Heritable changes in division speed accompany the diversification of single T cell fate

Abstract

Rapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. It enables the outgrowth of an individual T cell into thousands of clonal descendants that diversify into short-lived effectors and long-lived memory cells. Clonal expansion is thought to be programmed upon priming of a single naive T cell and then executed by homogenously fast divisions of all of its descendants. However, the actual speed of cell divisions in such an emerging “T cell family” has never been measured with single-cell resolution. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8+ T cell throughout up to ten divisions of activation-induced proliferation. This comprehensive mapping of T cell family trees identifies a short burst phase, in which division speed is homogenously fast and maintained independent of external cytokine availability or continued T cell receptor stimulation. Thereafter, however, division speed diversifies, and model-based computational analysis using a Bayesian inference framework for tree-structured data reveals a segregation into heritably fast- and slow-dividing branches. This diversification of division speed is preceded already during the burst phase by variable expression of the interleukin-2 receptor alpha chain. Later it is accompanied by selective expression of memory marker CD62L in slower dividing branches. Taken together, these data demonstrate that T cell clonal expansion is structured into subsequent burst and diversification phases, the latter of which coincides with specification of memory versus effector fate.

Inheritance of old mitochondria controls early CD8+T cell fate commitment and is regulated by autophagy

“…By using sorted populations or approaches that provide single-cell resolution, we determined that the metabolism, survival and proliferative capacity of these progenies is different. Exhibiting lower translation rates, higher frequencies of slow-dividing cells and CD62L expression and better survival capacity in absence of antigen, MitoSnap-cells clearly showed a stronger memory phenotype than MitoSnap+ cells 22,38 . Although, surprisingly, we could not observe significant differences in mitochondrial respiration rates, MitoSnap+ cells relied more on glycolysis, a feature seen in effector CD8 + T cells.…”

“…Effector CD8 + T cells are highly proliferative, while memory CD8 + T cells divide slower and are more quiescent. This is a feature that is established early on following T cell activation, with cell cycle speed predictive of CD8 + T cell fate 22,38 . CD8 + T cells with different clonal expansion rates have different metabolic demands, effector cells being more reliant on glycolysis, while long-lived naïve and memory cells mostly perform mitochondrial oxidative phosphorylation and fatty acid oxidation to produce ATP 32,33 .…”

On How, and Why, and When, We Grow Old

“…However, until recently we have not been able to characterize numbers of immune cells, and their lineages, with precision, but this has recently be achieved by Plambeck et al 52 Note the cell-lineage-tree of a T cell clone in FIGURE 1I of that paper, which is of identical appearance to the cell-lineage-tree of a nematode, from which we extracted values, in units of numbers of cells, N , which revealed that the whole body grows by the Universal Mitotic Fraction and Growth Equations , while the clonal parts of the nematode body grow by the Cellular Allometric Growth Equation (#4, see APPENDIX and Reference 6). There is no reason why the clones of the immune system, and the populations which they comprise, could not also be mapped with data determined by the methodology of Plambeck et al 52 to Cellular Phylodynamic equations, whose parameters could be traced to the Mitotic Fraction, m , and Cell Cycle Time, c , as well as measuring how the values of these parameters might change with age. Such an approach would allow us to understand the immune response, and its change with age, quantitatively, and design optimally effective strategies for improving immunization in the elderly, among other things.…”

Divide and Conquer: Phenotypic and Temporal Heterogeneity Within CD8+ T Cell Responses

“…This work further demonstrated that the stimuli controlling cell cycle duration differed by subset, with effector cells responding to IL2 signaling and central memory precursors dependent on TCR stimulation. Subsequent in vitro long-term live imaging experiments, using anti-CD3 stimulation and culture with IL2 to promote sustained expansion, found rapid progression of all cells from division 2 through divisions 3 or 4, followed by a heritable split of division speeds (81). Faster divisions were associated with expression of the high affinity IL2 receptor component CD25, while slower divisions were associated with expression of CD62L (81), suggesting a relationship to the effector and memory precursor populations observed in vivo (80).…”

“…Subsequent in vitro long-term live imaging experiments, using anti-CD3 stimulation and culture with IL2 to promote sustained expansion, found rapid progression of all cells from division 2 through divisions 3 or 4, followed by a heritable split of division speeds (81). Faster divisions were associated with expression of the high affinity IL2 receptor component CD25, while slower divisions were associated with expression of CD62L (81), suggesting a relationship to the effector and memory precursor populations observed in vivo (80). A similar rapid initial proliferation, followed by a split in division times associated with differentiation marker genes, was also observed in a murine model of influenza infection using cell-cycle-phase reporter mice (82).…”