The delivery of therapeutics to the central nervous system remains a major challenge in part due to the presence of the blood–brain barrier (BBB). Recently, cell-derived vesicles, particularly exosomes, have emerged as an attractive vehicle for targeting drugs to the brain, but whether or how they cross the BBB remains unclear. Here, we investigated the interactions between exosomes and brain microvascular endothelial cells (BMECs) in vitro under conditions that mimic the healthy and inflamed BBB in vivo. Transwell assays revealed that luciferase-carrying exosomes can cross a BMEC monolayer under stroke-like, inflamed conditions (TNF-α activated) but not under normal conditions. Confocal microscopy showed that exosomes are internalized by BMECs through endocytosis, co-localize with endosomes, in effect primarily utilizing the transcellular route of crossing. Together, these results indicate that cell-derived exosomes can cross the BBB model under stroke-like conditions in vitro. This study encourages further development of engineered exosomes as drug delivery vehicles or tracking tools for treating or monitoring neurological diseases.

Targeting the mechano-niche of the tumor microenvironment is a strategy to interrogate, detect, and treat cancer.

We present a new instrument for nanoscopic investigations by coupling an atomic force microscope (AFM) with a super resolution stimulated emission depletion (STED) microscope. This nanoscopic tool allows high resolution fluorescence imaging, topographical imaging and nano-mechanical imaging, such as, stiffness. Results obtained from technical and biological samples are shown illustrating different functions and the versatility of the presented tool. We assert that, this highly precise tractable tool paves the way to a new set of comprehensive studies in medicine, biology and materials science.

In this article, we describe and show the application of some of the most advanced fluorescence superresolution techniques, STED AFM and STORM AFM microscopy towards imaging of cytoskeletal structures, such as microtubule filaments. Mechanical and structural properties can play a relevant role in the investigation of cytoskeletal structures of interest, such as microtubules, that provide support to the cell structure. In fact, the mechanical properties, such as the local stiffness and the elasticity, can be investigated by AFM force spectroscopy with tens of nanometers resolution. Force curves can be analyzed in order to obtain the local elasticity (and the Young's modulus calculation by fitting the force curves from every pixel of interest), and the combination with STED/STORM microscopy integrates the measurement with high specificity and yields superresolution structural information. This hybrid modality of superresolution‐AFM working is a clear example of correlative multimodal microscopy.

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.