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Baldassano JF, MacLeod KM. Kv1 channels regulate variations in spike patterning and temporal reliability in the avian cochlear nucleus angularis. J Neurophysiol 2022; 127:116-129. [PMID: 34817286 PMCID: PMC8742726 DOI: 10.1152/jn.00460.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Diverse physiological phenotypes in a neuronal population can broaden the range of computational capabilities within a brain region. The avian cochlear nucleus angularis (NA) contains a heterogeneous population of neurons whose variation in intrinsic properties results in electrophysiological phenotypes with a range of sensitivities to temporally modulated input. The low-threshold potassium conductance (GKLT) is a key feature of neurons involved in fine temporal structure coding for sound localization, but a role for these channels in intensity or spectrotemporal coding has not been established. To determine whether GKLT affects the phenotypical variation and temporal properties of NA neurons, we applied dendrotoxin-I (DTX), a potent antagonist of Kv1-type potassium channels, to chick brain stem slices in vitro during whole cell patch-clamp recordings. We found a cell-type specific subset of NA neurons that was sensitive to DTX: single-spiking NA neurons were most profoundly affected, as well as a subset of tonic-firing neurons. Both tonic I (phasic onset bursting) and tonic II (delayed firing) neurons showed DTX sensitivity in their firing rate and phenotypical firing pattern. Tonic III neurons were unaffected. Spike time reliability and fluctuation sensitivity measured in DTX-sensitive NA neurons was also reduced with DTX. Finally, DTX reduced spike threshold adaptation in these neurons, suggesting that GKLT contributes to the temporal properties that allow coding of rapid changes in the inputs to NA neurons. These results suggest that variation in Kv1 channel expression may be a key factor in functional diversity in the avian cochlear nucleus.NEW & NOTEWORTHY The dendrotoxin-sensitive voltage-gated potassium conductance typically associated with neuronal coincidence detection in the timing pathway for sound localization is demonstrated to affect spiking patterns and temporal input sensitivity in the intensity pathway in the avian auditory brain stem. The Kv1-family channels appear to be present in a subset of cochlear nucleus angularis neurons, regulate spike threshold dynamics underlying high-pass membrane filtering, and contribute to intrinsic firing diversity.
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Brown DH, Hyson RL. Intrinsic physiological properties underlie auditory response diversity in the avian cochlear nucleus. J Neurophysiol 2019; 121:908-927. [PMID: 30649984 DOI: 10.1152/jn.00459.2018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Sensory systems exploit parallel processing of stimulus features to enable rapid, simultaneous extraction of information. Mechanisms that facilitate this differential extraction of stimulus features can be intrinsic or synaptic in origin. A subdivision of the avian cochlear nucleus, nucleus angularis (NA), extracts sound intensity information from the auditory nerve and contains neurons that exhibit diverse responses to sound and current injection. NA neurons project to multiple regions ascending the auditory brain stem including the superior olivary nucleus, lateral lemniscus, and avian inferior colliculus, with functional implications for inhibitory gain control and sound localization. Here we investigated whether the diversity of auditory response patterns in NA can be accounted for by variation in intrinsic physiological features. Modeled sound-evoked auditory nerve input was applied to NA neurons with dynamic clamp during in vitro whole cell recording at room temperature. Temporal responses to auditory nerve input depended on variation in intrinsic properties, and the low-threshold K+ current was implicated as a major contributor to temporal response diversity and neuronal input-output functions. An auditory nerve model of acoustic amplitude modulation produced synchrony coding of modulation frequency that depended on the intrinsic physiology of the individual neuron. In Primary-Like neurons, varying low-threshold K+ conductance with dynamic clamp altered temporal modulation tuning bidirectionally. Taken together, these data suggest that intrinsic physiological properties play a key role in shaping auditory response diversity to both simple and more naturalistic auditory stimuli in the avian cochlear nucleus. NEW & NOTEWORTHY This article addresses the question of how the nervous system extracts different information in sounds. Neurons in the cochlear nucleus show diverse responses to acoustic stimuli that may allow for parallel processing of acoustic features. The present studies suggest that diversity in intrinsic physiological features of individual neurons, including levels of a low voltage-activated K+ current, play a major role in regulating the diversity of auditory responses.
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Affiliation(s)
- David H Brown
- Program in Neuroscience, Department of Psychology, Florida State University , Tallahassee, Florida
| | - Richard L Hyson
- Program in Neuroscience, Department of Psychology, Florida State University , Tallahassee, Florida
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Ahn JH, Park JH, Choi SY, Lee TK, Cho JH, Kim IH, Lee JC, Choi JH, Hwang IK, Lee E, Park S, Lim J, Lee YJ, Seo K, Won MH. The distribution of calbindinD-28k and parvalbumin immunoreactive neurons in the somatosensory area of the pigeon pallium. Anat Histol Embryol 2017; 47:64-70. [DOI: 10.1111/ahe.12325] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Accepted: 10/21/2017] [Indexed: 12/15/2022]
Affiliation(s)
- J. H. Ahn
- Department of Biomedical Science and Research Institute for Bioscience and Biotechnology; Hallym University; Chuncheon South Korea
| | - J. H. Park
- Department of Biomedical Science and Research Institute for Bioscience and Biotechnology; Hallym University; Chuncheon South Korea
| | - S. Y. Choi
- Department of Biomedical Science and Research Institute for Bioscience and Biotechnology; Hallym University; Chuncheon South Korea
| | - T.-K. Lee
- Department of Neurobiology; School of Medicine; Kangwon National University; Chuncheon South Korea
| | - J. H. Cho
- Department of Neurobiology; School of Medicine; Kangwon National University; Chuncheon South Korea
| | - I. H. Kim
- Department of Neurobiology; School of Medicine; Kangwon National University; Chuncheon South Korea
| | - J.-C. Lee
- Department of Neurobiology; School of Medicine; Kangwon National University; Chuncheon South Korea
| | - J. H. Choi
- Department of Anatomy; College of Veterinary Medicine; Kangwon National University; Chuncheon South Korea
| | - I. K. Hwang
- Department of Anatomy and Cell Biology; College of Veterinary Medicine; Research Institute for Veterinary Science; Seoul National University; Seoul South Korea
| | - E. Lee
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Research Institute for Veterinary Science; Seoul National University; Seoul South Korea
| | - S. Park
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Research Institute for Veterinary Science; Seoul National University; Seoul South Korea
| | - J. Lim
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Research Institute for Veterinary Science; Seoul National University; Seoul South Korea
| | - Y. J. Lee
- Department of Emergency Medicine; Seoul Hospital; College of Medicine; Sooncheonhyang University; Seoul South Korea
| | - K. Seo
- Department of Veterinary Clinical Sciences; College of Veterinary Medicine; Research Institute for Veterinary Science; Seoul National University; Seoul South Korea
| | - M.-H. Won
- Department of Neurobiology; School of Medicine; Kangwon National University; Chuncheon South Korea
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Distinct Neural Properties in the Low-Frequency Region of the Chicken Cochlear Nucleus Magnocellularis. eNeuro 2017; 4:eN-NWR-0016-17. [PMID: 28413822 PMCID: PMC5388668 DOI: 10.1523/eneuro.0016-17.2017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 02/17/2017] [Accepted: 03/05/2017] [Indexed: 12/03/2022] Open
Abstract
Topography in the avian cochlear nucleus magnocellularis (NM) is represented as gradually increasing characteristic frequency (CF) along the caudolateral-to-rostromedial axis. In this study, we characterized the organization and cell biophysics of the caudolateral NM (NMc) in chickens (Gallus gallus). Examination of cellular and dendritic architecture first revealed that NMc contains small neurons and extensive dendritic processes, in contrast to adendritic, large neurons located more rostromedially. Individual dye-filling study further demonstrated that NMc is divided into two subregions, with NMc2 neurons having larger and more complex dendritic fields than NMc1. Axonal tract tracing studies confirmed that NMc1 and NMc2 neurons receive afferent inputs from the auditory nerve and the superior olivary nucleus, similar to the adendritic NM. However, the auditory axons synapse with NMc neurons via small bouton-like terminals, unlike the large end bulb synapses on adendritic NM neurons. Immunocytochemistry demonstrated that most NMc2 neurons express cholecystokinin but not calretinin, distinct from NMc1 and adendritic NM neurons that are cholecystokinin negative and mostly calretinin positive. Finally, whole-cell current clamp recordings revealed that NMc neurons require significantly lower threshold current for action potential generation than adendritic NM neurons. Moreover, in contrast to adendritic NM neurons that generate a single-onset action potential, NMc neurons generate multiple action potentials to suprathreshold sustained depolarization. Taken together, our data indicate that NMc contains multiple neuron types that are structurally, connectively, molecularly, and physiologically different from traditionally defined NM neurons, emphasizing specialized neural properties for processing low-frequency sounds.
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Murray E, Cho JH, Goodwin D, Ku T, Swaney J, Kim SY, Choi H, Park YG, Park JY, Hubbert A, McCue M, Vassallo S, Bakh N, Frosch MP, Wedeen VJ, Seung HS, Chung K. Simple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems. Cell 2016; 163:1500-14. [PMID: 26638076 DOI: 10.1016/j.cell.2015.11.025] [Citation(s) in RCA: 292] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 10/09/2015] [Accepted: 11/10/2015] [Indexed: 01/25/2023]
Abstract
Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.
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Affiliation(s)
- Evan Murray
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jae Hun Cho
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Daniel Goodwin
- Simons Center for Data Analysis, 160 Fifth Avenue, 8th Floor, New York, NY 10010, USA
| | - Taeyun Ku
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Justin Swaney
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sung-Yon Kim
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Heejin Choi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jeong-Yoon Park
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Austin Hubbert
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Margaret McCue
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sara Vassallo
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Naveed Bakh
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory of Neuropathology, Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Van J Wedeen
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - H Sebastian Seung
- Simons Center for Data Analysis, 160 Fifth Avenue, 8th Floor, New York, NY 10010, USA; Princeton Neuroscience Institute and Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Kwanghun Chung
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard University and Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
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