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Signature Redacted Thes Towards Optical Connectomics: Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and In Situ Molecular Barcoding Peilun Dai B.Eng. Electrical and Electronic Engineering Nanyang Technological University, 2014 Submitted to the Department of Brain and Cognitive Sciences in Partial Fulfillment of the Requirements for the Degree of Master of Science in Neuroscience at the Massachusetts Institute of Technology September 2018 C 2018 Massachusetts Institute of Technology. All rights reserved. Signature redacted Signature of author: Department ofBrairad-eg1ive ciences Signature redacted August 10, 2018 Certified by: Edward S. Boyden, III Y. Eva Tan Professor in Neurotechnology Associate Professor of Media Arts and Sciences, -Braifne Sciences, and Biological Engineering Thes Accepted by Signature redacted Matthew A. Wilson Sherman Fairchild Professor of Neroscience and Picower Scholar Director of Graduate Education for Brain and Cognitive Sciences MASSACHUSEFS INSTITE OF TECHNOLOGY OCT 112018 LIBRARIES ARCHIVES 2 Towards Optical Connectomics: Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and In Situ Molecular Barcoding by Peilun Dai Submitted to the Department of Brain and Cognitive Sciences on August 10, 2018 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Neuroscience Abstract Reconstruction of the 3D morphology of neurons is an essential step towards the analysis and understanding of the structures and functions of neural circuits. Optical microscopy has been a key technology for mapping brain circuits throughout the history of neuroscience due to its low cost, wide usage in biological sciences and ability to read out information-rich molecular signals. However, conventional optical microscopes have limited spatial resolution due to the diffraction limit of light, requiring a tradeoff between the density of neurons to be imaged and the ability to resolve finer structures. A new technology called expansion microscopy (ExM), which physically expands the specimen multiple times before optical imaging, enables us to image fine structures of biological tissues with super resolution using conventional optical microscopes. With the help of ExM, we can also read out molecular information in the neural tissues optically. In this thesis, I will introduce our study of the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Thesis Supervisor: Edward S. Boyden, III Title: Y. Eva Tan Professor in Neurotechnology, Associate Professor of Media Arts and Sciences, Brain and Cognitive Sciences, and Biological Engineering 3 4 Contents Chapter I Introduction 6 Chapter 2 Results and Discussion 12 2.1 Simulation of Expansion Microscopy Images 12 2.2 Segmentation 14 Boundary Detection 14 Over-Segmentation and Agglomeration 15 Quantification of Segmentation Accuracy 19 2.3 Discussion 21 2.4 Contribution 26 Bibliography 27 5 6 Chapter 1 Introduction Three-dimensional reconstruction of neural morphology is an essential step towards systematic analysis of neural circuits and their functions. Throughout the history of neuroscience, optical microscopy has been a key technology for mapping brain circuits (Llinais 2003; Wilt et al. 2009). However, due to the limitation in spatial resolution, conventional optical microscopy has been limited to imaging and tracking a subset of neurons in a given brain region (Kasthuri and Lichtman 2007; Miyasaka et al. 2014; Robles, Laurell, and Baier 2014). When the neurons are sparsely labeled, it is possible to image thin axons and dendritic spine necks smaller than the diffraction limit of optical microscopes (Dumitriu, Rodriguez, and Morrison 2011). In this way, however, only partial maps of the brain circuits can be obtained with light microscopy. By giving random colors to the images using molecular methods (Livet et al. 2007; Cai et al. 2013), it is possible to increase the number of neurons that can be resolved (Siimbiil et al. 2016), but the number is still much smaller than the number that is required to fully reconstruct the circuits in a modest size brain due to the local optical blurring effect from the stochastic nature of the distribution of fluorescent molecules within neurons. In contrast to earlier methods of magnification relying only on the optical lens to optically magnify images of preserved brain tissues, a new method called expansion microscopy (ExM) (F. Chen, Tillberg, and Boyden 2015) physically magnifies samples directly, which provides a new approach to imaging large-scale 3D brain circuits at nano-scale. ExM combines two sets of ideas: embedding preserved biological specimen in hydrogels such as polyacrylamide for imaging purposes (Hausen and Dreyer 1981; Germroth, Gourdie, and Thompson 1995), which dates back to the 1980s, and responsive polymers capable of vastly changing size in response to changes in chemical environment (Tanaka et al. 1980), which dates back to the 1970s. By synthesizing a swellable polyelectrolyte gel through a specimen, which binds to key biomolecules of interest, then mechanically homogenizing the specimen and dialyzing it in water, we can expand brain tissues ~4.5x in linear dimension, with low distortion (~1-4%) (F. Chen, Tillberg, and Boyden 2015). In this way, molecules initially located within the diffraction-limit of the optical microscope are physically pulled apart far enough to be resolved with conventional optical microscopes, resulting in an effective 7 resolution equal to the diffraction limit divided by the expansion factor. It has been recently shown that expansion can be performed multiple times iteratively on the same sample, which results in as much as ~20x linear expansion or even 100x linear expansion, for two and three iterations respectively (Chang et al. 2017). With iterative ExM, (Chang et al. 2017) have managed to resolve nanoscale structures at a resolution of ~25nm. The properties of ExM described above may allow for in-situ readout of nucleic acid "barcodes" that have been proposed to uniquely identify single neurons (Zador et al. 2012; Marblestone et al. 2014; Lu et al. 2011; Kebschull et al. 2016). A "barcode" is a sequence of RNA nucleic acid used to uniquely tag a particular neuron. The number of possible nucleic acid sequences grows exponentially with the length of the sequence, i.e., 4 N possible "barcodes" for sequences of length N. For example, a barcode of length 30 has a potential diversity of 430 or 1018, which vastly outnumbers the neurons even in the human brain. By expressing a randomly generated barcode in the neurons, each neuron in a brain will almost certainly express a unique tag when the barcode length is sufficiently long. Such barcodes can be read out optically in an expanded specimen through the use of fluorescent in-situ sequencing (FISSEQ) (J. H. Lee et al. 2014; Y.-S. Chen et al. 2017) or fluorescent in-situ hybridization (FISH) (Langer-Safer, Levine, and Ward 1982; F. Chen et al. 2016). When imaged optically, these barcodes appear as spatially localized fluorescent dots, which blink out a digital string of colors that uniquely encodes the identity of the cell that contains them, during the steps of in-situ sequencing. With the super-resolution capability enabled by ExM, combined with the molecular barcodes, we approach the challenge of dense 3D neural morphology reconstruction theoretically by asking two questions: what are the limits of an optical approach in the context of 3D morphology reconstruction with ExM, and how can we approach these limits through novel chemical tags and analytical protocols, as well as specific algorithms? Since such questions must be answered in the context of the detailed structural properties of neural tissues, we base our analysis on a simulation of ExM imaging which uses electron microscopic (EM) volume reconstruction of neural tissue (Figure 1A) as a "ground truth". Then our simulation produces images which emulate those which would be generated by ExM in a confocal microscope (Figure 1B), on a neural volume with a precisely known 8 structure previously obtained by electron microscopy. This approach is illustrated in Figure 1. a b c d Figure 1. Simulation of ExM volume reconstruction pipeline. (A) Ground truth input dataset obtained from fully reconstructed electron microscopic volume. (B) Simulated optical ExM image. (C) Boundary detection performed on simulated optical ExM image. (D) 3D neuron segmentation obtained from simulated optical ExM image. We take a conservative approach in our estimates of the performance of ExM as well as in the algorithmic
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