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Mizuta K, Sato M. Multiphoton imaging of hippocampal neural circuits: techniques and biological insights into region-, cell-type-, and pathway-specific functions. NEUROPHOTONICS 2024; 11:033406. [PMID: 38464393 PMCID: PMC10923542 DOI: 10.1117/1.nph.11.3.033406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 03/12/2024]
Abstract
Significance The function of the hippocampus in behavior and cognition has long been studied primarily through electrophysiological recordings from freely moving rodents. However, the application of optical recording methods, particularly multiphoton fluorescence microscopy, in the last decade or two has dramatically advanced our understanding of hippocampal function. This article provides a comprehensive overview of techniques and biological findings obtained from multiphoton imaging of hippocampal neural circuits. Aim This review aims to summarize and discuss the recent technical advances in multiphoton imaging of hippocampal neural circuits and the accumulated biological knowledge gained through this technology. Approach First, we provide a brief overview of various techniques of multiphoton imaging of the hippocampus and discuss its advantages, drawbacks, and associated key innovations and practices. Then, we review a large body of findings obtained through multiphoton imaging by region (CA1 and dentate gyrus), cell type (pyramidal neurons, inhibitory interneurons, and glial cells), and cellular compartment (dendrite and axon). Results Multiphoton imaging of the hippocampus is primarily performed under head-fixed conditions and can reveal detailed mechanisms of circuit operation owing to its high spatial resolution and specificity. As the hippocampus lies deep below the cortex, its imaging requires elaborate methods. These include imaging cannula implantation, microendoscopy, and the use of long-wavelength light sources. Although many studies have focused on the dorsal CA1 pyramidal cells, studies of other local and inter-areal circuitry elements have also helped provide a more comprehensive picture of the information processing performed by the hippocampal circuits. Imaging of circuit function in mouse models of Alzheimer's disease and other brain disorders such as autism spectrum disorder has also contributed greatly to our understanding of their pathophysiology. Conclusions Multiphoton imaging has revealed much regarding region-, cell-type-, and pathway-specific mechanisms in hippocampal function and dysfunction in health and disease. Future technological advances will allow further illustration of the operating principle of the hippocampal circuits via the large-scale, high-resolution, multimodal, and minimally invasive imaging.
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Affiliation(s)
- Kotaro Mizuta
- RIKEN BDR, Kobe, Japan
- New York University Abu Dhabi, Department of Biology, Abu Dhabi, United Arab Emirates
| | - Masaaki Sato
- Hokkaido University Graduate School of Medicine, Department of Neuropharmacology, Sapporo, Japan
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2
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Mim MS, Kumar N, Levis M, Unger MF, Miranda G, Gazzo D, Robinett T, Zartman JJ. Piezo regulates epithelial topology and promotes precision in organ size control. Cell Rep 2024; 43:114398. [PMID: 38935502 DOI: 10.1016/j.celrep.2024.114398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 05/09/2024] [Accepted: 06/10/2024] [Indexed: 06/29/2024] Open
Abstract
Mechanosensitive Piezo channels regulate cell division, cell extrusion, and cell death. However, systems-level functions of Piezo in regulating organogenesis remain poorly understood. Here, we demonstrate that Piezo controls epithelial cell topology to ensure precise organ growth by integrating live-imaging experiments with pharmacological and genetic perturbations and computational modeling. Notably, the knockout or knockdown of Piezo increases bilateral asymmetry in wing size. Piezo's multifaceted functions can be deconstructed as either autonomous or non-autonomous based on a comparison between tissue-compartment-level perturbations or between genetic perturbation populations at the whole-tissue level. A computational model that posits cell proliferation and apoptosis regulation through modulation of the cutoff tension required for Piezo channel activation explains key cell and tissue phenotypes arising from perturbations of Piezo expression levels. Our findings demonstrate that Piezo promotes robustness in regulating epithelial topology and is necessary for precise organ size control.
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Affiliation(s)
- Mayesha Sahir Mim
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Nilay Kumar
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Megan Levis
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria F Unger
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Gabriel Miranda
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - David Gazzo
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Trent Robinett
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jeremiah J Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA; Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.
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Wu Y, Xu Z, Liang S, Wang L, Wang M, Jia H, Chen X, Zhao Z, Liao X. NeuroSeg-III: efficient neuron segmentation in two-photon Ca 2+ imaging data using self-supervised learning. BIOMEDICAL OPTICS EXPRESS 2024; 15:2910-2925. [PMID: 38855703 PMCID: PMC11161377 DOI: 10.1364/boe.521478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 06/11/2024]
Abstract
Two-photon Ca2+ imaging technology increasingly plays an essential role in neuroscience research. However, the requirement for extensive professional annotation poses a significant challenge to improving the performance of neuron segmentation models. Here, we present NeuroSeg-III, an innovative self-supervised learning approach specifically designed to achieve fast and precise segmentation of neurons in imaging data. This approach consists of two modules: a self-supervised pre-training network and a segmentation network. After pre-training the encoder of the segmentation network via a self-supervised learning method without any annotated data, we only need to fine-tune the segmentation network with a small amount of annotated data. The segmentation network is designed with YOLOv8s, FasterNet, efficient multi-scale attention mechanism (EMA), and bi-directional feature pyramid network (BiFPN), which enhanced the model's segmentation accuracy while reducing the computational cost and parameters. The generalization of our approach was validated across different Ca2+ indicators and scales of imaging data. Significantly, the proposed neuron segmentation approach exhibits exceptional speed and accuracy, surpassing the current state-of-the-art benchmarks when evaluated using a publicly available dataset. The results underscore the effectiveness of NeuroSeg-III, with employing an efficient training strategy tailored for two-photon Ca2+ imaging data and delivering remarkable precision in neuron segmentation.
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Affiliation(s)
- Yukun Wu
- Guangxi Key Laboratory of Special Biomedicine and Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
| | - Zhehao Xu
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
| | - Shanshan Liang
- Brain Research Center, State Key Laboratory of Trauma and Chemical Poisoning, Third Military Medical University, Chongqing 400038, China
| | - Lukang Wang
- Brain Research Center, State Key Laboratory of Trauma and Chemical Poisoning, Third Military Medical University, Chongqing 400038, China
| | - Meng Wang
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
| | - Hongbo Jia
- Guangxi Key Laboratory of Special Biomedicine and Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
- Brain Research Instrument Innovation Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, Jiangsu, China
| | - Xiaowei Chen
- Guangxi Key Laboratory of Special Biomedicine and Advanced Institute for Brain and Intelligence, School of Medicine, Guangxi University, Nanning 530004, China
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China
| | - Zhikai Zhao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
| | - Xiang Liao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400030, China
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4
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Glaeser-Khan S, Savalia NK, Cressy J, Feng J, Li Y, Kwan AC, Kaye AP. Spatiotemporal Organization of Prefrontal Norepinephrine Influences Neuronal Activity. eNeuro 2024; 11:ENEURO.0252-23.2024. [PMID: 38702188 PMCID: PMC11134306 DOI: 10.1523/eneuro.0252-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/08/2024] [Accepted: 01/19/2024] [Indexed: 05/06/2024] Open
Abstract
Norepinephrine (NE), a neuromodulator released by locus ceruleus (LC) neurons throughout the cortex, influences arousal and learning through extrasynaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the prefrontal cortex (PFC) affect neuronal firing, we employed in vivo two-photon imaging of layer 2/3 of the PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. In this proof of principle study, we found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+ fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.
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Affiliation(s)
| | - Neil K Savalia
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06510
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut 06511
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853
| | - Jianna Cressy
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, Connecticut 06515
| | - Jiesi Feng
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Alex C Kwan
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853
| | - Alfred P Kaye
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, Connecticut 06515
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5
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Carbonero D, Noueihed J, Kramer MA, White JA. Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.11.561797. [PMID: 37905071 PMCID: PMC10614735 DOI: 10.1101/2023.10.11.561797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.
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Affiliation(s)
- Daniel Carbonero
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Jad Noueihed
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Mark A. Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
| | - John A. White
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
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Zak JD, Reddy G, Konanur V, Murthy VN. Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex. Nat Commun 2024; 15:3268. [PMID: 38627390 PMCID: PMC11021479 DOI: 10.1038/s41467-024-47366-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
Sensory systems are organized hierarchically, but feedback projections frequently disrupt this order. In the olfactory bulb (OB), cortical feedback projections numerically match sensory inputs. To unravel information carried by these two streams, we imaged the activity of olfactory sensory neurons (OSNs) and cortical axons in the mouse OB using calcium indicators, multiphoton microscopy, and diverse olfactory stimuli. Here, we show that odorant mixtures of increasing complexity evoke progressively denser OSN activity, yet cortical feedback activity is of similar sparsity for all stimuli. Also, representations of complex mixtures are similar in OSNs but are decorrelated in cortical axons. While OSN responses to increasing odorant concentrations exhibit a sigmoidal relationship, cortical axonal responses are complex and nonmonotonic, which can be explained by a model with activity-dependent feedback inhibition in the cortex. Our study indicates that early-stage olfactory circuits have access to local feedforward signals and global, efficiently formatted information about odor scenes through cortical feedback.
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Affiliation(s)
- Joseph D Zak
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, 60607, USA.
- Department of Psychology, University of Illinois Chicago, Chicago, IL, 60607, USA.
| | - Gautam Reddy
- Physics & Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA, 94085, USA
- Department of Physics, Princeton University, Princeton, NJ, 08540, USA
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Vaibhav Konanur
- Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, 60607, USA
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, 02134, USA
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7
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Wüstner D, Egebjerg JM, Lauritsen L. Dynamic Mode Decomposition of Multiphoton and Stimulated Emission Depletion Microscopy Data for Analysis of Fluorescent Probes in Cellular Membranes. SENSORS (BASEL, SWITZERLAND) 2024; 24:2096. [PMID: 38610307 PMCID: PMC11013970 DOI: 10.3390/s24072096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024]
Abstract
An analysis of the membrane organization and intracellular trafficking of lipids often relies on multiphoton (MP) and super-resolution microscopy of fluorescent lipid probes. A disadvantage of particularly intrinsically fluorescent lipid probes, such as the cholesterol and ergosterol analogue, dehydroergosterol (DHE), is their low MP absorption cross-section, resulting in a low signal-to-noise ratio (SNR) in live-cell imaging. Stimulated emission depletion (STED) microscopy of membrane probes like Nile Red enables one to resolve membrane features beyond the diffraction limit but exposes the sample to a lot of excitation light and suffers from a low SNR and photobleaching. Here, dynamic mode decomposition (DMD) and its variant, higher-order DMD (HoDMD), are applied to efficiently reconstruct and denoise the MP and STED microscopy data of lipid probes, allowing for an improved visualization of the membranes in cells. HoDMD also allows us to decompose and reconstruct two-photon polarimetry images of TopFluor-cholesterol in model and cellular membranes. Finally, DMD is shown to not only reconstruct and denoise 3D-STED image stacks of Nile Red-labeled cells but also to predict unseen image frames, thereby allowing for interpolation images along the optical axis. This important feature of DMD can be used to reduce the number of image acquisitions, thereby minimizing the light exposure of biological samples without compromising image quality. Thus, DMD as a computational tool enables gentler live-cell imaging of fluorescent probes in cellular membranes by MP and STED microscopy.
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Affiliation(s)
- Daniel Wüstner
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, DK-5230 Odense M, Denmark; (J.M.E.); (L.L.)
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8
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Paudel S, Yue M, Nalamalapu R, Saha MS. Deciphering the Calcium Code: A Review of Calcium Activity Analysis Methods Employed to Identify Meaningful Activity in Early Neural Development. Biomolecules 2024; 14:138. [PMID: 38275767 PMCID: PMC10813340 DOI: 10.3390/biom14010138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
The intracellular and intercellular flux of calcium ions represents an ancient and universal mode of signaling that regulates an extensive array of cellular processes. Evidence for the central role of calcium signaling includes various techniques that allow the visualization of calcium activity in living cells. While extensively investigated in mature cells, calcium activity is equally important in developing cells, particularly the embryonic nervous system where it has been implicated in a wide variety array of determinative events. However, unlike in mature cells, where the calcium dynamics display regular, predictable patterns, calcium activity in developing systems is far more sporadic, irregular, and diverse. This renders the ability to assess calcium activity in a consistent manner extremely challenging, challenges reflected in the diversity of methods employed to analyze calcium activity in neural development. Here we review the wide array of calcium detection and analysis methods used across studies, limiting the extent to which they can be comparatively analyzed. The goal is to provide investigators not only with an overview of calcium activity analysis techniques currently available, but also to offer suggestions for future work and standardization to enable informative comparative evaluations of this fundamental and important process in neural development.
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Affiliation(s)
- Sudip Paudel
- Wyss Institute, Harvard University, Boston, MA 02215, USA; (S.P.); (M.Y.)
| | - Michelle Yue
- Wyss Institute, Harvard University, Boston, MA 02215, USA; (S.P.); (M.Y.)
| | - Rithvik Nalamalapu
- School of Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA;
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Ratsifandrihamanana MR, Dard RF, Denis J, Cossart R, Picardo MA. Protocol to image and analyze hippocampal network dynamics in non-anesthetized mouse pups. STAR Protoc 2023; 4:102760. [PMID: 38041819 PMCID: PMC10701450 DOI: 10.1016/j.xpro.2023.102760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/03/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
Two-photon calcium imaging is a powerful technique that has revolutionized our understanding of how neural circuit dynamics supports different behaviors and cognitive processes. However, performing imaging during development remains challenging. Here, we provide a protocol to image CA1 neurons in mouse pups as well as a pipeline of analysis to analyze and share the data. We describe steps for intracerebroventricular injection, cranial window surgery, two-photon calcium imaging, and analysis of imaging data. For complete details on the use and execution of this protocol, please refer to Dard et al.1 and Denis et al.2.
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Affiliation(s)
| | - Robin F Dard
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France
| | - Julien Denis
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France
| | - Rosa Cossart
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France.
| | - Michel A Picardo
- Turing Centre for Living Systems, Aix-Marseille University, INSERM, INMED U1249, France.
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10
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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11
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Leng Y, Li X, Zheng F, Liu H, Wang C, Wang X, Liao Y, Liu J, Meng K, Yu J, Zhang J, Wang B, Tan Y, Liu M, Jia X, Li D, Li Y, Gu Z, Fan Y. Advances in In Vitro Models of Neuromuscular Junction: Focusing on Organ-on-a-Chip, Organoids, and Biohybrid Robotics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211059. [PMID: 36934404 DOI: 10.1002/adma.202211059] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/18/2023] [Indexed: 06/18/2023]
Abstract
The neuromuscular junction (NMJ) is a peripheral synaptic connection between presynaptic motor neurons and postsynaptic skeletal muscle fibers that enables muscle contraction and voluntary motor movement. Many traumatic, neurodegenerative, and neuroimmunological diseases are classically believed to mainly affect either the neuronal or the muscle side of the NMJ, and treatment options are lacking. Recent advances in novel techniques have helped develop in vitro physiological and pathophysiological models of the NMJ as well as enable precise control and evaluation of its functions. This paper reviews the recent developments in in vitro NMJ models with 2D or 3D cultures, from organ-on-a-chip and organoids to biohybrid robotics. Related derivative techniques are introduced for functional analysis of the NMJ, such as the patch-clamp technique, microelectrode arrays, calcium imaging, and stimulus methods, particularly optogenetic-mediated light stimulation, microelectrode-mediated electrical stimulation, and biochemical stimulation. Finally, the applications of the in vitro NMJ models as disease models or for drug screening related to suitable neuromuscular diseases are summarized and their future development trends and challenges are discussed.
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Affiliation(s)
- Yubing Leng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Xiaorui Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Fuyin Zheng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Hui Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Chunyan Wang
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xudong Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yulong Liao
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jiangyue Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Kaiqi Meng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jiaheng Yu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jingyi Zhang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Binyu Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yingjun Tan
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
| | - Meili Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Xiaoling Jia
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Deyu Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yinghui Li
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
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12
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Cai C, Dong C, Friedrich J, Rozsa M, Pnevmatikakis EA, Giovannucci A. FIOLA: an accelerated pipeline for fluorescence imaging online analysis. Nat Methods 2023; 20:1417-1425. [PMID: 37679524 DOI: 10.1038/s41592-023-01964-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 06/19/2023] [Indexed: 09/09/2023]
Abstract
Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.
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Affiliation(s)
- Changjia Cai
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cynthia Dong
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Marton Rozsa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Andrea Giovannucci
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Closed-Loop Engineering for Advanced Rehabilitation (CLEAR), North Carolina State University, Raleigh, NC, USA.
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13
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Glaeser-Khan S, Savalia NK, Cressy J, Feng J, Li Y, Kwan AC, Kaye AP. Spatiotemporal organization of prefrontal norepinephrine influences neuronal activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544191. [PMID: 37502881 PMCID: PMC10370029 DOI: 10.1101/2023.06.09.544191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Norepinephrine (NE), a neuromodulator released by locus coeruleus neurons throughout cortex, influences arousal and learning through extra-synaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the PFC affect neuronal firing, we employed in-vivo two-photon imaging of layer 2/3 of PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. We found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+ fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.
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14
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Zhou Z, Yip HM, Tsimring K, Sur M, Ip JPK, Tin C. Effective and efficient neural networks for spike inference from in vivo calcium imaging. CELL REPORTS METHODS 2023; 3:100462. [PMID: 37323579 PMCID: PMC10261900 DOI: 10.1016/j.crmeth.2023.100462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/21/2023] [Accepted: 03/31/2023] [Indexed: 06/17/2023]
Abstract
Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using ΔF/F0 calcium inputs based on a U-Net deep neural network. When testing on a large, ground-truth public database, it consistently outperformed state-of-the-art algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 can be applied to analyses of orientation selectivity in primary visual cortex neurons. We conclude that it would be a versatile inference system that may benefit diverse neuroscience studies.
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Affiliation(s)
- Zhanhong Zhou
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Hei Matthew Yip
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Katya Tsimring
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Mriganka Sur
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jacque Pak Kan Ip
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chung Tin
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China
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15
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Parmentier T, LaMarre J, Lalonde J. Evaluation of Neurotoxicity With Human Pluripotent Stem Cell-Derived Cerebral Organoids. Curr Protoc 2023; 3:e744. [PMID: 37068185 DOI: 10.1002/cpz1.744] [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: 04/19/2023]
Abstract
The recent development of human cerebral organoids provides an invaluable in vitro model of human brain development to assess the toxicity of natural or man-made toxic substances. By recapitulating key aspects of early human neurodevelopment, investigators can evaluate with this three-dimensional (3D) model the effect of certain compounds on the formation of neuronal networks and their electrophysiological properties with more physiological relevance than neurons grown in monolayers and in cultures composed of a unique cell type. This promising potential has contributed to the development of a large number of diverse protocols to generate human cerebral organoids, making interlaboratory comparisons of results difficult. Based on a previously published protocol to generate human cortical organoids (herein called cerebral organoids), we detail several approaches to evaluate the effect of chemicals on neurogenesis, apoptosis, and neuronal function when exogenously applied to cultured specimens. Here, we take as an example 4-aminopyridine, a potassium channel blocker that modulates the activity of neurons and neurogenesis, and describe a simple and cost-effective way to test the impact of this agent on cerebral organoids derived from human induced pluripotent stem cells. We also provide tested protocols to evaluate neurogenesis in cerebral organoids with ethynyl deoxyuridine labeling and neuronal activity with live calcium imaging and microelectrode arrays. Together, these protocols should facilitate the implementation of cerebral organoid technologies in laboratories wishing to evaluate the effects of specific compounds or conditions on the development and function of human neurons with only basic cell culture equipment. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Generation of human cerebral organoids from pluripotent stem cells Support Protocol 1: Human pluripotent stem cell culture Basic Protocol 2: Evaluation of neurogenesis in cerebral organoids with ethynyl deoxyuridine labeling Basic Protocol 3: Calcium imaging in cerebral organoids Basic Protocol 4: Electrophysiological evaluation of cerebral organoids with microelectrode arrays Support Protocol 2: Immunostaining of cerebral organoids.
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Affiliation(s)
- Thomas Parmentier
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- Current address: Département de Sciences Cliniques, Faculté de Médecine Vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - Jonathan LaMarre
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Jasmin Lalonde
- Department of Molecular and Cellular Biology, College of Biological Science, University of Guelph, Guelph, Ontario, Canada
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16
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Guo C, Wang A, Cheng H, Chen L. New imaging instrument in animal models: Two-photon miniature microscope and large field of view miniature microscope for freely behaving animals. J Neurochem 2023; 164:270-283. [PMID: 36281555 DOI: 10.1111/jnc.15711] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 09/19/2022] [Accepted: 10/12/2022] [Indexed: 11/30/2022]
Abstract
Over the past decade, novel optical imaging tools have been developed for imaging neuronal activities along with the evolution of fluorescence indicators with brighter expression and higher sensitivity. Miniature microscopes, as revolutionary approaches, enable the imaging of large populations of neuron ensembles in freely behaving rodents and mammals, which allows exploring the neural basis of behaviors. Recent progress in two-photon miniature microscopes and mesoscale single-photon miniature microscopes further expand those affordable methods to navigate neural activities during naturalistic behaviors. In this review article, two-photon miniature microscopy techniques are summarized historically from the first documented attempt to the latest ones, and comparisons are made. The driving force behind and their potential for neuroscientific inquiries are also discussed. Current progress in terms of the mesoscale, i.e., the large field-of-view miniature microscopy technique, is addressed as well. Then, pipelines for registering single cells from the data of two-photon and large field-of-view miniature microscopes are discussed. Finally, we present the potential evolution of the techniques.
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Affiliation(s)
- Changliang Guo
- Beijing Institute of Collaborative Innovation, Beijing, China.,State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Aimin Wang
- School of Electronics, Peking University, Beijing, China.,State Key Laboratory of Advanced Optical Communication System and Networks, Peking University, Beijing, China
| | - Heping Cheng
- State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.,Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, China
| | - Liangyi Chen
- State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Beijing, China.,Beijing Academy of Artificial Intelligence, Beijing, China
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17
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Naik A, Kenyon R, Taheri A, BergerWolf T, Ibrahim B, Shinagawa Y, Llano D. V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data. J Neurosci Res 2023; 101:217-231. [PMID: 36309817 PMCID: PMC9742979 DOI: 10.1002/jnr.25139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/07/2022] [Accepted: 10/12/2022] [Indexed: 12/14/2022]
Abstract
Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.
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Affiliation(s)
- A.G. Naik
- Department of Computer Science, University of Illinois at Chicago, USA
| | - R.V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, USA
| | - A. Taheri
- Department of Computer Science, University of Illinois at Chicago, USA
| | - T. BergerWolf
- Department of Computer Science Engineering, Ohio State University, USA
| | - B. Ibrahim
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - Y. Shinagawa
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
| | - D.A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign,Beckman Institute for Advanced Science and Technology, Urbana, Il 61801
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18
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Sardella D, Kristensen AM, Bordoni L, Kidmose H, Shahrokhtash A, Sutherland DS, Frische S, Schiessl IM. Serial intravital 2-photon microscopy and analysis of the kidney using upright microscopes. Front Physiol 2023; 14:1176409. [PMID: 37168225 PMCID: PMC10164931 DOI: 10.3389/fphys.2023.1176409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/03/2023] [Indexed: 05/13/2023] Open
Abstract
Serial intravital 2-photon microscopy of the kidney and other abdominal organs is a powerful technique to assess tissue function and structure simultaneously and over time. Thus, serial intravital microscopy can capture dynamic tissue changes during health and disease and holds great potential to characterize (patho-) physiological processes with subcellular resolution. However, successful image acquisition and analysis require significant expertise and impose multiple potential challenges. Abdominal organs are rhythmically displaced by breathing movements which hamper high-resolution imaging. Traditionally, kidney intravital imaging is performed on inverted microscopes where breathing movements are partly compensated by the weight of the animal pressing down. Here, we present a custom and easy-to-implement setup for intravital imaging of the kidney and other abdominal organs on upright microscopes. Furthermore, we provide image processing protocols and a new plugin for the free image analysis software FIJI to process multichannel fluorescence microscopy data. The proposed image processing pipelines cover multiple image denoising algorithms, sample drift correction using 2D registration, and alignment of serial imaging data collected over several weeks using landmark-based 3D registration. The provided tools aim to lower the barrier of entry to intravital microscopy of the kidney and are readily applicable by biomedical practitioners.
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Affiliation(s)
- Donato Sardella
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- *Correspondence: Ina Maria Schiessl, ; Donato Sardella,
| | | | - Luca Bordoni
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Hanne Kidmose
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Ali Shahrokhtash
- Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark
| | | | | | - Ina Maria Schiessl
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- *Correspondence: Ina Maria Schiessl, ; Donato Sardella,
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19
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Xu Z, Wu Y, Guan J, Liang S, Pan J, Wang M, Hu Q, Jia H, Chen X, Liao X. NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca 2+ imaging. Front Cell Neurosci 2023; 17:1127847. [PMID: 37091918 PMCID: PMC10117760 DOI: 10.3389/fncel.2023.1127847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.
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Affiliation(s)
- Zhehao Xu
- Advanced Institute for Brain and Intelligence, Medical College, Guangxi University, Nanning, China
| | - Yukun Wu
- Advanced Institute for Brain and Intelligence, Medical College, Guangxi University, Nanning, China
| | - Jiangheng Guan
- Department of Neurosurgery, The General Hospital of Chinese PLA Central Theater Command, Wuhan, China
| | - Shanshan Liang
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Junxia Pan
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Meng Wang
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
| | - Qianshuo Hu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Hongbo Jia
- Advanced Institute for Brain and Intelligence, Medical College, Guangxi University, Nanning, China
- Brain Research Instrument Innovation Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China
| | - Xiaowei Chen
- Advanced Institute for Brain and Intelligence, Medical College, Guangxi University, Nanning, China
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
- *Correspondence: Xiaowei Chen,
| | - Xiang Liao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
- Xiang Liao,
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20
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Gare S, Chel S, Abhinav TK, Dhyani V, Jana S, Giri L. Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach. Integr Biol (Camb) 2022; 14:184-203. [PMID: 36670549 DOI: 10.1093/intbio/zyac017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 11/22/2022] [Accepted: 11/30/2022] [Indexed: 01/22/2023]
Abstract
Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.
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Affiliation(s)
- Suman Gare
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Soumita Chel
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - T K Abhinav
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Vaibhav Dhyani
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Soumya Jana
- Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, India
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21
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Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. Gigascience 2022; 12:giad049. [PMID: 37401720 PMCID: PMC10318494 DOI: 10.1093/gigascience/giad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/20/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023] Open
Abstract
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
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Affiliation(s)
- Deepti Mittal
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Rebecca Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Kuner
- Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Rohini Kuner
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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22
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Endothelial cells regulate astrocyte to neural progenitor cell trans-differentiation in a mouse model of stroke. Nat Commun 2022; 13:7812. [PMID: 36535938 PMCID: PMC9763251 DOI: 10.1038/s41467-022-35498-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
The concept of the neurovascular unit emphasizes the importance of cell-cell signaling between neural, glial, and vascular compartments. In neurogenesis, for example, brain endothelial cells play a key role by supplying trophic support to neural progenitors. Here, we describe a surprising phenomenon where brain endothelial cells may release trans-differentiation signals that convert astrocytes into neural progenitor cells in male mice after stroke. After oxygen-glucose deprivation, brain endothelial cells release microvesicles containing pro-neural factor Ascl1 that enter into astrocytes to induce their trans-differentiation into neural progenitors. In mouse models of focal cerebral ischemia, Ascl1 is upregulated in endothelium prior to astrocytic conversion into neural progenitor cells. Injecting brain endothelial-derived microvesicles amplifies the process of astrocyte trans-differentiation. Endothelial-specific overexpression of Ascl1 increases the local conversion of astrocytes into neural progenitors and improves behavioral recovery. Our findings describe an unexpected vascular-regulated mechanism of neuroplasticity that may open up therapeutic opportunities for improving outcomes after stroke.
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23
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Zhu F, Grier HA, Tandon R, Cai C, Agarwal A, Giovannucci A, Kaufman MT, Pandarinath C. A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution. Nat Neurosci 2022; 25:1724-1734. [PMID: 36424431 PMCID: PMC9825112 DOI: 10.1038/s41593-022-01189-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/23/2022] [Indexed: 11/26/2022]
Abstract
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.
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Affiliation(s)
- Feng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
| | - Harrison A Grier
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Raghav Tandon
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Changjia Cai
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | | | - Andrea Giovannucci
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Closed-Loop Engineering for Advanced Rehabilitation (CLEAR), North Carolina State University, Raleigh, NC, USA.
| | - Matthew T Kaufman
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA.
- Neuroscience Institute, The University of Chicago, Chicago, IL, USA.
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA.
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Tippani M, Pattie EA, Davis BA, Nguyen CV, Wang Y, Sripathy SR, Maher BJ, Martinowich K, Jaffe AE, Page SC. CaPTure: Calcium PeakToolbox for analysis of in vitro calcium imaging data. BMC Neurosci 2022; 23:71. [PMID: 36451089 PMCID: PMC9710137 DOI: 10.1186/s12868-022-00751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/02/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Calcium imaging is a powerful technique for recording cellular activity across large populations of neurons. However, analysis methods capable of single-cell resolution in cultured neurons, especially for cultures derived from human induced pluripotent stem cells (hiPSCs), are lacking. Existing methods lack scalability to accommodate high-throughput comparisons between multiple lines, across developmental timepoints, or across pharmacological manipulations. RESULTS To address this need we developed CaPTure, a scalable, automated Ca2+ imaging analysis pipeline ( https://github.com/LieberInstitute/CaPTure ). CaPTuredetects neurons, classifies and quantifies spontaneous activity, quantifies synchrony metrics, and generates cell- and network-specific metrics that facilitate phenotypic discovery. The method is compatible with parallel processing on computing clusters without requiring significant user input or parameter modification. CONCLUSION CaPTure allows for rapid assessment of neuronal activity in cultured cells at cellular resolution, rendering it amenable to high-throughput screening and phenotypic discovery. The platform can be applied to both human- and rodent-derived neurons and is compatible with many imaging systems.
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Affiliation(s)
- Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Elizabeth A Pattie
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Brittany A Davis
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Claudia V Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Srinidhi Rao Sripathy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
| | - Brady J Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Stephanie Cerceo Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 855 North Wolfe Street, Suite 300, Baltimore, MD, 21205, USA.
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25
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Romero G, Park J, Koehler F, Pralle A, Anikeeva P. Modulating cell signalling in vivo with magnetic nanotransducers. NATURE REVIEWS. METHODS PRIMERS 2022; 2:92. [PMID: 38111858 PMCID: PMC10727510 DOI: 10.1038/s43586-022-00170-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 12/20/2023]
Abstract
Weak magnetic fields offer nearly lossless transmission of signals within biological tissue. Magnetic nanomaterials are capable of transducing magnetic fields into a range of biologically relevant signals in vitro and in vivo. These nanotransducers have recently enabled magnetic control of cellular processes, from neuronal firing and gene expression to programmed apoptosis. Effective implementation of magnetically controlled cellular signalling relies on careful tailoring of magnetic nanotransducers and magnetic fields to the responses of the intended molecular targets. This primer discusses the versatility of magnetic modulation modalities and offers practical guidelines for selection of appropriate materials and field parameters, with a particular focus on applications in neuroscience. With recent developments in magnetic instrumentation and nanoparticle chemistries, including those that are commercially available, magnetic approaches promise to empower research aimed at connecting molecular and cellular signalling to physiology and behaviour in untethered moving subjects.
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Affiliation(s)
- Gabriela Romero
- Department of Biomedical Engineering and Chemical Engineering, University of Texas at San Antonio, San Antonio, TX, USA
| | - Jimin Park
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Florian Koehler
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arnd Pralle
- Department of Physics, University at Buffalo, the State University of New York, Buffalo, NY, USA
| | - Polina Anikeeva
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
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26
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Cai Y, Wu J, Dai Q. Review on data analysis methods for mesoscale neural imaging in vivo. NEUROPHOTONICS 2022; 9:041407. [PMID: 35450225 PMCID: PMC9010663 DOI: 10.1117/1.nph.9.4.041407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.
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Affiliation(s)
- Yeyi Cai
- Tsinghua University, Department of Automation, Beijing, China
| | - Jiamin Wu
- Tsinghua University, Department of Automation, Beijing, China
| | - Qionghai Dai
- Tsinghua University, Department of Automation, Beijing, China
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27
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Benisty H, Song A, Mishne G, Charles AS. Review of data processing of functional optical microscopy for neuroscience. NEUROPHOTONICS 2022; 9:041402. [PMID: 35937186 PMCID: PMC9351186 DOI: 10.1117/1.nph.9.4.041402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
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Affiliation(s)
- Hadas Benisty
- Yale Neuroscience, New Haven, Connecticut, United States
| | - Alexander Song
- Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Gal Mishne
- UC San Diego, Halıcığlu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States
| | - Adam S. Charles
- Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States
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28
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Grienberger C, Giovannucci A, Zeiger W, Portera-Cailliau C. Two-photon calcium imaging of neuronal activity. NATURE REVIEWS. METHODS PRIMERS 2022; 2:67. [PMID: 38124998 PMCID: PMC10732251 DOI: 10.1038/s43586-022-00147-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/23/2023]
Abstract
In vivo two-photon calcium imaging (2PCI) is a technique used for recording neuronal activity in the intact brain. It is based on the principle that, when neurons fire action potentials, intracellular calcium levels rise, which can be detected using fluorescent molecules that bind to calcium. This Primer is designed for scientists who are considering embarking on experiments with 2PCI. We provide the reader with a background on the basic concepts behind calcium imaging and on the reasons why 2PCI is an increasingly powerful and versatile technique in neuroscience. The Primer explains the different steps involved in experiments with 2PCI, provides examples of what ideal preparations should look like and explains how data are analysed. We also discuss some of the current limitations of the technique, and the types of solutions to circumvent them. Finally, we conclude by anticipating what the future of 2PCI might look like, emphasizing some of the analysis pipelines that are being developed and international efforts for data sharing.
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Affiliation(s)
- Christine Grienberger
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA
| | - Andrea Giovannucci
- Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William Zeiger
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Carlos Portera-Cailliau
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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29
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Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Calcium imaging has rapidly become a methodology of choice for real-time in vivo neuron analysis. Its application to large sets of data requires automated tools to annotate and segment cells, allowing scalable image segmentation under reproducible criteria. In this paper, we review and summarize the most recent methods for computational segmentation of calcium imaging. The contributions of the paper are three-fold: we provide an overview of the main algorithms taxonomized in three categories (signal processing, matrix factorization and machine learning-based approaches), we highlight the main advantages and disadvantages of each category and we provide a summary of the performance of the methods that have been tested on public benchmarks (with links to the public code when available).
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30
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Johnston KG, Grieco SF, Zhang H, Jin S, Xu X, Nie Q. Tracking longitudinal population dynamics of single neuronal calcium signal using SCOUT. CELL REPORTS METHODS 2022; 2:100207. [PMID: 35637911 PMCID: PMC9142684 DOI: 10.1016/j.crmeth.2022.100207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/06/2022] [Accepted: 04/08/2022] [Indexed: 11/07/2022]
Abstract
In vivo calcium imaging enables simultaneous recording of large neuronal ensembles engaged in complex operations. Many experiments require monitoring and identification of cell populations across multiple sessions. Population cell tracking across multiple sessions is complicated by non-rigid transformations induced by cell movement and imaging field shifts. We introduce SCOUT (Single-Cell spatiOtemporal longitUdinal Tracking), a fast, robust cell-tracking method utilizing multiple cell-cell similarity metrics, probabilistic inference, and an adaptive clustering methodology, to perform cell identification across multiple sessions. By comparing SCOUT with earlier cell-tracking algorithms on simulated, 1-photon, and 2-photon recordings, we show that our approach significantly improves cell-tracking quality, particularly when recordings exhibit spatial footprint movement between sessions or sub-optimal neural extraction quality.
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Affiliation(s)
- Kevin G. Johnston
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Steven F. Grieco
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Hai Zhang
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Suoqin Jin
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- The Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92697, USA
- The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697, USA
| | - Qing Nie
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697, USA
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31
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Liu W, Pan J, Xu Y, Wang M, Jia H, Zhang K, Chen X, Li X, Liao X. Fast and Accurate Motion Correction for Two-Photon Ca 2+ Imaging in Behaving Mice. Front Neuroinform 2022; 16:851188. [PMID: 35559212 PMCID: PMC9088923 DOI: 10.3389/fninf.2022.851188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Two-photon Ca2+ imaging is a widely used technique for investigating brain functions across multiple spatial scales. However, the recording of neuronal activities is affected by movement of the brain during tasks in which the animal is behaving normally. Although post-hoc image registration is the commonly used approach, the recent developments of online neuroscience experiments require real-time image processing with efficient motion correction performance, posing new challenges in neuroinformatics. We propose a fast and accurate image density feature-based motion correction method to address the problem of imaging animal during behaviors. This method is implemented by first robustly estimating and clustering the density features from two-photon images. Then, it takes advantage of the temporal correlation in imaging data to update features of consecutive imaging frames with efficient calculations. Thus, motion artifacts can be quickly and accurately corrected by matching the features and obtaining the transformation parameters for the raw images. Based on this efficient motion correction strategy, our algorithm yields promising computational efficiency on imaging datasets with scales ranging from dendritic spines to neuronal populations. Furthermore, we show that the proposed motion correction method outperforms other methods by evaluating not only computational speed but also the quality of the correction performance. Specifically, we provide a powerful tool to perform motion correction for two-photon Ca2+ imaging data, which may facilitate online imaging experiments in the future.
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Affiliation(s)
- Weiyi Liu
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Junxia Pan
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Yuanxu Xu
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Meng Wang
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
| | - Hongbo Jia
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning, China
- Brain Research Instrument Innovation Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Kuan Zhang
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Xiaowei Chen
- Brain Research Center and State Key Laboratory of Trauma, Burns, and Combined Injury, Third Military Medical University, Chongqing, China
| | - Xingyi Li
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
| | - Xiang Liao
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
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32
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Non-rigid Registration for Two-photon Imaging Using Triangulation and Piecewise Affine Transformation. Neuroscience 2022; 491:1-12. [PMID: 35367292 DOI: 10.1016/j.neuroscience.2022.03.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/21/2022]
Abstract
Accurate and efficient non-rigid registration is important to investigate neural mechanisms in multi-session two-photon (2p) imaging across a few days. The 2p imaging recordings from different sessions usually possess certain complex misalignment or huge data variance due to relocation errors during experimental operations or brain recovery. Most of the reported neural image registration tools were able to solve the registration problem in the same session with small deformation. However, the registration of neural images across multi-sessions remains a challenge. In this study, we report the development of a non-rigid registration method for 2p imaging in mice based on image triangulation and piecewise affine transformation (TPAT) technologies. The TPAT method supported both automatic and semi-automatic operation types, and both showed great performance in the benchmark test of non-rigid neural image registration. The proposed method constitutes a step forward in promoting and accelerating discoveries from multi-session 2p imaging research.
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33
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Triplett MA, Goodhill GJ. Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data. Neural Comput 2022; 34:1143-1169. [PMID: 35344990 DOI: 10.1162/neco_a_01492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/11/2022] [Indexed: 11/04/2022]
Abstract
Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.
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Affiliation(s)
- Marcus A Triplett
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
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34
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Mattis J, Somarowthu A, Goff KM, Jiang E, Yom J, Sotuyo N, Mcgarry LM, Feng H, Kaneko K, Goldberg EM. Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome. eLife 2022; 11:e69293. [PMID: 35212623 PMCID: PMC8920506 DOI: 10.7554/elife.69293] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 02/24/2022] [Indexed: 11/13/2022] Open
Abstract
Dravet syndrome (DS) is a neurodevelopmental disorder due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit, characterized by treatment-resistant epilepsy, temperature-sensitive seizures, developmental delay/intellectual disability with features of autism spectrum disorder, and increased risk of sudden death. Convergent data suggest hippocampal dentate gyrus (DG) pathology in DS (Scn1a+/-) mice. We performed two-photon calcium imaging in brain slice to uncover a profound dysfunction of filtering of perforant path input by DG in young adult Scn1a+/- mice. This was not due to dysfunction of DG parvalbumin inhibitory interneurons (PV-INs), which were only mildly impaired at this timepoint; however, we identified enhanced excitatory input to granule cells, suggesting that circuit dysfunction is due to excessive excitation rather than impaired inhibition. We confirmed that both optogenetic stimulation of entorhinal cortex and selective chemogenetic inhibition of DG PV-INs lowered seizure threshold in vivo in young adult Scn1a+/- mice. Optogenetic activation of PV-INs, on the other hand, normalized evoked responses in granule cells in vitro. These results establish the corticohippocampal circuit as a key locus of pathology in Scn1a+/- mice and suggest that PV-INs retain powerful inhibitory function and may be harnessed as a potential therapeutic approach toward seizure modulation.
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Affiliation(s)
- Joanna Mattis
- Department of Neurology, The Perelman School of Medicine at The University of PennsylvaniaPhiladelphiaUnited States
| | - Ala Somarowthu
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Kevin M Goff
- Neuroscience Graduate Group, The University of Pennsylvania Perelman School of MedicinePhiladelphiaUnited States
| | - Evan Jiang
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Jina Yom
- College of Arts and Sciences, The University of PennsylvaniaPhiladelphiaUnited States
| | - Nathaniel Sotuyo
- Neuroscience Graduate Group, The University of Pennsylvania Perelman School of MedicinePhiladelphiaUnited States
| | - Laura M Mcgarry
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Huijie Feng
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Keisuke Kaneko
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Ethan M Goldberg
- Department of Neurology, The Perelman School of Medicine at The University of PennsylvaniaPhiladelphiaUnited States
- Division of Neurology, Department of Pediatrics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Neuroscience, The Perelman School of Medicine at The University of PennsylvaniaPhiladelphiaUnited States
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35
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Development of an Ergonomic User Interface Design of Calcium Imaging Processing System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
An optical brain-machine interface (O-BMI) system using calcium imaging has various advantages such as high resolution, a comprehensive view of large neural populations, abilities such as long-term stable recording, and applicability to freely behaving animals in neuroscience research. The present study developed an ergonomic user interface (UI) design, based on a use scenario for an O-BMI system that can be used for the acquisition and processing of calcium imaging in freely behaving rodents. The UI design was developed in three steps: (1) identification of design and function requirements of users, (2) establishment of a use scenario, and (3) development of a UI prototype. The UI design requirements were identified by a literature review, a benchmark of existing systems, and a focus group interview with five neuroscience researchers. Then, the use scenario was developed for tasks of data acquisition, feature extraction, and neural decoding for offline and online processing by considering the sequences of operations and needs of users. Lastly, a digital prototype incorporating an information architecture, graphic user interfaces, and simulated functions was fabricated. A usability test was conducted with five neuroscientists (work experience = 3.4 ± 1.1 years) and five ergonomic experts (work experience = 3.6 ± 2.7 years) to compare the digital prototypes with four existing systems (Miniscope, nVista, Mosaic, and Suite2p). The usability testing results showed that the ergonomic UI design was significantly preferred to the UI designs of the existing systems by reducing the task completion time by 10.1% to 70.2% on average, the scan path length by 14.4% to 88.7%, and perceived workload by 12.2% to 37.9%, increasing satisfaction by 11.3% to 74.3% in data acquisition and signal-extraction tasks. The present study demonstrates the significance of the user-centered design approach in the development of a system for neuroscience research. Further research is needed to validate the usability test results of the UI prototype as a corresponding real system is implemented.
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Fan W, Sullivan SJ, Sdrulla AD. Dorsal Column and Root Stimulation at Aβ-fiber Intensity Activate Superficial Dorsal Horn Glutamatergic and GABAergic Populations. Mol Pain 2022; 18:17448069221079559. [PMID: 35088625 PMCID: PMC8891844 DOI: 10.1177/17448069221079559] [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] [Indexed: 11/17/2022] Open
Abstract
Neurostimulation therapies are frequently used in patients with chronic pain conditions. They emerged from Gate Control Theory (GCT), which posits that Aβ-fiber activation recruits superficial dorsal horn (SDH) inhibitory networks to “close the gate” on nociceptive transmission, resulting in pain relief. However, the efficacy of current therapies is limited, and the underlying circuits remain poorly understood. For example, it remains unknown whether ongoing stimulation of Aβ-fibers is sufficient to drive activity in SDH neurons. We used multiphoton microscopy in spinal cords extracted from mice expressing the genetically encoded calcium indicator GCaMP6s in glutamatergic and GABAergic populations; activity levels were inferred from deconvolved calcium signals using CaImAn software. Sustained Aβ-fiber stimulation at the dorsal columns or dorsal roots drove robust yet transient activation of both SDH populations. Following the initial increase, activity levels decreased below baseline in glutamatergic neurons and were depressed after stimulation ceased in both populations. Surprisingly, only about half of GABAergic neurons responded to Aβ-fiber stimulation. This subset showed elevated activity for the entire duration of stimulation, while non-responders decreased with time. Our findings suggest that Aβ-fiber stimulation initially recruits both excitatory and inhibitory populations but has divergent effects on their activity, providing a foundation for understanding the analgesic effects of neurostimulation devices. Perspective: This article used microscopy to characterize the responses of mouse spinal cord cells to stimulation of non-painful nerve fibers. These findings deepen our understanding of how the spinal cord processes information and provide a foundation for improving pain-relieving therapies.
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Affiliation(s)
- Wei Fan
- Anesthesiology and Pain Management6684Oregon Health & Science University
| | - Steve J Sullivan
- Anesthesiology and Pain Management6684Oregon Health & Science University
| | - Andrei D Sdrulla
- Anesthesiology and Pain Management6684Oregon Health & Science University
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To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data. J Neurosci Methods 2022; 366:109431. [PMID: 34856319 DOI: 10.1016/j.jneumeth.2021.109431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND With the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. METHODS In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. RESULTS We found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods. COMPARISON WITH EXISTING METHODS Our simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods. CONCLUSION We conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.
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Custom-Built Operant Conditioning Setup for Calcium Imaging and Cognitive Testing in Freely Moving Mice. eNeuro 2022; 9:ENEURO.0430-21.2022. [PMID: 35105659 PMCID: PMC8856704 DOI: 10.1523/eneuro.0430-21.2022] [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: 10/08/2021] [Revised: 01/11/2022] [Accepted: 01/14/2022] [Indexed: 11/21/2022] Open
Abstract
Operant chambers are widely used in animal research to study cognition, motivation, and learning processes. Paired with the rapidly developing technologies for brain imaging and manipulations of brain activity, operant conditioning chambers are a powerful tool for neuroscience research. The behavioral testing and imaging setups that are commercially available are often quite costly. Here, we present a custom-built operant chamber that can be constructed in a few days by an unexperienced user with relatively inexpensive, widely available materials. The advantages of our operant setup compared with other open-source and closed-source solutions are its relatively low cost, its support of complex behavioral tasks, its user-friendly setup, and its validated functionality with video imaging of behavior and calcium imaging using the UCLA Miniscope. Using this setup, we replicate our previously published findings showing that mice exposed to social defeat stress in adolescence have inhibitory control impairments in the Go/No-Go task when they reach adulthood. We also present calcium imaging data of medial prefrontal cortex (mPFC) neuronal activity acquired during Go/No-Go testing in freely moving mice and show that neuronal population activity increases from day 1 to day 14 of the task. We propose that our operant chamber is a cheaper alternative to its commercially available counterparts and offers a better balance between versatility and user-friendly setup than other open-source alternatives.
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Imaging data analysis using non-negative matrix factorization. Neurosci Res 2021; 179:51-56. [PMID: 34953961 DOI: 10.1016/j.neures.2021.12.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 09/24/2021] [Accepted: 12/12/2021] [Indexed: 11/22/2022]
Abstract
The rapid progress of imaging devices such as two-photon microscopes has made it possible to measure the activity of thousands to tens of thousands of cells at single-cell resolution in a wide field of view (FOV) data. However, it is not possible to manually identify thousands of cells in such wide FOV data. Several research groups have developed machine learning methods for automatically detecting cells from wide FOV data. Many of the recently proposed methods using dynamic activity information rather than static morphological information are based on non-negative matrix factorization (NMF). In this review, we outline cell-detection methods related to NMF. For the purpose of raising issues on NMF cell detection, we introduce our current development of a non-NMF method that is capable of detecting about 17,000 cells in ultra-wide FOV data.
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Taniguchi M, Tezuka T, Vergara P, Srinivasan S, Hosokawa T, Cherasse Y, Naoi T, Sakurai T, Sakaguchi M. Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2997-3003. [PMID: 34891875 DOI: 10.1109/embc46164.2021.9629611] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We developed Carignan, a real-time calcium imaging software that can automatically detect activity patterns of neurons. Carignan can activate an external device when synchronized neural activity is detected in calcium imaging obtained by a one-photon (1p) miniscope. Combined with optogenetics, our software enables closed-loop experiments for investigating functions of specific types of neurons in the brain. In addition to making existing pattern detection algorithms run in real-time seamlessly, we developed a new classification module that distinguishes neurons from false-positives using deep learning. We used a combination of convolutional and recurrent neural networks to incorporate both spatial and temporal features in activity patterns. Our method performed better than existing neuron detection methods for false-positive neuron detection in terms of the F1 score. Using Carignan, experimenters can activate or suppress a group of neurons when specific neural activity is observed. Because the system uses a 1p miniscope, it can be used on the brain of a freely-moving animal, making it applicable to a wide range of experimental paradigms.
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Macpherson T, Churchland A, Sejnowski T, DiCarlo J, Kamitani Y, Takahashi H, Hikida T. Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Netw 2021; 144:603-613. [PMID: 34649035 DOI: 10.1016/j.neunet.2021.09.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain's cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry.
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Affiliation(s)
- Tom Macpherson
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Anne Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - Terry Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, CA, USA; Division of Biological Sciences, University of California San Diego, CA, USA
| | - James DiCarlo
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan.
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Abstract
Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast, and accurate active neuron segmentation is critical when processing these videos. In this work, we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets acquired by independent experimental groups; the difference in accuracy was enlarged when processing datasets containing few manually marked ground truths. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.
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43
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Song A, Gauthier JL, Pillow JW, Tank DW, Charles AS. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. J Neurosci Methods 2021; 358:109173. [PMID: 33839190 PMCID: PMC8217135 DOI: 10.1016/j.jneumeth.2021.109173] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/21/2021] [Accepted: 03/24/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible. NEW-METHOD We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. RESULTS We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. COMPARISON WITH EXISTING METHODS NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data. CONCLUSIONS By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.
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Affiliation(s)
- Alexander Song
- Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Department of Physics, Princeton University, Princeton, 08540 NJ, USA
| | - Jeff L Gauthier
- Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Department of Psychology, Princeton University, Princeton, 08540 NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Bezos Center for Neural Circuit Dynamics, Princeton University, Princeton, 08540 NJ, USA; Department of Molecular Biology, Princeton University, Princeton, 08540 NJ, USA
| | - Adam S Charles
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA; Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, 21218, MD, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, 21218, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, 21218, MD, USA
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44
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Fan W, Sdrulla AD. Differential modulation of excitatory and inhibitory populations of superficial dorsal horn neurons in lumbar spinal cord by Aβ-fiber electrical stimulation. Pain 2021; 161:1650-1660. [PMID: 32068665 DOI: 10.1097/j.pain.0000000000001836] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Activation of Aβ-fibers is fundamental to numerous analgesic therapies, yet its effects on dorsal horn neuronal activity remain unclear. We used multiphoton microscopy of the genetically encoded calcium indicator GCaMP6s to characterize the effects of Aβ-fiber electrical stimulation (Aβ-ES) on neural activity. Specifically, we quantified somatic responses evoked by C-fiber intensity stimulation before and after a 10-minute train of dorsal root Aβ-ES in superficial dorsal horn (SDH) neurons, in mouse lumbar spinal cord. Aβ-ES did not alter C-fiber-evoked activity when GCaMP6s was virally expressed in all neurons, in an intact lumbar spinal cord preparation. However, when we restricted the expression of GCaMP6s to excitatory or inhibitory populations, we observed that Aβ-ES modestly potentiated evoked activity of excitatory neurons and depressed that of inhibitory neurons. Aβ-ES had no significant effects in a slice preparation in either SDH population. A larger proportion of SDH neurons was activated by Aβ-ES when delivered at a root rostral or caudal to the segment where the imaging and C-fiber intensity stimulation occurred. Aβ-ES effects on excitatory and inhibitory populations depended on the root used. Our findings suggest that Aβ-ES differentially modulates lumbar spinal cord SDH populations in a cell type- and input-specific manner. Furthermore, they underscore the importance of the Aβ-ES delivery site, suggesting that Aβ stimulation at a segment adjacent to where the pain is may improve analgesic efficacy.
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Affiliation(s)
- Wei Fan
- Department of Anesthesiology and Perioperative Medicine, Oregon Health & Science University, Portland, OR, United States
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45
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Leinders-Zufall T, Storch U, Mederos y Schnitzler M, Ojha NK, Koike K, Gudermann T, Zufall F. A diacylglycerol photoswitching protocol for studying TRPC channel functions in mammalian cells and tissue slices. STAR Protoc 2021; 2:100527. [PMID: 34027485 PMCID: PMC8121987 DOI: 10.1016/j.xpro.2021.100527] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Small molecular probes designed for photopharmacology and opto-chemogenetics are rapidly gaining widespread recognition for investigations of transient receptor potential canonical (TRPC) channels. This protocol describes the use of three photoswitchable diacylglycerol analogs—PhoDAG-1, PhoDAG-3, and OptoDArG—for ultrarapid activation and deactivation of native TRPC2 channels in mouse vomeronasal sensory neurons and olfactory type B cells, as well as heterologously expressed human TRPC6 channels. Photoconversion can be achieved in mammalian tissue slices and enables all-optical stimulation and shutoff of TRPC channels. For complete details on the use and execution of this protocol, please refer to Leinders-Zufall et al. (2018). DAG photoswitching enables ultrarapid activation and deactivation of TRPC channels Multiple photoswitchable DAG analogs are now available DAG photoconversion is sufficient for the gating of TRPC2, TRPC3, and TRPC6 Photoswitching combined with Ca2+ imaging enables all-optical stimulation and recording
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Affiliation(s)
- Trese Leinders-Zufall
- Center for Integrative Physiology and Molecular Medicine, Saarland University, 66421 Homburg, Germany
| | - Ursula Storch
- Walther-Straub-Institut für Pharmakologie und Toxikologie, Ludwig-Maximilians-Universität München, 80336 München, Germany
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-Universität München, 80336 München, Germany
| | - Michael Mederos y Schnitzler
- Walther-Straub-Institut für Pharmakologie und Toxikologie, Ludwig-Maximilians-Universität München, 80336 München, Germany
| | - Navin K. Ojha
- Center for Integrative Physiology and Molecular Medicine, Saarland University, 66421 Homburg, Germany
| | - Kohei Koike
- Center for Integrative Physiology and Molecular Medicine, Saarland University, 66421 Homburg, Germany
| | - Thomas Gudermann
- Walther-Straub-Institut für Pharmakologie und Toxikologie, Ludwig-Maximilians-Universität München, 80336 München, Germany
| | - Frank Zufall
- Center for Integrative Physiology and Molecular Medicine, Saarland University, 66421 Homburg, Germany
- Corresponding author
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Neugornet A, O'Donovan B, Ortinski PI. Comparative Effects of Event Detection Methods on the Analysis and Interpretation of Ca 2+ Imaging Data. Front Neurosci 2021; 15:620869. [PMID: 33841076 PMCID: PMC8032960 DOI: 10.3389/fnins.2021.620869] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 01/25/2021] [Indexed: 01/04/2023] Open
Abstract
Calcium imaging has gained substantial popularity as a tool to profile the activity of multiple simultaneously active cells at high spatiotemporal resolution. Among the diverse approaches to processing of Ca2+ imaging data is an often subjective decision of how to quantify baseline fluorescence or F 0. We examine the effect of popular F 0 determination methods on the interpretation of neuronal and astrocyte activity in a single dataset of rats trained to self-administer intravenous infusions of cocaine and compare them with an F 0-independent wavelet ridgewalking event detection approach. We find that the choice of the processing method has a profound impact on the interpretation of widefield imaging results. All of the dF/F 0 thresholding methods tended to introduce spurious events and fragment individual transients, leading to smaller calculated event durations and larger event frequencies. Analysis of simulated datasets confirmed these observations and indicated substantial intermethod variability as to the events classified as significant. Additionally, most dF/F 0 methods on their own were unable to adequately account for bleaching of fluorescence, although the F 0 smooth approach and the wavelet ridgewalking algorithm both did so. In general, the choice of the processing method led to dramatically different quantitative and sometimes opposing qualitative interpretations of the effects of cocaine self-administration both at the level of individual cells and at the level of cell networks. Significantly different distributions of event duration, amplitude, frequency, and network measures were found across the majority of dF/F 0 approaches. The wavelet ridgewalking algorithm broadly outperformed dF/F 0-based methods for both neuron and astrocyte recordings. These results indicate the need for heightened awareness of the limitations and tendencies associated with decisions to use particular Ca2+ image processing pipelines. Both quantification and interpretation of the effects of experimental manipulations are strongly sensitive to such decisions.
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Affiliation(s)
- Austin Neugornet
- Department of Neuroscience, School of Medicine, University of Kentucky, Lexington, KY, United States
| | - Bernadette O'Donovan
- Department of Pharmacology, Physiology and Neuroscience, University of South Carolina School of Medicine, Columbia, SC, United States
| | - Pavel Ivanovich Ortinski
- Department of Neuroscience, School of Medicine, University of Kentucky, Lexington, KY, United States
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47
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Roth RH, Ding JB. From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior. BME FRONTIERS 2020; 2020:7190517. [PMID: 37849967 PMCID: PMC10521756 DOI: 10.34133/2020/7190517] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/27/2020] [Indexed: 10/19/2023] Open
Abstract
Understanding how brain activity encodes information and controls behavior is a long-standing question in neuroscience. This complex problem requires converging efforts from neuroscience and engineering, including technological solutions to perform high-precision and large-scale recordings of neuronal activity in vivo as well as unbiased methods to reliably measure and quantify behavior. Thanks to advances in genetics, molecular biology, engineering, and neuroscience, in recent decades, a variety of optical imaging and electrophysiological approaches for recording neuronal activity in awake animals have been developed and widely applied in the field. Moreover, sophisticated computer vision and machine learning algorithms have been developed to analyze animal behavior. In this review, we provide an overview of the current state of technology for neuronal recordings with a focus on optical and electrophysiological methods in rodents. In addition, we discuss areas that future technological development will need to cover in order to further our understanding of the neural activity underlying behavior.
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Affiliation(s)
- Richard H Roth
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Jun B Ding
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
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Campos P, Walker JJ, Mollard P. Diving into the brain: deep-brain imaging techniques in conscious animals. J Endocrinol 2020; 246:R33-R50. [PMID: 32380471 PMCID: PMC7354703 DOI: 10.1530/joe-20-0028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 05/07/2020] [Indexed: 01/28/2023]
Abstract
In most species, survival relies on the hypothalamic control of endocrine axes that regulate critical functions such as reproduction, growth, and metabolism. For decades, the complexity and inaccessibility of the hypothalamic-pituitary axis has prevented researchers from elucidating the relationship between the activity of endocrine hypothalamic neurons and pituitary hormone secretion. Indeed, the study of central control of endocrine function has been largely dominated by 'traditional' techniques that consist of studying in vitro or ex vivo isolated cell types without taking into account the complexity of regulatory mechanisms at the level of the brain, pituitary and periphery. Nowadays, by exploiting modern neuronal transfection and imaging techniques, it is possible to study hypothalamic neuron activity in situ, in real time, and in conscious animals. Deep-brain imaging of calcium activity can be performed through gradient-index lenses that are chronically implanted and offer a 'window into the brain' to image multiple neurons at single-cell resolution. With this review, we aim to highlight deep-brain imaging techniques that enable the study of neuroendocrine neurons in awake animals whilst maintaining the integrity of regulatory loops between the brain, pituitary and peripheral glands. Furthermore, to assist researchers in setting up these techniques, we discuss the equipment required and include a practical step-by-step guide to performing these deep-brain imaging studies.
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Affiliation(s)
- Pauline Campos
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Correspondence should be addressed to P Campos:
| | - Jamie J Walker
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
- Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Patrice Mollard
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
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49
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Tran LM, Mocle AJ, Ramsaran AI, Jacob AD, Frankland PW, Josselyn SA. Automated Curation of CNMF-E-Extracted ROI Spatial Footprints and Calcium Traces Using Open-Source AutoML Tools. Front Neural Circuits 2020; 14:42. [PMID: 32792911 PMCID: PMC7384547 DOI: 10.3389/fncir.2020.00042] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/19/2020] [Indexed: 11/17/2022] Open
Abstract
In vivo 1-photon (1p) calcium imaging is an increasingly prevalent method in behavioral neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium imaging datasets. Despite these advancements in pre-processing methods, manual curation of the extracted spatial footprints and calcium traces of neurons remains important for quality control. Here, we propose an additional semi-automated curation step for sorting spatial footprints and calcium traces from putative neurons extracted using the popular constrained non-negative matrixfactorization for microendoscopic data (CNMF-E) algorithm. We used the automated machine learning (AutoML) tools TPOT and AutoSklearn to generate classifiers to curate the extracted ROIs trained on a subset of human-labeled data. AutoSklearn produced the best performing classifier, achieving an F1 score >92% on the ground truth test dataset. This automated approach is a useful strategy for filtering ROIs with relatively few labeled data points and can be easily added to pre-existing pipelines currently using CNMF-E for ROI extraction.
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Affiliation(s)
- Lina M. Tran
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Postgraduate Affiliates Program, Vector Institute, Toronto, ON, Canada
| | - Andrew J. Mocle
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Adam I. Ramsaran
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Alexander D. Jacob
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Paul W. Frankland
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada
| | - Sheena A. Josselyn
- Hospital for Sick Children, Neurosciences and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Brain, Mind & Consciousness Program, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada
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50
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Tu M, Zhao R, Adler A, Gan WB, Chen ZS. Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus. Neural Comput 2020; 32:1144-1167. [PMID: 32343646 PMCID: PMC8011981 DOI: 10.1162/neco_a_01281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the animal's position. We validate our spike inference-free decoding methods in multiple in vivo calcium imaging recordings of the mouse hippocampus based on both supervised and unsupervised decoding analyses. We systematically investigate the decoding performance of our proposed methods with respect to the number of neurons, imaging frame rate, and signal-to-noise ratio. Our proposed supervised decoding analysis is ultrafast and robust, and thereby appealing for real-time position decoding applications based on calcium imaging.
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Affiliation(s)
- Mengyu Tu
- Department of Psychiatry, New York University School of Medicine, New York, NY 10016, U.S.A., and Nanyang Technological University, 639798, Singapore
| | - Ruohe Zhao
- Skirball Institute, Department of Neuroscience and Physiology and Department of Anesthesiology, New York University School of Medicine, New York, NY 10016, U.S.A., and Key Laboratory of Chemical Genomics, Peking University, Shenzhen Graduate School, Shenzhen 518055, China
| | - Avital Adler
- Skirball Institute, Department of Neuroscience and Physiology and Department of Anesthesiology, New York University School of Medicine, New York, NY 10016, U.S.A.
| | - Wen-Biao Gan
- Skirball Institute, Department of Neuroscience and Physiology, Department of Anesthesiology, and Neuroscience Institute, New York University School of Medicine, New York, NY 10016, U.S.A.
| | - Zhe S Chen
- Department of Psychiatry and Neuroscience Institute, New York University School of Medicine, New York, NY 10016, U.S.A.
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