Aging of the T Cell Compartment in Mice and Humans: From No Naive Expectations to Foggy Memories

Nikolich‐Žugich, Janko

doi:10.4049/jimmunol.1401174

Cited by 231 publications

(220 citation statements)

References 100 publications

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“…From the Old template, five clusters appeared to be memory CD4 T cells (CD3+CD4+CD8−CD45−), three expressed monocytic markers (CD3−CD14+CD11B+), and four expressed both T cell and monocyte markers and showed high FSC‐W values, consistent with T cell/monocyte conjugates. The T cell results fit well with previous studies that showed a shift in frequency from naïve to memory CD8 T cells on aging 18, 19, 20, 21, consistent with the accumulation of memory cells with continued antigen stimulation, and oligoclonal expansion of specific memory CD8 T cell populations 22, 23. Similarly, a decrease in gamma‐delta T cells has been reported previously 24, 25.…”

Section: Resultssupporting

confidence: 90%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets. © 2015 The Authors. Published Wiley Periodicals Inc.

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Section: Resultssupporting

confidence: 90%

Competitive SWIFT cluster templates enhance detection of aging changes

Rebhahn

Roumanes

et al. 2015

Cytometry Pt A

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“…However, different organs provide distinct and unique microenvironments for CLL cells, 20,21 and it is not known if the same holds true for T cells. Moreover, fundamental T-cell defects are also present in aging healthy individuals, 38 which needs to be taken into account because CLL is predominantly a disease of the elderly. AT of CLL into young mice allows elimination of the confounding variable of aging and is a widely accepted strategy to shorten disease latency 39 and decrease variability.…”

Section: Discussionmentioning

confidence: 99%

Mechanisms of PD-L1/PD-1–mediated CD8 T-cell dysfunction in the context of aging-related immune defects in the Eµ-TCL1 CLL mouse model

McClanahan

Riches

Miller

et al. 2015

Blood

109

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Key Points• PD-L1/PD-1-mediated CD8T-cell dysfunction develops with CLL in different organs, and similarities to agingrelated immune defects exist.• PD-1 1 normal T cells have markedly different effector functions than PD-1 1 CLL T cells.T-cell defects, immune suppression, and poor antitumor immune responses are hallmarks of chronic lymphocytic leukemia (CLL), and PD-1/PD-L1 inhibitory signaling has emerged as a major immunosuppressive mechanism. However, the effect of different microenvironments and the confounding influence of aging are poorly understood. The current study uses the Em-TCL1 mouse model, which replicates human T-cell defects, as a preclinical platform to longitudinally examine patterns of T-cell dysfunction alongside developing CLL and in different microenvironments, with a focus on PD-1/PD-L1 interactions. The development of CLL was significantly associated with changes in T-cell phenotype across all organs and function. Although partly mirrored in aging wild-type mice, CLL-specific T-cell changes were identified. Murine CLL cells highly expressed PD-L1 and PD-L2 in all organs, with high PD-L1 expression in the spleen. CD3

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“…Indeed, a substantial body of literature demonstrates that aging can exert a wide-spread and often deleterious influence on the efficient coordination of T cell responses by compromising or distorting relevant organ systems (thymic involution), the distribution of T cell subsets (loss of naive T cells [T N ], oligoclonal expansions of memory-phenotype T cells [T MP ]) and specificities (reduced TCR diversity), as well as altering T cell phenotypes, signal transduction, metabolism, telomere biology and functionalities (diminished responsiveness, exhaustion, replicative senescence, etc.) (5)(6)(7)(8)(9)(10). Although the available evidence suggests that CD8 + T M populations maintained under ideal conditions (i.e., in the absence of persisting or deliberately introduced antigen) are less adversely affected by the ravages of age (3,10), pathogenspecific CD8 + T M appear to slowly evolve, as documented by their gradual though seemingly limited phenotypic and functional conversion (11)(12)(13)(14)(15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%