Stem cell biology is population biology: differentiation of hematopoietic multipotent progenitors to common lymphoid and myeloid progenitors

Abstract

The hematopoietic stem cell (HSC) system is a demand control system, with the demand coming from the organism, since the products of the common myeloid and lymphoid progenitor (CMP, CLP respectively) cells are essential for activity and defense against disease. We show how ideas from population biology (combining population dynamics and evolutionary considerations) can illuminate the feedback control of the HSC system by the fully differentiated products, which has recently been verified experimentally. We develop models for the penultimate differentiation of HSC Multipotent Progenitors (MPPs) into CLP and CMP and introduce two concepts from population biology into stem cell biology. The first concept is the Multipotent Progenitor Commitment Response (MPCR) which is the probability that a multipotent progenitor cell follows a CLP route rather than a CMP route. The second concept is the link between the MPCR and a measure of Darwinian fitness associated with organismal performance and the levels of differentiated lymphoid and myeloid cells. We show that many MPCRs are consistent with homeostasis, but that they will lead to different dynamics of cells and signals following a wound or injury and thus have different consequences for Darwinian fitness. We show how coupling considerations of life history to dynamics of the HSC system and its products allows one to compute the selective pressures on cellular processes. We discuss ways that this framework can be used and extended.

Figure 1

A diagrammatic derivation of Eqns 1 to 6 (details given in Additional file1). a) In the most general case, we consider stem cells (S), a series of Multipotent Progenitor Cells (MPP), a Common Lymphoid Progenitor (CLP) and a Common Myeloid Progenitor (CMP). CLPs give rise to B, NK, and T cells; CMPs give rise to Erythrocytes (E), Granulocytes (G), and Platelets (P). We denote the total numbers of lymphoid and myeloid cells by L and M respectively, rates of differentiation by r_·(with subscript indicating the cell type involved), rates of development of MPP cells by λ_·, feedback from fully differentiated cells on those rates by Φ_·, and rates of cell death by μ_·. The feedback functions have the property that they are 1 when stem cell or fully differentiated cell numbers are low and decline as stem cells or fully differentiated cells increase. Thus, for example, stem cells renew (one stem cell becomes two) at rate r_sΦ_s(l,m)when the concentrations of lymphoid and myeloid cells are l and m respectively, asymmetrically differentiate (one stem cell becomes two stage-0 progenitors) at rate $2r_{p^{\prime }}{\Phi }_{p^{\prime }}(l,m){\Phi }_s(l,m)$, symmetrically differentiate (one stem cell becomes a stem cell and a stage-0 progenitor) at rate r_pΦ_s(l,m), and die at rate μ_s. Similar interpretations hold for other transitions. The Multipotent Commitment Response (MPCR), denoted by ρ(l,m), is the probability that a MPP in its final stage commits to the lymphoid route. b) To focus on the MPCR, we combine all of the fully differentiated cells into lymphoid and myeloid classes (L and M) and use Michaelis-Menten-like arguments to compress the MPP class into a single stage, assuming that steady states of intermediate stages are rapidly reached, characterized by combination of rate constants Ω_N.

Figure 2

a) The relationship between the parameters α and γ of the stem cell commitment response when homeostasis corresponds to 1 lymphoid cell per 1000 myeloid cells. b) Different values of γaffect how the MPCR varies with changes in the number of lymphoid and myeloid cells. In the presence of high numbers of myeloid cells, the demand response is to drive the MPPs to make more lymphoid cells.

Figure 3

We assume that the rate at which successful reproduction accumulates, Δf (m) is a parabolic function of the density of myeloid cells m.

Figure 4

We assume that the rate of mortality declines with increasing numbers of myeloid cells, which has the effect that annual survival increases with increasing densities of myeloid cells; here we artificially hold the myeloid cells constant.

Figure 5

Even in a laboratory environment, without wounding or infection, organism do not live forever, so that survival declines with age (panel a) with the consequence that accumulated fitness saturates.

Figure 6

Plotting lifetime accumulated fitness as a function of γ allows us to understand the strength of selection on γas determined by the environment in which the organism lives.

Figure 7

Ten realizations of the model with both wounds and infection, for the case of γ = 2.