Design and Development of Fluorescent Vemurafenib Analogs for In Vivo Imaging

Herein we describe fluorescent derivatives of vemurafenib to probe therapeutic BRAF inhibition in live cells and in vivo. The compounds were evaluated and compared by determining target binding, inhibition of mutant BRAF melanoma cell lines and live cell imaging. We show that vemurafenib-BODIPY is a superior imaging drug to visualize the targets of vemurafenib in live cells and in vivo in non-resistant and resistant melanoma tumors

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

ACCEPTED MANUSCRIPT Interactive Visualization of Multiresolution Image Stacks in 3D

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain

BrainMaps.org - Interactive High-Resolution Digital Brain Atlases and Virtual Microscopy

BrainMaps.org is an interactive high-resolution digital brain atlas and virtual microscope that is based on over 20 million megapixels of scanned images of serial sections of both primate and non-primate brains and that is integrated with a high-speed database for querying and retrieving data about brain structure and function over the internet. Complete brain datasets for various species, including Homo sapiens, Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, and Tyto alba, are accessible online. The methods and tools we describe are useful for both research and teaching, and can be replicated by labs seeking to increase accessibility and sharing of neuroanatomical data. These tools offer the possibility of visualizing and exploring completely digitized sections of brains at a sub-neuronal level, and can facilitate large-scale connectional tracing, histochemical and stereological analyses

Automated cell nucleus detection for large-volume electron microscopy of neural tissue

Volumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their connections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here. Using a hybrid approach that combines interactive learning, morphological processing, and object level feature classification, we demonstrate automated detection of cell nuclei at 92.4% recall and 95.1% precision. These algorithms are not RAM-limited and can scale to arbitrarily large datasets.Publisher's Versio