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The high metabolic demand of brain tissue is supported by a constant supply of blood through dense microvascular networks. Capillaries are the smallest class of vessels and vary in diameter between ∼2 to 5 μm in the brain. This diameter range plays a significant role in the optimization of blood flow resistance, blood cell distribution, and oxygen extraction. The control of capillary diameter has largely been ascribed to pericyte contractility, but it remains unclear if endothelial wall architecture also contributes to capillary diameter heterogeneity. Here, we use public, large-scale volume electron microscopy data from mouse cortex (MICrONS Explorer, Cortical MM^3) to examine how endothelial cell number, endothelial cell thickness, and pericyte coverage relates to microvascular lumen size. We find that transitional vessels near the penetrating arteriole and ascending venule are composed of 2 to 5 interlocked endothelial cells, while the numerous capillary segments intervening these zones are composed of either 1 or 2 endothelial cells, with roughly equal proportions. The luminal area and diameter is on average slightly larger with capillary segments composed of 2 interlocked endothelial cells versus 1 endothelial cell. However, this difference is insufficient to explain the full range of capillary diameters seen in vivo. This suggests that both endothelial structure and other influences, such as pericyte tone, contribute to the basal diameter and optimized perfusion of brain capillaries.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present “NEURD”, a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

To understand how the neocortex underlies our ability to perceive, think, and act, it is important to study the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron’s tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron’s receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the “like-to-like” connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. The extent to which the morphological diversity of excitatory neurons forms a continuum or is built from distinct clusters of cell types remains an open question. Here we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from a millimeter scale serial-section electron microscopy volume. We found a set of principles that captured the morphological diversity of the dendrites of excitatory neurons. First, their morphologies varied with respect to three major axes: soma depth, total apical and basal skeletal length. Second, neurons in layer 2/3 showed a strong trend of a decreasing width of their dendritic arbor and a smaller tuft with increasing cortical depth. Third, in layer 4, atufted neurons were primarily located in the primary visual cortex, while tufted neurons were more abundant in higher visual areas. Fourth, we discovered layer 4 neurons in V1 on the border to layer 5 which showed a tendency towards avoiding deeper layers with their dendrites. In summary, excitatory neurons exhibited a substantial degree of dendritic morphological variation, both within and across cortical layers, but this variation mostly formed a continuum, with only a few notable exceptions in deeper layers.

Serial-section electron microscopy (ssEM) is the method of choice for studying macroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so calledconnectomes. In order to use this data, consisting of up to 108individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipelineASAP(Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of theRender[18] services used in the volume assembly of the brain of adultDrosophila melanogaster[2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP is modular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25] at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. While many morphological surveys focus on branching patterns of individual cells, fewer have been devoted to sub-cellular structure of cells. Electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. Here we present the first systematic survey of the somatic region of nearly 100,000 cortical cells, using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to recapitulate many known aspects of cortical organization, while also revealing novel relationships. Parameters of cell size, nuclear infolding and somatic synaptic innervation co-vary with distinct patterns across depth and between types. Further, we describe how these subcellular features can be used to create highly accurate predictions of cell-types across large scale EM datasets. More generally, our results suggest that the shifts in cellular physiology and molecular programming seen across cell types accompany profound differences in the fine-scale structure of cells.

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

To understand the brain we must relate neurons’ functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1–3and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form1. This transformation is facilitated by the engulfment and degradation of excess axonal branches and inappropriate synapses by surrounding glial cells, including microglia and astrocytes2,3. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia has made it difficult to determine the contribution of these and other glial cell types to this process. Here, we used large scale, serial electron microscopy (ssEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors4, frequently surrounded terminal axon branches and included numerous phagolysosomes (PLs) containing fragments of axons and presynaptic terminals. Single- nucleus RNA sequencing indicated that cortical OPCs express key phagocytic genes, as well as neuronal transcripts, consistent with active axonal engulfment. PLs were ten times more abundant in OPCs than in microglia in P36 mice, and declined with age and lineage progression, suggesting that OPCs contribute very substantially to refinement of neuronal circuits during later phases of cortical development.

SummaryWe present a semi-automated reconstruction of L2/3 mouse primary visual cortex from 3 million cubic microns of electron microscopic images, including pyramidal and inhibitory neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are being made publicly available, along with tools for programmatic and 3D interactive access. The density of synaptic inputs onto inhibitory neurons varies across cell classes and compartments. We uncover a compartment-specific correlation between mitochondrial coverage and synapse density. Frequencies of connectivity motifs in the graph of pyramidal cells are predicted quite accurately from node degrees using the configuration model of random graphs. Cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. These example findings illustrate the resource’s utility for relating structure and function of cortical circuits as well as for neuronal cell biology.

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ~250×140×90 μm(3) of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

The activity and connectivity of inhibitory cells has a profound impact on the operation of neuronal networks. While the average connectivity of many inhibitory cell types has been characterized, we still lack an understanding of how individual interneurons distribute their synapses onto their targets and how heterogeneous the inhibition is onto different individual excitatory neurons. Here, we use large-scale volumetric electron microscopy (EM) and functional imaging to address this question for chandelier cells in layer 2/3 of mouse visual cortex. Using dense morphological reconstructions from EM, we mapped the complete chandelier input onto 153 pyramidal neurons. We find that the number of input synapses is highly variable across the population, but the variability is correlated with structural features of the target neuron: soma depth, soma size, and the number of perisomatic synapses received. Functionally, we found that chandelier cell activity in vivo was highly correlated and tracks pupil diameter, a proxy for arousal state. We propose that chandelier cells provide a global signal whose strength is individually adjusted for each target neuron. This approach, combining comprehensive structural analysis with functional recordings of identified cell types, will be a powerful tool to uncover the wiring rules across the diversity of cortical cell types.

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.

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al. 2007) by a log-normal distribution (Loewenstein, Kuras, and Rumpel 2011; de Vivo et al. 2017; Santuy et al. 2018). A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris 1993; Koester and Johnston 2005; Bartol et al. 2015; Kasthuri et al. 2015; Dvorkin and Ziv 2016; Bloss et al. 2018; Motta et al. 2019). We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma.

Serial-section electron microscopy is the method of choice for studying cellular structure and network connectivity in the brain. We have built a pipeline of parallel imaging using transmission electron automated microscopes (piTEAM) that scales this technology and enables the acquisition of petascale datasets containing local cortical microcircuits. The distributed platform is composed of multiple transmission electron microscopes that image, in parallel, different sections from the same block of tissue, all under control of a custom acquisition software (pyTEM) that implements 24/7 continuous autonomous imaging. The suitability of this architecture for large scale electron microscopy imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex spanning four different visual areas. Over 26,500 ultrathin tissue sections were imaged, yielding a dataset of more than 2 petabytes. Our current burst imaging rate is 500 Mpixel/s (image capture only) per microscope and net imaging rate is 100 Mpixel/s (including stage movement, image capture, quality control, and post processing). This brings the combined burst acquisition rate of the pipeline to 3 Gpixel/s and the net rate to 600 Mpixel/s with six microscopes running acquisition in parallel, which allowed imaging a cubic millimeter of mouse visual cortex at synaptic resolution in less than 6 months.

2-photon image quality degrades in volume-labeled tissue due to high background fluorescence. We apply 3-photon imaging to measuring functional responses in deep layers of primary visual cortex in pan-excitatory transgenic mice expressing GCaMP6s.

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.

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 constructing more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. We address these shortcomings with a new fast, flexible, learning-free method for sparse segmentation and reconstruction of neural volumes. 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, which are of sufficient quality for preliminary biological study. Compared to existing supervised learning methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

This protocol describes a method for registration of in vivo cortical retinotopic map with cytochrome c oxidase (CO) labeled architectonic maps of the same mouse brain through the alignment of vascular fiducials. By recording surface blood vessel pattern and sequential alignment at each step, this method overcomes the challenge imposed by tissue distortion during perfusion, mounting, sectioning and histology procedures. This method can also be generalized to register and align other types of in vivo functional maps like ocular dominance map and spatial/temporal frequency tuning map with various anatomical maps of mouse cortex.

Biomaterial-based tissue-engineered tumor models are now widely used in cancer biology studies. However, specific methods for efficient and reliable cell seeding into these and tissue-engineering constructs used for regenerative medicine often remain poorly defined. Here, we describe a capillary force-based method for seeding the human prostate cancer cell lines M12 and LNCaP C4-2 into sphere-templated poly(2-hydroxyethyl methacrylate) hydrogels. The capillary force seeding method improved the cell number and distribution within the porous scaffolds compared to well-established protocols such as static and centrifugation seeding. Seeding efficiency was found to be strongly dependent on the rounded cell diameter relative to the pore diameter and pore interconnect size, parameters that can be controllably modulated during scaffold fabrication. Cell seeding efficiency was evaluated quantitatively using a PicoGreen DNA assay, which demonstrated some variation in cell retention using the capillary force method. When cultured within the porous hydrogels, both cell lines attached and proliferated within the network, but histology showed the formation of a necrotic zone by 7 days likely due to oxygen and nutrient diffusional limitations. The necrotic zone thickness was decreased by dynamically culturing cells in an orbital shaker. Proliferation analysis showed that despite a variable seeding efficiency, by 7 days in culture, scaffolds contained a roughly consistent number of cells as they proliferated to fill the pores of the scaffold. These studies demonstrate that sphere-templated polymeric scaffolds have the potential to serve as an adaptable cell culture substrate for engineering a three-dimensional prostate cancer model.

Vascular calcification is common in chronic kidney disease, where cardiovascular mortality remains the leading cause of death. Patients with kidney disease are often prescribed vitamin D receptor agonists (VDRAs) that confer a survival benefit, but the underlying mechanisms remain unclear. Here we tested two VDRAs in a mouse chronic kidney disease model where dietary phosphate loading induced aortic medial calcification. Mice were given intraperitoneal calcitriol or paricalcitol three times per week for three weeks. These treatments were associated with half of the aortic calcification compared to no therapy, and there was no difference between the two agents. In the setting of a high phosphate diet, serum parathyroid hormone and calcium levels were not significantly altered by treatment. VDRA therapy was associated with increased serum and urine klotho levels, increased phosphaturia, correction of hyperphosphatemia, and lowering of serum fibroblast growth factor-23. There was no effect on elastin remodeling or inflammation, however, the expression of the anti-calcification factor, osteopontin, in aortic medial cells was increased. Paricalcitol upregulated osteopontin secretion from mouse vascular smooth muscle cells in culture. Thus, klotho and osteopontin were upregulated by VDRA therapy in chronic kidney disease, independent of changes in serum parathyroid hormone and calcium.

The sinus between skin and a percutaneous medical device is often a portal for infection. Epidermal integration into an optimized porous biomaterial could seal this sinus. In this study, we measured epithelial ingrowth into rods of sphere-templated porous poly(2-hydroxyethyl methacrylate) implanted percutaneously in mice. The rods contained spherical 20-, 40-, or 60-μm pores with and without surface modification. Epithelial migration was measured 3, 7, and 14 days post-implantation utilizing immunohistochemistry for pankeratins and image analysis. Our global results showed average keratinocyte migration distances of 81 ± 16.85 μm (SD). Migration was shorter through 20-μm pores (69.32 ± 21.73) compared with 40 and 60 μm (87.04 ± 13.38 μm and 86.63 ± 8.31 μm, respectively). Migration was unaffected by 1,1′ carbonyldiimidazole surface modification without considering factors of pore size and healing duration. Epithelial integration occurred quickly showing an average migration distance of 74.13 ± 12.54 μm after 3 days without significant progression over time. These data show that the epidermis closes the sinus within 3 days, migrates into the biomaterial (an average of 11% of total rod diameter), and stops. This process forms an integrated epithelial collar without evidence of marsupialization or permigration. © 2011 Wiley Periodicals, Inc. J Biomed Mater Res Part A:, 2011.

Stable isotope labeling may provide a novel method for tracking stem cells once they have been injected into a human or animal host. Here, we present a simple pilot study to determine the potential for using ToF-SIMS to detect and localize 15 N labeled cells in tissue biopsies for use in cell therapy studies. For this pilot study, 3T3 fibroblasts were grown in normal media and in two different media containing 15 N labeled amino acids. Samples containing a mixture of 15 N labeled and unlabeled cells were prepared, fixed and dried for analysis and were then imaged using a bunched Bi 3 + primary ion source. The cells containing 15 N labeled amino acids could be readily distinguished using nitrogen-containing peaks which have been previously associated with the labeled amino acids. Contrast was sufficient to allow easy identification of labeled cells in both sparsely and densely plated cultures. Multivariate analysis showed that the image contrast could be improved by including peaks originating from characteristic fragments of the labeled amino acids as well as lower mass NH 4 + and CH 4 N + peaks. Additional work is being pursued to determine and improve the longevity of the label.

Modern genetic approaches are powerful in providing access to diverse cell types in the brain and facilitating the study of their function. Here, we report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.

Purpose. Biomaterials which release locally high concentrations of antithrombotic agents should lessen the thrombogenicity of the materials. To evaluate this approach, we prepared novel polyurethane matrices loaded with hirudin and coated them with 2-hydroxyethyl methacrylate (HEMA) by glow discharge plasma deposition (GDPD) to reduce the release rate. Methods. Polyurethane (BioSpan®) matrices containing hirudin and pore former (d-mannitol or BSA) were prepared by the solvent casting method. HEMA plasma deposition was then applied using GDPD technique to create a diffusional barrier film on the surface of the matrices. The effect of pore former and HEMA plasma coating on the release of hirudin was systematically investigated. Surface properties of matrices was also studied using Scanning Electron Microscopy (SEM) and Electron Spectroscopy for Chemical Analysis (ESCA). Results. The release of hirudin from BioSpan® matrix could be controlled by changing the weight fraction and particle size of pore former. HEMA plasma treatment of matrices produced a thin, highly cross-linked film on the surface. The initial burst and subsequent release of hirudin was significantly reduced after HEMA plasma coating, which suggested that the plasma disposition acted as a diffusional barrier and limited the release of hirudin incorporated in the polyurethane matrix. Conclusions. The plasma coating served as a diffusional barrier, and could work to control the release kinetics of hirudin by changing the various plasma coating conditions. Local delivery of hirudin using these biomaterials at the site of cardiovascular diseases can have the advantage of regional high levels of hirudin, as well as lowering systemic hirudin exposure, thereby minimizing the possibility of side effects.

Hirudin is the most potent specific inhibitor of thrombin known. Hirudin was originally isolated from leeches, but it is now also available in synthetic form (recombinant hirudin). The inhibitor is currently undergoing clinical trials as a potential replacement for the extensively used thrombin inhibitor heparin. In this review, the biochemical and pharmacokinetic characteristics of hirudin (native and recombinant) and the efficacy of hirudin in treating and preventing cardiovascular disorders is discussed. The advantages of local controlled delivery of hirudin for treating cardiovascular disorders are then presented. Several implantable polymers applicable for controlled delivery system also are introduced. Finally, the feasibility of controlled delivery of r-hirudin for local therapy of cardiovascular disorders is addressed.

Chronically implanted biosensors typically lose sensitivity 1-2 months after implantation, due in large part to the development of a collagen-rich capsule that prevents analytes of interest from reaching the biosensor. Corticosteroids are likely candidates for reducing collagen deposition but these compounds have many serious side effects when given over a prolonged period. One method of assessing whether or not locally released corticosteroids have a systemic effect is to measure cortisol concentrations in venous serum. We hypothesized that a very low release rate of the potent corticosteroid, dexamethasone, would lead to a localized anti-inflammatory effect without systemic effects. We found that reduction in subcutaneous granulocytes (primarily eosinophils), and to a lesser extent, reduction of macrophages served as a good local indicator of the steroid effect. When released over a 28-day period, a total dexamethasone dose of < or =0.1 mg/kg led to a consistent reduction in the number of granulocytes and macrophages found in the local vicinity of the implant without a reduction of these cells at distant tissue locations. The lack of suppression of serum cortisol with these doses confirmed that low-release rates of dexamethasone can lead to consistent local anti-inflammatory effects without distant, systemic effects. (c) 2010 Wiley Periodicals, Inc. J Biomed Mater Res, 2010.