1
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Han X, Guo S, Ji N, Li T, Liu J, Ye X, Wang Y, Yun Z, Xiong F, Rong J, Liu D, Ma H, Wang Y, Huang Y, Zhang P, Wu W, Ding L, Hawrylycz M, Lein E, Ascoli GA, Xie W, Liu L, Zhang L, Peng H. Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy. SCIENCE ADVANCES 2023; 9:eadf3771. [PMID: 37824619 PMCID: PMC10569712 DOI: 10.1126/sciadv.adf3771] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 04/18/2023] [Indexed: 10/14/2023]
Abstract
Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.
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Affiliation(s)
- Xiaofeng Han
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Tian Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xiangqiao Ye
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhixi Yun
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Feng Xiong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jing Rong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Di Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Hui Ma
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yujin Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yue Huang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhao Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Krasnow Institute for Advanced Studies and Bioengineering Department, College of Engineering and Computing, George Mason University, Fairfax, VA, USA
| | - Wei Xie
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
- The Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Lijuan Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
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2
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Miranda C, Howell MR, Lusk JF, Marschall E, Eshima J, Anderson T, Smith BS. Automated microscope-independent fluorescence-guided micropipette. BIOMEDICAL OPTICS EXPRESS 2021; 12:4689-4699. [PMID: 34513218 PMCID: PMC8407805 DOI: 10.1364/boe.431372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
Glass micropipette electrodes are commonly used to provide high resolution recordings of neurons. Although it is the gold standard for single cell recordings, it is highly dependent on the skill of the electrophysiologist. Here, we demonstrate a method of guiding micropipette electrodes to neurons by collecting fluorescence at the aperture, using an intra-electrode tapered optical fiber. The use of a tapered fiber for excitation and collection of fluorescence at the micropipette tip couples the feedback mechanism directly to the distance between the target and electrode. In this study, intra-electrode tapered optical fibers provide a targeted robotic approach to labeled neurons that is independent of microscopy.
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Affiliation(s)
- Christopher Miranda
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
| | - Madeleine R. Howell
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
| | - Joel F. Lusk
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
| | - Ethan Marschall
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
| | - Jarrett Eshima
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
| | - Trent Anderson
- University of Arizona, College of Medicine – Phoenix, Phoenix, AZ 85004, USA
| | - Barbara S. Smith
- Arizona State University, School of Biological and Health Systems Engineering, Tempe, AZ 85210, USA
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3
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Machine Learning-Based Pipette Positional Correction for Automatic Patch Clamp In Vitro. eNeuro 2021; 8:8/4/ENEURO.0051-21.2021. [PMID: 34312222 PMCID: PMC8318343 DOI: 10.1523/eneuro.0051-21.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 11/21/2022] Open
Abstract
Patch clamp electrophysiology is a common technique used in neuroscience to understand individual neuron behavior, allowing one to record current and voltage changes with superior spatiotemporal resolution compared with most electrophysiology methods. While patch clamp experiments produce high fidelity electrophysiology data, the technique is onerous and labor intensive. Despite the emergence of patch clamp systems that automate key stages in the typical patch clamp procedure, full automation remains elusive. Patch clamp pipettes can miss the target cell during automated experiments because of positioning errors in the robotic manipulators, which can easily exceed the diameter of a neuron. Further, when patching in acute brain slices, the inherent light scattering from non-uniform brain tissue can complicate pipette tip identification. We present a convolutional neural network (CNN), based on ResNet101, to identify and correct pipette positioning errors before each patch clamp attempt, thereby preventing the deleterious effects of and accumulation of positioning errors. This deep-learning-based pipette detection method enabled superior localization of the pipette within 0.62 ± 0.58 μm, resulting in improved cell detection success rate and whole-cell patch clamp success rates by 71% and 59%, respectively, compared with the state-of-the-art cross-correlation method. Furthermore, this technique reduced the average time for pipette correction by 81%. This technique enables real-time correction of pipette position during patch clamp experiments with similar accuracy and quality of recording to manual patch clamp, making notable progress toward full human-out-of-the-loop automation for patch clamp electrophysiology.
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4
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Song B, Kang CY, Han JH, Kano M, Konnerth A, Bae S. In vivo genome editing in single mammalian brain neurons through CRISPR-Cas9 and cytosine base editors. Comput Struct Biotechnol J 2021; 19:2477-2485. [PMID: 34025938 PMCID: PMC8113754 DOI: 10.1016/j.csbj.2021.04.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 10/31/2022] Open
Abstract
Gene manipulation is a useful approach for understanding functions of genes and is important for investigating basic mechanisms of brain function on the level of single neurons and circuits. Despite the development and the wide range of applications of CRISPR-Cas9 and base editors (BEs), their implementation for an analysis of individual neurons in vivo remained limited. In fact, conventional gene manipulations are generally achieved only on the population level. Here, we combined either CRISPR-Cas9 or BEs with the targeted single-cell electroporation technique as a proof-of-concept test for gene manipulation in single neurons in vivo. Our assay consisted of CRISPR-Cas9- or BEs-induced gene knockout in single Purkinje cells in the cerebellum. Our results demonstrate the feasibility of both gene editing and base editing in single cells in the intact brain, providing a tool through which molecular perturbations of individual neurons can be used for analysis of circuits and, ultimately, behaviors.
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Affiliation(s)
- Beomjong Song
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Chan Young Kang
- Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul 04763, South Korea
| | - Jun Hee Han
- Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul 04763, South Korea
| | - Masanobu Kano
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Arthur Konnerth
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Institute of Neuroscience, Technical University of Munich, 80802 Munich, Germany.,Munich Cluster for Systems Neurology, Technical University of Munich, 80802 Munich, Germany
| | - Sangsu Bae
- Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul 04763, South Korea
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5
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Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021; 5:203-218. [PMID: 33589781 PMCID: PMC8118147 DOI: 10.1038/s41551-020-00681-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 12/17/2020] [Indexed: 02/08/2023]
Abstract
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.
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Affiliation(s)
- Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Nicholas P Reder
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Kevin W Eliceiri
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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6
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Automatic deep learning-driven label-free image-guided patch clamp system. Nat Commun 2021; 12:936. [PMID: 33568670 PMCID: PMC7875980 DOI: 10.1038/s41467-021-21291-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 01/18/2021] [Indexed: 01/13/2023] Open
Abstract
Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model is trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements are performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research. Patch clamp recording of neurons is slow and labor-intensive. Here the authors present a method for automated deep learning driven label-free image guided patch clamp physiology to perform measurements on hundreds of human and rodent neurons.
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7
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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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8
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Neuronal cell-subtype specificity of neural synchronization in mouse primary visual cortex. Nat Commun 2019; 10:2533. [PMID: 31182715 PMCID: PMC6557841 DOI: 10.1038/s41467-019-10498-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 04/27/2019] [Indexed: 11/14/2022] Open
Abstract
Spatiotemporally synchronised neuronal activity is central to sensation, motion and cognition. Brain circuits consist of dynamically interconnected neuronal cell-types, thus elucidating how neuron types synergise within the network is key to understand the neuronal orchestra. Here we show that in neocortex neuron-network coupling is neuronal cell-subtype specific. Employing in vivo two-photon (2-p) Calcium (Ca) imaging and 2-p targeted whole-cell recordings, we cell-type specifically investigated the coupling profiles of genetically defined neuron populations in superficial layers (L) of mouse primary visual cortex (V1). Our data reveal novel subtlety of neuron-network coupling in inhibitory interneurons (INs). Parvalbumin (PV)- and Vasoactive intestinal peptide (VIP)-expressing INs exhibit skewed distributions towards strong network-coupling; in Somatostatin (SST)-expressing INs, however, two physiological subpopulations are identified with distinct neuron-network coupling profiles, providing direct evidence for subtype specificity. Our results thus add novel functional granularity to neuronal cell-typing, and provided insights critical to simplifying/understanding neural dynamics. Synchronised neuronal activity is essential for cortical function, yet mechanistic insights into this process remain limited. Here, authors use a combination of in vivo imaging and targeted whole-cell recordings to demonstrate that Somatostatin neurons, in the superficial layers of the mouse primary visual cortex, exhibit functional heterogeneity and can be classified into two distinct subtypes characterized as either having type I uncorrelated, or type II highly correlated with network activity.
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9
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Jayant K, Wenzel M, Bando Y, Hamm JP, Mandriota N, Rabinowitz JH, Plante IJL, Owen JS, Sahin O, Shepard KL, Yuste R. Flexible Nanopipettes for Minimally Invasive Intracellular Electrophysiology In Vivo. Cell Rep 2019; 26:266-278.e5. [PMID: 30605681 PMCID: PMC7263204 DOI: 10.1016/j.celrep.2018.12.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/23/2018] [Accepted: 12/04/2018] [Indexed: 12/01/2022] Open
Abstract
Intracellular recordings in vivo remains the best technique to link single-neuron electrical properties to network function. Yet existing methods are limited in accuracy, throughput, and duration, primarily via washout, membrane damage, and movement-induced failure. Here, we introduce flexible quartz nanopipettes (inner diameters of 10-25 nm and spring constant of ∼0.08 N/m) as nanoscale analogs of traditional glass microelectrodes. Nanopipettes enable stable intracellular recordings (seal resistances of 500 to ∼800 MΩ, 5 to ∼10 cells/nanopipette, and duration of ∼1 hr) in anaesthetized and awake head-restrained mice, exhibit minimal diffusional flux, and facilitate precise recording and stimulation. When combined with quantum-dot labels and microprisms, nanopipettes enable two-photon targeted electrophysiology from both somata and dendrites, and even paired recordings from neighboring neurons, while permitting simultaneous population imaging across cortical layers. We demonstrate the versatility of this method by recording from parvalbumin-positive (Pv) interneurons while imaging seizure propagation, and we find that Pv depolarization block coincides with epileptic spread. Flexible nanopipettes present a simple method to procure stable intracellular recordings in vivo.
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Affiliation(s)
- Krishna Jayant
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA; Department of Biological Sciences, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
| | - Michael Wenzel
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Yuki Bando
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Jordan P Hamm
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Nicola Mandriota
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA
| | - Jake H Rabinowitz
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | - Ilan Jen-La Plante
- Department of Chemistry, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA
| | - Jonathan S Owen
- Department of Chemistry, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA
| | - Ozgur Sahin
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; Department of Physics, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA
| | - Kenneth L Shepard
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Rafael Yuste
- Department of Biological Sciences, Columbia University, New York, NY 10027, USA; NeuroTechnology Center, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
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10
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Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Abbasi-Asl R, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-Llavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C. Visual physiology of the layer 4 cortical circuit in silico. PLoS Comput Biol 2018; 14:e1006535. [PMID: 30419013 PMCID: PMC6258373 DOI: 10.1371/journal.pcbi.1006535] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 11/26/2018] [Accepted: 09/29/2018] [Indexed: 01/15/2023] Open
Abstract
Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations. How can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.
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Affiliation(s)
- Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nathan W Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yazan N Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ramakrishnan Iyer
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Zihao Xu
- University of California San Diego, La Jolla, CA, United States of America
| | - Reza Abbasi-Asl
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Michael Buice
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nuno da Costa
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Saskia de Vries
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Denman
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Severine Durand
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jérôme Lecoq
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Lu Li
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Gabriel K Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | | | - Staci A Sorensen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Massimo Scanziani
- Howard Hughes Medical Institute and Department of Physiology, University of California San Francisco, San Francisco, California, United States of America
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, United States of America
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11
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Meijering E, Carpenter AE, Peng H, Hamprecht FA, Olivo-Marin JC. Imagining the future of bioimage analysis. Nat Biotechnol 2018; 34:1250-1255. [PMID: 27926723 DOI: 10.1038/nbt.3722] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 11/10/2016] [Indexed: 01/13/2023]
Affiliation(s)
- Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Fred A Hamprecht
- Heidelberg Collaboratory for Image Processing, Heidelberg University, Heidelberg, Germany
| | - Jean-Christophe Olivo-Marin
- Institut Pasteur, Unité d'Analyse d'Images Biologiques, Centre National de la Recherche Scientifique Unité de Recherche Mixte, Paris, France
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Annecchino LA, Schultz SR. Progress in automating patch clamp cellular physiology. Brain Neurosci Adv 2018; 2:2398212818776561. [PMID: 32166142 PMCID: PMC7058203 DOI: 10.1177/2398212818776561] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/19/2018] [Indexed: 12/30/2022] Open
Abstract
Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since their inception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address both voltage- and ligand-gated channels, and showing the potential to play a pivotal role in drug discovery and biomedical research. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologically relevant cellular environments, or capture higher order aspects of synaptic physiology and network dynamics. In vivo patch clamp electrophysiology has the potential to yield more biologically complex information and be especially useful in reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, while capturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it is a difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, in vivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we document the development of automated patch clamp technology, from early systems based on multi-well plates through to automated planar-array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cell electrophysiological recordings in vivo.
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Affiliation(s)
- Luca A. Annecchino
- Centre for Neurotechnology and Department of Bioengineering, Imperial College London, London, UK
| | - Simon R. Schultz
- Centre for Neurotechnology and Department of Bioengineering, Imperial College London, London, UK
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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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Annecchino LA, Morris AR, Copeland CS, Agabi OE, Chadderton P, Schultz SR. Robotic Automation of In Vivo Two-Photon Targeted Whole-Cell Patch-Clamp Electrophysiology. Neuron 2017; 95:1048-1055.e3. [PMID: 28858615 PMCID: PMC5584670 DOI: 10.1016/j.neuron.2017.08.018] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 06/12/2017] [Accepted: 08/11/2017] [Indexed: 12/30/2022]
Abstract
Whole-cell patch-clamp electrophysiological recording is a powerful technique for studying cellular function. While in vivo patch-clamp recording has recently benefited from automation, it is normally performed "blind," meaning that throughput for sampling some genetically or morphologically defined cell types is unacceptably low. One solution to this problem is to use two-photon microscopy to target fluorescently labeled neurons. Combining this with robotic automation is difficult, however, as micropipette penetration induces tissue deformation, moving target cells from their initial location. Here we describe a platform for automated two-photon targeted patch-clamp recording, which solves this problem by making use of a closed loop visual servo algorithm. Our system keeps the target cell in focus while iteratively adjusting the pipette approach trajectory to compensate for tissue motion. We demonstrate platform validation with patch-clamp recordings from a variety of cells in the mouse neocortex and cerebellum.
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Affiliation(s)
- Luca A Annecchino
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK
| | - Alexander R Morris
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK
| | - Caroline S Copeland
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK
| | - Oshiorenoya E Agabi
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK
| | - Paul Chadderton
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK
| | - Simon R Schultz
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London SW7 2AZ, UK.
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A robot for high yield electrophysiology and morphology of single neurons in vivo. Nat Commun 2017; 8:15604. [PMID: 28569837 PMCID: PMC5461495 DOI: 10.1038/ncomms15604] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 04/06/2017] [Indexed: 01/13/2023] Open
Abstract
Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure–function relationship and neuronal cell-type classification. Here we report a robot for efficiently performing in vivo single-cell experiments in deep brain tissues optically difficult to access. This robot automates blind (non-visually guided) single-cell electroporation (SCE) and extracellular electrophysiology, and can be used to characterize neuronal morphological and physiological properties of, and/or manipulate genetic/chemical contents via delivering extraneous materials (for example, genes) into single neurons in vivo. Tested in the mouse brain, our robot successfully reveals the full morphology of single-infragranular neurons recorded in multiple neocortical regions, as well as deep brain structures such as hippocampal CA3, with high efficiency. Our robot thus can greatly facilitate the study of in vivo full morphology and electrophysiology of single neurons in the brain. Single-cell characterization and perturbation of neurons is critical for revealing the structure-function relationship of brain cells. Here the authors develop a robot that performs single-cell electroporation and extracellular electrophysiology and can be used for performing in vivo single-cell experiments in deep brain tissues optically difficult to access.
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Wu 吴秋雨 Q, Kolb I, Callahan BM, Su Z, Stoy W, Kodandaramaiah SB, Neve R, Zeng H, Boyden ES, Forest CR, Chubykin AA. Integration of autopatching with automated pipette and cell detection in vitro. J Neurophysiol 2016; 116:1564-1578. [PMID: 27385800 DOI: 10.1152/jn.00386.2016] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 07/04/2016] [Indexed: 11/22/2022] Open
Abstract
Patch clamp is the main technique for measuring electrical properties of individual cells. Since its discovery in 1976 by Neher and Sakmann, patch clamp has been instrumental in broadening our understanding of the fundamental properties of ion channels and synapses in neurons. The conventional patch-clamp method requires manual, precise positioning of a glass micropipette against the cell membrane of a visually identified target neuron. Subsequently, a tight "gigaseal" connection between the pipette and the cell membrane is established, and suction is applied to establish the whole cell patch configuration to perform electrophysiological recordings. This procedure is repeated manually for each individual cell, making it labor intensive and time consuming. In this article we describe the development of a new automatic patch-clamp system for brain slices, which integrates all steps of the patch-clamp process: image acquisition through a microscope, computer vision-based identification of a patch pipette and fluorescently labeled neurons, micromanipulator control, and automated patching. We validated our system in brain slices from wild-type and transgenic mice expressing channelrhodopsin 2 under the Thy1 promoter (line 18) or injected with a herpes simplex virus-expressing archaerhodopsin, ArchT. Our computer vision-based algorithm makes the fluorescent cell detection and targeting user independent. Compared with manual patching, our system is superior in both success rate and average trial duration. It provides more reliable trial-to-trial control of the patching process and improves reproducibility of experiments.
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Affiliation(s)
- Qiuyu Wu 吴秋雨
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Ilya Kolb
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Brendan M Callahan
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Zhaolun Su
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - William Stoy
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Suhasa B Kodandaramaiah
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Mechanical Engineering, University of Minnesota-Twin Cities, Minneapolis, Minnesota; and
| | - Rachael Neve
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington
| | - Edward S Boyden
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Craig R Forest
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia; George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia
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