Elucidating functional heterogeneity in hematopoietic progenitor cells: a combined experimental and modeling approach.

A detailed understanding of the mechanisms maintaining the hierarchical balance of cell types in hematopoiesis will be important for the therapeutic manipulation of normal and leukemic cells. Mathematical modeling is expected to make an important contribution to this area, but the iterative development of increasingly accurate models will rely on repeated validation using experimental data of sufficient resolution to distinguish between alternative model scenarios. The multipotent hematopoietic progenitor FDCP-Mix cells maintain a hierarchy from self-renewal to post-mitotic differentiation in vitro and are accessible to detailed analysis. Here, we report the development of a combined mathematical modeling and experimental approach to study the principles underlying heterogeneity in FDCP-Mix cultures. We adapt a single-cell based model of hematopoiesis to the conditions of cell culture and describe an association between proliferative history and phenotype of FDCP-Mix cells. While data derived from population studies are incapable of distinguishing between three mechanistically different model scenarios, statistical analysis of single cell tracking data provides a resolution sufficient to select one of them. This scenario favors differences between granulocytic and monocytic lineage with respect to their proliferative behavior and death rates as a mechanistic explanation for the observed heterogeneity. Our results demonstrate the power of a combined experimental/modeling approach in which single cell fate analysis is the key to revealing regulatory principles at the cellular level.

[1]  A. B. Lyons,et al.  Determination of lymphocyte division by flow cytometry. , 1994, Journal of immunological methods.

[2]  I. Glauche,et al.  Cellular aging leads to functional heterogeneity of hematopoietic stem cells: a modeling perspective , 2011, Aging cell.

[3]  D. Roose,et al.  Distributed parameter identification for a label-structured cell population dynamics model using CFSE histogram time-series data , 2009, Journal of mathematical biology.

[4]  Ingo Roeder,et al.  On the symmetry of siblings: automated single-cell tracking to quantify the behavior of hematopoietic stem cells in a biomimetic setup. , 2012, Experimental hematology.

[5]  Nico Scherf,et al.  Beyond genealogies: Mutual information of causal paths to analyse single cell tracking data , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[6]  D Hasenclever,et al.  A novel view on stem cell development: analysing the shape of cellular genealogies , 2009, Cell proliferation.

[7]  C. Werner,et al.  Maleic anhydride copolymers--a versatile platform for molecular biosurface engineering. , 2003, Biomacromolecules.

[8]  T. Dexter,et al.  Erythroid development of the FDCP‐Mix A4 multipotent cell line is governed by the relative concentrations of erythropoietin and interleukin 3 , 1995, British journal of haematology.

[9]  I. Weissman,et al.  Establishment of a normal hematopoietic and leukemia stem cell hierarchy. , 2008, Cold Spring Harbor symposia on quantitative biology.

[10]  D. Boettiger,et al.  Insertional mutagenesis as a route to identifying genes involved in self renewal of haemopoietic stem cells. , 2000, Current topics in microbiology and immunology.

[11]  A. B. Lyons,et al.  Analysing cell division in vivo and in vitro using flow cytometric measurement of CFSE dye dilution. , 2000, Journal of immunological methods.

[12]  Ingo Roeder,et al.  A novel dynamic model of hematopoietic stem cell organization based on the concept of within-tissue plasticity. , 2002, Experimental hematology.

[13]  Nico Scherf,et al.  FluidTracks - Combining Nonlinear Image Registration and Active Contours for Cell Tracking , 2012, Bildverarbeitung für die Medizin.

[14]  Nico Scherf,et al.  Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions , 2012, Bioinform..

[15]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[16]  A. Golubev,et al.  Exponentially modified Gaussian (EMG) relevance to distributions related to cell proliferation and differentiation. , 2010, Journal of theoretical biology.

[17]  Timm Schroeder,et al.  Long-term single-cell imaging of mammalian stem cells , 2011, Nature Methods.

[18]  C. Werner,et al.  Biomimetic microcavities based on poly(dimethylsiloxane) elastomers , 2009 .

[19]  T. Schroeder,et al.  Advances in tracking hematopoiesis at the single-cell level , 2012, Current opinion in hematology.

[20]  Ingo Roeder,et al.  Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications , 2006, Nature Medicine.

[21]  Gary D Bader,et al.  Dynamic interaction networks in a hierarchically organized tissue , 2010, Molecular systems biology.

[22]  P. Horan,et al.  Fluorescent cell labeling for in vivo and in vitro cell tracking. , 1990, Methods in cell biology.

[23]  V. Quaranta,et al.  Fractional Proliferation: A method to deconvolve cell population dynamics from single-cell data , 2012, Nature Methods.

[24]  Nico Scherf,et al.  Assisting the Machine Paradigms for Human-Machine Interaction in Single Cell Tracking , 2013, Bildverarbeitung für die Medizin.

[25]  C. Parish,et al.  New fluorescent dyes for lymphocyte migration studies. Analysis by flow cytometry and fluorescence microscopy. , 1990, Journal of immunological methods.

[26]  Michael Cross,et al.  Transient expression of PU.1 commits multipotent progenitors to a myeloid fate whereas continued expression favors macrophage over granulocyte differentiation. , 2003, Experimental hematology.

[27]  H. Karasuyama,et al.  Establishment of mouse cell lines which constitutively secrete large quantities of interleukin 2, 3, 4 or 5, using modified cDNA expression vectors , 1988, European journal of immunology.

[28]  F. Allgöwer,et al.  Analysis and Simulation of Division- and Label-Structured Population Models , 2012, Bulletin of mathematical biology.

[29]  Ingo Roeder,et al.  Characterization and quantification of clonal heterogeneity among hematopoietic stem cells: a model-based approach. , 2008, Blood.

[30]  Ingo Röder,et al.  Stem Cell Proliferation and Quiescence—Two Sides of the Same Coin , 2009, PLoS Comput. Biol..

[31]  Hulin Wu,et al.  Evaluation of Multitype Mathematical Models for CFSE-Labeling Experiment Data , 2012, Bulletin of mathematical biology.

[32]  J. Smith,et al.  Do cells cycle? , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[33]  T. Dexter,et al.  Self-renewal and differentiation of interleukin-3-dependent multipotent stem cells are modulated by stromal cells and serum factors. , 1986, Differentiation; research in biological diversity.

[34]  P. Quesenberry,et al.  The murine long-term multi-lineage renewal marrow stem cell is a cycling cell , 2014, Leukemia.

[35]  Michael Cross,et al.  Lineage Specification of Hematopoietic Stem Cells: Mathematical Modeling and Biological Implications , 2007, Stem cells.