Broad volume distributions indicate nonsynchronized growth and suggest sudden collapses of germinal center B cell populations.

Immunization with a T cell-dependent Ag leads to the formation of several hundred germinal centers (GCs) within secondary lymphoid organs, a key process in the maturation of the immune response. Although prevailing perceptions about affinity maturation intuitively assume simultaneous seeding, growth, and decay of GCs, our previous mathematical simulations led us to hypothesize that their growth might be nonsynchronized. To investigate this, we performed computer-aided three-dimensional reconstructions of splenic GCs to measure size distributions at consecutive time points following immunization of BALB/c mice with a conjugate of 2-phenyl-oxazolone and chicken serum albumin. Our analysis reveals a broad volume distribution of GCs, indicating that individual GCs certainly do not obey the average time course of the GC volumes and that their growth is nonsynchronized. To address the cause and implications of this behavior, we compared our empirical data with simulations of a stochastic mathematical model that allows for frequent and sudden collapses of GCs. Strikingly, this model succeeds in reproducing the empirical average kinetics of GC volumes as well as the underlying broad size distributions. Possible causes of GC B cell population collapses are discussed in the context of the affinity-maturation process.

Fluid dynamic simulation of rat brain vessels, geometrically reconstructed from MR-angiography and validated using phase contrast angiography.

The exact knowledge of the blood vessel geometry plays an important role, not only in clinical applications (stroke diagnosis, detection of stenosis), but also for deeper analysis of hemodynamic functional data, such as fMRI. Such vessel geometries can be obtained by different MR angiographic measurements. It is shown that simulations using computational fluid dynamics (CFD) can be used to validate the vessel geometry, automatically reconstructed from time of flight (TOF) angiograms or phase contrast angiography (PC-MRA) data. CFD simulations are based on PC-MRA data, since these data contain additionally rheological information (phases) besides merely amplitudes as is the case for TOF measurements. Parts of the rat brain vessel system are carefully modeled consisting of a main tube and second order branches. By analyzing velocity changes up and downstream of bifurcations, it is shown that CFD can be used to help detecting missing vessels in the TOF based reconstruction. It is demonstrated by artificially deleting a branch from the reconstruction and compared the flow in both resulting CFD simulations. Finally the simulations help to understand the effects of secondary branches on the flow in the main tube. The aim of this study is to compare the measured (PCA) flow data with the CFD simulation results, based on the vessel geometry gained from the PCA image using an in house reconstruction algorithm. If a more accurate simulation method is found and if in principal the simulation matches the PCA data, it might be possible to deduct that in cases where the measured data varies from the CFD simulation, the reconstruction is not complete, i.e. branches are missing or wrong branches were reconstructed.