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Suk HJ, Buie N, Xu G, Banerjee A, Boyden ES, Tsai LH. Vibrotactile stimulation at gamma frequency mitigates pathology related to neurodegeneration and improves motor function. Front Aging Neurosci 2023; 15:1129510. [PMID: 37273653 PMCID: PMC10233036 DOI: 10.3389/fnagi.2023.1129510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/27/2023] [Indexed: 06/06/2023] Open
Abstract
The risk for neurodegenerative diseases increases with aging, with various pathological conditions and functional deficits accompanying these diseases. We have previously demonstrated that non-invasive visual stimulation using 40 Hz light flicker ameliorated pathology and modified cognitive function in mouse models of neurodegeneration, but whether 40 Hz stimulation using another sensory modality can impact neurodegeneration and motor function has not been studied. Here, we show that whole-body vibrotactile stimulation at 40 Hz leads to increased neural activity in the primary somatosensory cortex (SSp) and primary motor cortex (MOp). In two different mouse models of neurodegeneration, Tau P301S and CK-p25 mice, daily exposure to 40 Hz vibrotactile stimulation across multiple weeks also led to decreased brain pathology in SSp and MOp. Furthermore, both Tau P301S and CK-p25 mice showed improved motor performance after multiple weeks of daily 40 Hz vibrotactile stimulation. Vibrotactile stimulation at 40 Hz may thus be considered as a promising therapeutic strategy for neurodegenerative diseases with motor deficits.
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Affiliation(s)
- Ho-Jun Suk
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Nicole Buie
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Guojie Xu
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Arit Banerjee
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Edward S. Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, United States
- Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Howard Hughes Medical Institute, Cambridge, MA, United States
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, United States
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Alon S, Goodwin DR, Sinha A, Wassie AT, Chen F, Daugharthy ER, Bando Y, Kajita A, Xue AG, Marrett K, Prior R, Cui Y, Payne AC, Yao CC, Suk HJ, Wang R, Yu CCJ, Tillberg P, Reginato P, Pak N, Liu S, Punthambaker S, Iyer EPR, Kohman RE, Miller JA, Lein ES, Lako A, Cullen N, Rodig S, Helvie K, Abravanel DL, Wagle N, Johnson BE, Klughammer J, Slyper M, Waldman J, Jané-Valbuena J, Rozenblatt-Rosen O, Regev A, Church GM, Marblestone AH, Boyden ES. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 2021; 371:eaax2656. [PMID: 33509999 PMCID: PMC7900882 DOI: 10.1126/science.aax2656] [Citation(s) in RCA: 157] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/13/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022]
Abstract
Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.
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Affiliation(s)
- Shahar Alon
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Faculty of Engineering, Gonda Brain Research Center and Institute of Nanotechnology, Bar-Ilan University, Ramat Gan, Israel
| | - Daniel R Goodwin
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Anubhav Sinha
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Asmamaw T Wassie
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Fei Chen
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan R Daugharthy
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Yosuke Bando
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Kioxia Corporation, Minato-ku, Tokyo, Japan
| | | | - Andrew G Xue
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | | | | | - Yi Cui
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Andrew C Payne
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chun-Chen Yao
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ho-Jun Suk
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Ru Wang
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Paul Tillberg
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Paul Reginato
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Nikita Pak
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA, USA
| | - Songlei Liu
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Sukanya Punthambaker
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Eswar P R Iyer
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Richie E Kohman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott Rodig
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel L Abravanel
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Nikhil Wagle
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Michal Slyper
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Martorell AJ, Paulson AL, Suk HJ, Abdurrob F, Drummond GT, Guan W, Young JZ, Kim DNW, Kritskiy O, Barker SJ, Mangena V, Prince SM, Brown EN, Chung K, Boyden ES, Singer AC, Tsai LH. Multi-sensory Gamma Stimulation Ameliorates Alzheimer's-Associated Pathology and Improves Cognition. Cell 2019; 177:256-271.e22. [PMID: 30879788 DOI: 10.1016/j.cell.2019.02.014] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 06/30/2018] [Accepted: 02/11/2019] [Indexed: 01/06/2023]
Abstract
We previously reported that inducing gamma oscillations with a non-invasive light flicker (gamma entrainment using sensory stimulus or GENUS) impacted pathology in the visual cortex of Alzheimer's disease mouse models. Here, we designed auditory tone stimulation that drove gamma frequency neural activity in auditory cortex (AC) and hippocampal CA1. Seven days of auditory GENUS improved spatial and recognition memory and reduced amyloid in AC and hippocampus of 5XFAD mice. Changes in activation responses were evident in microglia, astrocytes, and vasculature. Auditory GENUS also reduced phosphorylated tau in the P301S tauopathy model. Furthermore, combined auditory and visual GENUS, but not either alone, produced microglial-clustering responses, and decreased amyloid in medial prefrontal cortex. Whole brain analysis using SHIELD revealed widespread reduction of amyloid plaques throughout neocortex after multi-sensory GENUS. Thus, GENUS can be achieved through multiple sensory modalities with wide-ranging effects across multiple brain areas to improve cognitive function.
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Affiliation(s)
- Anthony J Martorell
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Abigail L Paulson
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
| | - Ho-Jun Suk
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; MIT Media Lab, Departments of Biological Engineering and Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Fatema Abdurrob
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gabrielle T Drummond
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Webster Guan
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
| | - Jennie Z Young
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - David Nam-Woo Kim
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Oleg Kritskiy
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Scarlett J Barker
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Vamsi Mangena
- Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Stephanie M Prince
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
| | - Emery N Brown
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Kwanghun Chung
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, MIT, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02139, USA
| | - Edward S Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; MIT Media Lab, Departments of Biological Engineering and Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Annabelle C Singer
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02139, USA.
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Piatkevich KD, Jung EE, Straub C, Linghu C, Park D, Suk HJ, Hochbaum DR, Goodwin D, Pnevmatikakis E, Pak N, Kawashima T, Yang CT, Rhoades JL, Shemesh O, Asano S, Yoon YG, Freifeld L, Saulnier JL, Riegler C, Engert F, Hughes T, Drobizhev M, Szabo B, Ahrens MB, Flavell SW, Sabatini BL, Boyden ES. Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat Chem Biol 2018. [DOI: 10.1038/s41589-018-0023-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Piatkevich KD, Suk HJ, Kodandaramaiah SB, Yoshida F, DeGennaro EM, Drobizhev M, Hughes TE, Desimone R, Boyden ES, Verkhusha VV. Near-Infrared Fluorescent Proteins Engineered from Bacterial Phytochromes in Neuroimaging. Biophys J 2017; 113:2299-2309. [PMID: 29017728 DOI: 10.1016/j.bpj.2017.09.007] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 09/03/2017] [Accepted: 09/06/2017] [Indexed: 11/17/2022] Open
Abstract
Several series of near-infrared (NIR) fluorescent proteins (FPs) were recently engineered from bacterial phytochromes but were not systematically compared in neurons. To fluoresce, NIR FPs utilize an enzymatic derivative of heme, the linear tetrapyrrole biliverdin, as a chromophore whose level in neurons is poorly studied. Here, we evaluated NIR FPs of the iRFP protein family, which were reported to be the brightest in non-neuronal mammalian cells, in primary neuronal culture, in brain slices of mouse and monkey, and in mouse brain in vivo. We applied several fluorescence imaging modes, such as wide-field and confocal one-photon and two-photon microscopy, to compare photochemical and biophysical properties of various iRFPs. The iRFP682 and iRFP670 proteins exhibited the highest brightness and photostability under one-photon and two-photon excitation modes, respectively. All studied iRFPs exhibited efficient binding of the endogenous biliverdin chromophore in cultured neurons and in the mammalian brain and can be readily applied to neuroimaging.
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Affiliation(s)
- Kiryl D Piatkevich
- Media Lab, MIT, Cambridge, Massachusetts; MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts
| | - Ho-Jun Suk
- Media Lab, MIT, Cambridge, Massachusetts; Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts
| | - Suhasa B Kodandaramaiah
- Media Lab, MIT, Cambridge, Massachusetts; MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts
| | - Fumiaki Yoshida
- MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts
| | - Ellen M DeGennaro
- MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts; Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts
| | - Mikhail Drobizhev
- Department of Cell Biology and Neuroscience, Montana State University, Bozeman, Montana
| | - Thomas E Hughes
- Department of Cell Biology and Neuroscience, Montana State University, Bozeman, Montana
| | - Robert Desimone
- MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts; Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts
| | - Edward S Boyden
- Media Lab, MIT, Cambridge, Massachusetts; MIT McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts; Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts; Department of Biological Engineering, MIT, Cambridge, Massachusetts; MIT Center for Neurobiological Engineering, MIT, Cambridge, Massachusetts.
| | - Vladislav V Verkhusha
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York; Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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8
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Grossman N, Bono D, Dedic N, Kodandaramaiah SB, Rudenko A, Suk HJ, Cassara AM, Neufeld E, Kuster N, Tsai LH, Pascual-Leone A, Boyden ES. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 2017; 169:1029-1041.e16. [PMID: 28575667 PMCID: PMC5520675 DOI: 10.1016/j.cell.2017.05.024] [Citation(s) in RCA: 371] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 02/22/2017] [Accepted: 05/12/2017] [Indexed: 12/22/2022]
Abstract
We report a noninvasive strategy for electrically stimulating neurons at depth. By delivering to the brain multiple electric fields at frequencies too high to recruit neural firing, but which differ by a frequency within the dynamic range of neural firing, we can electrically stimulate neurons throughout a region where interference between the multiple fields results in a prominent electric field envelope modulated at the difference frequency. We validated this temporal interference (TI) concept via modeling and physics experiments, and verified that neurons in the living mouse brain could follow the electric field envelope. We demonstrate the utility of TI stimulation by stimulating neurons in the hippocampus of living mice without recruiting neurons of the overlying cortex. Finally, we show that by altering the currents delivered to a set of immobile electrodes, we can steerably evoke different motor patterns in living mice.
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Affiliation(s)
- Nir Grossman
- Media Lab, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 0AZ London, UK
| | - David Bono
- Department of Materials Science and Engineering, MIT, Cambridge, MA 02139, USA
| | - Nina Dedic
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Suhasa B Kodandaramaiah
- Media Lab, MIT, Cambridge, MA 02139, USA; Department of Mechanical Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
| | - Andrii Rudenko
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Department of Biology, City College of the City University of York, New York, NY 10031, USA
| | - Ho-Jun Suk
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
| | - Antonino M Cassara
- IT'IS Foundation for Research on Information Technologies in Society, 8004 Zurich, Switzerland
| | - Esra Neufeld
- IT'IS Foundation for Research on Information Technologies in Society, 8004 Zurich, Switzerland
| | - Niels Kuster
- IT'IS Foundation for Research on Information Technologies in Society, 8004 Zurich, Switzerland; Swiss Federal Institute of Technology (ETHZ), 8092 Zurich, Switzerland
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA; Broad Institute of Harvard University and MIT, Cambridge, MA 02142, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Edward S Boyden
- Media Lab, MIT, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Broad Institute of Harvard University and MIT, Cambridge, MA 02142, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA.
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Tillberg PW, Chen F, Piatkevich KD, Zhao Y, Yu CCJ, English BP, Gao L, Martorell A, Suk HJ, Yoshida F, DeGennaro EM, Roossien DH, Gong G, Seneviratne U, Tannenbaum SR, Desimone R, Cai D, Boyden ES. Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. Nat Biotechnol 2016; 34:987-92. [PMID: 27376584 PMCID: PMC5068827 DOI: 10.1038/nbt.3625] [Citation(s) in RCA: 358] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 05/30/2016] [Indexed: 01/22/2023]
Abstract
Expansion microscopy (ExM) enables imaging of preserved specimens with nanoscale precision on diffraction-limited instead of specialized super-resolution microscopes. ExM works by physically separating fluorescent probes after anchoring them to a swellable gel. The first ExM method did not result in the retention of native proteins in the gel and relied on custom-made reagents that are not widely available. Here we describe protein retention ExM (proExM), a variant of ExM in which proteins are anchored to the swellable gel, allowing the use of conventional fluorescently labeled antibodies and streptavidin, and fluorescent proteins. We validated and demonstrated the utility of proExM for multicolor super-resolution (∼70 nm) imaging of cells and mammalian tissues on conventional microscopes.
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Affiliation(s)
- Paul W Tillberg
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Fei Chen
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kiryl D Piatkevich
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Yongxin Zhao
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Chih-Chieh Jay Yu
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Brian P English
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Linyi Gao
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Anthony Martorell
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ho-Jun Suk
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Fumiaki Yoshida
- Osaka University Medical School, Suita, Osaka, Japan.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ellen M DeGennaro
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Guanyu Gong
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Uthpala Seneviratne
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Steven R Tannenbaum
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Robert Desimone
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Dawen Cai
- University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Edward S Boyden
- Massachusetts Institute of Technology Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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10
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Oh YK, Joung HA, Han HS, Suk HJ, Kim MG. A three-line lateral flow assay strip for the measurement of C-reactive protein covering a broad physiological concentration range in human sera. Biosens Bioelectron 2014; 61:285-9. [PMID: 24906087 DOI: 10.1016/j.bios.2014.04.032] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 04/01/2014] [Accepted: 04/17/2014] [Indexed: 10/25/2022]
Abstract
The lateral flow assay (LFA) strip sensor possesses many advantages as a diagnostic device, including the capabilities of rapid, one-step assay performance, and high throughput production. A major limitation of the sensor, however, is its difficulty in measuring a broad concentration range of target proteins, including C-reactive protein (CRP), due to the "hook effect." In this study, we report the use of a three-line LFA strip sensor, adding an antigen line to the conventional two-line LFA sensor, for detecting CRP within a broad concentration range in human sera. We introduced an antigen line between test and control lines in the LFA sensor. The antigen line was formed by dispensing a CRP antibody solution followed by a CRP solution in nitrocellulose membrane. All other conditions were identical to those applied to the conventional LFA strip sensor. The CRP level in test samples was generated by data processing from the intensities of three lines. The strip sensor measured a linear detection range of CRP concentration from 1 ng/mL to 500 μg/mL within 10 min, with a calculated detection range of 0.69 ng/mL-1.02 mg/mL. Using the developed three-line LFA sensor, 50 clinical samples were measured at a detection range of 0.4-84.7 μg/mL. This novel and easy-to-use CRP sensor can be a useful tool for rapid, sensitive, and cost-effective detection of a broad physiological concentration range of CRP capabilities that are vital for various diagnostic applications.
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Affiliation(s)
- Young Kyoung Oh
- INGIbio Co. Ltd., R&D Center, 206, APRI, 123 Chemdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea
| | - Hyou-Arm Joung
- School of Physics and Chemistry, Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea
| | - Hyung Soo Han
- Department of Physiology, Kyungpook National University School of Medicine, Daegu 700-422, Republic of Korea
| | - Ho-Jun Suk
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Min-Gon Kim
- INGIbio Co. Ltd., R&D Center, 206, APRI, 123 Chemdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea; School of Physics and Chemistry, Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea.
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11
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Abstract
Particle manipulation based on dielectrophoresis (DEP) can be a versatile and useful tool in lab-on-chip systems for a wide range of cell patterning and tissue engineering applications. Even though there are extensive reports on the use of DEP for cell patterning applications, the development of approaches that make DEP even more affordable and common place is still desirable. In this study, we present the use of interdigitated electrodes on a printed circuit board (PCB) that can be reused to manipulate and position HeLa cells and polystyrene particles over 100 microm thick glass cover slips using DEP. An open-well or a closed microfluidic channel, both made of PDMS, was placed on the glass coverslip, which was then placed directly over the PCB. An AC voltage was applied to the electrodes on the PCB to induce DEP on the particles through the thin glass coverslip. The HeLa cells patterned with DEP were subsequently grown to confirm the lack of any adverse affects from the electric fields. This alternative and reusable platform for DEP particle manipulation can provide a convenient and rapid method for prototyping a DEP-based lab-on-chip system, cost-sensitive lab-on-chip applications, and a wide range of tissue engineering applications.
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Affiliation(s)
- Kidong Park
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
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Watkins H, McKenna WJ, Thierfelder L, Suk HJ, Anan R, O'Donoghue A, Spirito P, Matsumori A, Moravec CS, Seidman JG. Mutations in the genes for cardiac troponin T and alpha-tropomyosin in hypertrophic cardiomyopathy. N Engl J Med 1995; 332:1058-64. [PMID: 7898523 DOI: 10.1056/nejm199504203321603] [Citation(s) in RCA: 608] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Familial hypertrophic cardiomyopathy can be caused by mutations in the genes for beta cardiac myosin heavy chain, alpha-tropomyosin, or cardiac troponin T. It is not known how often the disease is caused by mutations in the tropomyosin and troponin genes, and the associated clinical phenotypes have not been carefully studied. METHODS Linkage between polymorphisms of the alpha-tropomyosin gene or the cardiac troponin T gene and hypertrophic cardiomyopathy was assessed in 27 families. In addition, 100 probands were screened for mutations in the alpha-tropomyosin gene, and 26 were screened for mutations in the cardiac troponin T gene. Life expectancy, the incidence of sudden death, and the extent of left ventricular hypertrophy were compared in patients with different mutations. RESULTS Genetic analyses identified only one alpha-tropomyosin mutation, identical to one previously described. Five novel mutations in cardiac troponin were identified, as well as a further example of a previously described mutation. The clinical phenotype of four troponin T mutations in seven unrelated families was similar and was characterized by a poor prognosis (life expectancy, approximately 35 years) and a high incidence of sudden death. The mean (+/- SD) maximal thickness of the left ventricular wall in subjects with cardiac troponin T mutations (16.7 +/- 5.5 mm) was significantly less than that in subjects with beta cardiac myosin heavy-chain mutations (23.7 +/- 7.7 mm, P < 0.001). CONCLUSIONS Mutations in alpha-tropomyosin are a rare cause of familial hypertrophic cardiomyopathy, accounting for approximately 3 percent of cases. Mutations in cardiac troponin T account for approximately 15 percent of cases of familial hypertrophic cardiomyopathy in this referral-center population. These mutations are characterized by relatively mild and sometimes subclinical hypertrophy but a high incidence of sudden death. Genetic testing may therefore be especially important in this group.
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Affiliation(s)
- H Watkins
- Howard Hughes Medical Institute, Boston, MA
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