3D Structure From 2D Microscopy Images Using Deep Learning

Experimental and numerical responses of fibroblast and epithelial cells to the frequency of electric toothbrush

In the oral environment, fibroblast and gingival epithelial cells undergo distinct forces. Chewing, brushing, or force interactions with dental materials like implants can produce these forces. The behavior and response of these cells to forces are determined by their stiffness. Additionally, this behavior can be crucial in mechanosensory and tissue development. In this study, after being cultured using nanomagnet materials, fibroblast and epithelial cells were subjected to magnetic tweezers cytometry testing, and the viscoelastic model was used to determine their stiffness. The reaction of single gingival cells was modeled by determining the stiffness of cells at Gel Point frequencies and the operating frequency of electric toothbrushes and employing the Finite Element Method (FEM). Epithelial cell and fibroblast gel points took place at frequencies of 5Hz and 3Hz, respectively. At these frequencies, the behavior of cells is both quasi-solid and fluid. In addition, the findings of the finite element analysis demonstrated that the cells undergo a greater degree of deformation at the Gel point frequency compared to the operating frequency of toothbrushes. This quantity was approximately 331 times greater in epithelial cells, which reached a maximum of 7.114 μm. Additionally, the maximal fibroblast cell deformation at 3Hz frequency was determined to be 2.981 μm, which is roughly 117 times that at 150Hz frequency.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Mapping molecular complexes with super-resolution microscopy and single-particle analysis

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.