An approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system.
Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.
Neural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure. Background and SummaryWhether focusing on a large swath of cortex or a single subcortical nucleus, consistent and reliable visualization of cytoarchitecture is critical for the creation of reference points which demarcate the brain's landscape [1]. This is true not only for the identification of landmarks (or regions of interest), but also the study of local circuits therein. Indeed, it is the distinguishing features in brain cytoarchitecture which arise at small, local scales (i.e., through clusters of cells which are packed in discrete barrels or layers; see [2,3]) that continue to emerge across larger spatial scales to reveal the presence of functionally distinct regions. Thus, detailed views into the brain's architecture can be used to experimentally manipulate circuits, and to advance the field's understanding and integration of each of these overarching systems.With advances in the reconstruction and analysis of significantly larger brain volumes, neuroscientists are now able to visualize patterns of microarchitecture that arise at a scale previously inaccessible using traditional methods [4,5,6]. Examples such as CLARITY [7], expansion microscopy [8], serial two photon tomography [9, 10, 11], multi-beam scanning electron microscopy [12], and X-ray microtomography [13,14,15,16,17], now provide access to several regions of interest within a volume of tissue simultaneously, providing rich context to study both local circuitry and long-range projections. With many of these new techniques, it is possible to image and analyze large intact anatomical samples that preserve the connectivity between multiple regions of interest [18,19], thus providing a lens into the heterogeneity of neural structure within and across different brain areas.Here, we introduce a three-dimensional neuroanatomical dataset extracted from a validated, in-vitro mouse thalamocortical sample spanning six anatomically distinct regions of interest (somatosensory cortex, two thalamic nuclei, zona incerta, striatum and hypothalamus) [18]. This dataset was reconstructed using X-ray microtomography to reveal a diverse composition of microstructures (e.g., myelinated axons, cell bodies, and vasculature) within each region at isotr...
Serial synchrotron crystallography enables the study of protein structures under physiological temperature and reduced radiation damage by collection of data from thousands of crystals. The Structural Biology Center at Sector 19 of the Advanced Photon Source has implemented a fixed-target approach with a new 3D-printed mesh-holder optimized for sample handling. The holder immobilizes a crystal suspension or droplet emulsion on a nylon mesh, trapping and sealing a near-monolayer of crystals in its mother liquor between two thin Mylar films. Data can be rapidly collected in scan mode and analyzed in near real-time using piezoelectric linear stages assembled in an XYZ arrangement, controlled with a graphical user interface and analyzed using a high-performance computing pipeline. Here, the system was applied to two β-lactamases: a class D serine β-lactamase from Chitinophaga pinensis DSM 2588 and L1 metallo-β-lactamase from Stenotrophomonas maltophilia K279a.
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