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McNulty P, Wu R, Yamaguchi A, Heckscher ES, Haas A, Nwankpa A, Skanata MM, Gershow M. CRASH2p: Closed-loop Two Photon Imaging in Freely Moving Animals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595209. [PMID: 38826435 PMCID: PMC11142166 DOI: 10.1101/2024.05.22.595209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Direct measurement of neural activity in freely moving animals is essential for understanding how the brain controls and represents behaviors. Genetically encoded calcium indicators report neural activity as changes in fluorescence intensity, but brain motion confounds quantitative measurement of fluorescence. Translation, rotation, and deformation of the brain and the movements of intervening scattering or auto-fluorescent tissue all alter the amount of fluorescent light captured by a microscope. Compared to single-photon approaches, two photon microscopy is less sensitive to scattering and off-target fluorescence, but more sensitive to motion, and two photon imaging has always required anchoring the microscope to the brain. We developed a closed-loop resonant axial-scanning high-speed two photon (CRASH2p) microscope for real-time 3D motion correction in unrestrained animals, without implantation of reference markers. We complemented CRASH2p with a novel scanning strategy and a multistage registration pipeline. We performed volumetric ratiometrically corrected functional imaging in the CNS of freely moving Drosophila larvae and discovered previously unknown neural correlates of behavior.
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Affiliation(s)
- Paul McNulty
- Department of Physics,New York University, New York, USA
| | - Rui Wu
- Department of Physics,New York University, New York, USA
| | | | - Ellie S. Heckscher
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL
| | - Andrew Haas
- Department of Physics,New York University, New York, USA
| | | | | | - Marc Gershow
- Department of Physics,New York University, New York, USA
- Center for Neural Science,New York University, New York, USA
- Neuroscience Institute, New York University, New York, USA
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2
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Eom M, Han S, Park P, Kim G, Cho ES, Sim J, Lee KH, Kim S, Tian H, Böhm UL, Lowet E, Tseng HA, Choi J, Lucia SE, Ryu SH, Rózsa M, Chang S, Kim P, Han X, Piatkevich KD, Choi M, Kim CH, Cohen AE, Chang JB, Yoon YG. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat Methods 2023; 20:1581-1592. [PMID: 37723246 PMCID: PMC10555843 DOI: 10.1038/s41592-023-02005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 08/10/2023] [Indexed: 09/20/2023]
Abstract
Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.
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Affiliation(s)
- Minho Eom
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Seungjae Han
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Pojeong Park
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Gyuri Kim
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Eun-Seo Cho
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Jueun Sim
- Department of Materials Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Kang-Han Lee
- Department of Biology, Chungnam National University, Daejeon, Republic of Korea
| | - Seonghoon Kim
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - He Tian
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Urs L Böhm
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité University of Medicine Berlin, Berlin, Germany
| | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jieun Choi
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Stephani Edwina Lucia
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Seung Hyun Ryu
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Republic of Korea
| | - Márton Rózsa
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Sunghoe Chang
- Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Pilhan Kim
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
- Graduate School of Nanoscience and Technology, KAIST, Daejeon, Republic of Korea
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Kiryl D Piatkevich
- Research Center for Industries of the Future and School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Myunghwan Choi
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - Cheol-Hee Kim
- Department of Biology, Chungnam National University, Daejeon, Republic of Korea
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Jae-Byum Chang
- Department of Materials Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Young-Gyu Yoon
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea.
- Department of Semiconductor System Engineering, KAIST, Daejeon, Republic of Korea.
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Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat Methods 2021; 18:1395-1400. [PMID: 34400836 DOI: 10.1038/s41592-021-01225-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 06/28/2021] [Indexed: 12/11/2022]
Abstract
Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits.
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Zhang Z, Cong L, Bai L, Wang K. Light-field microscopy for fast volumetric brain imaging. J Neurosci Methods 2021; 352:109083. [PMID: 33484746 DOI: 10.1016/j.jneumeth.2021.109083] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/23/2020] [Accepted: 01/14/2021] [Indexed: 01/06/2023]
Abstract
Recording neural activities over large populations is critical for a better understanding of the functional mechanisms of animal brains. Traditional optical imaging technologies for in vivo neural activity recording are usually limited in throughput and cannot cover a large imaging volume at high speed. Light-field microscopy features a highly parallelized imaging collection mechanism and can simultaneously record optical signals from different depths. Therefore, it can potentially increase the imaging throughput substantially. Furthermore, its unique instantaneous volumetric imaging capability enables the capture of highly dynamic processes, such as recording whole-animal neural activities in freely moving Caenorhabditis elegans and whole-brain neural activity in freely swimming larval zebrafish during prey capture. Here, we summarize the principles of and considerations in the practical implementation of light-field microscopy as currently applied in biological imaging experiments. We also discuss the strategies that light-field microscopy can employ when imaging thick tissues in the presence of scattering and background interference. Finally, we present a few examples of applying light-field microscopy in neuroscientific studies in several important animal models.
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Affiliation(s)
- Zhenkun Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lin Cong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lu Bai
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 201210, China.
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Biswas T, Bishop WE, Fitzgerald JE. Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings. Curr Opin Neurobiol 2020; 65:138-145. [PMID: 33248437 PMCID: PMC8754199 DOI: 10.1016/j.conb.2020.10.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 11/24/2022]
Abstract
Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.
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Affiliation(s)
- Tirthabir Biswas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - William E Bishop
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - James E Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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Fu Z, Xiang J. Aptamers, the Nucleic Acid Antibodies, in Cancer Therapy. Int J Mol Sci 2020; 21:ijms21082793. [PMID: 32316469 PMCID: PMC7215806 DOI: 10.3390/ijms21082793] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 04/09/2020] [Accepted: 04/15/2020] [Indexed: 02/06/2023] Open
Abstract
The arrival of the monoclonal antibody (mAb) technology in the 1970s brought with it the hope of conquering cancers to the medical community. However, mAbs, on the whole, did not achieve the expected wonder in cancer therapy although they do have demonstrated successfulness in the treatment of a few types of cancers. In 1990, another technology of making biomolecules capable of specific binding appeared. This technique, systematic evolution of ligands by exponential enrichment (SELEX), can make aptamers, single-stranded DNAs or RNAs that bind targets with high specificity and affinity. Aptamers have some advantages over mAbs in therapeutic uses particularly because they have little or no immunogenicity, which means the feasibility of repeated use and fewer side effects. In this review, the general properties of the aptamer, the advantages and limitations of aptamers, the principle and procedure of aptamer production with SELEX, particularly the undergoing studies in aptamers for cancer therapy, and selected anticancer aptamers that have entered clinical trials or are under active investigations are summarized.
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Affiliation(s)
- Zhaoying Fu
- Department of Biochemistry and Molecular Biology, College of Medicine, Yanan University, Yanan 716000, China
- Correspondence: (Z.F.); (J.X.)
| | - Jim Xiang
- Division of Oncology, University of Saskatchewan, Saskatoon, SA S7N 4H4, Canada
- Correspondence: (Z.F.); (J.X.)
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Demir E, Yaman YI, Basaran M, Kocabas A. Dynamics of pattern formation and emergence of swarming in Caenorhabditis elegans. eLife 2020; 9:52781. [PMID: 32250243 PMCID: PMC7202895 DOI: 10.7554/elife.52781] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 04/05/2020] [Indexed: 01/10/2023] Open
Abstract
Many animals collectively form complex patterns to tackle environmental difficulties. Several biological and physical factors, such as animal motility, population densities, and chemical cues, play significant roles in this process. However, very little is known about how sensory information interplays with these factors and controls the dynamics of pattern formation. Here, we study the direct relation between oxygen sensing, pattern formation, and emergence of swarming in active Caenorhabditis elegans aggregates. We find that when thousands of animals gather on food, bacteria-mediated decrease in oxygen level slows down the animals and triggers motility-induced phase separation. Three coupled factors—bacterial accumulation, aerotaxis, and population density—act together and control the entire dynamics. Furthermore, we find that biofilm-forming bacterial lawns including Bacillus subtilis and Pseudomonas aeruginosa strongly alter the collective dynamics due to the limited diffusibility of bacteria. Additionally, our theoretical model captures behavioral differences resulting from genetic variations and oxygen sensitivity.
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Affiliation(s)
- Esin Demir
- Bio-Medical Sciences and Engineering Program, Koç University, Sarıyer, Istanbul, Turkey
| | - Y Ilker Yaman
- Department of Physics, Koç University, Sarıyer, Istanbul, Turkey
| | - Mustafa Basaran
- Bio-Medical Sciences and Engineering Program, Koç University, Sarıyer, Istanbul, Turkey
| | - Askin Kocabas
- Bio-Medical Sciences and Engineering Program, Koç University, Sarıyer, Istanbul, Turkey.,Department of Physics, Koç University, Sarıyer, Istanbul, Turkey.,Koç University Surface Science and Technology Center, Koç University, Sarıyer, Istanbul, Turkey.,Koç University Research Center for Translational Medicine, Koç University, Sarıyer, Istanbul, Turkey
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8
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Valle AF, Seelig JD. Two-photon Bessel beam tomography for fast volume imaging. OPTICS EXPRESS 2019; 27:12147-12162. [PMID: 31052759 DOI: 10.1364/oe.27.012147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 04/01/2019] [Indexed: 06/09/2023]
Abstract
Light microscopy on dynamic samples, for example neural activity in the brain, often requires imaging volumes that extend over several 100 µm in axial direction at a rate of at least several tens of Hertz. Here, we develop a tomography approach for scanning fluorescence microscopy which allows recording a volume image in a single frame scan. Volumes are imaged by simultaneously recording four independent projections at different angles using temporally multiplexed, tilted Bessel beams. From the resulting projections, three-dimensional images are reconstructed using inverse Radon transforms combined with convolutional neural networks (U-net).
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