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Zhang Y, Rózsa M, Liang Y, Bushey D, Wei Z, Zheng J, Reep D, Broussard GJ, Tsang A, Tsegaye G, Narayan S, Obara CJ, Lim JX, Patel R, Zhang R, Ahrens MB, Turner GC, Wang SSH, Korff WL, Schreiter ER, Svoboda K, Hasseman JP, Kolb I, Looger LL. Fast and sensitive GCaMP calcium indicators for imaging neural populations. Nature 2023; 615:884-891. [PMID: 36922596 PMCID: PMC10060165 DOI: 10.1038/s41586-023-05828-9] [Citation(s) in RCA: 160] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/10/2023] [Indexed: 03/17/2023]
Abstract
Calcium imaging with protein-based indicators1,2 is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators3-8. The resulting 'jGCaMP8' sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.
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Affiliation(s)
- Yan Zhang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Márton Rózsa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Yajie Liang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel Bushey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jihong Zheng
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Daniel Reep
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Arthur Tsang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Getahun Tsegaye
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sujatha Narayan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | | | - Jing-Xuan Lim
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Ronak Patel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Rongwei Zhang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Glenn C Turner
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Samuel S-H Wang
- Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | - Wyatt L Korff
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Eric R Schreiter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Allen Institute for Neural Dynamics, Seattle, WA, USA.
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Jeremy P Hasseman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| | - Ilya Kolb
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Loren L Looger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Genetically Encoded Neural Indicator and Effector (GENIE) Project, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.
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2
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High-speed imaging of light-induced photoreceptor microsaccades in compound eyes. Commun Biol 2022; 5:203. [PMID: 35241794 PMCID: PMC8894348 DOI: 10.1038/s42003-022-03142-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 02/09/2022] [Indexed: 11/09/2022] Open
Abstract
Inside compound eyes, photoreceptors contract to light changes, sharpening retinal images of the moving world in time. Current methods to measure these so-called photoreceptor microsaccades in living insects are spatially limited and technically challenging. Here, we present goniometric high-speed deep pseudopupil (GHS-DPP) microscopy to assess how the rhabdomeric insect photoreceptors and their microsaccades are organised across the compound eyes. This method enables non-invasive rhabdomere orientation mapping, whilst their microsaccades can be locally light-activated, revealing the eyes' underlying active sampling motifs. By comparing the microsaccades in wild-type Drosophila's open rhabdom eyes to spam-mutant eyes, reverted to an ancestral fused rhabdom state, and honeybee's fused rhabdom eyes, we show how different eye types sample light information. These results show different ways compound eyes initiate the conversion of spatial light patterns in the environment into temporal neural signals and highlight how this active sampling can evolve with insects' visual needs.
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Song Z, Zhou Y, Feng J, Juusola M. Multiscale 'whole-cell' models to study neural information processing - New insights from fly photoreceptor studies. J Neurosci Methods 2021; 357:109156. [PMID: 33775669 DOI: 10.1016/j.jneumeth.2021.109156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 11/26/2022]
Abstract
Understanding a neuron's input-output relationship is a longstanding challenge. Arguably, these signalling dynamics can be better understood if studied at three levels of analysis: computational, algorithmic and implementational (Marr, 1982). But it is difficult to integrate such analyses into a single platform that can realistically simulate neural information processing. Multiscale dynamical "whole-cell" modelling, a recent systems biology approach, makes this possible. Dynamical "whole-cell" models are computational models that aim to account for the integrated function of numerous genes or molecules to behave like virtual cells in silico. However, because constructing such models is laborious, only a couple of examples have emerged since the first one, built for Mycoplasma genitalium bacterium, was reported in 2012. Here, we review dynamic "whole-cell" neuron models for fly photoreceptors and how these have been used to study neural information processing. Specifically, we review how the models have helped uncover the mechanisms and evolutionary rules of quantal light information sampling and integration, which underlie light adaptation and further improve our understanding of insect vision.
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Affiliation(s)
- Zhuoyi Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Shanghai, China.
| | - Yu Zhou
- School of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Mikko Juusola
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, UK; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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Lewallen CF, Wan Q, Maminishkis A, Stoy W, Kolb I, Hotaling N, Bharti K, Forest CR. High-yield, automated intracellular electrophysiology in retinal pigment epithelia. J Neurosci Methods 2019; 328:108442. [PMID: 31562888 PMCID: PMC7071944 DOI: 10.1016/j.jneumeth.2019.108442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/20/2019] [Accepted: 09/24/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND Recent advancements with induced pluripotent stem cell-derived (iPSC) retinal pigment epithelium (RPE) have made disease modeling and cell therapy for macular degeneration feasible. However, current techniques for intracellular electrophysiology - used to validate epithelial function - are painstaking and require manual skill; limiting experimental throughput. NEW METHOD A five-stage algorithm, leveraging advances in automated patch clamping, systematically derived and optimized, improves yield and reduces skill when compared to conventional, manual techniques. RESULTS The automated algorithm improves yield per attempt from 17% (manually, n = 23) to 22% (automated, n = 120) (chi-squared, p = 0.004). Specifically for RPE, depressing the local cell membrane by 6 μm and electroporating (buzzing) just prior to this depth (5 μm) maximized yield. COMPARISON WITH EXISTING METHOD Conventionally, intracellular epithelial electrophysiology is performed by manually lowering a pipette with a micromanipulator, blindly, towards a monolayer of cells and spontaneously stopping when the magnitude of the instantaneous measured membrane potential decreased below a predetermined threshold. The new method automatically measures the pipette tip resistance during the descent, detects the cell surface, indents the cell membrane, and briefly buzzes to electroporate the membrane while descending, overall achieving a higher yield than conventional methods. CONCLUSIONS This paper presents an algorithm for high-yield, automated intracellular electrophysiology in epithelia; optimized for human RPE. Automation reduces required user skill and training while, simultaneously, improving yield. This algorithm could enable large-scale exploration of drug toxicity and physiological function verification for numerous kinds of epithelia.
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Affiliation(s)
- Colby F Lewallen
- Georgia Institute of Technology, G.W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA.
| | - Qin Wan
- National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Arvydas Maminishkis
- National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William Stoy
- Georgia Institute of Technology, Wallace H Coulter Department of Biomedical Engineering, Atlanta, GA 30332, USA
| | - Ilya Kolb
- Georgia Institute of Technology, Wallace H Coulter Department of Biomedical Engineering, Atlanta, GA 30332, USA; HHMI Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA 20147, USA
| | - Nathan Hotaling
- National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kapil Bharti
- National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Craig R Forest
- Georgia Institute of Technology, G.W. Woodruff School of Mechanical Engineering, Atlanta, GA 30332, USA
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5
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Li X, Abou Tayoun A, Song Z, Dau A, Rien D, Jaciuch D, Dongre S, Blanchard F, Nikolaev A, Zheng L, Bollepalli MK, Chu B, Hardie RC, Dolph PJ, Juusola M. Ca 2+-Activated K + Channels Reduce Network Excitability, Improving Adaptability and Energetics for Transmitting and Perceiving Sensory Information. J Neurosci 2019; 39:7132-7154. [PMID: 31350259 PMCID: PMC6733542 DOI: 10.1523/jneurosci.3213-18.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 05/28/2019] [Accepted: 05/31/2019] [Indexed: 11/21/2022] Open
Abstract
Ca2+-activated K+ channels (BK and SK) are ubiquitous in synaptic circuits, but their role in network adaptation and sensory perception remains largely unknown. Using electrophysiological and behavioral assays and biophysical modeling, we discover how visual information transfer in mutants lacking the BK channel (dSlo- ), SK channel (dSK- ), or both (dSK- ;; dSlo- ) is shaped in the female fruit fly (Drosophila melanogaster) R1-R6 photoreceptor-LMC circuits (R-LMC-R system) through synaptic feedforward-feedback interactions and reduced R1-R6 Shaker and Shab K+ conductances. This homeostatic compensation is specific for each mutant, leading to distinctive adaptive dynamics. We show how these dynamics inescapably increase the energy cost of information and promote the mutants' distorted motion perception, determining the true price and limits of chronic homeostatic compensation in an in vivo genetic animal model. These results reveal why Ca2+-activated K+ channels reduce network excitability (energetics), improving neural adaptability for transmitting and perceiving sensory information.SIGNIFICANCE STATEMENT In this study, we directly link in vivo and ex vivo experiments with detailed stochastically operating biophysical models to extract new mechanistic knowledge of how Drosophila photoreceptor-interneuron-photoreceptor (R-LMC-R) circuitry homeostatically retains its information sampling and transmission capacity against chronic perturbations in its ion-channel composition, and what is the cost of this compensation and its impact on optomotor behavior. We anticipate that this novel approach will provide a useful template to other model organisms and computational neuroscience, in general, in dissecting fundamental mechanisms of homeostatic compensation and deepening our understanding of how biological neural networks work.
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Affiliation(s)
- Xiaofeng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Ahmad Abou Tayoun
- Department of Biology, Dartmouth College, Hanover, New Hampshire 03755
| | - Zhuoyi Song
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China, and
| | - An Dau
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Diana Rien
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - David Jaciuch
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Sidhartha Dongre
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Florence Blanchard
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Anton Nikolaev
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Lei Zheng
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Murali K Bollepalli
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China, and
- Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge CB2 3DY, United Kingdom
| | - Brian Chu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China, and
- Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge CB2 3DY, United Kingdom
| | - Roger C Hardie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, and Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China, and
- Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge CB2 3DY, United Kingdom
| | - Patrick J Dolph
- Department of Biology, Dartmouth College, Hanover, New Hampshire 03755,
| | - Mikko Juusola
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China,
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom
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6
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Richter FG, Fendl S, Haag J, Drews MS, Borst A. Glutamate Signaling in the Fly Visual System. iScience 2018; 7:85-95. [PMID: 30267688 PMCID: PMC6135900 DOI: 10.1016/j.isci.2018.08.019] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 08/13/2018] [Accepted: 08/23/2018] [Indexed: 10/28/2022] Open
Abstract
For a proper understanding of neural circuit function, it is important to know which signals neurons relay to their downstream partners. Calcium imaging with genetically encoded calcium sensors like GCaMP has become the default approach for mapping these responses. How well such measurements represent the true neurotransmitter output of any given cell, however, remains unclear. Here, we demonstrate the viability of the glutamate sensor iGluSnFR for 2-photon in vivo imaging in Drosophila melanogaster and prove its usefulness for estimating spatiotemporal receptive fields in the visual system. We compare the results obtained with iGluSnFR with the ones obtained with GCaMP6f and find that the spatial aspects of the receptive fields are preserved between indicators. In the temporal domain, however, measurements obtained with iGluSnFR reveal the underlying response properties to be much faster than those acquired with GCaMP6f. Our approach thus offers a more accurate description of glutamatergic neurons in the fruit fly.
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Affiliation(s)
| | - Sandra Fendl
- Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany
| | - Jürgen Haag
- Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany
| | - Michael S Drews
- Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany
| | - Alexander Borst
- Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany.
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7
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Juusola M, Dau A, Song Z, Solanki N, Rien D, Jaciuch D, Dongre SA, Blanchard F, de Polavieja GG, Hardie RC, Takalo J. Microsaccadic sampling of moving image information provides Drosophila hyperacute vision. eLife 2017; 6:26117. [PMID: 28870284 PMCID: PMC5584993 DOI: 10.7554/elife.26117] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 07/25/2017] [Indexed: 11/13/2022] Open
Abstract
Small fly eyes should not see fine image details. Because flies exhibit saccadic visual behaviors and their compound eyes have relatively few ommatidia (sampling points), their photoreceptors would be expected to generate blurry and coarse retinal images of the world. Here we demonstrate that Drosophila see the world far better than predicted from the classic theories. By using electrophysiological, optical and behavioral assays, we found that R1-R6 photoreceptors’ encoding capacity in time is maximized to fast high-contrast bursts, which resemble their light input during saccadic behaviors. Whilst over space, R1-R6s resolve moving objects at saccadic speeds beyond the predicted motion-blur-limit. Our results show how refractory phototransduction and rapid photomechanical photoreceptor contractions jointly sharpen retinal images of moving objects in space-time, enabling hyperacute vision, and explain how such microsaccadic information sampling exceeds the compound eyes’ optical limits. These discoveries elucidate how acuity depends upon photoreceptor function and eye movements. Fruit flies have five eyes: two large compound eyes which support vision, plus three smaller single lens eyes which are used for navigation. Each compound eye monitors 180° of space and consists of roughly 750 units, each containing eight light-sensitive cells called photoreceptors. This relatively wide spacing of photoreceptors is thought to limit the sharpness, or acuity, of vision in fruit flies. The area of the human retina (the light-sensitive surface at back of our eyes) that generates our sharpest vision contains photoreceptors that are 500 times more densely packed. Despite their differing designs, human and fruit fly eyes work via the same general principles. If we, or a fruit fly, were to hold our gaze completely steady, the world would gradually fade from view as the eye adapted to the unchanging visual stimulus. To ensure this does not happen, animals continuously make rapid, automatic eye movements called microsaccades. These refresh the image on the retina and prevent it from fading. Yet it is not known why do they not also cause blurred vision. Standard accounts of vision assume that the retina and the brain perform most of the information processing required, with photoreceptors simply detecting how much light enters the eye. However, Juusola, Dau, Song et al. now challenge this idea by showing that photoreceptors are specially adapted to detect the fluctuating patterns of light that enter the eye as a result of microsaccades. Moreover, fruit fly eyes resolve small moving objects far better than would be predicted based on the spacing of their photoreceptors. The discovery that photoreceptors are well adapted to deal with eye movements changes our understanding of insect vision. The findings also disprove the 100-year-old dogma that the spacing of photoreceptors limits the sharpness of vision in compound eyes. Further studies are required to determine whether photoreceptors in the retinas of other animals, including humans, have similar properties.
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Affiliation(s)
- Mikko Juusola
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - An Dau
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Zhuoyi Song
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Narendra Solanki
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Diana Rien
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - David Jaciuch
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Sidhartha Anil Dongre
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Florence Blanchard
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
| | - Gonzalo G de Polavieja
- Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
| | - Roger C Hardie
- Department of Physiology Development and Neuroscience, Cambridge University, Cambridge, United Kingdom
| | - Jouni Takalo
- Department of Biomedical Science, University of Sheffield, Sheffield, United Kingdom
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8
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Juusola M, Song Z. How a fly photoreceptor samples light information in time. J Physiol 2017; 595:5427-5437. [PMID: 28233315 PMCID: PMC5556158 DOI: 10.1113/jp273645] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/17/2017] [Indexed: 11/08/2022] Open
Abstract
A photoreceptor's information capture is constrained by the structure and function of its light‐sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white‐noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.
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Affiliation(s)
- Mikko Juusola
- Department of Biomedical Science, University of Sheffield, Sheffield, S10 T2N, UK.,National Key laboratory of Cognitive Neuroscience and Learning, Beijing, Beijing Normal University, Beijing, 100875, China
| | - Zhuoyi Song
- Department of Biomedical Science, University of Sheffield, Sheffield, S10 T2N, UK
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