Uncovering structural themes across cilia microtubule inner proteins with implications for human cilia function.

Centrosomes and cilia are microtubule-based superstructures vital for cell division, signaling, and motility. The once thought hollow lumen of their microtubule core structures was recently found to hold a rich meshwork of microtubule inner proteins (MIPs). To address the outstanding question of how distinct MIPs evolved to recognize microtubule inner surfaces, we applied computational sequence analyses, structure predictions, and experimental validation to uncover evolutionarily conserved microtubule- and MIP-binding modules named NWE, SNYG, and ELLEn, and PYG and GFG-repeat by their signature motifs. These modules intermix with MT-binding DM10-modules and Mn-repeats in 24 Chlamydomonas and 33 human proteins. The modules molecular characteristics provided keys to identify elusive cross-species homologs, hitherto unknown human MIP candidates, and functional properties for seven protein subfamilies, including the microtubule seam-binding NWE and ELLEn families. Our work defines structural innovations that underpin centriole and axoneme assembly and demonstrates that MIPs co-evolved with centrosomes and cilia.

Airway Cells 3D Reconstruction via Manual and Machine-Learning Aided Segmentation of Volume EM Datasets.

Volume electron microscopy (vEM) is a high-resolution imaging technique capable of revealing the 3D structure of cells, tissues, and model organisms. This imaging modality is gaining prominence due to its ability to provide a comprehensive view of cells at the nanometer scale. The visualization and quantitative analysis of individual subcellular structures however requires segmentation of each 2D electron micrograph slice of the 3D vEM dataset; this process is extremely laborious de facto limiting its applications and throughput. To address these limitations, deep learning approaches have been recently developed including Empanada-Napari plugin, an open-source tool for automated segmentation based on a Panoptic-DeepLab (PDL) architecture. In this chapter, we provide a step-by-step protocol describing the process of manual segmentation using 3dMOD within the IMOD package and the process of automated segmentation using Empanada-Napari plugins for the 3D reconstruction of airway cellular structures.

Phase-shift nanodroplets as an emerging sonoresponsive nanomaterial for imaging and drug delivery applications.

Nanodroplets - emerging phase-changing sonoresponsive materials - have attracted substantial attention in biomedical applications for both tumour imaging and therapeutic purposes due to their unique response to ultrasound. As ultrasound is applied at different frequencies and powers, nanodroplets have been shown to cavitate by the process of acoustic droplet vapourisation (ADV), causing the development of mechanical forces which promote sonoporation through cellular membranes. This allows drugs to be delivered efficiently into deeper tissues where tumours are located. Recent reviews on nanodroplets are mostly focused on the mechanism of cavitation and their applications in biomedical fields. However, the chemistry of the nanodroplet components has not been discussed or reviewed yet. In this review, the commonly used materials and preparation methods of nanodroplets are summarised. More importantly, this review provides examples of variable chemistry components in nanodroplets which link them to their efficiency as ultrasound-multimodal imaging agents to image and monitor drug delivery. Finally, the drawbacks of current research, future development, and future direction of nanodroplets are discussed.