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Mapping Cell Types, Dynamics, and Connections in Neural Circuits

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Mapping Cell Types, Dynamics, and Connections in Neural Circuits Mapping Cell Types, Dynamics, and Connections in Neural Circuits by Samuel Gordon Rodriques B.Sc. in Physics Haverford College, 2013 M.Phil. in Engineering Churchill College, University of Cambridge, 2014 Submitted to the Department of Physics in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY IN PHYSICS at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUNE 2019 © 2019 Massachusetts Institute of Technology. All rights reserved. Signature of Author: _____________________________________________ Samuel G. Rodriques Certified by: __________________________________________________ Jeff Gore, Associate Professor of Physics, Supervisor Accepted by: _________________________________________________ Nergis Mavalvala, Associate Department Head 1 2 Mapping Cell Types, Dynamics, and Connections in Neural Circuits by Samuel Gordon Rodriques Submitted to the Department of Physics on May 17th, 2019, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Physics. ABSTRACT Neuroscience is limited by the difficulty of recording neural activity, identifying cell types, and mapping connectivity in high throughput. In this thesis, I present several scalable technologies aimed at improving our ability to characterize the activity, composition, and connectivity of neural circuits. My primary contributions include the design for a nanofabricated electrical recording device and a new approach to nanofabrication within swellable hydrogels; a high- throughput method for mapping the locations of cell types in tissue; an approach to direct sequencing of proteins at the single molecule level; an approach to directly recording neural activity into the sequence of RNA, enabling it to be detected by DNA sequencing; and a method for molecular barcoding of neurons, with the goal of enabling a high-throughput approach to neural circuit mapping. I conclude with a consideration of the limitations of the academic incentive structure as concerns the development and deployment of new technologies, and propose a structure for basic science research, complementary to the academic structure, based on the systematic establishment of well-funded, highly focused research projects with clear goals, an incentive to rapidly disseminate information, and limited lifetimes. Thesis Supervisor: Jeff Gore Title: Associate Professor of Physics Thesis Co-supervisor: Edward S. Boyden Title: Associate Professor of Media Arts and Sciences 3 Contents Chapter 1 – Introduction ............................................................................................................................. 14 Chapter 2 – Optical Reflectometry for Recording Neural Activity ............................................................... 19 Summary .................................................................................................................................................. 20 Introduction ............................................................................................................................................. 20 Design Principles: ..................................................................................................................................... 21 Fiber-Optic Reflectometry ................................................................................................................... 22 Electrooptic Modulation ....................................................................................................................... 26 Material Selection for the Capacitor Layer .............................................................................................. 34 Discussion ................................................................................................................................................ 35 Chapter 3 – Implosion Fabrication............................................................................................................... 39 Summary .................................................................................................................................................. 41 Introduction ............................................................................................................................................. 41 Results ..................................................................................................................................................... 41 Chapter 4 – Slide-seq ................................................................................................................................... 49 Summary: ................................................................................................................................................. 51 Introduction ............................................................................................................................................. 51 Results ..................................................................................................................................................... 51 Chapter 5 – Protein Sequencing ................................................................................................................... 59 Summary .................................................................................................................................................. 60 Introduction: ............................................................................................................................................ 60 Problem Overview .................................................................................................................................... 63 Results ..................................................................................................................................................... 64 Distinguishability of amino acids based on their NAAB binding profiles ............................................. 64 Model Parameters ................................................................................................................................ 65 Methods of Data Analysis .................................................................................................................... 66 Simulations .......................................................................................................................................... 69 Measurements of 푘퐷 ............................................................................................................................ 70 Identifying Amino Acids ...................................................................................................................... 73 Application to Randomized Affinity Matrices ...................................................................................... 76 Discussion ................................................................................................................................................ 76 Primary Uncertainties .......................................................................................................................... 77 4 Calibration Error ................................................................................................................................. 77 Parallelization ...................................................................................................................................... 78 Conclusion ............................................................................................................................................... 79 Chapter 6 – Tickertape ................................................................................................................................ 80 Summary: ................................................................................................................................................. 81 Introduction ............................................................................................................................................. 81 Results: .................................................................................................................................................... 84 RNA Tickertape Infers the Timing of Isolated Transcriptional Events with High Resolution .............. 84 RNA Tickertape Infers the Timing and Magnitude of Complex Transcriptional Programs .................. 85 RNA Tickertape Operates in Single Mammalian Cells ......................................................................... 88 RNA Tickertape can be used to infer the timing of neural activity ...................................................... 90 Discussion: ............................................................................................................................................... 91 Chapter 7 – Connectomics ........................................................................................................................... 92 Summary: ................................................................................................................................................. 94 Introduction ............................................................................................................................................. 94 Scalable arbitrary-color optical super-resolution ................................................................................... 94 The power of multiple colors: ..............................................................................................................
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