Mass spectrometry-based analytical tools for the molecular protein characterization of human plasma lipoproteins

Lipoproteins are a heterogeneous population of blood plasma particles composed of apolipoproteins and lipids. Lipoproteins transport exogenous and endogenous triglycerides and cholesterol from sites of absorption and formation to sites of storage and usage. Three major classes of lipoproteins are distinguished according to their density: high-density (HDL), low-density (LDL) and very low-density lipoproteins (VLDL). While HDLs contain mainly apolipoproteins of lower molecular weight, the two other classes contain apolipoprotein B and apolipoprotein (a) together with triglycerides and cholesterol. HDL concentrations were found to be inversely related to coronary heart disease and LDL/VLDL concentrations directly related. Although many studies have been published in this area, few have concentrated on the exact protein composition of lipoprotein particles. Lipoproteins were separated by density gradient ultracentrifugation into different subclasses. Native gel electrophoresis revealed different gel migration behaviour of the particles, with less dense particles having higher apparent hydrodynamic radii than denser particles. Apolipoprotein composition profiles were measured by matrix-assisted laser desorption/ionization-mass spectrometry on a macromizer instrument, equipped with the recently introduced cryodetector technology, and revealed differences in apolipoprotein composition between HDL subclasses. By combining these profiles with protein identifications from native and denaturing polyacrylamide gels by liquid chromatography-tandem mass spectrometry, we characterized comprehensively the exact protein composition of different lipoprotein particles. We concluded that the differential display of protein weight information acquired by macromizer mass spectrometry is an excellent tool for revealing structural variations of different lipoprotein particles, and hence the foundation is laid for the screening of cardiovascular disease risk factors associated with lipoproteins.

SNNAP: A Tool for Teaching Neuroscience

Simulation tools aid in learning neuroscience by providing the student with an interactive environment to carry out simulated experiments and test hypotheses. The field of neuroscience is well suited for the use of simulation tools since nerve cell signaling can be described by mathematical equations and solved by computer. Neural signaling entails the propagation of electrical current along nerve membrane and transmission to neighboring neurons through synaptic connections. Action potentials and synaptic transmission can be simulated and results displayed for visualization and analysis. The neurosimulator SNNAP (Simulator for Neural Networks and Action Potentials) is a simulation environment that provides users with editors for model building, simulator engine and visual display editor. This paper presents several modeling examples that illustrate some of the capabilities and features of SNNAP. First, the Hodgkin-Huxley (HH) model is presented and the threshold phenomenon is illustrated. Second, small neural networks are described with HH models using various synaptic connections available with SNNAP. Synaptic connections may be modulated through facilitation or depression with SNNAP. A study of vesicle pool dynamics is presented using an AMPA receptor model. Finally, a central pattern generator model of the Aplysia feeding circuit is illustrated as an example of a complex network that may be studied with SNNAP. Simulation code is provided for each case study described and tasks are suggested for further investigation.

VR Based Visualization and Exploration of Plant Biological Data

This paper investigates the use of virtual reality (VR) technologies to facilitate the analysis of plant biological data in distinctive steps in the application pipeline. Reconstructed three-dimensional biological models (primary polygonal models) transferred to a virtual environment support scientists' collaborative exploration of biological datasets so that they obtain accurate analysis results and uncover information hidden in the data. Examples of the use of virtual reality in practice are provided and a complementary user study was performed.

Visualization and stereological characterization of individual rat lung acini by high-resolution X-ray tomographic microscopy

The small trees of gas-exchanging pulmonary airways which are fed by the most distal purely conducting airways are called acini and represent the functional gas-exchanging units. The three-dimensional architecture of the acini has a strong influence on ventilation and particle deposition. Due to the difficulty to identify individual acini on microscopic lung sections the knowledge about the number of acini and their biological parameters like volume, surface area, and number of alveoli per acinus are limited. We developed a method to extract individual acini from lungs imaged by high-resolution synchrotron radiation based X-ray tomographic microscopy and estimated their volume, surface area and number of alveoli. Rat acini were isolated by semiautomatically closing the airways at the transition from conducting to gas-exchanging airways. We estimated a mean internal acinar volume of 1.148mm(3), a mean acinar surface area of 73.9mm(2), and a mean of 8470 alveoli per acinus. Assuming that the acini are similarly sized throughout different regions of the lung, we calculated that a rat lung contains 5470±833 acini. We conclude that our novel approach is well suited for the fast and reliable characterization of a large number of individual acini in healthy, diseased, or transgenic lungs of different species including humans.