Modelling of the SDF-1/CXCR4 regulated in vivo homing of therapeutic mesenchymal stem/stromal cells in mice

Abstract

BackgroundMesenchymal stem/stromal cells (MSCs) are a promising tool for cell-based therapies in the treatment of tissue injury. The stromal cell-derived factor-1 (SDF-1)/CXC chemokine receptor 4 (CXCR4) axis plays a significant role in directing MSC homing to sites of injury. However in vivo MSC distribution following intravenous transplantation remains poorly understood, potentially hampering the precise prediction and evaluation of therapeutic efficacy.MethodsA murine model of partial ischemia/reperfusion (I/R) is used to induce liver injury, increase the hepatic levels of SDF-1, and study in vivo MSC distribution. Hypoxia-preconditioning increases the expression of CXCR4 in human bone marrow-derived MSCs. Quantitative assays for human DNA using droplet digital PCR (ddPCR) allow us to examine the in vivo kinetics of intravenously infused human MSCs in mouse blood and liver. A mathematical model-based system is developed to characterize in vivo homing of human MSCs in mouse models with SDF-1 levels in liver and CXCR4 expression on the transfused MSCs. The model is calibrated to experimental data to provide novel estimates of relevant parameter values.ResultsImages of immunohistochemistry for SDF-1 in the mouse liver with I/R injury show a significantly higher SDF-1 level in the I/R injured liver than that in the control. Correspondingly, the ddPCR results illustrate a higher MSC concentration in the I/R injured liver than the normal liver. CXCR4 is overexpressed in hypoxia-preconditioned MSCs. An increased number of hypoxia-preconditioned MSCs in the I/R injured liver is observed from the ddPCR results. The model simulations align with the experimental data of control and hypoxia-preconditioned human MSC distribution in normal and injured mouse livers, and accurately predict the experimental outcomes with different MSC doses.DiscussionThe modelling results suggest that SDF-1 in organs is an effective in vivo attractant for MSCs through the SDF-1/CXCR4 axis and reveal the significance of the SDF-1/CXCR4 chemotaxis on in vivo homing of MSCs. This in vivo modelling approach allows qualitative characterization and prediction of the MSC homing to normal and injured organs on the basis of clinically accessible variables, such as the MSC dose and SDF-1 concentration in blood. This model could also be adapted to abnormal conditions and/or other types of circulating cells to predict in vivo homing patterns.

The synergistic effect of electroacupuncture and bone mesenchymal stem cell transplantation on repairing thin endometrial injury in rats

“…The number of stem cells in the BMSC + EA group was slightly higher than that in the BMSC-only transplantation group. In general, stem cell migration is closely related to the SDF-1/CXCR4 axis [63]. In the present study, western blotting and qRT-PCR also revealed high expression of SDF-1 and CXCR4 after combining BMSCs with EA.…”

Immunosuppressive Property of MSCs Mediated by Cell Surface Receptors

“…CXCR4 SDF-1 Experimental colitis mouse model (134) Traumatic Brain Injury rats model (135) The I/R injured liver mouse model (7,137) (Continued) Mouse model (139,155) Cartilage defect Rabbitsmodel (156) Experimental mouse models (141) A rat model of acute myocardial infarction.…”

“…SDF-1 pretreatment could promote the expression of CXCR4 in MSCs (135). Hypoxia conditions or other oxidative stress conditions have the same effect as SDF-1 pretreatment (136)(137)(138). Since the inflammatory response promotes the secretion of SDF-1, MSCs may express more CXCR4 when the human body is in an inflammatory state.…”

Regenerative medicine meets mathematical modelling: developing symbiotic relationships

“… I want to model… My data are… Consider this kind of maths Example refs Basic regenerative biology Gene regulation & transcriptional control Transcriptomics, time-course of gene/protein expression, live imaging of gene/protein expression Differential equations for e.g. concentration of mRNA, stochastic models for control of gene expression by proteins 69 – 71 Cell migration Cell tracking, population snapshot, time-course of population Cell-based, differential equations, statistical 17 , 18 , 26 , 27 Cell-cell signalling Cell counts, time-course of cell number, signalling molecule concentration Differential equations, hybrid models 37 – 40 , 62 , 64 Relevant to bioreactors Lineage choice, differentiation, cell reprogramming Clonal tracking, live imaging of cell phenotype, transcriptomics, epigenomics, metabolic activity Stochastic and deterministic differential equation models of cell division & differentiation Relevant models from different applications: 19 , 28 – 30 , 72 Pattern formation, growth High-content microscopy images/movies Cell-based, differential equations 9 , 11 , 14 , 31 , 32 , 41 , 46 – 48 , 50 , 51 , 53 – 58 , 73 Bioreactor optimisation Flow rates, inlet and outlet solute concentrations, cell distributions Differential equations …”

“…In liver regeneration, mesenchymal stem/stromal cells are directed to sites of injury by SDF-1, which has potential for cell-based therapies. A differential equation model has recapitulated the in vivo response to treatment of liver injury for different SDF-1 concentrations and doses of transplanted cells 64 , including the beneficial effect of hypoxia-preconditioning to increase the CXCR4 receptor concentration.…”