Automated Mosaic Imaging for CN Map

Creating the CN map of the retina requires digitizing each tissue section and registering it to its neighbors. Creating a volume of this scale is a significant undertaking: The CN map for the rabbit inner plexiform layer in the visual streak requires a volume delimited by a canonical field 250 μm in diameter × 30 μm high: roughly 1.47 × 10⁶ μm³. A cylindrical volume is a more efficient capture object than rhomboidal prisms that will have extremities clipped out as sections are rotated during registration. While the issue is irrelevant at small volumes [18 (link)], it tremendously impacts beam time at canonical scales. In practice, at a magnification of 5,000× on the JEOL JEM-1400, we capture 950–1,100 images or tiles/section and ≈333 sections at 70–90-nm thickness. Storage of unprocessed 16-bit images requires 10.4 terabytes. With a time of capture at roughly 30 s/frame, this requires some 70–100 calendar days on a single TEM, which argues for automated capture scripts and efficient capture geometries. To ensure the images can be positioned properly in the total mosaic, each image has 15% area overlap with its neighbors. With some of the software tools developed below, it is also evident that such tasks can be parallelized across microscopes and users.
We capture ssTEM data using SerialEM software developed by D. Mastronarde at the University of Colorado at Boulder [72 (link)]. Though originally developed for TEM tomography, SerialEM is ideal for large-scale mosaicking. The most recent build version of SerialEM allows definition of multiple circular or polygonal regions of interest on a grid and automates stage drive and image capture within the regions of interest on the JEOL JEM 1400 TEM (and other recent TEMs as well such as FEI Tecnais) and, critically, stores stage position metadata for each tile. This greatly reduces the computational cost of the initial positioning of mosaic tiles from O(n²) to O(n). The program includes a scripting capability that provides the flexibility needed to optimize the acquisition strategy, for example, by focusing only on an appropriately spaced subset of the image tiles. While automated capture is ideal for the microscope's Gatan Ultrascan 4000 (4K × 4K) camera, it can also serve on a smaller scale with film capture.