Cells communicate with the outside world through membrane receptors that recognize one of many possible stimuli (hormones, antibodies, peptides) in the extracellular environment and translate this information to intracellular responses. Stimulation of the cells produces changes in the organization and dynamics of the receptors that are critical to signal transduction. Problems in signaling networks are important in understanding many diseases including cancer, allergy and asthma, so there is great interest in understanding these changes. Biologists in the Spatiotemporal Modeling of Cell Signaling Center (STMC) have generated a large amount of data about the high affinity receptor FceRI, that is found in mast cells and basophils. The activation of this receptor starts when IgE bound to FceRI is crosslinked by a stimulus, that is, a multivalent antigen, initiating a tyrosine kinase signaling cascade that triggers histamine release and other preformed inflammatory mediators that are stored in cytoplasmic granules. My STMC collaborators have created two kinds of data about receptor organization and dynamics. They produce static snapshots of the organization of the receptors by fixing the cells and then labeling the receptors with nano-gold particles and imaging the cell membrane using high-resolution transmission electron microscopy. They study the motion of the receptors by labeling them with quantum dots in living cells and then making movies of the motion of the dots using high resolution fluorescence microscopy and video imaging. All of the data are dose-response where the dose is the amount of stimulus given to the cell and the responses are given by the distribution and dynamics. The main goal of this thesis is to quantify the changes in receptor distribution and dynamics during signaling. Previously, the organization of the receptors was studied using spatial statistics. We have improved this analysis using hierarchical clustering and dendrogram analysis. Clusters of receptors are determined by choosing a distance and then putting any two particles in the same cluster if they are closer than this distance. The problem is how to choose this distance? Our algorithm produces the intrinsic clustering distance that is determined from the data using the hierarchical clustering algorithm. Next, we compare this number to the number provided by randomly generated data to produce the clustering ratio that we use to quantify how clustering increases with increasing stimulus. Previously, the dynamic data were analyzed using the mean squared displacement to produce a diffusion coefficient. We use time-series analysis applied to the jumps, the difference in the position of a particle in two successive frames of the movies, to provide significantly more nano-scale information about the motion. A serious difficulty that we overcame is that the quantum dots blink, so there are missing data when the dots are off. For unstimulated cells, one important result is that the jumps are not normally distributed because there is an excess of short jumps, indicating the presence of small (less than 70nm in diameter) confinement zones in the membrane. When the cells are stimulated, the motion rapidly slows and the jumps show an even greater excess of small jumps indicating a further level of receptor confinement.
Level of Degree
Mathematics & Statistics
First Committee Member (Chair)
Second Committee Member
Third Committee Member
The New Mexico Center for the Spatiotemporal Modeling of Cell Signaling
Cellular signal transduction, Cell membranes, Cell receptors, Mast cells--Physiology, Basophils--Physiology, Quantum dots.
Espinoza Hidalgo, Flor Aurelia. "Analysis of the organization and dynamics of proteins in cell membranes." (2011). http://digitalrepository.unm.edu/math_etds/16