Fly EM
FlyEM has explored multiple strategies for large-scale imaging, in particular, serial section TEM and FIB-SEM.
ssTEM
To reconstruct large regions of Drosophila nervous tissue, we optimized specimen preparation (eg. high-pressure freezing followed by freeze-substitution) for automated image processing, as well as serial sectioning. The fly is amenable to large-scale EM imaging, in part, because the larval and adult nervous systems are within the range of scales suitable for serial sectioning (10^3 – 10^6 µm3). To date, we have collected over 3000 serial sections through the lamina and medulla and over 5000 serial sections through the entire L1 CNS with conventional ultramicrotomy. Imaging these sections at a resolution sufficient to detect synapses requires hundreds of thousands of images, a time consuming process without automation. We improved imaging throughput by customizing the Leginon Automated TEM Image Acquisition System (developed by NanoImaging Services, Inc. in La Jolla, CA).
Transmission electron micrographs of fly nervous tissue. From left to right: Section through medulla neuropile (synapse circled in yellow); produced by Zhiyuan Lu and Shin-ya Takemura. Cross-section of L1 CNS at level of ventral nerve cord; produced by Rick Fetter.
FIB-SEM
Before 2012, our group acquired datasets primarily using ssTEM with anisotropic resolution (e.g., 3 x 3 x 50 nm). The relatively poor resolution in the Z direction limited our ability to trace very fine processes and the effectiveness of automatic image segmentation. Therefore, we are now refining and improving techniques using FIB-SEM (spearheaded by Harald Hess's Lab) to produce our datasets. While FIB-SEM technology has a smaller field of view when compared to ssTEM, improvements to scalability have allowed our group to generate datasets encompassing more than seven complete columns of medulla data with isotropic resolution of 10 x 10 x 10 nm and minimal imaging artifacts.
Employing methods from electrical engineering and computer vision, the Fly EM team lead by Chklovskii and Scheffer labs developed a semi-automated pipeline for serial section TEM reconstruction (Chklovskii et al. 2010), which consists of three main image processing tasks: registration, segmentation, and linkage. Briefly, registration involves horizontal alignment of images to produce a “panorama” of each EM section, and vertical alignment of each section to produce an image stack. Segmentation partitions the stack into voxels, defining profiles that belong to neurons. Linkage is the final process by which voxels belonging to putative neurons are linked across adjacent sections. Despite our ability to automate image processing and segment large datasets, the resulting reconstruction contains errors, which must be corrected in a proofreading step. We have adapted this procedure to now work with isotropic FIB-SEM datasets. While some of these techniques remain similar in spirit, there is no need for an explicit linkage step since segmentation that was previously applied separately on each section in 2D can now be applied in 3D.
The last step of the EM reconstruction pipeline is proofreading and synapse annotation, which is performed using custom written visualization and editing software called Raveler. Raveler includes tools for correcting computer segmentation errors (such as erroneously assigned voxels), annotating location of synapses and highlighting other areas of interest. To maintain performance while proofreading large datasets (> 2-3 terabytes), Raveler splits images into tiles that are dynamically loaded like Google Maps.
At the core of the proofreading process is a team of 9 proofreaders (see Gallery) who participate in a training curriculum that includes (1) proofreader training datasets, (2) proofreader instruction manual, (3) comparison software for identifying discrepancies between proofread datasets, and (4) automated tracking system for monitoring proofreading rate, as well as cataloging proofread datasets. At present, our proofreading team is comprised of 6 full-time and 2 part-time proofreaders.
Our proofreading workflow is divided by objectives. The first objective is to pull out cell shapes and identify cells of interest. The second objective is to identify and trace post-synaptic dendrites back to identified cells. To accomplish the first objective, ~ 80% of the volume must be proofread. Neuroanatomists identify relevant cell shapes and annotate the location of pre- and post-synaptic sites using tools in Raveler. To accomplish the second objective, proofreaders link the annotated post-synaptic sites to cells of interest and the final connectome is produced.
As we transition to FIB-SEM datasets, our algorithms are more capable of automatically segmenting the volume and restricting the parts that must be examined by proofreaders (focused proofreading). For example, we determine the significance and probability of potential errors in the segmentation and have proofreaders avoid areas where we are either confident of correctness or where an error minimally impacts the topology/reconstruction (Plaza, Scheffer, Saunder 2012).
Team Members Groups
