Defining the dynamics of naive CD4 and CD8 T cells across the mouse lifespan

Naive CD4 and CD8 T cells are part of the foundation of adaptive immune responses, but multiple aspects of their behaviour remain elusive. Newly generated T cells continue to develop after they leave the thymus and their dynamics and 'rules of entry' into the mature naive population are challenging to define. The extents to which naive T cells' capacities to survive or self-renew change as they age are also unclear. Further, much of what we know about their behaviour derives from studies in adults, both mouse and human. We know much less about naive T cell dynamics early in life, during which the thymus is highly active and peripheral T cell populations are rapidly established. For example, it has been suggested that neonatal mice are lymphopenic; if so, does this environment impact the behaviour of the earliest thymic emigrants, for example through altered rates of division and loss? In this study we integrate data from multiple experimental systems to construct models of naive CD4 and CD8 T cell population dynamics across the entire mouse lifespan. We infer that both subsets progressively increase their capacity to persist through survival mechanisms rather than through self-renewal, and find that this very simple model of adaptation describes the population dynamics of naive CD4 T cells from birth into old age. In addition, we find that newly generated naive CD8 T cells are lost at an elevated rate for the first 3-4 weeks of life, which may derive from transiently increased recruitment into conventional and virtual memory populations. We find no evidence for elevated rates of division of naive CD4 or CD8 T cells early in life and indeed estimate that these cells divide extremely rarely. Markers of proliferation within peripheral naive T cells are instead inherited from division during thymic development. We also find no evidence for feedback regulation of rates of division or loss of naive T cells at any age in healthy mice, challenging the dogma that their numbers are homeostatically regulated. Our analyses show how confronting an array of mechanistic mathematical models with diverse datasets can move us closer to a complete, and remarkably simple, picture of naive CD4 and CD8 T cell dynamics in mice.

Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues

Molecular clocks that record cell ancestry mutate too slowly to measure the short-timescale dynamics of cell renewal in adult tissues. Here, we show that fluctuating DNA methylation marks can be used as clocks in cells where ongoing methylation and demethylation cause repeated ‘flip–flops’ between methylated and unmethylated states. We identify endogenous fluctuating CpG (fCpG) sites using standard methylation arrays and develop a mathematical model to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than the colon, with slower stem cell replacement in the small intestine. Germline APC mutation increased the number of replacements per crypt. In blood, we measured rapid expansion of acute leukemia and slower growth of chronic disease. Thus, the patterns of human somatic cell birth and death are measurable with fluctuating methylation clocks (FMCs).

Influence of the NAT2 gene polymorphic markers on the effectiveness and safety of treatment in patients with newly diagnosed pulmonary tuberculosis based on peripheral red blood cell dynamics

Background. Individual sensitivity to isoniazid in tuberculosis patients is determined by the presence of N-acetyltransferase 2 (NAT2) enzyme gene allelic variants in genome. Evaluation of quantitative and qualitative alterations in peripheral blood can be used for diagnosis, disease severity estimation, or as a clue for estimation of anti-tuberculosis chemotherapy effectiveness and safety.Aim: Find associations between acetylation type and peripheral red blood cell (RBC) dynamics; determine the effect of NAT2 acetylation rate on the effectiveness and safety of treatment in patients with newly identified pulmonary tuberculosis (TB) residing in the Sakha Republic (Yakutia).Methods. This study included 146 patients with various clinical forms of newly diagnosed pulmonary TB. Oral isoniazid, rifampicin, pyrazinamide, and ethambutol were administered patients. Genotyping was performed via real time PCR.Results. Rapid and intermediate acetylators showed an increase in hemoglobin concentrations and RBC erythrocyte hemoglobin content by the end of chemotherapy (P<0.05). Incidence of anemia was lower in intermediate acetylators, compared to rapid or slow acetylators (P=0.013). Negative correlation was established between absolute RBC count and slow acetylation type (P=0.017). Patients with rapid acetylation type showed increased RBC distribution width indexes RDW-CV and RDW-SD (P<0.05).Conclusions. An adequate therapeutic effect was achieved with standard doses of anti-TB medications in patients with intermediate acetylation type. Rapid and slow acetylators required anti-TB medication dose correction. Genotyping for NAT2 gene in patients with pulmonary TB enables clinicians to choose the optimal dose of anti-TB medications, specifically, isoniazid dose.

Learning developmental mode dynamics from single-cell trajectories

Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at various stages of embryonic development. This rapid experimental progress poses the theoretical challenge of translating high-dimensional imaging data into predictive low-dimensional models that capture the essential ordering principles governing developmental cell migration in complex geometries. Here, we combine mode decomposition ideas that have proved successful in condensed matter physics and turbulence theory with recent advances in sparse dynamical systems inference to realize a computational framework for learning quantitative continuum models from single-cell imaging data. Considering pan-embryo cell migration during early gastrulation in zebrafish as a widely studied example, we show how cell trajectory data on a curved surface can be coarse-grained and compressed with suitable harmonic basis functions. The resulting low-dimensional representation of the collective cell dynamics enables a compact characterization of developmental symmetry breaking and the direct inference of an interpretable hydrodynamic model, which reveals similarities between pan-embryo cell migration and active Brownian particle dynamics on curved surfaces. Due to its generic conceptual foundation, we expect that mode-based model learning can help advance the quantitative biophysical understanding of a wide range of developmental structure formation processes.

Quantification of T-cell dynamics during latent cytomegalovirus infection in humans

Cytomegalovirus (CMV) infection has a major impact on the T-cell pool, which is thought to be associated with ageing of the immune system. The effect on the T-cell pool has been interpreted as an effect of CMV on non-CMV specific T-cells. However, it remains unclear whether the effect of CMV could simply be explained by the presence of large, immunodominant, CMV-specific memory CD8+ T-cell populations. These have been suggested to establish through gradual accumulation of long-lived cells. However, little is known about their maintenance. We investigated the effect of CMV infection on T-cell dynamics in healthy older adults, and aimed to unravel the mechanisms of maintenance of large numbers of CMV-specific CD8+ T-cells. We studied the expression of senescence, proliferation, and apoptosis markers and quantified the in vivo dynamics of CMV-specific and other memory T-cell populations using in vivo deuterium labelling. Increased expression of late-stage differentiation markers by CD8+ T-cells of CMV+ versus CMV- individuals was not solely explained by the presence of large, immunodominant CMV-specific CD8+ T-cell populations. The lifespans of circulating CMV-specific CD8+ T-cells did not differ significantly from those of bulk memory CD8+ T-cells, and the lifespans of bulk memory CD8+ T-cells did not differ significantly between CMV- and CMV+ individuals. Memory CD4+ T-cells of CMV+ individuals showed increased expression of late-stage differentiation markers and decreased Ki-67 expression. Overall, the expression of senescence markers on T-cell populations correlated positively with their expected in vivo lifespan. Together, this work suggests that i) large, immunodominant CMV-specific CD8+ T-cell populations do not explain the phenotypical differences between CMV+ and CMV- individuals, ii) CMV infection hardly affects the dynamics of the T-cell pool, and iii) large numbers of CMV-specific CD8+ T-cells are not due to longer lifespans of these cells.

Quantitative Action Spectroscopy Reveals ARPE-19 Sensitivity to Long-Wave Ultraviolet Radiation at 350 nm and 380 nm

The role of ultraviolet radiation (UVR) exposure in the pathology of age-related macular degeneration (AMD) has been debated for decades with epidemiological evidence failing to find a clear consensus for or against it playing a role. A key reason for this is a lack of foundational research into the response of living retinal tissue to UVR in regard to AMD-specific parameters of tissue function. We, therefore, explored the response of cultured retinal pigmented epithelium (RPE), the loss of which heralds advanced AMD, to specific wavelengths of UVR across the UV-B and UV-A bands found in natural sunlight. Using a bespoke in vitro UVR exposure apparatus coupled with bandpass filters we exposed the immortalised RPE cell line, ARPE-19, to 10nm bands of UVR between 290 and 405nm. Physical cell dynamics were assessed during exposure in cells cultured upon specialist electrode culture plates which allow for continuous, non-invasive electrostatic interrogation of key cell parameters during exposure such as monolayer coverage and tight-junction integrity. UVR exposures were also utilised to quantify wavelength-specific effects using a rapid cell viability assay and a phenotypic profiling assay which was leveraged to simultaneously quantify intracellular reactive oxygen species (ROS), nuclear morphology, mitochondrial stress, epithelial integrity and cell viability as part of a phenotypic profiling approach to quantifying the effects of UVR. Electrical impedance assessment revealed unforeseen detrimental effects of UV-A, beginning at 350nm, alongside previously demonstrated UV-B impacts. Cell viability analysis also highlighted increased effects at 350nm as well as 380nm. Effects at 350nm were further substantiated by high content image analysis which highlighted increased mitochondrial dysfunction and oxidative stress. We conclude that ARPE-19 cells exhibit a previously uncharacterised sensitivity to UV-A radiation, specifically at 350nm and somewhat less at 380nm. If upheld in vivo, such sensitivity will have impacts upon geoepidemiological risk scoring of AMD.

Analysis of Cell and Molecular Phenotypes of a Highly Replicated Longevity-Associated FOXO3 Variant in Older Adults

Aging demographics in the US, and other industrialized nations, are resulting in rapidly increasing health care costs from age-related diseases. New therapeutic interventions to extend healthspan in older adults requires understanding connections between basic aging biology and human longevity factors. Using clinical samples from the Kuakini Honolulu Heart Program (HHP) and their Offspring, we are examining potential links between molecular and cellular mechanisms of aging and the longevity associated FOXO3 genotype (carriage of SNP rs2802292 “G” allele). Telomere dynamics in leucocytes (LTL) have shown strong correlation with multiple lifestyle and health factors. We previously demonstrated a significant protective relation between FOXO3 longevity genotype and LTL in a cross-sectional study. Now we are assessing a longitudinal relation, at three time points over 20+ years, in older men. We are also exploring stem cell frequency and differentiation capacity in neurological and peripheral blood samples to assess FOXO3 genotype and human cell dynamics.

Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.