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Bukowski L, Strøm ME, Andersen JL, Maesen JB, Tian L, Sinning S. 5-HT_FAsTR: a versatile, label-free, high-throughput, fluorescence-based microplate assay to quantify serotonin transport and release. Sci Rep 2024; 14:6541. [PMID: 38504103 PMCID: PMC10951269 DOI: 10.1038/s41598-024-56712-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/09/2024] [Indexed: 03/21/2024] Open
Abstract
The neurotransmitter serotonin plays a pivotal role in mood and depression. It also acts as a vasoconstrictor within blood vessels and is the main neurotransmitter in the gastrointestinal system. In neurotransmission, released serotonin is taken up by serotonin transporters, which are principal targets of antidepressants and the psychostimulant, ecstasy. The investigation of serotonin transporters have relied almost exclusively on the use of radiolabeled serotonin in heterogenous end-point assays. Here we adapt the genetically encoded fluorescent biosensor, iSeroSnFR, to establish and validate the Serotonin (5-HT) Fluorescence Assay for Transport and Release (5-HT_FAsTR) for functional and pharmacological studies of serotonin transport and release. We demonstrate the applicability of the method for the study of a neuronal, high-affinity, low-capacity serotonin transporter (SERT) as well as an extraneuronal low-affinity, high-capacity organic cation transporter and mutants thereof. 5HT_FAsTR offers an accessible, versatile and reliable semi-homogenous assay format that only relies on a fluorescence plate reader for repeated, real-time measurements of serotonin influx and efflux. 5HT_FAsTR accelerates and democratizes functional characterization and pharmacological studies of serotonin transporters and genetic variants thereof in disease states such as depression, anxiety and ADHD.
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Affiliation(s)
- Lina Bukowski
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark
| | - Markus Emanuel Strøm
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark
| | - Jens Lindengren Andersen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark
| | - Jannick Bang Maesen
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, University of California, Davis, CA, 95616-8635, USA
- Max Planck Florida Institute for Neuroscience, Jupiter, FL, 33458, USA
| | - Steffen Sinning
- Department of Forensic Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark.
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2
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Otanuly M, Kubitschke M, Masseck OA. A Bright Future? A Perspective on Class C GPCR Based Genetically Encoded Biosensors. ACS Chem Neurosci 2024; 15:889-897. [PMID: 38380648 PMCID: PMC10921406 DOI: 10.1021/acschemneuro.3c00854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
One of the major challenges in molecular neuroscience today is to accurately monitor neurotransmitters, neuromodulators, peptides, and various other biomolecules in the brain with high temporal and spatial resolution. Only a comprehensive understanding of neuromodulator dynamics, their release probability, and spatial distribution will unravel their ultimate role in cognition and behavior. This Perspective offers an overview of potential design strategies for class C GPCR-based biosensors. It briefly highlights current applications of GPCR-based biosensors, with a primary focus on class C GPCRs and their unique structural characteristics compared with other GPCR subfamilies. The discussion offers insights into plausible future design approaches for biosensor development targeting members of this specific GPCR subfamily. It is important to note that, at this stage, we are contemplating possibilities rather than presenting a concrete guide, as the pipeline is still under development.
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Affiliation(s)
- Margulan Otanuly
- Synthetische Biologie, Universität Bremen, Bremen 28359, Germany
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3
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Dong C, Zheng Y, Long-Iyer K, Wright EC, Li Y, Tian L. Fluorescence Imaging of Neural Activity, Neurochemical Dynamics, and Drug-Specific Receptor Conformation with Genetically Encoded Sensors. Annu Rev Neurosci 2022; 45:273-294. [PMID: 35316611 PMCID: PMC9940643 DOI: 10.1146/annurev-neuro-110520-031137] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in fluorescence imaging permit large-scale recording of neural activity and dynamics of neurochemical release with unprecedented resolution in behaving animals. Calcium imaging with highly optimized genetically encoded indicators provides a mesoscopic view of neural activity from genetically defined populations at cellular and subcellular resolutions. Rigorously improved voltage sensors and microscopy allow for robust spike imaging of populational neurons in various brain regions. In addition, recent protein engineering efforts in the past few years have led to the development of sensors for neurotransmitters and neuromodulators. Here, we discuss the development and applications of these genetically encoded fluorescent indicators in reporting neural activity in response to various behaviors in different biological systems as well as in drug discovery. We also report a simple model to guide sensor selection and optimization.
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Affiliation(s)
- Chunyang Dong
- Graduate Program in Biochemistry, Molecular, Cellular, and Developmental Biology, University of California, Davis, California, USA
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, California, USA;
| | - Yu Zheng
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences; PKU-IDG/McGovern Institute for Brain Research; and Peking-Tsinghua Center for Life Sciences, Beijing, China;
| | - Kiran Long-Iyer
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, California, USA;
- Neuroscience Graduate Program, University of California, Davis, California, USA
| | - Emily C Wright
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, California, USA;
| | - Yulong Li
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences; PKU-IDG/McGovern Institute for Brain Research; and Peking-Tsinghua Center for Life Sciences, Beijing, China;
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, California, USA;
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4
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Koveal D, Rosen PC, Meyer DJ, Díaz-García CM, Wang Y, Cai LH, Chou PJ, Weitz DA, Yellen G. A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors. Nat Commun 2022; 13:2919. [PMID: 35614105 PMCID: PMC9133083 DOI: 10.1038/s41467-022-30685-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/11/2022] [Indexed: 12/30/2022] Open
Abstract
Genetically encoded fluorescent biosensors are powerful tools used to track chemical processes in intact biological systems. However, the development and optimization of biosensors remains a challenging and labor-intensive process, primarily due to technical limitations of methods for screening candidate biosensors. Here we describe a screening modality that combines droplet microfluidics and automated fluorescence imaging to provide an order of magnitude increase in screening throughput. Moreover, unlike current techniques that are limited to screening for a single biosensor feature at a time (e.g. brightness), our method enables evaluation of multiple features (e.g. contrast, affinity, specificity) in parallel. Because biosensor features can covary, this capability is essential for rapid optimization. We use this system to generate a high-performance biosensor for lactate that can be used to quantify intracellular lactate concentrations. This biosensor, named LiLac, constitutes a significant advance in metabolite sensing and demonstrates the power of our screening approach.
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Affiliation(s)
- Dorothy Koveal
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Paul C Rosen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dylan J Meyer
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Carlos Manlio Díaz-García
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Yongcheng Wang
- Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Liangzhu Laboratory, Zhejiang University Medical Center, 1369 West Wenyi Road, Hangzhou, 311121, China
| | - Li-Heng Cai
- Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, USA
| | - Peter J Chou
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - David A Weitz
- Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Gary Yellen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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5
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. NEUROPHOTONICS 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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6
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Lin L, Gupta S, Zheng WS, Si K, Zhu JJ. Genetically encoded sensors enable micro- and nano-scopic decoding of transmission in healthy and diseased brains. Mol Psychiatry 2021; 26:443-455. [PMID: 33277628 PMCID: PMC7850973 DOI: 10.1038/s41380-020-00960-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 10/06/2020] [Accepted: 11/10/2020] [Indexed: 12/11/2022]
Abstract
Neural communication orchestrates a variety of behaviors, yet despite impressive effort, delineating transmission properties of neuromodulatory communication remains a daunting task due to limitations of available monitoring tools. Recently developed genetically encoded neurotransmitter sensors, when combined with superresolution and deconvolution microscopic techniques, enable the first micro- and nano-scopic visualization of neuromodulatory transmission. Here we introduce this image analysis method by presenting its biophysical foundation, practical solutions, biological validation, and broad applicability. The presentation illustrates how the method resolves fundamental synaptic properties of neuromodulatory transmission, and the new data unveil unexpected fine control and precision of rodent and human neuromodulation. The findings raise the prospect of rapid advances in the understanding of neuromodulatory transmission essential for resolving the physiology or pathogenesis of various behaviors and diseases.
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Affiliation(s)
- Li Lin
- Department of Neurosurgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, China. .,School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China.
| | - Smriti Gupta
- grid.27755.320000 0000 9136 933XDepartment of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
| | - W. Sharon Zheng
- grid.27755.320000 0000 9136 933XDepartment of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908 USA ,grid.27755.320000 0000 9136 933XBiomedical Engineering Class of 2021, University of Virginia School of Medicine, Charlottesville, VA USA
| | - Ke Si
- grid.13402.340000 0004 1759 700XCollege of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027 China ,grid.13402.340000 0004 1759 700XSchool of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310027 China
| | - J. Julius Zhu
- grid.27755.320000 0000 9136 933XDepartment of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA 22908 USA
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7
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Sabatini BL, Tian L. Imaging Neurotransmitter and Neuromodulator Dynamics In Vivo with Genetically Encoded Indicators. Neuron 2020; 108:17-32. [PMID: 33058762 DOI: 10.1016/j.neuron.2020.09.036] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/10/2020] [Accepted: 09/25/2020] [Indexed: 12/16/2022]
Abstract
The actions of neuromodulation are thought to mediate the ability of the mammalian brain to dynamically adjust its functional state in response to changes in the environment. Altered neurotransmitter (NT) and neuromodulator (NM) signaling is central to the pathogenesis or treatment of many human neurological and psychiatric disorders, including Parkinson's disease, schizophrenia, depression, and addiction. To reveal the precise mechanisms by which these neurochemicals regulate healthy and diseased neural circuitry, one needs to measure their spatiotemporal dynamics in the living brain with great precision. Here, we discuss recent development, optimization, and applications of optical approaches to measure the spatial and temporal profiles of NT and NM release in the brain using genetically encoded sensors for in vivo studies.
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Affiliation(s)
- Bernardo L Sabatini
- Howard Hughes Medical Institute, Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
| | - Lin Tian
- Departments of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA.
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8
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Labouesse MA, Cola RB, Patriarchi T. GPCR-Based Dopamine Sensors-A Detailed Guide to Inform Sensor Choice for In vivo Imaging. Int J Mol Sci 2020; 21:E8048. [PMID: 33126757 PMCID: PMC7672611 DOI: 10.3390/ijms21218048] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 09/25/2020] [Accepted: 09/26/2020] [Indexed: 12/12/2022] Open
Abstract
Understanding how dopamine (DA) encodes behavior depends on technologies that can reliably monitor DA release in freely-behaving animals. Recently, red and green genetically encoded sensors for DA (dLight, GRAB-DA) were developed and now provide the ability to track release dynamics at a subsecond resolution, with submicromolar affinity and high molecular specificity. Combined with rapid developments in in vivo imaging, these sensors have the potential to transform the field of DA sensing and DA-based drug discovery. When implementing these tools in the laboratory, it is important to consider there is not a 'one-size-fits-all' sensor. Sensor properties, most importantly their affinity and dynamic range, must be carefully chosen to match local DA levels. Molecular specificity, sensor kinetics, spectral properties, brightness, sensor scaffold and pharmacology can further influence sensor choice depending on the experimental question. In this review, we use DA as an example; we briefly summarize old and new techniques to monitor DA release, including DA biosensors. We then outline a map of DA heterogeneity across the brain and provide a guide for optimal sensor choice and implementation based on local DA levels and other experimental parameters. Altogether this review should act as a tool to guide DA sensor choice for end-users.
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Affiliation(s)
- Marie A. Labouesse
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA;
- Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY 10032, USA
| | - Reto B. Cola
- Anatomy and Program in Neuroscience, University of Fribourg, 1700 Fribourg, Switzerland;
- Institute of Pharmacology and Toxicology, University of Zurich, 8057 Zurich, Switzerland
| | - Tommaso Patriarchi
- Institute of Pharmacology and Toxicology, University of Zurich, 8057 Zurich, Switzerland
- Neuroscience Center Zurich, University and ETH Zurich, 8057 Zurich, Switzerland
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9
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Moreaux LC, Yatsenko D, Sacher WD, Choi J, Lee C, Kubat NJ, Cotton RJ, Boyden ES, Lin MZ, Tian L, Tolias AS, Poon JKS, Shepard KL, Roukes ML. Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time. Neuron 2020; 108:66-92. [PMID: 33058767 PMCID: PMC8061790 DOI: 10.1016/j.neuron.2020.09.043] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/18/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022]
Abstract
We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm3 volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.
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Affiliation(s)
- Laurent C Moreaux
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Dimitri Yatsenko
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wesley D Sacher
- Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Max Planck Institute for Microstructure Physics, Halle, Germany
| | - Jaebin Choi
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Changhyuk Lee
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA; Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology, Korea
| | - Nicole J Kubat
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA
| | - R James Cotton
- Shirley Ryan AbilityLab, Northwestern University, Chicago, IL 60611, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Edward S Boyden
- Howard Hughes Medical Institute, Cambridge, MA, USA; McGovern Institute, MIT, Cambridge, USA; Koch Institute, MIT, Cambridge, USA; Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, USA
| | - Michael Z Lin
- Departments of Neurobiology and Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, CA 95616, USA
| | - Andreas S Tolias
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Joyce K S Poon
- Max Planck Institute for Microstructure Physics, Halle, Germany; Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, ON M5S 3G4, Canada
| | - Kenneth L Shepard
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Michael L Roukes
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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10
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Rabut C, Yoo S, Hurt RC, Jin Z, Li H, Guo H, Ling B, Shapiro MG. Ultrasound Technologies for Imaging and Modulating Neural Activity. Neuron 2020; 108:93-110. [PMID: 33058769 PMCID: PMC7577369 DOI: 10.1016/j.neuron.2020.09.003] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/25/2020] [Accepted: 09/01/2020] [Indexed: 02/06/2023]
Abstract
Visualizing and perturbing neural activity on a brain-wide scale in model animals and humans is a major goal of neuroscience technology development. Established electrical and optical techniques typically break down at this scale due to inherent physical limitations. In contrast, ultrasound readily permeates the brain, and in some cases the skull, and interacts with tissue with a fundamental resolution on the order of 100 μm and 1 ms. This basic ability has motivated major efforts to harness ultrasound as a modality for large-scale brain imaging and modulation. These efforts have resulted in already-useful neuroscience tools, including high-resolution hemodynamic functional imaging, focused ultrasound neuromodulation, and local drug delivery. Furthermore, recent breakthroughs promise to connect ultrasound to neurons at the genetic level for biomolecular imaging and sonogenetic control. In this article, we review the state of the art and ongoing developments in ultrasonic neurotechnology, building from fundamental principles to current utility, open questions, and future potential.
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Affiliation(s)
- Claire Rabut
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Sangjin Yoo
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Robert C Hurt
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Zhiyang Jin
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - Hongyi Li
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Hongsun Guo
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Bill Ling
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Mikhail G Shapiro
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
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11
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Pal A, Tian L. Imaging voltage and brain chemistry with genetically encoded sensors and modulators. Curr Opin Chem Biol 2020; 57:166-176. [PMID: 32823064 DOI: 10.1016/j.cbpa.2020.07.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 01/21/2023]
Abstract
Neurons and glia are functionally organized into circuits and higher-order structures that allow the precise information processing required for complex behaviors. To better understand the structure and function of the brain, we must understand synaptic connectivity, action potential generation and propagation, as well as well-orchestrated molecular signaling. Recently, dramatically improved sensors for voltage, intracellular calcium, and neurotransmitters/modulators, combined with advanced microscopy provide new opportunities for in vivo dissection of cellular and circuit activity in awake, behaving animals. This review focuses on the current trends in genetically encoded sensors for molecules and cellular events and their potential applicability to the study of nervous system in health and disease.
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Affiliation(s)
- Akash Pal
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA.
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