CytoPy: An autonomous cytometry analysis framework.

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.

CD57+ Memory T Cells Proliferate In Vivo.

A central paradigm in the field of lymphocyte biology asserts that replicatively senescent memory T cells express the carbohydrate epitope CD57. These cells nonetheless accumulate with age and expand numerically in response to persistent antigenic stimulation. Here, we use in vivo deuterium labeling and ex vivo analyses of telomere length, telomerase activity, and intracellular expression of the cell-cycle marker Ki67 to distinguish between two non-exclusive scenarios: (1) CD57+ memory T cells do not proliferate and instead arise via phenotypic transition from the CD57- memory T cell pool; and/or (2) CD57+ memory T cells self-renew via intracompartmental proliferation. Our results provide compelling evidence in favor of the latter scenario and further suggest in conjunction with mathematical modeling that self-renewal is by far the most abundant source of newly generated CD57+ memory T cells. Immunological memory therefore appears to be intrinsically sustainable among highly differentiated subsets of T cells that express CD57.

Human TSCM cell dynamics in vivo are compatible with long-lived immunological memory and stemness.

Adaptive immunity relies on the generation and maintenance of memory T cells to provide protection against repeated antigen exposure. It has been hypothesised that a self-renewing population of T cells, named stem cell-like memory T (TSCM) cells, are responsible for maintaining memory. However, it is not clear if the dynamics of TSCM cells in vivo are compatible with this hypothesis. To address this issue, we investigated the dynamics of TSCM cells under physiological conditions in humans in vivo using a multidisciplinary approach that combines mathematical modelling, stable isotope labelling, telomere length analysis, and cross-sectional data from vaccine recipients. We show that, unexpectedly, the average longevity of a TSCM clone is very short (half-life < 1 year, degree of self-renewal = 430 days): far too short to constitute a stem cell population. However, we also find that the TSCM population is comprised of at least 2 kinetically distinct subpopulations that turn over at different rates. Whilst one subpopulation is rapidly replaced (half-life = 5 months) and explains the rapid average turnover of the bulk TSCM population, the half-life of the other TSCM subpopulation is approximately 9 years, consistent with the longevity of the recall response. We also show that this latter population exhibited a high degree of self-renewal, with a cell residing without dying or differentiating for 15% of our lifetime. Finally, although small, the population was not subject to excessive stochasticity. We conclude that the majority of TSCM cells are not stem cell-like but that there is a subpopulation of TSCM cells whose dynamics are compatible with their putative role in the maintenance of T cell memory.

Human Stem Cell-like Memory T Cells Are Maintained in a State of Dynamic Flux.

Adaptive immunity requires the generation of memory T cells from naive precursors selected in the thymus. The key intermediaries in this process are stem cell-like memory T (TSCM) cells, multipotent progenitors that can both self-renew and replenish more differentiated subsets of memory T cells. In theory, antigen specificity within the TSCM pool may be imprinted statically as a function of largely dormant cells and/or retained dynamically by more transitory subpopulations. To explore the origins of immunological memory, we measured the turnover of TSCM cells in vivo using stable isotope labeling with heavy water. The data indicate that TSCM cells in both young and elderly subjects are maintained by ongoing proliferation. In line with this finding, TSCM cells displayed limited telomere length erosion coupled with high expression levels of active telomerase and Ki67. Collectively, these observations show that TSCM cells exist in a state of perpetual flux throughout the human lifespan.

Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives.

Human neutrophils have traditionally been thought to have a short half-life in blood; estimates vary from 4 to 18 hours. This dogma was recently challenged by stable isotope labeling studies with heavy water, which yielded estimates in excess of 3 days. To investigate this disparity, we generated new stable isotope labeling data in healthy adult subjects using both heavy water (n = 4) and deuterium-labeled glucose (n = 9), a compound with more rapid labeling kinetics. To interpret results, we developed a novel mechanistic model and applied it to previously published (n = 5) and newly generated data. We initially constrained the ratio of the blood neutrophil pool to the marrow precursor pool (ratio = 0.26; from published values). Analysis of heavy water data sets yielded turnover rates consistent with a short blood half-life, but parameters, particularly marrow transit time, were poorly defined. Analysis of glucose-labeling data yielded more precise estimates of half-life (0.79 ± 0.25 days; 19 hours) and marrow transit time (5.80 ± 0.42 days). Substitution of this marrow transit time in the heavy water analysis gave a better-defined blood half-life of 0.77 ± 0.14 days (18.5 hours), close to glucose-derived values. Allowing the ratio of blood neutrophils to mitotic neutrophil precursors (R) to vary yielded a best-fit value of 0.19. Reanalysis of the previously published model and data also revealed the origin of their long estimates for neutrophil half-life: an implicit assumption that R is very large, which is physiologically untenable. We conclude that stable isotope labeling in healthy humans is consistent with a blood neutrophil half-life of less than 1 day.

Reconciling Estimates of Cell Proliferation from Stable Isotope Labeling Experiments.

Stable isotope labeling is the state of the art technique for in vivo quantification of lymphocyte kinetics in humans. It has been central to a number of seminal studies, particularly in the context of HIV-1 and leukemia. However, there is a significant discrepancy between lymphocyte proliferation rates estimated in different studies. Notably, deuterated (2)H2-glucose (D2-glucose) labeling studies consistently yield higher estimates of proliferation than deuterated water (D2O) labeling studies. This hampers our understanding of immune function and undermines our confidence in this important technique. Whether these differences are caused by fundamental biochemical differences between the two compounds and/or by methodological differences in the studies is unknown. D2-glucose and D2O labeling experiments have never been performed by the same group under the same experimental conditions; consequently a direct comparison of these two techniques has not been possible. We sought to address this problem. We performed both in vitro and murine in vivo labeling experiments using identical protocols with both D2-glucose and D2O. This showed that intrinsic differences between the two compounds do not cause differences in the proliferation rate estimates, but that estimates made using D2-glucose in vivo were susceptible to difficulties in normalization due to highly variable blood glucose enrichment. Analysis of three published human studies made using D2-glucose and D2O confirmed this problem, particularly in the case of short term D2-glucose labeling. Correcting for these inaccuracies in normalization decreased proliferation rate estimates made using D2-glucose and slightly increased estimates made using D2O; thus bringing the estimates from the two methods significantly closer and highlighting the importance of reliable normalization when using this technique.