Mechanisms of blood homeostasis: lineage tracking and a neutral model of cell populations in rhesus macaques

Goyal, Sidhartha; Kim, Sanggu; Chen, Irvin S. Y.; Chou, Tom

doi:10.1186/s12915-015-0191-8

Cited by 33 publications

(60 citation statements)

References 54 publications

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“…For nontransient populations, assuming sequential blood samples of the output from IS-barcoded progenitors, Goyal et al [60] developed a mathematical model with a simplified network consisting of HSCs, pooled transit-amplifying progenitor cells, and fully differentiated nucleated blood cells. The aim is not to challenge the haematopoietic tree or the sequential output from individual clones, but to better understand the evolution of the clone-size distribution.…”

Section: Barcoding Datamentioning

confidence: 99%

Retracing the in vivo haematopoietic tree using single‐cell methods

Perié

Duffy

2016

FEBS Letters

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The dynamic process by which self-renewing stem cells and their offspring proliferate and differentiate to create the erythroid, myeloid and lymphoid lineages of the blood system has long since been an important topic of study. A range of recent single cell and family tracing methodologies such as massively parallel single-cell RNA-sequencing, mass cytometry, integration site barcoding, cellular barcoding and transposon barcoding are enabling unprecedented analysis, dissection and re-evaluation of the haematopoietic tree. In addition to the substantial experimental advances, these new techniques have required significant theoretical development in order to make biological deductions from their data. Here, we review these approaches from both an experimental and inferential point of view, considering their discoveries to date, their capabilities, limitations and opportunities for further development.

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Section: Barcoding Datamentioning

confidence: 99%

Retracing the in vivo haematopoietic tree using single‐cell methods

Perié

Duffy

2016

FEBS Letters

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“…We now use the results for the mean steady-state population N * to find the clone counts averaged over all realizations of the underlying stochastic process. The mean-field equations for the dynamics of these mean clone counts in the neutral model were derived in [34,35] and are reviewed in Appendix A. In the neutral model, we assume that all effective clones Q are associated with the same rates α and r so that the mean field evolution equation for c k (α, r) is [34,35]…”

Section: Mean-field Model Of Clone Countsmentioning

confidence: 99%

How heterogeneous thymic output and homeostatic proliferation shape naive T cell receptor clone abundance distributions

Dessalles

D’Orsogna

Chou³

2019

Preprint

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The set of T cells that express the same T cell receptor (TCR) sequence represent a T cell clone. The number of different naive T cell clones in an organism reflects the number of different T cell receptors (TCRs) arising from recombination of the V(D)J gene segments during T cell development in the thymus. TCR diversity and more specifically, the clone abundance distribution is an important factor in immune function. Specific recombination patterns occur more frequently than others while subsequent interactions between TCRs and self-antigens are known to trigger proliferation and sustain naive T cell survival. These processes are TCR-dependent, leading to clone-dependent thymic export and naive T cell proliferation rates. Using a mean-field approximation to the solution of a regulated birth-death-immigration model, we systematically quantify how TCR-dependent heterogeneities in immigration and proliferation rates affect the shape of clone abundance distributions (the number of different clones that are represented by a specific number of cells). By comparing predicted clone abundances derived from our heterogeneous birth-death-immigration model with experimentally sampled clone abundances, we quantify the heterogeneity necessary to generate the observed abundances. Our findings indicate that heterogeneity in proliferation rates is more likely the mechanism underlying the observed clone abundance distributions than heterogeneity in immigration rates. Author SummaryThe abundance distribution of different T cell receptors (TCRs) expressed on naive T cells depends on their rates of thymic output, homeostatic proliferation, and death. However, measured TCR count distributions do not match, even qualitatively, those predicted from a multiclone birth death-immigration process when constant birth, death, and immigration rates are used (a neutral model). We show how non-neutrality in the birth-death-immigration process, where naive T cells with different TCRs are produced and proliferate with a distribution of rates shape the predicted sampled clone abundance distributions (the clone counts). Using physiological parameters, we find that heterogeneity in proliferation rates, and not in thymic output rates, is the main determinant in generating the observed clone counts. These findings are consistent with proliferation-driven maintenance of the T cell population in humans.

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“…Glauche et al (2007); Marciniak-Czochra et al (2009); Foo et al (2009); Stiehl and Marciniak-Czochra (2012); Goyal et al (2015). There are several mathematical papers focusing on MPN and related disorders.…”

Section: Introductionmentioning

confidence: 99%

Determining the role of inflammation in the selection of JAK2 mutant cells in myeloproliferative neoplasms

Zhang

Fleischman²,

Wodarz

et al. 2017

Journal of Theoretical Biology

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Myeloproliferative neoplasm (MPN) is a hematologic malignancy characterized by the clonal outgrowth of hematopoietic cells with a somatically acquired mutation most commonly in JAK2 (JAK2V617F). This mutation endows upon myeloid progenitors cytokine independent growth and consequently leads to excessive production of myeloid lineage cells. It has been previously suggested that inflammation may play a role in the clonal evolution of JAK2V617F mutants. In particular, it is possible that one or more cellular kinetic parameters of hematopoietic stem cells (HSCs) are affected by inflammation, such as division or death rates of cells, and the probability of HSC differentiation. This suggests a mechanism that can steer the outcome of the cellular competition in favor of the mutants, initiating the disease. In this paper we create a number of mathematical evolutionary models, from very abstract to more concrete, that describe cellular competition in the context of inflammation. It is possible to build a model axiomatically, where only very general assumptions are imposed on the modeling components and no arbitrary (and generally unknown) functional forms are used, and still generate a set of testable predictions. In particular, we show that, if HSC death is negligible, the evolutionary advantage of mutant cells can only be conferred by an increase in differentiation probability of HSCs in the presence of inflammation, and if death plays a significant role in the dynamics, an additional mechanism may be an increase of HSC’s division-to-death ratio in the presence of inflammation. Further, we show that in the presence of inflammation, the wild type cell population is predicted to shrink under inflammation (even in the absence of mutants). Finally, it turns out that if only the differentiation probability is affected by the inflammation, then the resulting steady state population of wild type cells will contain a relatively smaller percentage of HSCs under inflammation. If the division-to-death rate is also affected, then the percentage of HSCs under inflammation can either decrease or increase, depending on other parameters.

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Mechanisms of blood homeostasis: lineage tracking and a neutral model of cell populations in rhesus macaques

Cited by 33 publications

References 54 publications

Retracing the in vivo haematopoietic tree using single‐cell methods

Retracing the in vivo haematopoietic tree using single‐cell methods

How heterogeneous thymic output and homeostatic proliferation shape naive T cell receptor clone abundance distributions

Determining the role of inflammation in the selection of JAK2 mutant cells in myeloproliferative neoplasms

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