No abstract

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 mm 3 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.

Combining two-photon calcium imaging (2PCI) and electron microscopy (EM) provides arguably the most powerful current approach for connecting function to structure in neural circuits. Recent years have seen dramatic advances in obtaining and processing CI and EM data separately. In addition, several joint CI-EM datasets (with CI performed in vivo, followed by EM reconstruction of the same volume) have been collected. However, no automated analysis tools yet exist that can match each signal extracted from the CI data to a cell segment extracted from EM; previous efforts have been largely manual and focused on analyzing calcium activity in cell bodies, neglecting potentially rich functional information from axons and dendrites. There are two major roadblocks to solving this matching problem: first, dense EM reconstruction extracts orders of magnitude more segments than are visible in the corresponding CI field of view, and second, due to optical constraints and non-uniform brightness of the calcium indicator in each cell, direct matching of EM and CI spatial components is nontrivial.In this work we develop a pipeline for fusing CI and densely-reconstructed EM data. We model the observed CI data using a constrained nonnegative matrix factorization (CNMF) framework, in which segments extracted from the EM reconstruction serve to initialize and constrain the spatial components of the matrix factorization. We develop an efficient iterative procedure for solving the resulting combined matching and matrix factorization problem and apply this procedure to joint CI-EM data from mouse visual cortex. The method recovers hundreds of dendritic components from the CI data, visible across multiple functional scans at different depths, matched with densely-reconstructed three-dimensional neural segments recovered from the EM volume. We publicly release the output of this analysis as a new gold standard dataset that can be used to score algorithms for demixing signals from 2PCI data. Finally, we show that this database can be exploited to (1) learn a mapping from 3d EM segmentations to predict the corresponding 2d spatial components estimated from CI data, and (2) train a neural network to denoise these estimated spatial components. This neural network denoiser is a stand-alone module that can be dropped in to enhance any existing 2PCI analysis pipeline.

PAX3 is a member of the PAX3/7 subfamily of the paired box proteins. In vertebrates, Pax3 is essential for skeletal myogenesis by activating a cascade of transcriptional events that are necessary and sufficient for skeletal myogenesis. Four related basic helix-loop-helix transcription factors, MyoD, Myf5, Mrf4, and Myogenin, are targets of PAX3 and serve as myogenic regulatory factors. Although the role of Pax3 in myogenesis is well studied in vertebrates, little is known about invertebrate PAX-3 proteins and myogenesis. Here, we took advantage of viable alleles of pax-3 in the nematode satellite model organism Pristionchus pacificus to investigate the function of PAX-3 in myogenesis. Two strong reduction-of-function alleles of Ppa-pax-3 show severe muscle-derived abnormalities and phalloidin staining indicates a disruption of body wall muscle patterning. Furthermore, we identified a myogenic regulatory factor-related gene Ppa-hlh-1/MyoD and a serum response factor-related gene Ppa-unc-120. Expression of both genes in Ppa-pax-3 mutant animals is down regulated suggesting that Ppa-pax-3 acts upstream in the regulatory network. Together, our results provide the first genetic evidence for a conserved function of PAX-3 in myogenesis between vertebrates and nematodes.