A novel quantitative method for analyzing the distributions of nanoparticles between different tissue and intracellular compartments

The penetration, translocation, and distribution of ultrafine and nanoparticles in tissues and cells are challenging issues in aerosol research. This article describes a set of novel quantitative microscopic methods for evaluating particle distributions within sectional images of tissues and cells by addressing the following questions: (1) is the observed distribution of particles between spatial compartments random? (2) Which compartments are preferentially targeted by particles? and (3) Does the observed particle distribution shift between different experimental groups? Each of these questions can be addressed by testing an appropriate null hypothesis. The methods all require observed particle distributions to be estimated by counting the number of particles associated with each defined compartment. For studying preferential labeling of compartments, the size of each of the compartments must also be estimated by counting the number of points of a randomly superimposed test grid that hit the different compartments. The latter provides information about the particle distribution that would be expected if the particles were randomly distributed, that is, the expected number of particles. From these data, we can calculate a relative deposition index (RDI) by dividing the observed number of particles by the expected number of particles. The RDI indicates whether the observed number of particles corresponds to that predicted solely by compartment size (for which RDI = 1). Within one group, the observed and expected particle distributions are compared by chi-squared analysis. The total chi-squared value indicates whether an observed distribution is random. If not, the partial chi-squared values help to identify those compartments that are preferential targets of the particles (RDI > 1). Particle distributions between different groups can be compared in a similar way by contingency table analysis. We first describe the preconditions and the way to implement these methods, then provide three worked examples, and finally discuss the advantages, pitfalls, and limitations of this method.

Size parameters, size-class distribution and area-number relationship of microscopic charcoal: relevance for fire reconstruction

Charcoal analysis was conducted on sediment cores from three lakes to assess the relationship between the area and number of charcoal particles. Three charcoal-size parameters (maximum breadth, maximum length and area) were measured on sediment samples representing various vegetation types, including shrub tundra, boreal forest and temperate forest. These parameters and charcoal size-class distributions do not differ statistically between two sites where the same preparation technique (glycerine pollen slides) was used, but they differ for the same core when different techniques were applied. Results suggest that differences in charcoal size and size-class distribution are mainly caused by different preparation techniques and are not related to vegetation-type variation. At all three sites, the area and number concentrations of charcoal particles are highly correlated in standard pollen slides; 82–83% of the variability of the charcoal-area concentration can be explained by the particle-number concentration. Comparisons between predicted and measured area concentrations show that regression equations linking charcoal number and area concentrations can be used across sites as long as the same pollen-preparation technique is used. Thus it is concluded that it is unnecessary to measure charcoal areas in standard pollen slides – a time-consuming and tedious process.