Developing 3D SEM in a broad biological context.

When electron microscopy (EM) was introduced in the 1930s it gave scientists their first look into the nanoworld of cells. Over the last 80 years EM has vastly increased our understanding of the complex cellular structures that underlie the diverse functions that cells need to maintain life. One drawback that has been difficult to overcome was the inherent lack of volume information, mainly due to the limit on the thickness of sections that could be viewed in a transmission electron microscope (TEM). For many years scientists struggled to achieve three-dimensional (3D) EM using serial section reconstructions, TEM tomography, and scanning EM (SEM) techniques such as freeze-fracture. Although each technique yielded some special information, they required a significant amount of time and specialist expertise to obtain even a very small 3D EM dataset. Almost 20 years ago scientists began to exploit SEMs to image blocks of embedded tissues and perform serial sectioning of these tissues inside the SEM chamber. Using first focused ion beams (FIB) and subsequently robotic ultramicrotomes (serial block-face, SBF-SEM) microscopists were able to collect large volumes of 3D EM information at resolutions that could address many important biological questions, and do so in an efficient manner. We present here some examples of 3D EM taken from the many diverse specimens that have been imaged in our core facility. We propose that the next major step forward will be to efficiently correlate functional information obtained using light microscopy (LM) with 3D EM datasets to more completely investigate the important links between cell structures and their functions.

Bone mineral content in Hawaiian, Asian, and Filipino children.

Os calcis bone mineral content (BMC) was measured by single photon absorptiometry in 86 children, ages 6 to 13 years from Hawaiian, Oriental, Caucasian, and Filipino ethnic groups. Pearson correlations indicated significant positive correlations between BMC and age, height, and weight. However, there were no significant differences in age, height or weight between ethnic groups. ANOVA revealed a significant effect of ethnic group on BMC with the Hawaiian group having a significantly higher BMC than the Asian or Caucasian groups. When age, height and weight were controlled for, ANCOVA still showed a significant effect of ethnicity on BMC. The current findings suggest that ethnic differences can develop early in life.