Optimization of negative stage bias potential for faster imaging in large-scale electron microscopy.

Large-scale electron microscopy (EM) allows analysis of both tissues and macromolecules in a semi-automated manner, but acquisition rate forms a bottleneck. We reasoned that a negative bias potential may be used to enhance signal collection, allowing shorter dwell times and thus increasing imaging speed. Negative bias potential has previously been used to tune penetration depth in block-face imaging. However, optimization of negative bias potential for application in thin section imaging will be needed prior to routine use and application in large-scale EM. Here, we present negative bias potential optimized through a combination of simulations and empirical measurements. We find that the use of a negative bias potential generally results in improvement of image quality and signal-to-noise ratio (SNR). The extent of these improvements depends on the presence and strength of a magnetic immersion field. Maintaining other imaging conditions and aiming for the same image quality and SNR, the use of a negative stage bias can allow for a 20-fold decrease in dwell time, thus reducing the time for a week long acquisition to less than 8 h. We further show that negative bias potential can be applied in an integrated correlative light electron microscopy (CLEM) application, allowing fast acquisition of a high precision overlaid LM-EM dataset. Application of negative stage bias potential will thus help to solve the current bottleneck of image acquisition of large fields of view at high resolution in large-scale microscopy.

Combinatorial Surface Roughness Effects on Osteoclastogenesis and Osteogenesis.

Implant surface properties are a key factor in bone responses to metallic bone implants. In view of the emerging evidence on the important role of osteoclasts in bone regeneration, we here studied how surface roughness affects osteoclastic differentiation and to what extent these osteoclasts have stimulatory effects on osteogenic differentiation of osteoprogenitor cells. For this, we induced osteoclasts derived from RAW264.7 cell line and primary mouse macrophages on titanium surfaces with different roughness ( Ra 0.02-3.63 µm) and analyzed osteoclast behavior in terms of cell number, morphology, differentiation, and further anabolic effect on osteoblastic cells. Surfaces with different roughness induced the formation of osteoclasts with distinct phenotypes, based on total osteoclast numbers, morphology, size, cytoskeletal organization, nuclearity, and osteoclastic features. Furthermore, these different osteoclast phenotypes displayed differential anabolic effects toward the osteogenic differentiation of osteoblastic cells, for which the clastokine CTHRC1 was identified as a causative factor. Morphologically, osteoclast potency to stimulate osteogenic differentiation of osteoblastic cells was found to logarithmically correlate with the nuclei number per osteoclast. Our results demonstrate the existence of a combinatorial effect of surface roughness, osteoclastogenesis, and osteogenic differentiation. These insights open up a new dimension for designing and producing metallic implants by considering the implant roughness to locally regulate osseointegration through coupling osteoclastogenesis with osteogenesis.