Differing Lectin Binding Profiles among Human Embryonic Stem Cells and Derivatives Aid in the Isolation of Neural Progenitor Cells

Human embryonic stem cells (hESCs) and their differentiated progeny allow for investigation of important changes/events during normal embryonic development. Currently most of the research is focused on proteinacous changes occurring as a result of differentiation of stem cells and little is known about changes in cell surface glycosylation patterns. Identification of cell lineage specific glycans can help in understanding their role in maintenance, proliferation and differentiation. Furthermore, these glycans can serve as markers for isolation of homogenous populations of cells. Using a panel of eight biotinylated lectins, the glycan expression of hESCs, hESCs-derived human neural progenitors (hNP) cells, and hESCs-derived mesenchymal progenitor (hMP) cells was investigated. Our goal was to identify glycans that are unique for hNP cells and use the corresponding lectins for cell isolation. Flow cytometry and immunocytochemistry were used to determine expression and localization of glycans, respectively, in each cell type. These results show that the glycan expression changes upon differentiation of hESCs and is different for neural and mesenchymal lineage. For example, binding of PHA-L lectin is low in hESCs (14±4.4%) but significantly higher in differentiated hNP cells (99±0.4%) and hMP cells (90±3%). Three lectins: VVA, DBA and LTL have low binding in hESCs and hMP cells, but significantly higher binding in hNP cells. Finally, VVA lectin binding was used to isolate hNP cells from a mixed population of hESCs, hNP cells and hMP cells. This is the first report that compares glycan expression across these human stem cell lineages and identifies significant differences. Also, this is the first study that uses VVA lectin for isolation for human neural progenitor cells

Simulation of surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model

AbstractWRF/Chem, a fully coupled meteorology–chemistry model, was used for the simulation of surface ozone pollution over the Central Gulf Coast region in Southeast United States of America (USA). Two ozone episodes during June 8–11, 2006 and July 18–22, 2006 characterized with hourly mixing ratios of 60–100ppbv, were selected for the study. Suite of sensitivity experiments were conducted with three different planetary boundary layer (PBL) schemes and three land surface models (LSM). The results indicate that Yonsei–University (YSU) PBL scheme in combination with NOAH and SOIL LSMs produce better simulations of both the meteorological and chemical species than others. YSU PBL scheme in combination with NOAH LSM had slightly better simulation than with SOIL scheme. Spatial comparison with observations showed that YSUNOAH experiment well simulated the diurnal mean ozone mixing ratio, timing of diurnal cycle as well as range in ozone mixing ratio at most monitoring stations with an overall correlation of 0.726, bias of –1.55ppbv, mean absolute error of 8.11ppbv and root mean square error of 14.5ppbv; and with an underestimation of 7ppbv in the daytime peak ozone and about 8% in the daily average ozone. Model produced 1–hr, and 8–hr average ozone values were well correlated with corresponding observed means. The minor underestimation of daytime ozone is attributed to the slight underestimation of air temperature which tend to slow–down the ozone production and overestimation of wind speeds which transport the produced ozone at a faster rate. Simulated mean horizontal and vertical flow patterns suggest the role of the horizontal transport and the PBL diffusion in the development of high ozone during the episode. Overall, the model is found to perform reasonably well to simulate the ozone and other precursor pollutants with good correlations and low error metrics. Thus the study demonstrates the potential of WRF/Chem model for air quality prediction in coastal environments

A Threshold Equation for Action Potential Initiation

In central neurons, the threshold for spike initiation can depend on the stimulus and varies between cells and between recording sites in a given cell, but it is unclear what mechanisms underlie this variability. Properties of ionic channels are likely to play a role in threshold modulation. We examined in models the influence of Na channel activation, inactivation, slow voltage-gated channels and synaptic conductances on spike threshold. We propose a threshold equation which quantifies the contribution of all these mechanisms. It provides an instantaneous time-varying value of the threshold, which applies to neurons with fluctuating inputs. We deduce a differential equation for the threshold, similar to the equations of gating variables in the Hodgkin-Huxley formalism, which describes how the spike threshold varies with the membrane potential, depending on channel properties. We find that spike threshold depends logarithmically on Na channel density, and that Na channel inactivation and K channels can dynamically modulate it in an adaptive way: the threshold increases with membrane potential and after every action potential. Our equation was validated with simulations of a previously published multicompartemental model of spike initiation. Finally, we observed that threshold variability in models depends crucially on the shape of the Na activation function near spike initiation (about −55 mV), while its parameters are adjusted near half-activation voltage (about −30 mV), which might explain why many models exhibit little threshold variability, contrary to experimental observations. We conclude that ionic channels can account for large variations in spike threshold

Flow cytometry histograms of lectin binding in hESCs, hNPs and hMPs.

<p>Percentage of cells binding to 8 different lectins was determined by flow cytometry. A representative lectin histogram plot is shown for one of 4 experimental replicates. In each panel far left grey fill peak in the histogram plot correlates with cells stained with secondary antibody only, and the shifted black tracing peak represents cells binding to a lectin. Panels in the left column show histograms of 8 different lectins binding to hESCs. Panels in the middle column are for hNP cells and panels in the right column are for hMP cells. Gating for each histogram indicates % of cells positive for the lectin-binding.</p

Fluorescence assisted cell sorting of hNPs using VVA lectin.

<p>hMP cells (A), hESCs (B) and hNP cells (C) stained with VVA-fluorescein lectin, analyzed for binding and used as controls. A mixed population of hESCs, hNPs and hMPs (1∶1∶1 ratio) was stained with VVA-fluorescein lectin and sorted for fluorescein positive cells (D). The sorted cells stained positive for Nestin and SOX2 (E); and were negative for OCT4 (F), and CD166 (G). Cells were stained with DAPI for nucleus. Scale bar: 10 µm.</p

Immunocytochemistry of hNP cell cultures for Nestin expression and binding to lectins.

<p>The panels in the left column show hNP cells staining for Nestin and DAPI (panels A, D, G, J, M, P, S, and V). In the same field of view as the left panel, the middle panels show binding of ConA(B), Pha-L (E), MMA (H), VVA (K), DBA (N), Pha-E (Q), LTL (T), and PNA (W) lectins in hNP cells. The merge view of lectin and Nestin staining is shown in panels in the right column. The staining amount roughly correlates with flow cytometry analysis. Scale bar: 10 µm.</p