Knife-Edge Scanning Microscopy Research Articles

MRI size assessment of cerebral microvasculature using diffusion-time-dependent stimulated-echo acquisition: A feasibility study in rodent

In this study, a stimulated-echo (STE) method was employed to robustify the cerebral vessel size estimation near air-tissue, bone-tissue interfaces, and large vessels. The proposed solution is to replace the relaxation rate change from gradient-echo (GRE) with that from STE with long diffusion time after the injection of an intravascular contrast agent, superparamagnetic iron oxide nanoparticles. The corresponding diffusion length of STE is shorter than the length over which the unwanted macroscopic field inhomogeneities but is still longer than the correlation length of the fields induced by small vessels. Therefore, the unwanted field inhomogeneities are refocused, while preserving microscopic susceptibility contrast from cerebral vessels. The mean vessel diameter (dimensionless) derived from the diffusion-time-varying STE method was compared to the mean vessel diameter obtained by a conventional spin-echo (SE) and GRE combination based on Monte-Carlo proton diffusion simulations and in vivo rat experiments at 7 ​T. The in vivo mean vessel diameter from the MRI experiments was directly compared to available reference mouse brain vasculature obtained by a knife-edge scanning microscope (KESM), which is considered to be the gold standard. Monte-Carlo simulation revealed that SE and GRE-based MR relaxation rate changes (ΔR2 and ΔR2∗, respectively) can be enhanced using single STE-based MR relaxation rate change (ΔRSTE) by regulating diffusion time, especially for small vessels. The in vivo mean vessel diameter from the STE method demonstrated a closer agreement with that from the KESM compared to the combined SE and GRE method, especially in the olfactory bulb and cortex. This study demonstrates that STE relaxation rate changes can be used as consistent measures for assessing small cerebral microvasculature, where macroscopic field inhomogeneity is severe and signal contamination from adjacent large vessels is significant.

SPARC: Distribution of Intrinsic Cardiac Neurons in 3D Reconstructed Hearts of F344 Rat

The intrinsic cardiac nervous system (ICN) is involved in the autonomic control of the heart and functions as an integrative center for local sensory and extrinsic autonomic inputs. There has been increasing interest in the functions of ICN neurons as well as their ablation as a therapeutic intervention for atrial fibrillation. The goal of the present study is to assess the distribution of ICN neurons between individuals with rat as a model using a novel data acquisition pipeline combining Knife Edge Scanning Microscope (KESM) with the custom software developed by MBF (Tissue Mapper) to perform single cell mapping of ICN neurons in 3D reconstructed rat hearts. Fischer 344 male rats (n = 3) were perfused through the abdominal aorta and whole‐mount perfusion stained with Cresyl Echt Violet. The hearts were then embedded into paraffin and mounted onto a robotic XYZ stage for simultaneous sectioning and imaging by a KESM. Individual section images that consisted of 1400, 1570, and 2580 sections for each heart that was cut at a 5 μm thickness were assembled into an image stack for anatomical cardiac digitization and single cell mapping of ICN neurons in Tissue Mapper for 3D heart reconstruction. The distribution of ICN neurons between individuals was assessed in two different planes of section: parallel sections showing 4‐chambers, and cross sections perpendicular to the long axis of the heart. The number of neurons counted in each of the heart samples were 2676, 2973, and 2885, with an average of 2844/rat. ICN neurons mainly localized around the posterior surface of the left and right atria and the hilum ‐ pulmonary veins, interatrial septum, along the pulmonary artery, and the root of the superior vena cava. We performed computational analysis of ICN neuron distribution using a partial projection approach based on principal component analysis of the 3D coordinates. We analyzed the first two principal components to compute a distribution of packing density in the 2D projection corresponding to maximum variability in the spatial location of single neurons. In parallel, we conducted unsupervised clustering of 3D coordinates of single neurons using partitioning around medoids algorithm and annotated the packing density distributions and 3D ICN neuron maps with the resultant clusters. Our analysis revealed that the density distribution, numbers of clusters and orientation of ICN neurons were similar across males with 7–10 clusters within each ICN. This demonstrates the robustness of the technique where single ICN neurons were well‐preserved and they localized around the same regions despite variation in section orientation. Thus, this technique can potentially be applied to study the distribution of ICN neurons in 3D reconstructed hearts of large animals and humans as well as to examine the sex differences and diseased‐induced remodeling of the ICN for future studies.Support or Funding InformationThis work is supported by NIH SPARC Program, grant #3OT2OD023848‐01 (PD/PI: K. Shivkumar; Subaward PIs: J. S. Schwaber, R. Vadigepalli, and Z. J. Cheng)

A Queryable Graph Representation of Vascular Connectivity in the Whole Mouse Brain.

We construct a graph representation for the topology and geometry of the vasculature presenting across the whole mouse brain (dataset: Knife-Edge Scanning Microscope Brain Atlas India Ink). We use our graph representation to calculate preliminary estimates of the average radius as 4:8 μm, total vascular volume as 1:1000 mm3, total vascular surface area as 6:5511 cm2, and total vascular length of 2866:6567 cm. We then isolate a posterior cerebral region, derive its graph representation, and then import that representation to a Neo4j graph database. We then detail how researchers can query this database online to isolate specific vascular networks for further analysis and reconstruction.

Murine Mouse Models of Metastatic Colorectal Cancer in Lungs: Foci Counts in Three Dimensions

In standard histological slide preparations, only a few representative samples are taken, which introduces sampling bias and necessarily eschews information about the full tissue sample. However, it is possible to obtain a full 3D data set of an entire tissue sample using the Knife Edge Scanning Microscope (KESM), an automated simultaneous sectioning and imaging microscope. In this study, we performed manual counts of metastases of colorectal cancer in mouse model lungs stained with India ink and hematoxylin and eosin, which were then sectioned and imaged in their entirety. We were able to get more accurate count of tumor foci than the current standard of surface nodule count can allow; multiple tumor foci were buried hundreds of microns deep into the parenchyma and hence not grossly visible from the surface. Volumetric analysis of whole samples also revealed unusual tumor morphology and permitted the visualization of unique architectural features where multiple tumor foci had grown together. Following the manual count of tumor foci, we developed a semi‐automated algorithm that could increase the efficiency of screening for foci, and create the opportunity for digital 3D reconstruction and morphology interrogation. Without access to a full dataset of the lungs, it is impossible to determine true foci number or morphology.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Tracing and analysis of the whole mouse brain vasculature with systematic cleaning to remove and consolidate erroneous images.

Whole mouse brain microvascular images at submicrometer scale can be obtained by Knife-Edge Scanning Microscopy (KESM). However, due to the large size of the image dataset and the noise from the serial sectioning process of the KESM, whole mouse brain vascular reconstruction and analysis with submicrometer resolution have not been achieved yet, while several previous studies demonstrated manually selected small noise-free portion of the KESM dataset. In addition to the KESM dataset, there have been studies for vessel reconstruction and analysis of the whole mouse brain at lower resolution or of partial brain regions at submicrometer resolution. However, to the best of our knowledge, there has been no study for vessel reconstruction and analysis of the whole mouse brain at submicrometer resolution. In this paper, we propose a framework for the 3D reconstruction and analysis of the whole KESM mouse brain vasculature dataset with rich vasculature information extracted at submicrometer resolution. The framework consists of two methods. The propose methods provide the systematic cleaning to remove and consolidate erroneous images automatically, which enables the full tracing and analysis of the whole KESM mouse brain dataset with richer vasculature information.

Tracing Tubular Structures from Teravoxel-Sized Microscope Images.

Tracing vasculature and neurites from teravoxel sized light-microscopy data-sets is a challenge impeding the availability of processed data to the research community. This is because (1) Holding terabytes of data during run-time is not easy for a regular PC. (2) Processing all the data at once would be slow and inefficient. In this paper, we propose a way to mitigate this challenge by Divide Conquer and Combine (DCC) method. We first split the volume into many smaller and manageable sub-volumes before tracing. These sub-volumes can then be traced individually in parallel (or otherwise). We propose an algorithm to stitch together the traced data from these sub-volumes. This algorithm is robust and handles challenging scenarios like (1) sub-optimal tracing at edges (2) densely packed structures and (3) different depths of trace termination. We validate our results using whole mouse brain vasculature data-set obtained from the Knife-Edge Scanning Microscopy (KESM) based automated tissue scanner.

Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting.

High-throughput imaging techniques, such as Knife-Edge Scanning Microscopy (KESM),are capable of acquiring three-dimensional whole-organ images at sub-micrometer resolution. These images are challenging to segment since they can exceed several terabytes (TB) in size, requiring extremely fast and fully automated algorithms. Staining techniques are limited to contrast agents that can be applied to large samples and imaged in a single pass. This requires maximizing the number of structures labeled in a single channel, resulting in images that are densely packed with spatial features. In this paper, we propose a three-dimensional approach for locating cells based on iterative voting. Due to the computational complexity of this algorithm, a highly efficient GPU implementation is required to make it practical on large data sets. The proposed algorithm has a limited number of input parameters and is highly parallel.

Knife-Edge Scanning Microscopy for Bright-Field Multi-Cubic Centimeter Analysis of Microvasculature

Modern light microscopy has benefited from the use of serial section microscopy such as in the Allen Brain Atlas and the creation of new rodent models for Alzheimer’s disease research [1, 2]. Typically, slides are prepared in the conventional manner, where images are collected using traditional light microscopy and digitally scanned. The resulting images then must be computationally registered to one another to create a 3D image. However, numerous issues have inhibited widespread adoption of this method. These include the manual nature of slide preparation and staining, the introduction of artifacts from manual sectioning and mounting on slides, the time taken for whole-slide scanning an entire sample, the difficult nature of image registration and data handling, and the simple fact that it results in too much data for modern infrastructures to handle. The knife-edge scanning microscope (KESM) combines sectioning and imaging into a single step, automates a large part of the traditional pathology workflow, and allows for a greater speed, precision, throughput, and scale at which tissues are digitized (Figure 1). The KESM achieves tissue data collection at a resolution of submicron pixels with a maximum sample volume of over 100 cm3, exceeding the depth offered by confocal or 2-photon microscopy. To handle high-resolution data of over a terabyte per cm3, sophisticated data processing software is applied to model 3D tissue reconstructions, provide interactive image views, and apply quantitative analytics. This allows quantitative analysis to be performed on 3D image stacks of whole mouse organs, which is extremely difficult, expensive, and time-consuming with traditional manual techniques.

Mapping the full vascular network in the mouse brain at submicrometer resolution.

Mapping the microvascular networks in the brain can lead to significant scientific and clinical insights. We developed a serial sectioning microscopy technique called the Knife-Edge Scanning Microscopy (KESM) to section and image the entire mouse brain at submicrometer resolution. In our effort to map the entire vascular network in the mouse brain, we perfused the vessels with India ink, and used KESM to image the prepared brain. This results in about 1.5 TB of raw image data which poses a serious challenge in terms of analysis. In this paper, we will present our data set, and computational algorithms we developed to trace and analyze morphological properties of the mouse brain vascular network. Since the data is available across the entire brain in full detail (the smallest capillaries can be observed in our data), it enables the comparison of regional differences in morphological properties. We expect our results to provide rich insights to brain and neuroscience researchers.

Learning to distinguish cerebral vasculature data from mechanical chatter in India-ink images acquired using knife-edge scanning microscopy.

We introduce a simple, yet effective, procedure for accurate classification of connected components embedded in biological images. In our method, a training set is generated from user-delineated features of manually-labeled examples; we subsequently train a classifier using the resultant training set. The overall process is described using imaging data acquired from an India-ink perfused C57BL/6J mouse brain using Knife Edge Scanning Microscopy. We illustrate the procedure through segmentation of cerebral vasculature structures from mechanical noise using trained classifiers. The features extracted by our procedure show high discriminatory power between classes; the classifiers (linear SVM, Gaussian SVM, and GentleBoost decision tree ensemble) trained using these features achieved high performance: F1-scores reported for linear SVM, Gaussian SVM, and GentleBoost decision tree ensemble were 0.963, 0.956, and 0.963 respectively.

Fast submicrometer-scale imaging of whole zebrafish using the knife-edge scanning microscope.

Advances in high-resolution 3D microscopy have enabled the investigation of subcellular microstructures in biological specimen. For a full understanding of the organism's structure and function, it is mandatory to obtain data from the whole animal, not just parts of it. In this paper, we present our work with the Knife-Edge Scanning Microscope (KESM) for imaging a Nissl-stained whole zebrafish larva. KESM combines a diamond microtome and line-scan imaging for simultaneous sectioning and imaging in 3D. We show that using the KESM, a zebrafish, less than 3 mm long and diameter less than 500 μm, can be imaged within 1 hour at a resolution of 0.6 μm × 0.7 μm × 1.0 μm. We also present new results on using a vibrating microtome to improve sectioning and imaging robustness.

Automated neurovascular tracing and analysis of the knife-edge scanning microscope Rat Nissl data set using a computing cluster.

We present a novel, parallelizable algorithm capable of automatically reconstructing and calculating anatomical statistics of cerebral vascular networks embedded in large volumes of Rat Nissl-stained data. In this paper, we report the results of our method using Rattus somatosensory cortical data acquired using Knife-Edge Scanning Microscopy. Our algorithm performs the reconstruction task with averaged precision, recall, and F2-score of 0.978, 0.892, and 0.902 respectively. Calculated anatomical statistics show some conformance to values previously reported. The results that can be obtained from our method are expected to help explicate the relationship between the structural organization of the microcirculation and normal (and abnormal) cerebral functioning.

Knife-edge scanning microscopy for in silico study of cerebral blood flow: From biological imaging data to flow simulations.

Knife-edge scanning microscopy provides the capability to image whole-brain cerebral microvasculature of small organisms, such as mice, at sub-micron resolution, providing a feasible foundation for the reconstruction of circulatory pathways from the systemic to cellular scale. In this paper, we illustrate the feasibility of using this data to model cerebral blood flow using numerical simulations. Starting with a small vascular element in microcirculation of interest, we present its segmentation from the imaging-data volume, construction of its triangular surface mesh, assembly of its tetrahedral volumetric mesh from the surface, and then conclude with Stokes flow simulation of plasma through the microvascular vessel.

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

Fast macro-scale transmission imaging of microvascular networks using KESM

Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.

Knife-Edge Scanning Microscope Brain Atlas: A Web-Based, Light-Weight 3D Mouse Brain Atlas

Event Abstract Back to Event Knife-Edge Scanning Microscope Brain Atlas: A Web-Based, Light-Weight 3D Mouse Brain Atlas Chul Sung1*, Ji Ryang Chung1, David Mayerich2, Jaerock Kwon3, Daniel Miller1, Todd Huffman4, John Keyser1, Louise Abbott1 and Yoonsuck Choe1 1 Texas A&M University, United States 2 University of Illinois, United States 3 Kettering University, United States 4 3Scan, United States High-throughput, high-resolution 3D microscopy techniques such as the Knife-Edge Scanning Microscopy (KESM) are enabling the acquisition of whole-brain-scale data from small animals such as the mouse. Using the fully automated KESM, we have been able to obtain whole mouse brain data sets at a resolution of 0.6 um x 0.7 um x 1.0 um, enough to show detailed neuronal morphology across the entire brain. Staining for our data sets were done with Golgi (neuronal morphology), Nissl (soma distribution), and India ink (vascular network). In this abstract, we describe a novel web-based 3D atlas we developed to enable broader access to our KESM data sets. The atlas framework, named the Knife-Edge Scanning Microscope Brain Atlas (KESMBA), customizes and extends the Google Maps API to serve the nearly 2TB (per brain) volume data in a multiscale manner, combined with a novel 3D rendering method using image overlays. The standard Google Maps navigation features (pan, zoom, etc.) are supported by default, allowing a multiscale exploration. Individual images in the data sets represent 1um-thin sections, so by themselves, they are not informative. We extended the Google Maps API to overcome this limitation, by overlaying images so that a local 3D volume can be visualized within the browser (see the figure). Other features include scale bar, navigation in the z (depth) direction, overlay control (number of overlays and interval), and location indicator. Experimental features include 3 standard sectioning planes (to be fully supported in the near future). KESMBA requires minimal client-side computing capability (a generic browser with javascript support), thus it enables broader dissemination of the KESM data sets to the neuroscience community. Also, we expect our framework to be general enough to accommodate other data modalities that have large volumes and fine details. On-going and future works include registration of the KESM data sets to the Waxholm space, and custom download and annotation functions. Acknowledgments: NSF CRCNS #0905041, NIH/NINDS \#1R01-NS54252, NSF MRI \#0079874, NSF ITR \#CCR-0220047, Texas ATP\#000512-0146-2001, Texas A\&M Research Foundation, Texas Engineering Experiment Station, and 3Scan. The appearance of the Google logo in the figure is due to the use of the Google Maps API by the KESM Brain Atlas. Besides that this work was done independent of Google. Figure 1 Keywords: digital atlasing, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Digital atlasing Citation: Sung C, Chung J, Mayerich D, Kwon J, Miller D, Huffman T, Keyser J, Abbott L and Choe Y (2011). Knife-Edge Scanning Microscope Brain Atlas: A Web-Based, Light-Weight 3D Mouse Brain Atlas. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00036 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Chul Sung, Texas A&M University, College Station, United States, csung@tamu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Chul Sung Ji Ryang Chung David Mayerich Jaerock Kwon Daniel Miller Todd Huffman John Keyser Louise Abbott Yoonsuck Choe Google Chul Sung Ji Ryang Chung David Mayerich Jaerock Kwon Daniel Miller Todd Huffman John Keyser Louise Abbott Yoonsuck Choe Google Scholar Chul Sung Ji Ryang Chung David Mayerich Jaerock Kwon Daniel Miller Todd Huffman John Keyser Louise Abbott Yoonsuck Choe PubMed Chul Sung Ji Ryang Chung David Mayerich Jaerock Kwon Daniel Miller Todd Huffman John Keyser Louise Abbott Yoonsuck Choe Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope

The common octopus, or Octopus vulgaris, has the largest nervous system of any invertebrate, and has been shown to possess learning and memory capabilities that in many ways rival those of some vertebrates [1]. Nevertheless, the neural architecture of this cephalopod mollusk differs markedly from that of any vertebrate. Investigating the differences and similarities between the neural architecture—or connectome—of the octopus and mammals, such as the mouse, may lead to deep insights into the computational principles underlying animal cognition. The octopus brain provides some unique advantages for anatomical research, since its axons are generally thick and unmyelinated, allowing traditional staining methods, such as Golgi, to be used effectively. With this in mind, we first imaged the brain using the Knife-Edge Scanning Microscope [2], a custom serial sectioning microscope that can image large blocks of tissue (1 cm3) at sub-micrometer resolution. We imaged large portions of the octopus subesophageal mass (SUB) and the optic lobe (OL) which were stained using Golgi. In order to extract the geometry of the neuronal morphology, we used our Maximum Intensity Projection (MIP)-based tracing algorithm [3]. The imaging results are shown in Figure 1(a-d), and tracing results are shown in 1(e). Although quite preliminary, to our knowledge this is the first time large volumes of the octopus brain have been imaged at sub-micrometer resolution, allowing us to resolve many of the processes that make up the neural network. We expect that this pilot study and the more detailed investigations to follow will allow fruitful comparisons of the neural circuitries of individual octopuses with different ecological life histories, as well as of animals that have been exposed to a variety of neurodegenerative insults. Moreover, such explorations will engender a greater understanding of how functional neural architecture is altered by learning in invertebrates such as the octopus and vertebrates such as the mouse. In sum, this approach should contribute greatly to our understanding of the computational architecture of invertebrates and ultimately provide insights into the differences between invertebrate and vertebrate cognitive capabilities. Figure 1 Octopus subesophageal mass (SUB) and optic lobe (OL) imaged with the KESM (a–d), and tracing results (e). Scale (block width): (a) 1.44 mm, (b) 0.72 mm, (c) 1.44 mm, (d-f) 76.8 μm. Voxel resolution: 0.6 μm x 0.7 μm x 1.0 ...

Knife‐edge scanning microscopy for imaging and reconstruction of three‐dimensional anatomical structures of the mouse brain

Anatomical information at the cellular level is important in many fields, including organ systems development, computational biology and informatics. Creating data sets at resolutions that provide enough detail to reconstruct cellular structures across tissue volumes from 1 to 100 mm(3) has proven to be difficult and time-consuming. In this paper, we describe a new method for staining and imaging large volumes of tissue at sub-micron resolutions. Serial sections are cut using an automated ultra-microtome, whereas concurrently each section is imaged through a light microscope with a high-speed line-scan camera. This technique, knife-edge scanning microscopy, allows us to view and record large volumes of tissue in a relatively small amount of time (approximately 7 mm(2) s(-1)). The resolution and scanning speed of knife-edge scanning microscopy provides a new method for imaging tissue at sufficient resolution to reconstruct maps of cellular distribution and morphology. We show that these techniques preserve the alignment of serial sections accurately enough to allow for reconstruction of neuronal processes and microvasculature. Expanding these techniques to other tissues opens up the possibility of creating fully reconstructed cellular maps of entire organs.