theory accommodates previous ageing theories developed since the 1950s, such as the free radical theory of ageing (Ref. Reference Harman69), which offered a mechanistic cause of ageing, wherein ROS species generated as a consequence of metabolism randomly damage cells, with this damage gradually accumulating and resulting in senescence, or the oxidative mitochondrial theory of ageing (Ref. Reference Miquel70), which states that the mitochondria are probably the first target of oxidation as oxygen is mainly used in respiration for metabolic processes, thus leading to vulnerability of the mitochondrial genome in differentiated postmitotic cells. Although this theory provides an ordered perspective of mechanistic contributors to immunosenescence, one fundamental question that remains to be addressed is whether the random accumulation of oxidative molecular damage over time is sufficient to cause ageing. Following oxidative damage, the theory proposes that accumulated damage is the main driver of cellular ageing. However, it is unclear why somatic cells cannot remove or repair this particular damage, or decrease its generation by evolving more efficient maintenance mechanisms. Yet interestingly, the oxi-inflamm-ageing model has given guidance on how to efficiently search for biomarkers of age-related diseases and interventions (Refs Reference De la Fuente71, Reference De la Fuente72). A number of studies have been set up to focus specifically on robust biomarkers based on inflammageing markers of T cells, as immune cells favour the production of pro-inflammatory over anti-inflammatory cytokines, contributing to the accumulation of cellular and molecular damage in ageing tissues. For further reading, Dufour and Larsson provide an overview on the global measures in T cells of oxidative damage and how oxidants and antioxidants affect longevity (Ref. Reference Dufour and Larsson73).

Ageing and genetic and epigenetic alterations

Another major view in the understanding of T-cell ageing is based on the findings supporting that, over time, cells accumulate changes in the genome and epigenome, ultimately contributing to T-cell ageing. In the past decade, evidence has been collected from the genome analysis of aged T cells. Alteration of several gene networks and pathways that are associated with aged T cells have now been identified in humans and mice, including T-cell receptor (TCR) and activation-related molecules, alteration of chemokine/chemokine receptor expression, gain of natural killer cell receptors and function (Ref. Reference Chen, Lustig and Weng74). Whether these identified alterations of gene expressions occur in all cells or in subsets of defined T-cell populations remains to be determined. In addition, it is not really clear whether the wild-type genotype affects ageing via (i) accumulated molecular damage, (ii) antagonistic pleiotropy (including trade-offs) or (iii) programmatic mechanisms including futile programme run-on and costly programmes (Ref. Reference Gems and de Magalhães56).

Changes in the epigenome also have a large influence on T-cell ageing, through age-dependent changes in the level of histone protein concentrations and DNA methylation and histone modifications that alter chromatin structure and accessibility. Recently, novel techniques enabled the performance of genome-wide gene-specific epigenetic studies in infrequent cell populations such as T-cell subsets. In particular, chromatin accessibility mapping has been informative to define the epigenetic state of naive T cells versus effector or memory T cells and follow the chromatin changes that occur in antigen-specific T cells when they are activated in a viral infection and differentiate into various effector T cells and memory T cells (Ref. Reference Goronzy75). These studies have led to the recognition that T cells undergo large changes in chromatin structure with more than 20% of accessible sites either opening or closing (Ref. Reference Kanno76). For further reading, Chen et al. review the gene networks and signalling pathways that are altered with ageing in T cells (Ref. Reference Chen, Lustig and Weng74), whereas Goronzy et al. review how studies of the epigenetic landscape in human T cells are beginning to be informative to understand the mechanisms that drive T-cell ageing (Ref. Reference Goronzy75). Certainly, more studies are needed to dissect the primary causes of the impaired genetic and epigenetic expression of age-related genes and their consequences. In fact, rather than being a driving force of ageing, genetic and epigenetic alterations could be correlates of prior events causing T-cell ageing: epigenetic and genetic changes might represent the effects of T-cell ageing rather than the primary causes. Because age-related epigenetic changes in T cells provide molecular correlates of chronological age in human and vertebrates, they are useful for evaluating rates of ageing and interventions focused on healthy ageing (Ref. Reference Goronzy75).

Ageing and T-cell senescence

Accumulation of senescent cells is considered to be an additional driver of age-related phenotypes in many cells. Cellular senescence can be defined as the stable exit from the cell cycle in response to various stimuli. Senescent cells are metabolically active and participate in diverse effector programmes depending on the type of cell. Replicative senescence is thought to be beneficial for tissue homoeostasis but, if senescent cells persist in tissues, they can be detrimental to the tissue microenvironment, participating in pathological conditions. Increasing evidence indicates that some cell types gradually acquire a secretory phenotype called senescence-associated phenotype (SASP), which is a highly heterogenous feature of senescence (Ref. Reference Sheekey and Narita77). SASP contains a variety of factors, including inflammatory proteins, cytokines, chemokines, growth factors, and matrix-remodelling enzymes which negatively influence tissue homoeostasis. The most general biomarker of senescence is senescence-associated beta-galactosidase; other markers such as lipofuscin are often used when the studies cannot be limited to fresh tissue samples (Refs Reference Georgakopoulou78, Reference Gorgoulis79). Recent works on the search for specific markers associated with T-cell senescence propose that they can be valuable biomarker candidates for the chronic inflammatory phenotype (Ref. Reference Baker80).

To define such biomarkers, it is necessary to distinguish between exhausted and senescent T cells. Data show that T cells become ‘senescent’ cells when (i) they express CD57 and killer-cell lectin-like receptor subfamily G member 1 (KLRG-1) but show a downregulation of CD27 and CD28, (ii) are resistant to apoptosis increasing the accumulation of memory T cells and (iii) adopt a pro-inflammatory profile (Ref. Reference Ng81). On the other hand, ‘exhausted’ T cells (I) express various markers associated with programmed cell death, lymphocyte activation and cytotoxic genes (PD-1, LAG-3, TIM-3, CTLA-4) and (II) are unable to proliferate and secrete molecules upon stimulation. Considering T-cell replicative senescence, it has been suggested that rejuvenation of such T cells would provide beneficial age-related immune responses, such as an improved anti-influenza vaccine response (Ref. Reference Lanna82). Nonetheless, the senescent phenotype in T cells is highly heterogenous, and the SASP profile remains to be well defined in T cells (Ref. Reference Xu and Larbi83). Indeed, the premise that T-cell ageing is partially governed by replicative senescence is generally viewed as problematic, as it is not certain whether T-cell arrested states represent true replicative senescence or alternative immunosurveillance mechanisms. For instance, although CD8+CD27CD28CD57+ and KLRG-1+ T cells show some similarities to replicative senescence, they can still be stimulated to proliferate. For further reading, Zhao et al. provides an in-depth overview on senescent and exhausted T-cell phenotypes (Refs Reference Zhao, Shao and Peng84, Reference Pawelec85).

The ‘hallmarks of T-cell ageing’ scheme

The various explanations of T-cell ageing based on biochemical events leading to the accumulation of molecular/cellular damage give different weight to causes of ageing, as each of them focuses on a particular aspect of the process. In order to integrate the different relationships, a novel step on the search of the causes of T-cell ageing has been made using on a multifactorial approach. Borrowing the concept of the ‘hallmarks of ageing’, a synthetic paper has proposed a list of 10 hallmarks of T-cell ageing, including primary hallmarks (thymic involution, mitochondrial dysfunction, genetic and epigenetic alterations, and loss of proteostasis) and secondary hallmarks (reduction of the TCR repertoire, naive-memory imbalance, T-cell senescence and lack of effector plasticity), which together would explain immunosenescence hallmarks (immunodeficiency and inflammageing) (Ref. Reference Mittelbrunn and Kroemer60). Similarly in the case of the hallmarks of ageing, future work in the field of T-cell ageing involves a closer examination of the interactions between the hallmarks.

Enhancing the biochemical picture: biophysical principles of T-cell ageing

Emerging mechanobiological data suggest that the links between the ultimate causes of T-cell ageing are more complex than previously accepted in the hallmarks scheme, as biophysical properties and mechanical loading, in turn, also alter cellular states and functions, for instance shown for fibroblasts (Ref. Reference Phillip86), RBCs (Ref. Reference Garcia-Herreros87) and T-cells (Ref. Reference González-Bermúdez26) (see Table 1). These findings imply novel feedback loops between biophysical and biochemical damage triggering T-cell ageing.

Indeed, a biophysical point of departure for investigating the mechanical age-related changes in T cells is that changes in the mechanical properties of cells are hallmarks of ageing (Refs Reference Starodubtseva88, Reference Mayya89). Cell mechanical behaviour has been largely overlooked in the context of the immune system (Refs Reference Harris, Wirtz and Wu90, Reference Moreno-Flores91). Outside of the immune system, studies have demonstrated that there is a strong correlation between age and cell stiffness, in numerous diseases, including vascular degeneration, cardiac dysfunction and cancer (Ref. Reference Harris, Wirtz and Wu90). Studies that have applied atomic force microscopy to adherent human cells (epithelial cells (Ref. Reference Berdyyeva, Woodworth and Sokolov92), fibroblasts (Ref. Reference Dulińska-Molak93) and cardiac myocytes (Ref. Reference Lieber94)) seeded on flat substrates have shown that cells consistently respond to mechanical deformation with a stiffening response as a function of increasing age. Moreover, this stiffening has been observed in all cell regions (the cell edge, cytoplasm and perinuclear region) (Ref. Reference Berdyyeva, Woodworth and Sokolov92). Even suspended samples of RBCs derived from healthy donors experience reduced deformability as a result of stiffening with increasing age (Ref. Reference Ajmani and Rifkind95). It has been hypothesised that in some cases cell mechanical properties are altered with increased lifespan as a result of age-dependent changes to the composition and organisation of the extra-cellular matrix (Refs Reference Pelissier96, Reference Sokolov, Iyer and Woodworth97).

There are many biophysical questions still open in the context of T-cell ageing, that have been only recently addressed: how does age alter the biophysical properties of immune T cells? Do mechanical properties of T-cell subsets differ, and are these differences functionally important? Do distinct membrane, nuclear and cytoskeletal structures that mediate different types of functions (synapses, migration, etc.) change with age, and if so, how? Could there be validated biophysical immune biomarkers of T-cell ageing? If so, could T-cells be mechanically reprogrammed, as recent examples in fibroblasts? (Refs Reference Phillip86, Reference Roy98) Considering the longitudinal studies of Table 1 integrating mechanical, morphological, biomolecular and functional data (i.e. spontaneous migratory behaviour and immune synapses), two models of T-cell ageing are outlined next.

Ageing and T-cell stiffening of the plasma membrane

It is considered that the mechanical properties of T-cell membrane could impact immune synapses. Therefore, the use of the measure of the bending stiffness of the T-cell membrane could be useful as a marker of a decline in immune synapse during ageing, and stem from changes in its lipid composition and the distribution of lipid rafts (Refs Reference Mayya89, Reference Fulop99). The change in the composition of the plasma membrane led researchers to hypothesise that the increase of the bending stiffness could be associated with an intrinsic alteration of cholesterol metabolism in older adults, which ultimately alters the efficiency of the immune synapse (see Fig. 3) (Refs Reference Mayya89, Reference Fulop99). This description is rooted on the fact that the plasma membrane is composed of privileged signal transduction microdomains, known as lipid rafts, that participate in the creation of functional dynamic environment for immune synapses and assemble of signalling pathways. The idea provides a working framework to address the molecular mechanisms of immunosenescence. The regulation of the mechanical properties of the membrane, for example targeting cholesterol, could serve to reverse the dysfunctional immune synapses observed in the elders. However, the plasma membrane (mean thickness: 5 nm) is likely a minor contributor to the apparent stiffening of cells with age, and studies on T cells have shown that the effect of nuclear relative size (mean nuclear radius: 3 μm) is determinant compared with the effect of cytoskeleton (mean thickness: 0.3 μm) (Refs Reference González-Bermúdez16, Reference González-Bermúdez26). For further reading, the role of cholesterol in lipid rafts in T-cell ageing is reviewed in Fulop et al. (Ref. Reference Fulop99).

Fig. 3. (a) Hypothetical model that integrates age-related changes in T cells. Top: Schematic representations of T-cell cytoskeleton, nucleus, lamin B1, chromatin, reactive oxygen species (ROS), pro-inflammatory molecules, lipid rafts and unfit molecules (in particular lipofuscin). Bottom: Overview of how the different components of the T-cell are affected by ageing. (b) Functional consequences of age-related changes in T cells. Top: Trans-endothelial migration declines during ageing. Bottom: T-cell activation is compromised during ageing; schematic representation of the immunological synapse between a T-cell (expressing CD4 or CD8, T-cell receptor and CD28) and an antigen-presenting cell or APC (with a major-histocompatibility complex (MHC) and membrane protein B7). The stiffer plasma membrane and loss of CD28 can compromise the activation with advancing age. The interrelations between such changes and deteriorative immune functions are mentioned (see text for description and references).

Ageing and T-cell stiffening

A recent longitudinal study included the measurement of a set of 111 parameters in four T-cell populations (CD4+ and CD8+ T cells, in both memory and naive state), that was enlarged for the case of CD4+ memory T cells with geometrical and internal-ordering characterisation (Ref. Reference González-Bermúdez26). In view of the data, a biophysical model was proposed to describe the age-related changes that ultimately lead to a loss of the ability of T cells to spontaneously migrate, as synthesised in Figure 3.

Firstly, the average relative size of the T-cell nucleus increases with age. The progressive reduction of average DNA methylation (i.e. reduction of 5-methylcytosine) may plausibly induce this relative nuclear-size increase, as previous works have reported that a larger extent of DNA methylation leads to a more compact nucleosome structure (Ref. Reference Choy100). Complementarily, a reduction of nucleoskeletal lamin B1 thickness may also induce it, since this structure is crucial for maintaining nuclear morphology, as demonstrated by previous works examining how the depletion of lamin B1 resulted in larger chromatin volume (Ref. Reference Camps, Erdos and Ried101). The weak decrease of cytoskeletal proteins content could be associated both with an increase in the relative nuclear volume (and the consequent relative reduction of cytoplasmic volume), and with the progressive accumulation of fluorescent material, which was assumed to essentially consist of lipofuscin, as detected by self-fluorescence. Considering that the nuclear material is significantly stiffer than the cytoplasm (Ref. Reference Lammerding and Terjung102), the authors reasoned that the natural key mechanobiological mechanism that explains T-cell stiffening with age is the increase of the relative nuclear size. Indeed, in a previous multiple-measurement study of the same single T cells, it was also found that the relative nuclear size was the main contributor to T-cell apparent deformability (Ref. Reference González-Bermúdez16), whereas the average cytoskeletal-protein content had minor influence (Ref. Reference González-Bermúdez16).

According to previous studies, impaired immunity in aged individuals can be partially attributed to a relative decline in T-cell migration (Ref. Reference Garcia and Miller103). Overall, three ageing-related changes may plausibly play an important role in the reduction of cell migration observed in the longitudinal study: the growth of relative size of the nucleus, and the associated stiffness increase, and the reduction of myosin content. Although cell passive deformability was measured in the study, the active deformation of the cell is associated with passive deformation. Notably, the nuclear size and nucleus stiffness are highly relevant in T-cell migration, because of its higher rigidity (Ref. Reference Fruleux and Hawkins104). Myosin participates in the migration process through actomyosin contraction at the rear of the T cell (Ref. Reference Serrador, Nieto and Sánchez-Madrid105). Furthermore, it has been shown that T lymphocytes require histone methylation in key positions in histones' proteins, a process induced by actomyosin contractility in three-dimensional environments, in order to undergo nuclear softening and confined migration (Ref. Reference Wang106). In line with this view, that mechanobiological study of T-cells highlights the relationship between cell migration, nuclear stiffness and the cytoskeleton. Based on the combination of results of this study and a previous one with T cells (Ref. Reference González-Bermúdez16), the authors considered reasonable to assume that the increase of the relative size of the nucleus is the main contributor to T-cell stiffening with increasing age.

Future perspectives

Because of the limited scope of empirical evidence interrelating biophysical and biochemical features of T cells during ageing, there is no single coherent model which grasps all aspects of the T-cell ageing process. Such a model could help to understand the multiple causal connections between ageing and biophysical–chemical features in T cells, and potentially control them, for instance by selecting T-cell biomarkers that indicate the benefit to the patient from a treatment, compared with their condition at baseline (e.g. predictive T-cell biomarkers of ageing rates, lifespan, all-cause mortality). Based on the information described in the previous sections, it would be reasonable to continue exploring the possible use of T-cell deformability parameters as biomarkers of biological ageing, since they reflect aggregately the molecular content and internal ordering. Future works could explore the convenience of using mechanical models allowing characterising T-cell deformability by a set of parameters, including time dependency (see Section ‘Measurement of biophysical parameters’). The use of one single mechanical parameter has been found sufficient to show a progressive stiffening of T cells during ageing (Ref. Reference González-Bermúdez26). However, the complexity and variety of the cellular changes (Ref. Reference González-Bermúdez26) suggest that improving the mechanical characterisation with a higher number of parameters could be beneficial to develop a computation of useful mechanical biomarkers.

Future studies could analyse whether different candidate alternatives of senescent T cells and exhaust T cells have a distinct signature in the biophysical and biomolecular parameters, in order to address the mechanisms involved in the relationship between cell internal ordering, phenotype and function, as well as to design interventions to rejuvenate cells (Refs Reference De la Fuente71, Reference De la Fuente72). In particular, it would be interesting to determine whether telomere shortening is associated with a change of certain features, in parallel with the relative size of the nucleus of senescent and exhaust T cells. Besides, considering the ageing of the population in the next decades, on the basis of the understanding described above, such studies would be of interest to determine which intervention might be most effective to improve immunity, in the process of immunosenescence (Ref. Reference Carr107) and to define effective vaccines against those pathogens contributing to increased morbidity and mortality in the elderly (Ref. Reference Pinti108). Finally, large open questions that remain are whether there could be validated biophysical immune biomarkers of ageing, and if so, in which ways may cells be mechanically reprogrammed, as pioneering examples of fibroblasts have recently suggested.

Acknowledgements

The authors received support from the Comunidad de Madrid, Spain, grant Tec4Bio-CM/P2018/NMT-4443.

References

Minciullo, PL et al. (2016) Inflammaging and anti-inflammaging: the role of cytokines in extreme longevity. Archivum Immunologiae et Therapiae Experimentalis 64, 111126.CrossRefGoogle ScholarPubMed
Aw, D, Silva, AB and Palmer, DB (2007) Immunosenescence: emerging challenges for an ageing population. Immunology 120, 435446.CrossRefGoogle ScholarPubMed
Clark, G et al. (2016) Nomenclature of CD molecules from the tenth human leucocyte differentiation antigen workshop. Clinical & Translational Immunology 5, e57.CrossRefGoogle ScholarPubMed
Rodriguez, IJ et al. (2021) Immunosenescence study of T cells: a systematic review. Frontiers in Immunology 11, 604591.CrossRefGoogle ScholarPubMed
Restifo, NP and Gattinoni, L (2013) Lineage relationship of effector and memory T cells. Current Opinion in Immunology 25, 556563.CrossRefGoogle ScholarPubMed
Crompton, JG et al. (2016) Lineage relationship of CD8+ T cell subsets is revealed by progressive changes in the epigenetic landscape. Cellular & Molecular Immunology 13, 502513.CrossRefGoogle ScholarPubMed
Flynn, KJ et al. (1998) Virus-specific CD8+ T cells in primary and secondary influenza pneumonia. Immunity 8, 683691.CrossRefGoogle ScholarPubMed
Woodland, DL and Kohlmeier, JE (2009) Migration, maintenance and recall of memory T cells in peripheral tissues. Nature Reviews Immunology 9, 153161.CrossRefGoogle ScholarPubMed
Teijeira, A et al. (2017) T cell migration from inflamed skin to draining lymph nodes requires intralymphatic crawling supported by ICAM-1/LFA-1 interactions. Cell Reports 18, 857865.CrossRefGoogle ScholarPubMed
Fung, YC (1997) Biomechanics. New York, NY: Springer.CrossRefGoogle Scholar
Jackson, DG (2019) Leucocyte trafficking via the lymphatic vasculature – mechanisms and consequences. Frontiers in Immunology 10, 471.CrossRefGoogle ScholarPubMed
van der Woude, LL et al. (2017) Migrating into the tumor: a roadmap for T cells. Trends in Cancer 3, 797808.CrossRefGoogle ScholarPubMed
Nicolas-Boluda, A and Donnadieu, E (2019) Obstacles to T cell migration in the tumor microenvironment. Comparative Immunology, Microbiology and Infectious Diseases 63, 2230.CrossRefGoogle Scholar
Carman, CV and Springer, TA (2004) A transmigratory cup in leukocyte diapedesis both through individual vascular endothelial cells and between them. Journal of Cell Biology 167, 377388.CrossRefGoogle ScholarPubMed
Guillou, L et al. (2016) T-lymphocyte passive deformation is controlled by unfolding of membrane surface reservoirs. Molecular Biology of the Cell 27, 35743582.CrossRefGoogle ScholarPubMed
González-Bermúdez, B et al. (2020) Single-cell biophysical study reveals deformability and internal ordering relationship in T cells. Soft Matter 16, 56695678.CrossRefGoogle ScholarPubMed
Plaza, GR et al. (2015) Study of the influence of actin-binding proteins using linear analyses of cell deformability. Soft Matter 11, 54355446.CrossRefGoogle ScholarPubMed
González-Bermúdez, B, Guinea, GV and Plaza, GR (2019) Advances in micropipette aspiration: applications in cell biomechanics, models, and extended studies. Biophysical Journal 116, 587594.CrossRefGoogle ScholarPubMed
Haase, K and Pelling, AE (2015) Investigating cell mechanics with atomic force microscopy. Journal of the Royal Society Interface 12, 2014097020140970.CrossRefGoogle ScholarPubMed
Urbanska, M et al. (2020) A comparison of microfluidic methods for high-throughput cell deformability measurements. Nature Methods 17, 587593.CrossRefGoogle ScholarPubMed
Munevar, S, Wang, Y and Dembo, M (2001) Traction force microscopy of migrating normal and H-Ras transformed 3T3 fibroblasts. Biophysical Journal 80, 17441757.CrossRefGoogle ScholarPubMed
Sabass, B et al. (2008) High resolution traction force microscopy based on experimental and computational advances. Biophysical Journal 94, 207220.CrossRefGoogle ScholarPubMed
López-Fagundo, C et al. (2014) Three-dimensional traction forces of Schwann cells on compliant substrates. Journal of the Royal Society Interface 11, 20140247.CrossRefGoogle ScholarPubMed
Coppola, S et al. (2019) Quantifying cellular forces and biomechanical properties by correlative micropillar traction force and Brillouin microscopy. Biomedical Optics Express 10, 22022212.CrossRefGoogle ScholarPubMed
Hervas-Raluy, S et al. (2021) A new 3D finite element-based approach for computing cell surface tractions assuming nonlinear conditions. PLoS ONE 16, e0249018.CrossRefGoogle ScholarPubMed
González-Bermúdez, B et al. (2022) Aging is accompanied by T-cell stiffening and reduced interstitial migration through dysfunctional nuclear organization. Immunology. doi: 10.1111/imm.13559.CrossRefGoogle ScholarPubMed
Alpert, A et al. (2019) A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nature Medicine 25, 487495.CrossRefGoogle ScholarPubMed
Youm, Y-H et al. (2013) Canonical NLRP3 inflammasome links systemic low-grade inflammation to functional decline in aging. Cell Metabolism 18, 519532.CrossRefGoogle Scholar
Czesnikiewicz-Guzik, M et al. (2008) T cell subset-specific susceptibility to aging. Clinical Immunology 127, 107118.CrossRefGoogle ScholarPubMed
Strindhall, J et al. (2007) No immune risk profile among individuals who reach 100 years of age: findings from the Swedish NONA immune longitudinal study. Experimental Gerontology 42, 753761.CrossRefGoogle ScholarPubMed
Vescovini, R et al. (2007) Massive load of functional effector CD4+ and CD8+ T cells against cytomegalovirus in very old subjects. The Journal of Immunology 179, 42834291.CrossRefGoogle ScholarPubMed
Wallace, DL et al. (2004) Direct measurement of T cell subset kinetics in vivo in elderly men and women. The Journal of Immunology 173, 17871794.CrossRefGoogle ScholarPubMed
Deng, Y et al. (2004) Age-related impaired type 1 T cell responses to influenza: reduced activation ex vivo, decreased expansion in CTL culture in vitro, and blunted response to influenza vaccination in vivo in the elderly. The Journal of Immunology 172, 34373446.CrossRefGoogle ScholarPubMed
Wikby, A et al. (1998) Changes in CD8 and CD4 lymphocyte subsets, T cell proliferation responses and non-survival in the very old: the Swedish longitudinal OCTO-immune study. Mechanisms of Ageing and Development 102, 187198.CrossRefGoogle ScholarPubMed
Kouli, A et al. (2021) T lymphocyte senescence is attenuated in Parkinson's disease. Journal of Neuroinflammation 18, 228.CrossRefGoogle ScholarPubMed
Rao, KM et al. (1992) Age-related alterations in actin cytoskeleton and receptor expression in human leukocytes. Journal of Gerontology 47, B37B44.CrossRefGoogle ScholarPubMed
Thomas, S et al. (2003) Effects of X-ray radiation on the rheologic properties of platelets and lymphocytes. Transfusion 43, 502508.CrossRefGoogle ScholarPubMed
Ruef, P, Böhler, T and Linderkamp, O (1991) Deformability and volume of neonatal and adult leukocytes. Pediatric Research 29, 128132.CrossRefGoogle ScholarPubMed
Hu, M et al. (2009) Nanostructure and nanomechanics analysis of lymphocyte using AFM: from resting, activated to apoptosis. Journal of Biomechanics 42, 15131519.CrossRefGoogle Scholar
De la Fuente, M et al. (2008) Vitamin E ingestion improves several immune functions in elderly men and women. Free Radical Research 42, 272280.CrossRefGoogle ScholarPubMed
Stohlawetz, P et al. (1996) The effect of age on the transendothelial migration of human T lymphocytes. Scandinavian Journal of Immunology 44, 530534.CrossRefGoogle ScholarPubMed
Hallgren, HM et al. (1988) Lymphocyte proliferative response to PHA and anti-CD3/Ti monoclonal antibodies, T cell surface marker expression, and serum IL-2 receptor levels as biomarkers of age and health. Mechanisms of Ageing and Development 43, 175185.CrossRefGoogle ScholarPubMed
Martínez de Toda, I et al. (2016) Immune function parameters as markers of biological age and predictors of longevity. Aging 8, 31103119.CrossRefGoogle ScholarPubMed
Keenan, CR and Allan, RS (2019) Epigenomic drivers of immune dysfunction in aging. Aging Cell 18, e12878.CrossRefGoogle Scholar
Márquez, EJ et al. (2020) Sexual-dimorphism in human immune system aging. Nature Communications 11, 751.CrossRefGoogle ScholarPubMed
Weinberger, B et al. (2007) Healthy aging and latent infection with CMV lead to distinct changes in CD8+ and CD4+ T-cell subsets in the elderly. Human Immunology 68, 8690.CrossRefGoogle ScholarPubMed
Adrover, JM, Nicolás-Ávila, JA and Hidalgo, A (2016) Aging: a temporal dimension for neutrophils. Trends in Immunology 37, 334345.CrossRefGoogle ScholarPubMed
Tanji-Matsuba, K (1998) Functional changes in aging polymorphonuclear leukocytes. Circulation 97, 9198.CrossRefGoogle ScholarPubMed
Luten, M et al. (2008) Survival of red blood cells after transfusion: a comparison between red cells concentrates of different storage periods. Transfusion 48, 14781485.CrossRefGoogle ScholarPubMed
Piomelli, S and Seaman, C (1993) Mechanism of red blood cell aging: relationship of cell density and cell age. American Journal of Hematology 42, 4652.CrossRefGoogle ScholarPubMed
Salbaş, K (1994) Effect of acute smoking on red blood cell deformability in healthy young and elderly non-smokers, and effect of verapamil on age- and acute smoking-induced change in red blood cell deformability. Scandinavian Journal of Clinical and Laboratory Investigation 54, 411416.CrossRefGoogle Scholar
Li, Z et al. (2017) Aging-impaired filamentous actin polymerization signaling reduces alveolar macrophage phagocytosis of Bacteria. Journal of Immunology 199, 31763186.CrossRefGoogle ScholarPubMed
Kim, O-H et al. (2017) Impaired phagocytosis of apoptotic cells causes accumulation of bone marrow-derived macrophages in aged mice. BMB Reports 50, 4348.CrossRefGoogle ScholarPubMed
Alonso-Fernandez, P and De la Fuente, M (2011) Role of the immune system in aging and longevity. Current Aging Science 4, 78100.CrossRefGoogle ScholarPubMed
Gladyshev, VN (2016) Aging: progressive decline in fitness due to the rising deleteriome adjusted by genetic, environmental, and stochastic processes. Aging Cell 15, 594602.CrossRefGoogle Scholar
Gems, D and de Magalhães, JP (2021) The hoverfly and the wasp: a critique of the hallmarks of aging as a paradigm. Ageing Research Reviews 70, 101407.CrossRefGoogle ScholarPubMed
López-Otín, C et al. (2013) The hallmarks of aging. Cell 153, 11941217.CrossRefGoogle ScholarPubMed
Ligotti, ME et al. (2021) Analysis of T and NK cell subsets in the Sicilian population from young to supercentenarian: the role of age and gender. Clinical and Experimental Immunology 205, 198212.CrossRefGoogle Scholar
Hashimoto, K (2019) Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians. PNAS 116, 2424224251.CrossRefGoogle ScholarPubMed
Mittelbrunn, M and Kroemer, G (2021) Hallmarks of T cell aging. Nature Immunology 22, 687698.CrossRefGoogle ScholarPubMed
Franceschi, C et al. (2006) Inflamm-aging: an evolutionary perspective on immunosenescence. Annals of the New York Academy of Sciences 908, 244254.CrossRefGoogle Scholar
Fagiolo, U et al. (1993) Increased cytokine production in mononuclear cells of healthy elderly people. European Journal of Immunology 23, 23752378.CrossRefGoogle ScholarPubMed
Monney, L et al. (2002) Th1-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature 415, 536541.CrossRefGoogle ScholarPubMed
Ginaldi, L et al. (2005) Chronic antigenic load and apoptosis in immunosenescence. Trends in Immunology 26, 7984.CrossRefGoogle ScholarPubMed
De Martinis, M et al. (2005) Inflamm-ageing and lifelong antigenic load as major determinants of ageing rate and longevity. FEBS Letters 579, 20352039.CrossRefGoogle ScholarPubMed
De la Fuente, M and Miquel, J (2009) An update of the oxidation-inflammation theory of aging: the involvement of the immune system in oxi-inflamm-aging. Current Pharmaceutical Design 15, 30033026.CrossRefGoogle ScholarPubMed
Brunk, UT and Terman, A (2002) Lipofuscin: mechanisms of age-related accumulation and influence on cell function. Free Radical Biology & Medicine 33, 611619.CrossRefGoogle ScholarPubMed
Alvarado, C et al. (2006) Dietary supplementation with antioxidants improves functions and decreases oxidative stress of leukocytes from prematurely aging mice. Nutrition (Burbank, Los Angeles County, Calif.) 22, 767777.CrossRefGoogle ScholarPubMed
Harman, D (1956) Aging: a theory based on free radical and radiation chemistry. Journal of Gerontology 11, 298300.CrossRefGoogle ScholarPubMed
Miquel, J (1992) An update on the mitochondrial-DNA mutation hypothesis of cell aging. Mutation Research/DNAging 275, 209216.CrossRefGoogle ScholarPubMed
De la Fuente, M et al. (2011) Strategies to improve the functions and redox state of the immune system in aged subjects. Current Pharmaceutical Design 17, 39663993.CrossRefGoogle ScholarPubMed
De la Fuente, M et al. (2020) Vitamin C and vitamin C plus E improve the immune function in the elderly. Experimental Gerontology 142, 111118.CrossRefGoogle ScholarPubMed
Dufour, E and Larsson, N-G (2004) Understanding aging: revealing order out of chaos. Biochimica et Biophysica Acta (BBA) – Bioenergetics 1658, 122132.CrossRefGoogle Scholar
Chen, G, Lustig, A and Weng, N (2013) T cell aging: a review of the transcriptional changes determined from genome-wide analysis. Frontiers in Immunology 4, 121.CrossRefGoogle ScholarPubMed
Goronzy, JJ et al. (2018) Epigenetics of T cell aging. Journal of Leukocyte Biology 104, 691699.CrossRefGoogle ScholarPubMed
Kanno, Y et al. (2012) Transcriptional and epigenetic control of T helper cell specification: molecular mechanisms underlying commitment and plasticity. Annual Review of Immunology 30, 707731.CrossRefGoogle ScholarPubMed
Sheekey, E and Narita, M (2022) p53 in senescence – it's a marathon not a sprint. The FEBS Journal. doi: 10.1111/febs.16325.Google Scholar
Georgakopoulou, E et al. (2012) Specific lipofuscin staining as a novel biomarker to detect replicative and stress-induced senescence. A method applicable in cryo-preserved and archival tissues. Aging (Albany, NY) 5, 3750.CrossRefGoogle Scholar
Gorgoulis, V et al. (2019) Cellular senescence: defining a path forward. Cell 179, 813827.CrossRefGoogle ScholarPubMed
Baker, DJ et al. (2011) Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders. Nature 479, 232236.CrossRefGoogle ScholarPubMed
Ng, TP et al. (2015) Markers of T-cell senescence and physical frailty: insights from Singapore longitudinal ageing studies. npj Aging and Mechanisms of Disease 1, 15005.CrossRefGoogle ScholarPubMed
Lanna, A (2017) A sestrin-dependent Erk–Jnk–p38 MAPK activation complex inhibits immunity during aging. Nature Immunology 18, 354363.CrossRefGoogle ScholarPubMed
Xu, W and Larbi, A (2017) Markers of T cell senescence in humans. International Journal of Molecular Sciences 18, 1742.CrossRefGoogle ScholarPubMed
Zhao, Y, Shao, Q and Peng, G (2020) Exhaustion and senescence: two crucial dysfunctional states of T cells in the tumor microenvironment. Cellular & Molecular Immunology 17, 2735.CrossRefGoogle ScholarPubMed
Pawelec, G (2019) Is there a positive side to T cell exhaustion? Frontiers in Immunology 10, 111.CrossRefGoogle Scholar
Phillip, JM et al. (2017) Biophysical and biomolecular determination of cellular age in humans. Nature Biomedical Engineering 1, 0093.CrossRefGoogle ScholarPubMed
Garcia-Herreros, A et al. (2021) Cyclic mechanical stresses alter erythrocyte membrane composition and microstructure and trigger macrophage phagocytosis, 2021.09.10.459518.Google Scholar
Starodubtseva, MN (2011) Mechanical properties of cells and ageing. Ageing Research Reviews 10, 1625.CrossRefGoogle ScholarPubMed
Mayya, V et al. (2019) Cutting edge: synapse propensity of human memory CD8 T cells confers competitive advantage over naive counterparts. The Journal of Immunology 203, 601606.CrossRefGoogle ScholarPubMed
Harris, MJ, Wirtz, D and Wu, P-H (2019) Dissecting cellular mechanics: implications for aging, cancer, and immunity. Seminars in Cell & Developmental Biology 93, 1625.CrossRefGoogle Scholar
Moreno-Flores, S (2020) Hallmarks of life in single cell contact mechanics: outstanding challenges and perspectives. Frontiers in Mechanical Engineering 6, 58.CrossRefGoogle Scholar
Berdyyeva, TK, Woodworth, CD and Sokolov, I (2005) Human epithelial cells increase their rigidity with ageing in vitro: direct measurements. Physics in Medicine and Biology 50, 8192.CrossRefGoogle ScholarPubMed
Dulińska-Molak, I et al. (2014) Age-related changes in the mechanical properties of human fibroblasts and its prospective reversal after anti-wrinkle tripeptide treatment. International Journal of Peptide Research and Therapeutics 20, 7785.CrossRefGoogle ScholarPubMed
Lieber, SC et al. (2004) Aging increases stiffness of cardiac myocytes measured by atomic force microscopy nanoindentation. American Journal of Physiology: Heart and Circulatory Physiology 287, H645H651.Google ScholarPubMed
Ajmani, RS and Rifkind, JM (1998) Hemorheological changes during human aging. Gerontology 44, 111120.CrossRefGoogle ScholarPubMed
Pelissier, FA et al. (2014) Age-related dysfunction in mechanotransduction impairs differentiation of human mammary epithelial progenitors. Cell Reports 7, 19261939.CrossRefGoogle ScholarPubMed
Sokolov, I, Iyer, S and Woodworth, CD (2006) Recovery of elasticity of aged human epithelial cells in vitro. Nanomedicine: Nanotechnology, Biology, and Medicine 2, 3136.CrossRefGoogle ScholarPubMed
Roy, B et al. (2020) Fibroblast rejuvenation by mechanical reprogramming and redifferentiation. Proceedings of the National Academy of Sciences 117, 1013110141.CrossRefGoogle ScholarPubMed
Fulop, T et al. (2012) Aging, immunosenescence and membrane rafts: the lipid connection. Longevity & Healthspan 1, 6.CrossRefGoogle ScholarPubMed
Choy, JS et al. (2010) DNA methylation increases nucleosome compaction and rigidity. Journal of the American Chemical Society 132, 17821783.CrossRefGoogle ScholarPubMed
Camps, J, Erdos, MR and Ried, T (2015) The role of lamin B1 for the maintenance of nuclear structure and function. Nucleus 6, 814.CrossRefGoogle ScholarPubMed
Lammerding, J (2011) Mechanics of the nucleus. In Terjung, R (ed.), Comprehensive Physiology. Hoboken, NJ, USA: John Wiley & Sons, Inc, https://doi.org/10.1002/cphy.c100038.Google Scholar
Garcia, GG and Miller, RA (2011) Age-related defects in the cytoskeleton signaling pathways of CD4 T cells. Ageing Research Reviews 10, 2634.CrossRefGoogle ScholarPubMed
Fruleux, A and Hawkins, RJ (2016) Physical role for the nucleus in cell migration. Journal of Physics: Condensed Matter 28, 363002.Google ScholarPubMed
Serrador, JM, Nieto, M and Sánchez-Madrid, F (1999) Cytoskeletal rearrangement during migration and activation of T lymphocytes. Trends in Cell Biology 9, 228233.CrossRefGoogle ScholarPubMed
Wang, P et al. (2018) WDR5 modulates cell motility and morphology and controls nuclear changes induced by a 3D environment. Proceedings of the National Academy of Sciences 115, 85818586.CrossRefGoogle ScholarPubMed
Carr, EJ et al. (2016) The cellular composition of the human immune system is shaped by age and cohabitation. Nature Immunology 17, 461.CrossRefGoogle ScholarPubMed
Pinti, M et al. (2016) Aging of the immune system: focus on inflammation and vaccination. European Journal of Immunology 46, 22862301.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Scheme of the main steps of differentiation, migration and proliferation of T cells. After being selected in the thymus, naive T cells are present in the lymphatic and the circulatory systems. Following activation in a lymphatic node by encountering an antigen presenting cell, naive T cells become effector T cells and some of them migrate and act of the infected tissue. The long-lived memory T cells may be found in tissues (tissue resident memory T cells) and in the lymphatic and circulatory systems (central memory T cells).

Figure 1

Fig. 2. Main examples of biochemical, functional and biophysical features studied for T cells in research works in which the effect of the age of the donor is considered. The figure is based on Table 1.

Figure 2

Table 1. Representative list of the relatively limited number of works studying T-cell features for various ages of the donors

Figure 3

Fig. 3. (a) Hypothetical model that integrates age-related changes in T cells. Top: Schematic representations of T-cell cytoskeleton, nucleus, lamin B1, chromatin, reactive oxygen species (ROS), pro-inflammatory molecules, lipid rafts and unfit molecules (in particular lipofuscin). Bottom: Overview of how the different components of the T-cell are affected by ageing. (b) Functional consequences of age-related changes in T cells. Top: Trans-endothelial migration declines during ageing. Bottom: T-cell activation is compromised during ageing; schematic representation of the immunological synapse between a T-cell (expressing CD4 or CD8, T-cell receptor and CD28) and an antigen-presenting cell or APC (with a major-histocompatibility complex (MHC) and membrane protein B7). The stiffer plasma membrane and loss of CD28 can compromise the activation with advancing age. The interrelations between such changes and deteriorative immune functions are mentioned (see text for description and references).