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Liu Z, Lu X, Villette V, Gou Y, Colbert KL, Lai S, Guan S, Land MA, Lee J, Assefa T, Zollinger DR, Korympidou MM, Vlasits AL, Pang MM, Su S, Cai C, Froudarakis E, Zhou N, Patel SS, Smith CL, Ayon A, Bizouard P, Bradley J, Franke K, Clandinin TR, Giovannucci A, Tolias AS, Reimer J, Dieudonné S, St-Pierre F. Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy. Cell 2022; 185:3408-3425.e29. [PMID: 35985322 PMCID: PMC9563101 DOI: 10.1016/j.cell.2022.07.013] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/13/2022] [Accepted: 07/18/2022] [Indexed: 11/23/2022]
Abstract
Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.
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Affiliation(s)
- Zhuohe Liu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Xiaoyu Lu
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX 77005, USA
| | - Vincent Villette
- Institut de Biologie de l'École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, Paris 75005, France
| | - Yueyang Gou
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kevin L Colbert
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shujuan Lai
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sihui Guan
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michelle A Land
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jihwan Lee
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX 77005, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tensae Assefa
- Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Daniel R Zollinger
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Maria M Korympidou
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany; Center for Integrative Neuroscience, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany; Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Baden-Württemberg, 72076, Germany
| | - Anna L Vlasits
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany; Center for Integrative Neuroscience, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany
| | - Michelle M Pang
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Sharon Su
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Changjia Cai
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion 70013, Greece
| | - Na Zhou
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Saumil S Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Cameron L Smith
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Annick Ayon
- Institut de Biologie de l'École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, Paris 75005, France
| | - Pierre Bizouard
- Institut de Biologie de l'École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, Paris 75005, France
| | - Jonathan Bradley
- Institut de Biologie de l'École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, Paris 75005, France
| | - Katrin Franke
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany; Center for Integrative Neuroscience, University of Tübingen, Tübingen, Baden-Württemberg 72076, Germany; Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Baden-Württemberg, 72076, Germany
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94305, USA
| | - Andrea Giovannucci
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, Chapel Hill, NC 27599, USA
| | - Andreas S Tolias
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Stéphane Dieudonné
- Institut de Biologie de l'École Normale Supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Research University, Paris 75005, France
| | - François St-Pierre
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX 77005, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
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Zhang X, Meeks JP. Paradoxically Sparse Chemosensory Tuning in Broadly Integrating External Granule Cells in the Mouse Accessory Olfactory Bulb. J Neurosci 2020; 40:5247-5263. [PMID: 32503886 PMCID: PMC7329303 DOI: 10.1523/jneurosci.2238-19.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 04/20/2020] [Accepted: 04/23/2020] [Indexed: 12/20/2022] Open
Abstract
The accessory olfactory bulb (AOB), the first neural circuit in the mouse accessory olfactory system, is critical for interpreting social chemosignals. Despite its importance, AOB information processing is poorly understood compared with the main olfactory bulb (MOB). Here, we sought to fill gaps in the understanding of AOB interneuron function. We used 2-photon GCaMP6f Ca2+ imaging in an ex vivo preparation to study chemosensory tuning in AOB external granule cells (EGCs), interneurons hypothesized to broadly inhibit activity in excitatory mitral cells (MCs). In ex vivo preparations from mice of both sexes, we measured MC and EGC tuning to natural chemosignal blends and monomolecular ligands, finding that EGC tuning was sparser, not broader, than upstream MCs. Simultaneous electrophysiological recording and Ca2+ imaging showed no differences in GCaMP6f-to-spiking relationships in these cell types during simulated sensory stimulation, suggesting that measured EGC sparseness was not due to cell type-dependent variability in GCaMP6f performance. Ex vivo patch-clamp recordings revealed that EGC subthreshold responsivity was far broader than indicated by GCaMP6f Ca2+ imaging, and that monomolecular ligands rarely elicited EGC spiking. These results indicate that EGCs are selectively engaged by chemosensory blends, suggesting different roles for EGCs than analogous interneurons in the MOB.SIGNIFICANCE STATEMENT The mouse accessory olfactory system (AOS) interprets social chemosignals, but we poorly understand AOS information processing. Here, we investigate the functional properties of external granule cells (EGCs), a major class of interneurons in the accessory olfactory bulb (AOB). We hypothesized that EGCs, which are densely innervated by excitatory mitral cells (MCs), would show broad chemosensory tuning, suggesting a role in divisive normalization. Using ex vivo GCaMP6f imaging, we found that EGCs were instead more sparsely tuned than MCs. This was not due to weaker GCaMP6f signaling in EGCs than in MCs. Instead, we found that many MC-activating chemosignals caused only subthreshold EGC responses. This indicates a different role for AOB EGCs compared with analogous cells in the main olfactory bulb.
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Affiliation(s)
- Xingjian Zhang
- University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Julian P Meeks
- University of Texas Southwestern Medical Center, Dallas, Texas 75390
- University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642
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Suk HJ, Boyden ES, van Welie I. Advances in the automation of whole-cell patch clamp technology. J Neurosci Methods 2019; 326:108357. [PMID: 31336060 DOI: 10.1016/j.jneumeth.2019.108357] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/05/2019] [Accepted: 07/10/2019] [Indexed: 12/22/2022]
Abstract
Electrophysiology is the study of neural activity in the form of local field potentials, current flow through ion channels, calcium spikes, back propagating action potentials and somatic action potentials, all measurable on a millisecond timescale. Despite great progress in imaging technologies and sensor proteins, none of the currently available tools allow imaging of neural activity on a millisecond timescale and beyond the first few hundreds of microns inside the brain. The patch clamp technique has been an invaluable tool since its inception several decades ago and has generated a wealth of knowledge about the nature of voltage- and ligand-gated ion channels, sub-threshold and supra-threshold activity, and characteristics of action potentials related to higher order functions. Many techniques that evolve to be standardized tools in the biological sciences go through a period of transformation in which they become, at least to some degree, automated, in order to improve reproducibility, throughput and standardization. The patch clamp technique is currently undergoing this transition, and in this review, we will discuss various aspects of this transition, covering advances in automated patch clamp technology both in vitro and in vivo.
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Affiliation(s)
- Ho-Jun Suk
- Health Sciences and Technology, MIT, Cambridge, MA 02139, USA; Media Lab, MIT, Cambridge, MA 02139, USA; McGovern Institute, MIT, Cambridge, MA 02139, USA
| | - Edward S Boyden
- Media Lab, MIT, Cambridge, MA 02139, USA; McGovern Institute, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
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Suk HJ, van Welie I, Kodandaramaiah SB, Allen B, Forest CR, Boyden ES. Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo. Neuron 2017; 95:1037-1047.e11. [PMID: 28858614 DOI: 10.1016/j.neuron.2017.08.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 06/27/2017] [Accepted: 08/04/2017] [Indexed: 01/02/2023]
Abstract
Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits.
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Affiliation(s)
- Ho-Jun Suk
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ingrid van Welie
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Suhasa B Kodandaramaiah
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Brian Allen
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Craig R Forest
- G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Edward S Boyden
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Desai NS, Siegel JJ, Taylor W, Chitwood RA, Johnston D. MATLAB-based automated patch-clamp system for awake behaving mice. J Neurophysiol 2015; 114:1331-45. [PMID: 26084901 PMCID: PMC4725114 DOI: 10.1152/jn.00025.2015] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 06/14/2015] [Indexed: 11/22/2022] Open
Abstract
Automation has been an important part of biomedical research for decades, and the use of automated and robotic systems is now standard for such tasks as DNA sequencing, microfluidics, and high-throughput screening. Recently, Kodandaramaiah and colleagues (Nat Methods 9: 585-587, 2012) demonstrated, using anesthetized animals, the feasibility of automating blind patch-clamp recordings in vivo. Blind patch is a good target for automation because it is a complex yet highly stereotyped process that revolves around analysis of a single signal (electrode impedance) and movement along a single axis. Here, we introduce an automated system for blind patch-clamp recordings from awake, head-fixed mice running on a wheel. In its design, we were guided by 3 requirements: easy-to-use and easy-to-modify software; seamless integration of behavioral equipment; and efficient use of time. The resulting system employs equipment that is standard for patch recording rigs, moderately priced, or simple to make. It is written entirely in MATLAB, a programming environment that has an enormous user base in the neuroscience community and many available resources for analysis and instrument control. Using this system, we obtained 19 whole cell patch recordings from neurons in the prefrontal cortex of awake mice, aged 8-9 wk. Successful recordings had series resistances that averaged 52 ± 4 MΩ and required 5.7 ± 0.6 attempts to obtain. These numbers are comparable with those of experienced electrophysiologists working manually, and this system, written in a simple and familiar language, will be useful to many cellular electrophysiologists who wish to study awake behaving mice.
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Affiliation(s)
- Niraj S Desai
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas
| | - Jennifer J Siegel
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas
| | - William Taylor
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas
| | - Raymond A Chitwood
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas
| | - Daniel Johnston
- Center for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas
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