ABSTRACT
BACKGROUND: Needle-free jet injection (NFJI) systems enable a controlled and targeted delivery of drugs into skin tissue. However, a scarce understanding of their underlying mechanisms has been a major deterrent to the development of an efficient system. Primarily, the lack of a suitable visualization technique that could capture the dynamics of the injected fluid-tissue interaction with a microsecond range temporal resolution has emerged as a main limitation. A conventional needle-free injection system may inject the fluids within a few milliseconds and may need a temporal resolution in the microsecond range for obtaining the required images. However, the presently available imaging techniques for skin tissue visualization fail to achieve these required spatial and temporal resolutions. Previous studies on injected fluid-tissue interaction dynamics were conducted using in vitro media with a stiffness similar to that of skin tissue. However, these media are poor substitutes for real skin tissue, and the need for an imaging technique having ex vivo or in vivo imaging capability has been echoed in the previous reports. METHODS: A near-infrared imaging technique that utilizes the optical absorption and fluorescence emission of indocyanine green dye, coupled with a tissue clearing technique, was developed for visualizing a NFJI in an ex vivo porcine skin tissue. RESULTS: The optimal imaging conditions obtained by considering the optical properties of the developed system and mechanical properties of the cleared ex vivo samples are presented. Crucial information on the dynamic interaction of the injected liquid jet with the ex vivo skin tissue layers and their interfaces could be obtained. CONCLUSIONS: The reported technique can be instrumental for understanding the injection mechanism and for the development of an efficient transdermal NFJI system as well.
ABSTRACT
Over the past decade, our understanding of human diseases has rapidly grown from the rise of single-cell spatial biology. While conventional tissue imaging has focused on visualizing morphological features, the development of multiplex tissue imaging from fluorescence-based methods to DNA- and mass cytometry-based methods has allowed visualization of over 60 markers on a single tissue section. The advancement of spatial biology with a single-cell resolution has enabled the visualization of cell-cell interactions and the tissue microenvironment, a crucial part to understanding the mechanisms underlying pathogenesis. Alongside the development of extensive marker panels which can distinguish distinct cell phenotypes, multiplex tissue imaging has facilitated the analysis of high dimensional data to identify novel biomarkers and therapeutic targets, while considering the spatial context of the cellular environment. This mini-review provides an overview of the recent advancements in multiplex imaging technologies and examines how these methods have been used in exploring pathogenesis and biomarker discovery in cancer, autoimmune and infectious diseases.
ABSTRACT
BACKGROUND: Efficient tools allowing the extraction of 2D surfaces from 3D-microscopy data are essential for studies aiming to decipher the complex cellular choreography through which epithelium morphogenesis takes place during development. Most existing methods allow for the extraction of a single and smooth manifold of sufficiently high signal intensity and contrast, and usually fail when the surface of interest has a rough topography or when its localization is hampered by other surrounding structures of higher contrast. Multiple surface segmentation entails laborious manual annotations of the various surfaces separately. RESULTS: As automating this task is critical in studies involving tissue-tissue or tissue-matrix interaction, we developed the Zellige software, which allows the extraction of a non-prescribed number of surfaces of varying inclination, contrast, and texture from a 3D image. The tool requires the adjustment of a small set of control parameters, for which we provide an intuitive interface implemented as a Fiji plugin. CONCLUSIONS: As a proof of principle of the versatility of Zellige, we demonstrate its performance and robustness on synthetic images and on four different types of biological samples, covering a wide range of biological contexts.