Dougal Maclaurin
Harvard University
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Featured researches published by Dougal Maclaurin.
Nature Methods | 2012
Joel M. Kralj; Adam D. Douglass; Daniel Hochbaum; Dougal Maclaurin; Adam E. Cohen
Reliable optical detection of single action potentials in mammalian neurons has been one of the longest-standing challenges in neuroscience. Here we achieved this goal by using the endogenous fluorescence of a microbial rhodopsin protein, Archaerhodopsin 3 (Arch) from Halorubrum sodomense, expressed in cultured rat hippocampal neurons. This genetically encoded voltage indicator exhibited an approximately tenfold improvement in sensitivity and speed over existing protein-based voltage indicators, with a roughly linear twofold increase in brightness between −150 mV and +150 mV and a sub-millisecond response time. Arch detected single electrically triggered action potentials with an optical signal-to-noise ratio >10. Arch(D95N) lacked endogenous proton pumping and had 50% greater sensitivity than wild type but had a slower response (41 ms). Nonetheless, Arch(D95N) also resolved individual action potentials. Microbial rhodopsin–based voltage indicators promise to enable optical interrogation of complex neural circuits and electrophysiology in systems for which electrode-based techniques are challenging.
Nature Methods | 2014
Daniel Hochbaum; Yongxin Zhao; Samouil L Farhi; Nathan Cao Klapoetke; Christopher A. Werley; Vikrant Kapoor; Peng Zou; Joel M. Kralj; Dougal Maclaurin; Niklas Smedemark-Margulies; Jessica L. Saulnier; Gabriella L. Boulting; Christoph Straub; Yong Ku Cho; Michael Melkonian; Gane Ka-Shu Wong; Venkatesh N. Murthy; Bernardo L. Sabatini; Edward S. Boyden; Robert E. Campbell; Adam E. Cohen
All-optical electrophysiology—spatially resolved simultaneous optical perturbation and measurement of membrane voltage—would open new vistas in neuroscience research. We evolved two archaerhodopsin-based voltage indicators, QuasAr1 and QuasAr2, which show improved brightness and voltage sensitivity, have microsecond response times and produce no photocurrent. We engineered a channelrhodopsin actuator, CheRiff, which shows high light sensitivity and rapid kinetics and is spectrally orthogonal to the QuasArs. A coexpression vector, Optopatch, enabled cross-talk–free genetically targeted all-optical electrophysiology. In cultured rat neurons, we combined Optopatch with patterned optical excitation to probe back-propagating action potentials (APs) in dendritic spines, synaptic transmission, subcellular microsecond-timescale details of AP propagation, and simultaneous firing of many neurons in a network. Optopatch measurements revealed homeostatic tuning of intrinsic excitability in human stem cell–derived neurons. In rat brain slices, Optopatch induced and reported APs and subthreshold events with high signal-to-noise ratios. The Optopatch platform enables high-throughput, spatially resolved electrophysiology without the use of conventional electrodes.
Proceedings of the National Academy of Sciences of the United States of America | 2013
Dougal Maclaurin; Veena Venkatachalam; Hohjai Lee; Adam E. Cohen
Microbial rhodopsins were recently introduced as genetically encoded fluorescent indicators of membrane voltage. An understanding of the mechanism underlying this function would aid in the design of improved voltage indicators. We asked, what states can the protein adopt, and which states are fluorescent? How does membrane voltage affect the photostationary distribution of states? Here, we present a detailed spectroscopic characterization of Archaerhodopsin 3 (Arch). We performed fluorescence spectroscopy on Arch and its photogenerated intermediates in Escherichia coli and in single HEK293 cells under voltage-clamp conditions. These experiments probed the effects of time-dependent illumination and membrane voltage on absorption, fluorescence, membrane current, and membrane capacitance. The fluorescence of Arch arises through a sequential three-photon process. Membrane voltage modulates protonation of the Schiff base in a 13-cis photocycle intermediate (M ⇌ N equilibrium), not in the ground state as previously hypothesized. We present experimental protocols for optimized voltage imaging with Arch, and we discuss strategies for engineering improved rhodopsin-based voltage indicators.
Journal of the American Chemical Society | 2014
Veena Venkatachalam; Daan Brinks; Dougal Maclaurin; Daniel Hochbaum; Joel M. Kralj; Adam E. Cohen
We developed a technique, “flash memory”, to record a photochemical imprint of the activity state—firing or not firing—of a neuron at a user-selected moment in time. The key element is an engineered microbial rhodopsin protein with three states. Two nonfluorescent states, D1 and D2, exist in a voltage-dependent equilibrium. A stable fluorescent state, F, is reached by a photochemical conversion from D2. When exposed to light of a wavelength λwrite, population transfers from D2 to F, at a rate determined by the D1 ⇌ D2 equilibrium. The population of F maintains a record of membrane voltage which persists in the dark. Illumination at a later time at a wavelength λread excites fluorescence of F, probing this record. An optional third flash at a wavelength λreset converts F back to D2, for a subsequent write–read cycle. The flash memory method offers the promise to decouple the recording of neural activity from its readout. In principle, the technique may enable one to generate snapshots of neural activity in a large volume of neural tissue, e.g., a complete mouse brain, by circumventing the challenge of imaging a large volume with simultaneous high spatial and high temporal resolution. The proof-of-principle flash memory sensors presented here will need improvements in sensitivity, speed, brightness, and membrane trafficking before this goal can be realized.
Nature Materials | 2016
Rafael Gómez-Bombarelli; Jorge Aguilera-Iparraguirre; Timothy D. Hirzel; David K. Duvenaud; Dougal Maclaurin; Martin A. Blood-Forsythe; Hyun Sik Chae; Markus Einzinger; Dong-Gwang Ha; Tony Wu; Georgios Markopoulos; Soonok Jeon; Ho-Suk Kang; Hiroshi Miyazaki; Masaki Numata; Sunghan Kim; Wenliang Huang; Seong Ik Hong; Marc A. Baldo; Ryan P. Adams; Alán Aspuru-Guzik
international conference on machine learning | 2015
Dougal Maclaurin; David K. Duvenaud; Ryan P. Adams
neural information processing systems | 2015
David K. Duvenaud; Dougal Maclaurin; Jorge Aguilera-Iparraguirre; Rafael Gómez-Bombarelli; Timothy D. Hirzel; Alán Aspuru-Guzik; Ryan P. Adams
international conference on artificial intelligence and statistics | 2016
David K. Duvenaud; Dougal Maclaurin; Ryan P. Adams
uncertainty in artificial intelligence | 2014
Dougal Maclaurin; Ryan P. Adams
Archive | 2012
Adam E. Cohen; Dougal Maclaurin; Daniel Hochbaum; Joel M. Kralj