Fluorescence-intensity distribution analysis and its application in biomolecular detection technology

A methodology, fluorescence-intensity distribution analysis, has been developed for confocal microscopy studies in which the fluorescence intensity of a sample with a heterogeneous brightness profile is monitored. An adjustable formula, modeling the spatial brightness distribution, and the technique of generating functions for calculation of theoretical photon count number distributions serve as the two cornerstones of the methodology. The method permits the simultaneous determination of concentrations and specific brightness values of a number of individual fluorescent species in solution. Accordingly, we present an extremely sensitive tool to monitor the interaction of fluorescently labeled molecules or other microparticles with their respective biological counterparts that should find a wide application in life sciences, medicine, and drug discovery. Its potential is demonstrated by studying the hybridization of 5′-(6-carboxytetramethylrhodamine)-labeled and nonlabeled complementary oligonucleotides and the subsequent cleavage of the DNA hybrids by restriction enzymes.

Modeling the dynamics of human hair cycles by a follicular automaton

The hair follicle cycle successively goes through the anagen, catagen, telogen, and latency phases, which correspond, respectively, to hair growth, arrest, shedding, and absence before a new anagen phase is initiated. Experimental observations collected over a period of 14 years in a group of 10 male volunteers, alopecic and nonalopecic, allowed us to determine the characteristics of scalp hair follicle cycles. On the basis of these observations, we propose a follicular automaton model to simulate the dynamics of human hair cycles. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states anagen, telogen, and latency, and the subsequent return to anagen. The transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a variance. The follicular automaton model accounts both for the dynamical transitions observed in a single follicle and for the behavior of an ensemble of independently cycling follicles. Thus, the model successfully reproduces the evolution of the fractions of follicle populations in each of the three phases, which fluctuate around steady-state or slowly drifting values. We apply the follicular automaton model to the study of spatial patterns of follicular growth that result from a spatially heterogeneous distribution of parameters such as the mean duration of anagen phase. When considering that follicles die or miniaturize after going through a critical number of successive cycles, the model can reproduce the evolution to hair patterns similar to well known types of diffuse or androgenetic alopecia.