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Abstract
Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.
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Affiliation(s)
- Francesco Cutrale
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
| | - Scott E. Fraser
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA
| | - Le A. Trinh
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA
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102
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Gouwens NW, Sorensen SA, Berg J, Lee C, Jarsky T, Ting J, Sunkin SM, Feng D, Anastassiou CA, Barkan E, Bickley K, Blesie N, Braun T, Brouner K, Budzillo A, Caldejon S, Casper T, Castelli D, Chong P, Crichton K, Cuhaciyan C, Daigle TL, Dalley R, Dee N, Desta T, Ding SL, Dingman S, Doperalski A, Dotson N, Egdorf T, Fisher M, de Frates RA, Garren E, Garwood M, Gary A, Gaudreault N, Godfrey K, Gorham M, Gu H, Habel C, Hadley K, Harrington J, Harris JA, Henry A, Hill D, Josephsen S, Kebede S, Kim L, Kroll M, Lee B, Lemon T, Link KE, Liu X, Long B, Mann R, McGraw M, Mihalas S, Mukora A, Murphy GJ, Ng L, Ngo K, Nguyen TN, Nicovich PR, Oldre A, Park D, Parry S, Perkins J, Potekhina L, Reid D, Robertson M, Sandman D, Schroedter M, Slaughterbeck C, Soler-Llavina G, Sulc J, Szafer A, Tasic B, Taskin N, Teeter C, Thatra N, Tung H, Wakeman W, Williams G, Young R, Zhou Z, Farrell C, Peng H, Hawrylycz MJ, Lein E, Ng L, Arkhipov A, Bernard A, Phillips JW, Zeng H, Koch C. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 2019; 22:1182-1195. [PMID: 31209381 PMCID: PMC8078853 DOI: 10.1038/s41593-019-0417-0] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 04/25/2019] [Indexed: 12/21/2022]
Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.
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Affiliation(s)
| | | | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Susan M Sunkin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Eliza Barkan
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kris Bickley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nicole Blesie
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Thomas Braun
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Krissy Brouner
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Agata Budzillo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Dan Castelli
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Peter Chong
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tanya L Daigle
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rachel Dalley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tsega Desta
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Samuel Dingman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Tom Egdorf
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Michael Fisher
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Emma Garren
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Amanda Gary
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Keith Godfrey
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Melissa Gorham
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hong Gu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Caroline Habel
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kristen Hadley
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alex Henry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - DiJon Hill
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sam Josephsen
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lisa Kim
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Matthew Kroll
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Tracy Lemon
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Xiaoxiao Liu
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Medea McGraw
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Gabe J Murphy
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | - Aaron Oldre
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Daniel Park
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Sheana Parry
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Jed Perkins
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - David Sandman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | | | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Aaron Szafer
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Naz Taskin
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Corinne Teeter
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Grace Williams
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Rob Young
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Colin Farrell
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, Washington, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, USA.
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, USA
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103
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Mao M, Nair A, Augustine GJ. A Novel Type of Neuron Within the Dorsal Striatum. Front Neural Circuits 2019; 13:32. [PMID: 31164808 PMCID: PMC6536632 DOI: 10.3389/fncir.2019.00032] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 04/12/2019] [Indexed: 11/13/2022] Open
Abstract
The striatum is predominantly composed of medium spiny projection neurons, with the remaining neurons consisting of several types of interneurons. Among the interneurons are a group of cells that express tyrosine hydroxylase (TH). Although the intrinsic electrical properties of these TH-expressing interneurons have been characterized, there is no agreement on the number of TH-expressing cell types and their electrical properties. Here, we have used transgenic mice in which YFP-tagged channelrhodopsin-2 (ChR2) was expressed in potential TH-expressing cells in a Cre-dependent manner. We found that the YFP+ neurons in the striatum were heterogeneous in their intrinsic electrical properties; unbiased clustering indicated that there are three main neuronal subtypes. One population of neurons had aspiny dendrites with high-frequency action potential (AP) firing and plateau potentials, resembling the TH interneurons (THIN) described previously. A second, very small population of labeled neurons resembled medium-sized spiny neurons (MSN). The third population of neurons had dendrites with an intermediate density of spines, showed substantial AP adaptation and generated prolonged spikes. This type of striatal neuron has not been previously identified in the adult mouse and we have named it the Frequency-Adapting Neuron with Spines (FANS). Because of their distinctive properties, FANS may play a unique role in striatal information processing.
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Affiliation(s)
- Miaomiao Mao
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Institute of Molecular and Cell Biology, Singapore, Singapore
| | - Aditya Nair
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Institute of Molecular and Cell Biology, Singapore, Singapore
- Life Sciences Programme, Faculty of Science, National University of Singapore, Singapore, Singapore
- Singapore Bioimaging Consortium, Agency of Science, Technology and Research, Singapore, Singapore
| | - George J. Augustine
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Institute of Molecular and Cell Biology, Singapore, Singapore
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104
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Extreme Compartmentalization in a Drosophila Amacrine Cell. Curr Biol 2019; 29:1545-1550.e2. [PMID: 31031119 DOI: 10.1016/j.cub.2019.03.070] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/08/2019] [Accepted: 03/28/2019] [Indexed: 11/22/2022]
Abstract
A neuron is conventionally regarded as a single processing unit. It receives input from one or several presynaptic cells, transforms these signals, and transmits one output signal to its postsynaptic partners. Exceptions exist: amacrine cells in the mammalian retina [1-3] or interneurons in the locust mesothoracic ganglion [4] are thought to represent many electrically isolated microcircuits within one neuron. An extreme case of such an amacrine cell has recently been described in the Drosophila visual system. This cell, called CT1, reaches into two neuropils of the optic lobe, where it visits each of 700 repetitive columns, thereby covering the whole visual field [5, 6]. Due to its unusual morphology, CT1 has been suspected to perform local computations [6, 7], but this has never been proven. Using 2-photon calcium imaging and visual stimulation, we find highly compartmentalized retinotopic response properties in neighboring terminals of CT1, with each terminal acting as an independent functional unit. Model simulations demonstrate that this extreme case of compartmentalization is at the biophysical limit of neural computation.
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105
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Diverse synaptic and dendritic mechanisms of complex spike burst generation in hippocampal CA3 pyramidal cells. Nat Commun 2019; 10:1859. [PMID: 31015414 PMCID: PMC6478939 DOI: 10.1038/s41467-019-09767-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 03/27/2019] [Indexed: 01/21/2023] Open
Abstract
Complex spike bursts (CSBs) represent a characteristic firing pattern of hippocampal pyramidal cells (PCs). In CA1PCs, CSBs are driven by regenerative dendritic plateau potentials, produced by correlated entorhinal cortical and CA3 inputs that simultaneously depolarize distal and proximal dendritic domains. However, in CA3PCs neither the generation mechanisms nor the computational role of CSBs are well elucidated. We show that CSBs are induced by dendritic Ca2+ spikes in CA3PCs. Surprisingly, the ability of CA3PCs to produce CSBs is heterogeneous, with non-uniform synaptic input-output transformation rules triggering CSBs. The heterogeneity is partly related to the topographic position of CA3PCs; we identify two ion channel types, HCN and Kv2 channels, whose proximodistal activity gradients contribute to subregion-specific modulation of CSB propensity. Our results suggest that heterogeneous dendritic integrative properties, along with previously reported synaptic connectivity gradients, define functional subpopulations of CA3PCs that may support CA3 network computations underlying associative memory processes.
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106
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Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo. Neuroimage 2019; 191:457-469. [PMID: 30818026 DOI: 10.1016/j.neuroimage.2019.02.046] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/21/2019] [Accepted: 02/19/2019] [Indexed: 01/08/2023] Open
Abstract
Reactive astrocytes exhibit hypertrophic morphology and altered metabolism. Deciphering astrocytic status would be of great importance to understand their role and dysregulation in pathologies, but most analytical methods remain highly invasive or destructive. The diffusion of brain metabolites, as non-invasively measured using diffusion-weighted magnetic resonance spectroscopy (DW-MRS) in vivo, depends on the structure of their micro-environment. Here we perform advanced DW-MRS in a mouse model of reactive astrocytes to determine how cellular compartments confining metabolite diffusion are changing. This reveals myo-inositol as a specific intra-astrocytic marker whose diffusion closely reflects astrocytic morphology, enabling non-invasive detection of astrocyte hypertrophy (subsequently confirmed by confocal microscopy ex vivo). Furthermore, we measure massive variations of lactate diffusion properties, suggesting that intracellular lactate is predominantly astrocytic under control conditions, but predominantly neuronal in case of astrocyte reactivity. This indicates massive remodeling of lactate metabolism, as lactate compartmentation is tightly linked to the astrocyte-to-neuron lactate shuttle mechanism.
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107
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Skibbe H, Reisert M, Nakae K, Watakabe A, Hata J, Mizukami H, Okano H, Yamamori T, Ishii S. PAT-Probabilistic Axon Tracking for Densely Labeled Neurons in Large 3-D Micrographs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:69-78. [PMID: 30010551 DOI: 10.1109/tmi.2018.2855736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A major goal of contemporary neuroscience research is to map the structural connectivity of mammalian brain using microscopy imaging data. In this context, the reconstruction of densely labeled axons from two-photon microscopy images is a challenging and important task. The visually overlapping, crossing, and often strongly distorted images of the axons allow many ambiguous interpretations to be made. We address the problem of tracking axons in densely labeled samples of neurons in large image data sets acquired from marmoset brains. Our high-resolution images were acquired using two-photon microscopy and they provided whole brain coverage, occupying terabytes of memory. Both the image distortions and the large data set size frequently make it impractical to apply present-day neuron tracing algorithms to such data due to the optimization of such algorithms to the precise tracing of either single or sparse sets of neurons. Thus, new tracking techniques are needed. We propose a probabilistic axon tracking algorithm (PAT). PAT tackles the tracking of axons in two steps: locally (L-PAT) and globally (G-PAT). L-PAT is a probabilistic tracking algorithm that can tackle distorted, cluttered images of densely labeled axons. L-PAT divides a large micrograph into smaller image stacks. It then processes each image stack independently before mapping the axons in each image to a sparse model of axon trajectories. G-PAT merges the sparse L-PAT models into a single global model of axon trajectories by minimizing a global objective function using a probabilistic optimization method. We demonstrate the superior performance of PAT over standard approaches on synthetic data. Furthermore, we successfully apply PAT to densely labeled axons in large images acquired from marmoset brains.
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108
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Liu S, Zhang D, Song Y, Peng H, Cai W. Automated 3-D Neuron Tracing With Precise Branch Erasing and Confidence Controlled Back Tracking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2441-2452. [PMID: 29993997 DOI: 10.1109/tmi.2018.2833420] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backward from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing with the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one user specified parameter for background segmentation. We bench tested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results.
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109
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Systematic Analysis of Transmitter Coexpression Reveals Organizing Principles of Local Interneuron Heterogeneity. eNeuro 2018; 5:eN-NWR-0212-18. [PMID: 30294668 PMCID: PMC6171738 DOI: 10.1523/eneuro.0212-18.2018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 09/07/2018] [Accepted: 09/13/2018] [Indexed: 01/02/2023] Open
Abstract
Broad neuronal classes are surprisingly heterogeneous across many parameters, and subclasses often exhibit partially overlapping traits including transmitter coexpression. However, the extent to which transmitter coexpression occurs in predictable, consistent patterns is unknown. Here, we demonstrate that pairwise coexpression of GABA and multiple neuropeptide families by olfactory local interneurons (LNs) of the moth Manduca sexta is highly heterogeneous, with a single LN capable of expressing neuropeptides from at least four peptide families and few instances in which neuropeptides are consistently coexpressed. Using computational modeling, we demonstrate that observed coexpression patterns cannot be explained by independent probabilities of expression of each neuropeptide. Our analyses point to three organizing principles that, once taken into consideration, allow replication of overall coexpression structure: (1) peptidergic neurons are highly likely to coexpress GABA; (2) expression probability of allatotropin depends on myoinhibitory peptide expression; and (3) the all-or-none coexpression patterns of tachykinin neurons with several other neuropeptides. For other peptide pairs, the presence of one peptide was not predictive of the presence of the other, and coexpression probability could be replicated by independent probabilities. The stochastic nature of these coexpression patterns highlights the heterogeneity of transmitter content among LNs and argues against clear-cut definition of subpopulation types based on the presence of single neuropeptides. Furthermore, the receptors for all neuropeptides and GABA were expressed within each population of principal neuron type in the antennal lobe (AL). Thus, activation of any given LN results in a dynamic cocktail of modulators that have the potential to influence every level of olfactory processing within the AL.
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110
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Kalinin AA, Allyn-Feuer A, Ade A, Fon GV, Meixner W, Dilworth D, Husain SS, de Wet JR, Higgins GA, Zheng G, Creekmore A, Wiley JW, Verdone JE, Veltri RW, Pienta KJ, Coffey DS, Athey BD, Dinov ID. 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification. Sci Rep 2018; 8:13658. [PMID: 30209281 PMCID: PMC6135819 DOI: 10.1038/s41598-018-31924-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/29/2018] [Indexed: 02/08/2023] Open
Abstract
Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.
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Affiliation(s)
- Alexandr A Kalinin
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Ari Allyn-Feuer
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Alex Ade
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gordon-Victor Fon
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Walter Meixner
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - David Dilworth
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Syed S Husain
- Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Jeffrey R de Wet
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gerald A Higgins
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gen Zheng
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Amy Creekmore
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John W Wiley
- Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James E Verdone
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert W Veltri
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenneth J Pienta
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Donald S Coffey
- Department of Urology, James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian D Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
| | - Ivo D Dinov
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA. .,Statistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA. .,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, USA.
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111
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Kisner A, Slocomb JE, Sarsfield S, Zuccoli ML, Siemian J, Gupta JF, Kumar A, Aponte Y. Electrophysiological properties and projections of lateral hypothalamic parvalbumin positive neurons. PLoS One 2018; 13:e0198991. [PMID: 29894514 PMCID: PMC5997303 DOI: 10.1371/journal.pone.0198991] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 05/30/2018] [Indexed: 11/18/2022] Open
Abstract
Cracking the cytoarchitectural organization, activity patterns, and neurotransmitter nature of genetically-distinct cell types in the lateral hypothalamus (LH) is fundamental to develop a mechanistic understanding of how activity dynamics within this brain region are generated and operate together through synaptic connections to regulate circuit function. However, the precise mechanisms through which LH circuits orchestrate such dynamics have remained elusive due to the heterogeneity of the intermingled and functionally distinct cell types in this brain region. Here we reveal that a cell type in the mouse LH identified by the expression of the calcium-binding protein parvalbumin (PVALB; LHPV) is fast-spiking, releases the excitatory neurotransmitter glutamate, and sends long range projections throughout the brain. Thus, our findings challenge long-standing concepts that define neurons with a fast-spiking phenotype as exclusively GABAergic. Furthermore, we provide for the first time a detailed characterization of the electrophysiological properties of these neurons. Our work identifies LHPV neurons as a novel functional component within the LH glutamatergic circuitry.
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Affiliation(s)
- Alexandre Kisner
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Julia E. Slocomb
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Sarah Sarsfield
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Maria Laura Zuccoli
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
- Department of Internal Medicine, Pharmacology and Toxicology Unit, University of Genoa, Genoa, Italy
| | - Justin Siemian
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Jay F. Gupta
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Arvind Kumar
- Department of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yeka Aponte
- Neuronal Circuits and Behavior Unit, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, Maryland, United States of America
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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112
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Zhou Z, Kuo HC, Peng H, Long F. DeepNeuron: an open deep learning toolbox for neuron tracing. Brain Inform 2018; 5:3. [PMID: 29876679 PMCID: PMC5990497 DOI: 10.1186/s40708-018-0081-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 11/10/2022] Open
Abstract
Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.
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Affiliation(s)
- Zhi Zhou
- Allen Institute for Brain Science, Seattle, USA.,Southeast University - Allen Institute Joint Center for Neuron Morphology, Southeast University, Nanjing, China
| | | | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, USA. .,Southeast University - Allen Institute Joint Center for Neuron Morphology, Southeast University, Nanjing, China.
| | - Fuhui Long
- Allen Institute for Brain Science, Seattle, USA.
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113
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Chatterjee S, Sullivan HA, MacLennan BJ, Xu R, Hou Y, Lavin TK, Lea NE, Michalski JE, Babcock KR, Dietrich S, Matthews GA, Beyeler A, Calhoon GG, Glober G, Whitesell JD, Yao S, Cetin A, Harris JA, Zeng H, Tye KM, Reid RC, Wickersham IR. Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons. Nat Neurosci 2018; 21:638-646. [PMID: 29507411 PMCID: PMC6503322 DOI: 10.1038/s41593-018-0091-7] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Accepted: 01/14/2018] [Indexed: 12/25/2022]
Abstract
Recombinant rabies viral vectors have proven useful for applications including retrograde targeting of projection neurons and monosynaptic tracing, but their cytotoxicity has limited their use to short-term experiments. Here we introduce a new class of double-deletion-mutant rabies viral vectors that left transduced cells alive and healthy indefinitely. Deletion of the viral polymerase gene abolished cytotoxicity and reduced transgene expression to trace levels but left vectors still able to retrogradely infect projection neurons and express recombinases, allowing downstream expression of other transgene products such as fluorophores and calcium indicators. The morphology of retrogradely targeted cells appeared unperturbed at 1 year postinjection. Whole-cell patch-clamp recordings showed no physiological abnormalities at 8 weeks. Longitudinal two-photon structural and functional imaging in vivo, tracking thousands of individual neurons for up to 4 months, showed that transduced neurons did not die but retained stable visual response properties even at the longest time points imaged.
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Affiliation(s)
| | - Heather A Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Ran Xu
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - YuanYuan Hou
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas K Lavin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas E Lea
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jacob E Michalski
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kelsey R Babcock
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephan Dietrich
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gillian A Matthews
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna Beyeler
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gwendolyn G Calhoon
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gordon Glober
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kay M Tye
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
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114
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Miller MA, Chandra R, Cuccarese MF, Pfirschke C, Engblom C, Stapleton S, Adhikary U, Kohler RH, Mohan JF, Pittet MJ, Weissleder R. Radiation therapy primes tumors for nanotherapeutic delivery via macrophage-mediated vascular bursts. Sci Transl Med 2018; 9:9/392/eaal0225. [PMID: 28566423 DOI: 10.1126/scitranslmed.aal0225] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 01/23/2017] [Accepted: 04/24/2017] [Indexed: 12/13/2022]
Abstract
Efficient delivery of therapeutic nanoparticles (TNPs) to tumors is critical in improving efficacy, yet strategies that universally maximize tumoral targeting by TNP modification have been difficult to achieve in the clinic. Instead of focusing on TNP optimization, we show that the tumor microenvironment itself can be therapeutically primed to facilitate accumulation of multiple clinically relevant TNPs. Building on the recent finding that tumor-associated macrophages (TAM) can serve as nanoparticle drug depots, we demonstrate that local tumor irradiation substantially increases TAM relative to tumor cells and, thus, TNP delivery. High-resolution intravital imaging reveals that after radiation, TAM primarily accumulate adjacent to microvasculature, elicit dynamic bursts of extravasation, and subsequently enhance drug uptake in neighboring tumor cells. TAM depletion eliminates otherwise beneficial radiation effects on TNP accumulation and efficacy, and controls with unencapsulated drug show that radiation effects are more pronounced with TNPs. Priming with combined radiation and cyclophosphamide enhances vascular bursting and tumoral TNP concentration, in some cases leading to a sixfold increase of TNP accumulation in the tumor, reaching 6% of the injected dose per gram of tissue. Radiation therapy alters tumors for enhanced TNP delivery in a TAM-dependent fashion, and these observations have implications for the design of next-generation tumor-targeted nanomaterials and clinical trials for adjuvant strategies.
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Affiliation(s)
- Miles A Miller
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA.,Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Ravi Chandra
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA.,Harvard Radiation Oncology Program, 55 Fruit Street, Boston, MA 02114, USA
| | - Michael F Cuccarese
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Christina Pfirschke
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Camilla Engblom
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Shawn Stapleton
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Utsarga Adhikary
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Rainer H Kohler
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - James F Mohan
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA
| | - Mikael J Pittet
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA.,Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, MA 02114, USA. .,Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA.,Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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115
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Schubert FK, Hagedorn N, Yoshii T, Helfrich-Förster C, Rieger D. Neuroanatomical details of the lateral neurons of Drosophila melanogaster support their functional role in the circadian system. J Comp Neurol 2018; 526:1209-1231. [PMID: 29424420 PMCID: PMC5873451 DOI: 10.1002/cne.24406] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 01/30/2018] [Accepted: 01/30/2018] [Indexed: 12/29/2022]
Abstract
Drosophila melanogaster is a long‐standing model organism in the circadian clock research. A major advantage is the relative small number of about 150 neurons, which built the circadian clock in Drosophila. In our recent work, we focused on the neuroanatomical properties of the lateral neurons of the clock network. By applying the multicolor‐labeling technique Flybow we were able to identify the anatomical similarity of the previously described E2 subunit of the evening oscillator of the clock, which is built by the 5th small ventrolateral neuron (5th s‐LNv) and one ITP positive dorsolateral neuron (LNd). These two clock neurons share the same spatial and functional properties. We found both neurons innervating the same brain areas with similar pre‐ and postsynaptic sites in the brain. Here the anatomical findings support their shared function as a main evening oscillator in the clock network like also found in previous studies. A second quite surprising finding addresses the large lateral ventral PDF‐neurons (l‐LNvs). We could show that the four hardly distinguishable l‐LNvs consist of two subgroups with different innervation patterns. While three of the neurons reflect the well‐known branching pattern reproduced by PDF immunohistochemistry, one neuron per brain hemisphere has a distinguished innervation profile and is restricted only to the proximal part of the medulla‐surface. We named this neuron “extra” l‐LNv (l‐LNvx). We suggest the anatomical findings reflect different functional properties of the two l‐LNv subgroups.
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Affiliation(s)
- Frank K Schubert
- Neurobiology and Genetics, Theodor-Boveri Institute, Biocenter, University of Würzburg, Würzburg, 97074, Germany
| | - Nicolas Hagedorn
- Neurobiology and Genetics, Theodor-Boveri Institute, Biocenter, University of Würzburg, Würzburg, 97074, Germany
| | - Taishi Yoshii
- Graduate School of Natural Science and Technology, Okayama University, Okayama, 700-8530, Japan
| | - Charlotte Helfrich-Förster
- Neurobiology and Genetics, Theodor-Boveri Institute, Biocenter, University of Würzburg, Würzburg, 97074, Germany
| | - Dirk Rieger
- Neurobiology and Genetics, Theodor-Boveri Institute, Biocenter, University of Würzburg, Würzburg, 97074, Germany
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116
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Gould EA, Busquet N, Shepherd D, Dietz RM, Herson PS, Simoes de Souza FM, Li A, George NM, Restrepo D, Macklin WB. Mild myelin disruption elicits early alteration in behavior and proliferation in the subventricular zone. eLife 2018; 7:34783. [PMID: 29436368 PMCID: PMC5828668 DOI: 10.7554/elife.34783] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 02/01/2018] [Indexed: 11/16/2022] Open
Abstract
Myelin, the insulating sheath around axons, supports axon function. An important question is the impact of mild myelin disruption. In the absence of the myelin protein proteolipid protein (PLP1), myelin is generated but with age, axonal function/maintenance is disrupted. Axon disruption occurs in Plp1-null mice as early as 2 months in cortical projection neurons. High-volume cellular quantification techniques revealed a region-specific increase in oligodendrocyte density in the olfactory bulb and rostral corpus callosum that increased during adulthood. A distinct proliferative response of progenitor cells was observed in the subventricular zone (SVZ), while the number and proliferation of parenchymal oligodendrocyte progenitor cells was unchanged. This SVZ proliferative response occurred prior to evidence of axonal disruption. Thus, a novel SVZ response contributes to the region-specific increase in oligodendrocytes in Plp1-null mice. Young adult Plp1-null mice exhibited subtle but substantial behavioral alterations, indicative of an early impact of mild myelin disruption.
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Affiliation(s)
- Elizabeth A Gould
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, United States.,Rocky Mountain Taste and Smell Center, University of Colorado Anschutz Medical Campus, Aurora, United States.,Neuroscience Program, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Nicolas Busquet
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Douglas Shepherd
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, United States.,Pediatric Heart Lung Center, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Robert M Dietz
- Department of Anesthesiology, University of Colorado School of Medicine, Aurora, United States
| | - Paco S Herson
- Department of Anesthesiology, University of Colorado School of Medicine, Aurora, United States
| | | | - Anan Li
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, China
| | - Nicholas M George
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, United States.,Rocky Mountain Taste and Smell Center, University of Colorado Anschutz Medical Campus, Aurora, United States.,Neuroscience Program, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Diego Restrepo
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, United States.,Rocky Mountain Taste and Smell Center, University of Colorado Anschutz Medical Campus, Aurora, United States.,Neuroscience Program, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Wendy B Macklin
- Department of Cell and Developmental Biology, University of Colorado Anschutz Medical Campus, Aurora, United States.,Rocky Mountain Taste and Smell Center, University of Colorado Anschutz Medical Campus, Aurora, United States.,Neuroscience Program, University of Colorado Anschutz Medical Campus, Aurora, United States
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117
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Abstract
The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.
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118
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Liu S, Zhang D, Liu S, Feng D, Peng H, Cai W. Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking. Neuroinformatics 2018; 14:387-401. [PMID: 27184384 DOI: 10.1007/s12021-016-9302-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The digital reconstruction of single neurons from 3D confocal microscopic images is an important tool for understanding the neuron morphology and function. However the accurate automatic neuron reconstruction remains a challenging task due to the varying image quality and the complexity in the neuronal arborisation. Targeting the common challenges of neuron tracing, we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises. By evaluating the proposed pipeline with the data provided by the Diadem challenge and the recent BigNeuron project, Rivulet is shown to be robust to challenging microscopic imagestacks. We discussed the algorithm design in technical details regarding the relationships between the proposed algorithm and the other state-of-the-art neuron tracing algorithms.
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Affiliation(s)
- Siqi Liu
- School of Information Technologies, University of Sydney, Darlington, NSW, Australia.
| | - Donghao Zhang
- School of Information Technologies, University of Sydney, Darlington, NSW, Australia
| | - Sidong Liu
- School of Information Technologies, University of Sydney, Darlington, NSW, Australia
| | - Dagan Feng
- School of Information Technologies, University of Sydney, Darlington, NSW, Australia
| | | | - Weidong Cai
- School of Information Technologies, University of Sydney, Darlington, NSW, Australia.
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119
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Nanda S, Chen H, Das R, Bhattacharjee S, Cuntz H, Torben-Nielsen B, Peng H, Cox DN, De Schutter E, Ascoli GA. Design and implementation of multi-signal and time-varying neural reconstructions. Sci Data 2018; 5:170207. [PMID: 29360104 PMCID: PMC5779069 DOI: 10.1038/sdata.2017.207] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 12/19/2017] [Indexed: 11/09/2022] Open
Abstract
Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.
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Affiliation(s)
- Sumit Nanda
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Hanbo Chen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ravi Das
- Neuroscience Institute, Georgia State University, Atlanta, GA 30303, USA
| | | | - Hermann Cuntz
- Ernst Strüngmann Institute (ESI), Frankfurt/Main D-60528, Germany
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt/Main D-60438, Germany
| | | | - Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Daniel N. Cox
- Neuroscience Institute, Georgia State University, Atlanta, GA 30303, USA
| | | | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
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120
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Das R, Bhattacharjee S, Patel AA, Harris JM, Bhattacharya S, Letcher JM, Clark SG, Nanda S, Iyer EPR, Ascoli GA, Cox DN. Dendritic Cytoskeletal Architecture Is Modulated by Combinatorial Transcriptional Regulation in Drosophila melanogaster. Genetics 2017; 207:1401-1421. [PMID: 29025914 PMCID: PMC5714456 DOI: 10.1534/genetics.117.300393] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 10/04/2017] [Indexed: 01/08/2023] Open
Abstract
Transcription factors (TFs) have emerged as essential cell autonomous mediators of subtype specific dendritogenesis; however, the downstream effectors of these TFs remain largely unknown, as are the cellular events that TFs control to direct morphological change. As dendritic morphology is largely dictated by the organization of the actin and microtubule (MT) cytoskeletons, elucidating TF-mediated cytoskeletal regulatory programs is key to understanding molecular control of diverse dendritic morphologies. Previous studies in Drosophila melanogaster have demonstrated that the conserved TFs Cut and Knot exert combinatorial control over aspects of dendritic cytoskeleton development, promoting actin and MT-based arbor morphology, respectively. To investigate transcriptional targets of Cut and/or Knot regulation, we conducted systematic neurogenomic studies, coupled with in vivo genetic screens utilizing multi-fluor cytoskeletal and membrane marker reporters. These analyses identified a host of putative Cut and/or Knot effector molecules, and a subset of these putative TF targets converge on modulating dendritic cytoskeletal architecture, which are grouped into three major phenotypic categories, based upon neuromorphometric analyses: complexity enhancer, complexity shifter, and complexity suppressor. Complexity enhancer genes normally function to promote higher order dendritic growth and branching with variable effects on MT stabilization and F-actin organization, whereas complexity shifter and complexity suppressor genes normally function in regulating proximal-distal branching distribution or in restricting higher order branching complexity, respectively, with spatially restricted impacts on the dendritic cytoskeleton. Collectively, we implicate novel genes and cellular programs by which TFs distinctly and combinatorially govern dendritogenesis via cytoskeletal modulation.
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Affiliation(s)
- Ravi Das
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
| | | | - Atit A Patel
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
| | - Jenna M Harris
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
| | | | - Jamin M Letcher
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
| | - Sarah G Clark
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
| | - Sumit Nanda
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia 22030
| | | | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia 22030
| | - Daniel N Cox
- Neuroscience Institute, Georgia State University, Atlanta, Georgia 30302
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121
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Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 2017; 18:529. [PMID: 29187165 PMCID: PMC5708080 DOI: 10.1186/s12859-017-1934-z] [Citation(s) in RCA: 3066] [Impact Index Per Article: 438.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 11/14/2017] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. RESULTS We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.
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Affiliation(s)
- Curtis T Rueden
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Johannes Schindelin
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Mark C Hiner
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Barry E DeZonia
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Alison E Walter
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Ellen T Arena
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
- Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Kevin W Eliceiri
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.
- Morgridge Institute for Research, Madison, Wisconsin, USA.
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122
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Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 2017. [PMID: 29187165 DOI: 10.1186/s12859-017-1934-z.] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. RESULTS We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. CONCLUSIONS Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.
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Affiliation(s)
- Curtis T Rueden
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Johannes Schindelin
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.,Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Mark C Hiner
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Barry E DeZonia
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA
| | - Alison E Walter
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.,Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Ellen T Arena
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA.,Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Kevin W Eliceiri
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin at Madison, Madison, Wisconsin, USA. .,Morgridge Institute for Research, Madison, Wisconsin, USA.
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Stegmaier J, Mikut R. Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines. PLoS One 2017; 12:e0187535. [PMID: 29095927 PMCID: PMC5667823 DOI: 10.1371/journal.pone.0187535] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 10/20/2017] [Indexed: 11/19/2022] Open
Abstract
Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout.
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Affiliation(s)
- Johannes Stegmaier
- Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- * E-mail:
| | - Ralf Mikut
- Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
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124
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Impact of protein content on physical and microstructural properties of extruded rice starch-pea protein snacks. J FOOD ENG 2017. [DOI: 10.1016/j.jfoodeng.2017.05.024] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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125
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Morphological determinants of dendritic arborization neurons in Drosophila larva. Brain Struct Funct 2017; 223:1107-1120. [PMID: 29094302 DOI: 10.1007/s00429-017-1541-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 10/19/2017] [Indexed: 01/08/2023]
Abstract
Pairing in vivo imaging and computational modeling of dendritic arborization (da) neurons from the fruit fly larva provides a unique window into neuronal growth and underlying molecular processes. We image, reconstruct, and analyze the morphology of wild-type, RNAi-silenced, and mutant da neurons. We then use local and global rule-based stochastic simulations to generate artificial arbors, and identify the parameters that statistically best approximate the real data. We observe structural homeostasis in all da classes, where an increase in size of one dendritic stem is compensated by a reduction in the other stems of the same neuron. Local rule models show that bifurcation probability is determined by branch order, while branch length depends on path distance from the soma. Global rule simulations suggest that most complex morphologies tend to be constrained by resource optimization, while simpler neuron classes privilege path distance conservation. Genetic manipulations affect both the local and global optimal parameters, demonstrating functional perturbations in growth mechanisms.
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126
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Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications. Transl Res 2017; 189:1-12. [PMID: 28784428 PMCID: PMC5659947 DOI: 10.1016/j.trsl.2017.07.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 06/26/2017] [Accepted: 07/18/2017] [Indexed: 12/12/2022]
Abstract
Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research.
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127
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Hedgehog signaling regulates ciliary localization of mouse odorant receptors. Proc Natl Acad Sci U S A 2017; 114:E9386-E9394. [PMID: 29078327 DOI: 10.1073/pnas.1708321114] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The ciliary localization of odorant receptors (ORs) is evolutionary conserved and essential for olfactory transduction. However, how the transport of ORs is regulated in mammalian olfactory sensory neurons is poorly understood. Here we demonstrate that odorant responsiveness and OR transport is regulated by the Hedgehog pathway. OR transport is inhibited by conditional gene inactivation of the Hedgehog signal mediator Smoothened (Smo) as well as by systemic administration of the Smo inhibitor vismodegib, a clinically used anticancer drug reported to distort smell perception in patients. The ciliary phenotype of Smo inhibition is haploinsufficient, cell autonomous, and correlates with the accumulation of OR-containing putative transport vesicles in the cytosol. The Smo-dependent OR transport route works in parallel with a low basal transport of vesicle containing both ORs and other olfactory transduction components. These findings both define a physiological function of Hedgehog signaling in olfaction and provide an important evolutionary link between olfaction and the requirement of a ciliary compartment for Hedgehog signaling.
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128
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Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat Biomed Eng 2017; 1:796-806. [DOI: 10.1038/s41551-017-0139-0] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 08/31/2017] [Indexed: 12/13/2022]
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129
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Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy. Nat Commun 2017; 8:612. [PMID: 28931809 PMCID: PMC5606987 DOI: 10.1038/s41467-017-00514-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 06/30/2017] [Indexed: 11/17/2022] Open
Abstract
Optical tissue clearing has revolutionized researchers’ ability to perform fluorescent measurements of molecules, cells, and structures within intact tissue. One common complication to all optically cleared tissue is a spatially heterogeneous refractive index, leading to light scattering and first-order defocus. We designed C-DSLM (cleared tissue digital scanned light-sheet microscopy) as a low-cost method intended to automatically generate in-focus images of cleared tissue. We demonstrate the flexibility and power of C-DSLM by quantifying fluorescent features in tissue from multiple animal models using refractive index matched and mismatched microscope objectives. This includes a unique measurement of myelin tracks within intact tissue using an endogenous fluorescent reporter where typical clearing approaches render such structures difficult to image. For all measurements, we provide independent verification using standard serial tissue sectioning and quantification methods. Paired with advancements in volumetric image processing, C-DSLM provides a robust methodology to quantify sub-micron features within large tissue sections. Optical clearing of tissue has enabled optical imaging deeper into tissue due to significantly reduced light scattering. Here, Ryan et al. tackle first-order defocus, an artefact of a non-uniform refractive index, extending light-sheet microscopy to partially cleared samples.
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130
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Conde-Sousa E, Szücs P, Peng H, Aguiar P. N3DFix: an Algorithm for Automatic Removal of Swelling Artifacts in Neuronal Reconstructions. Neuroinformatics 2017; 15:5-12. [PMID: 27412029 DOI: 10.1007/s12021-016-9308-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
It is well established that not only electrophysiology but also morphology plays an important role in shaping the functional properties of neurons. In order to properly quantify morphological features it is first necessary to translate observational histological data into 3-dimensional geometric reconstructions of the neuronal structures. This reconstruction process, independently of being manual or (semi-)automatic, requires several preparation steps (e.g. histological processing) before data acquisition using specialized software. Unfortunately these processing steps likely produce artifacts which are then carried to the reconstruction, such as tissue shrinkage and formation of swellings. If not accounted for and corrected, these artifacts can change significantly the results from morphometric analysis and computer simulations. Here we present N3DFix, an open-source software which uses a correction algorithm to automatically find and fix swelling artifacts in neuronal reconstructions. N3DFix works as a post-processing tool and therefore can be used in either manual or (semi-)automatic reconstructions. The algorithm's internal parameters have been defined using a "ground truth" dataset produced by a neuroanatomist, involving two complementary manual reconstruction procedures: in the first, neuronal topology was faithfully reconstructed, including all swelling artifacts; in the second procedure a meticulous correction of the artifacts was manually performed directly during neuronal tracing. The internal parameters of N3DFix were set to minimize the differences between manual amendments and the algorithm's corrections. It is shown that the performance of N3DFix is comparable to careful manual correction of the swelling artifacts. To promote easy access and wide adoption, N3DFix is available in NEURON, Vaa3D and Py3DN.
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Affiliation(s)
- Eduardo Conde-Sousa
- CBMA - Centre of Molecular and Environmental Biology, Department of Biology, University of Minho, Braga, Portugal
- CIBIO-InBIO - Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal
- CMUP - Centro de Matemática, Universidade do Porto, Porto, Portugal
| | - Peter Szücs
- MTA-DE-NAP B-Pain Control Research Group, Debrecen, Hungary
- Department of Physiology, University of Debrecen, Debrecen, Hungary
| | | | - Paulo Aguiar
- CMUP - Centro de Matemática, Universidade do Porto, Porto, Portugal.
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.
- INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Porto, Portugal.
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131
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Farahani N, Braun A, Jutt D, Huffman T, Reder N, Liu Z, Yagi Y, Pantanowitz L. Three-dimensional Imaging and Scanning: Current and Future Applications for Pathology. J Pathol Inform 2017; 8:36. [PMID: 28966836 PMCID: PMC5609355 DOI: 10.4103/jpi.jpi_32_17] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 07/03/2017] [Indexed: 11/04/2022] Open
Abstract
Imaging is vital for the assessment of physiologic and phenotypic details. In the past, biomedical imaging was heavily reliant on analog, low-throughput methods, which would produce two-dimensional images. However, newer, digital, and high-throughput three-dimensional (3D) imaging methods, which rely on computer vision and computer graphics, are transforming the way biomedical professionals practice. 3D imaging has been useful in diagnostic, prognostic, and therapeutic decision-making for the medical and biomedical professions. Herein, we summarize current imaging methods that enable optimal 3D histopathologic reconstruction: Scanning, 3D scanning, and whole slide imaging. Briefly mentioned are emerging platforms, which combine robotics, sectioning, and imaging in their pursuit to digitize and automate the entire microscopy workflow. Finally, both current and emerging 3D imaging methods are discussed in relation to current and future applications within the context of pathology.
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Affiliation(s)
| | - Alex Braun
- 3Scan, Inc., San Francisco, California, USA
| | - Dylan Jutt
- 3Scan, Inc., San Francisco, California, USA
| | | | - Nick Reder
- Department of Pathology, University of Washington, Seattle, Washington, USA
| | - Zheng Liu
- Department of Pathology, Saint Barnabas Medical Center, Livingston, New Jersey, USA
| | - Yukako Yagi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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132
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SmartScope2: Simultaneous Imaging and Reconstruction of Neuronal Morphology. Sci Rep 2017; 7:9325. [PMID: 28839271 PMCID: PMC5571186 DOI: 10.1038/s41598-017-10067-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 07/21/2017] [Indexed: 11/12/2022] Open
Abstract
Quantitative analysis of neuronal morphology is critical in cell type classification and for deciphering how structure gives rise to function in the brain. Most current approaches to imaging and tracing neuronal 3D morphology are data intensive. We introduce SmartScope2, the first open source, automated neuron reconstruction machine integrating online image analysis with automated multiphoton imaging. SmartScope2 takes advantage of a neuron’s sparse morphology to improve imaging speed and reduce image data stored, transferred and analyzed. We show that SmartScope2 is able to produce the complex 3D morphology of human and mouse cortical neurons with six-fold reduction in image data requirements and three times the imaging speed compared to conventional methods.
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133
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Li Y, Gong H, Yang X, Yuan J, Jiang T, Li X, Sun Q, Zhu D, Wang Z, Luo Q, Li A. TDat: An Efficient Platform for Processing Petabyte-Scale Whole-Brain Volumetric Images. Front Neural Circuits 2017; 11:51. [PMID: 28824382 PMCID: PMC5534480 DOI: 10.3389/fncir.2017.00051] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 07/17/2017] [Indexed: 11/13/2022] Open
Abstract
Three-dimensional imaging of whole mammalian brains at single-neuron resolution has generated terabyte (TB)- and even petabyte (PB)-sized datasets. Due to their size, processing these massive image datasets can be hindered by the computer hardware and software typically found in biological laboratories. To fill this gap, we have developed an efficient platform named TDat, which adopts a novel data reformatting strategy by reading cuboid data and employing parallel computing. In data reformatting, TDat is more efficient than any other software. In data accessing, we adopted parallelization to fully explore the capability for data transmission in computers. We applied TDat in large-volume data rigid registration and neuron tracing in whole-brain data with single-neuron resolution, which has never been demonstrated in other studies. We also showed its compatibility with various computing platforms, image processing software and imaging systems.
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Affiliation(s)
- Yuxin Li
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Hui Gong
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Xiaoquan Yang
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Jing Yuan
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Tao Jiang
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Xiangning Li
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Qingtao Sun
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Dan Zhu
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Zhenyu Wang
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Qingming Luo
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
| | - Anan Li
- Collaborative Innovation Center for Biomedical Engineering, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and TechnologyWuhan, China
- Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and TechnologyWuhan, China
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134
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Optical clearing and fluorescence deep-tissue imaging for 3D quantitative analysis of the brain tumor microenvironment. Angiogenesis 2017; 20:533-546. [PMID: 28699046 PMCID: PMC5660146 DOI: 10.1007/s10456-017-9565-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 06/27/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Three-dimensional visualization of the brain vasculature and its interactions with surrounding cells may shed light on diseases where aberrant microvascular organization is involved, including glioblastoma (GBM). Intravital confocal imaging allows 3D visualization of microvascular structures and migration of cells in the brain of mice, however, with limited imaging depth. To enable comprehensive analysis of GBM and the brain microenvironment, in-depth 3D imaging methods are needed. Here, we employed methods for optical tissue clearing prior to 3D microscopy to visualize the brain microvasculature and routes of invasion of GBM cells. METHODS We present a workflow for ex vivo imaging of optically cleared brain tumor tissues and subsequent computational modeling. This workflow was used for quantification of the microvasculature in relation to nuclear or cellular density in healthy mouse brain tissues and in human orthotopic, infiltrative GBM8 and E98 glioblastoma models. RESULTS Ex vivo cleared mouse brain tissues had a >10-fold imaging depth as compared to intravital imaging of mouse brain in vivo. Imaging of optically cleared brain tissue allowed quantification of the 3D microvascular characteristics in healthy mouse brains and in tissues with diffuse, infiltrative growing GBM8 brain tumors. Detailed 3D visualization revealed the organization of tumor cells relative to the vasculature, in both gray matter and white matter regions, and patterns of multicellular GBM networks collectively invading the brain parenchyma. CONCLUSIONS Optical tissue clearing opens new avenues for combined quantitative and 3D microscopic analysis of the topographical relationship between GBM cells and their microenvironment.
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135
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Singh JN, Nowlin TM, Seedorf GJ, Abman SH, Shepherd DP. Quantifying three-dimensional rodent retina vascular development using optical tissue clearing and light-sheet microscopy. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:76011. [PMID: 28717817 PMCID: PMC5514054 DOI: 10.1117/1.jbo.22.7.076011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 06/23/2017] [Indexed: 05/03/2023]
Abstract
Retinal vasculature develops in a highly orchestrated three-dimensional (3-D) sequence. The stages of retinal vascularization are highly susceptible to oxygen perturbations. We demonstrate that optical tissue clearing of intact rat retinas and light-sheet microscopy provides rapid 3-D characterization of vascular complexity during retinal development. Compared with flat mount preparations that dissect the retina and primarily image the outermost vascular layers, intact cleared retinas imaged using light-sheet fluorescence microscopy display changes in the 3-D retinal vasculature rapidly without the need for point scanning techniques. Using a severe model of retinal vascular disruption, we demonstrate that a simple metric based on Sholl analysis captures the vascular changes observed during retinal development in 3-D. Taken together, these results provide a methodology for rapidly quantifying the 3-D development of the entire rodent retinal vasculature.
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Affiliation(s)
- Jasmine N. Singh
- University of Colorado Denver, Department of Physics, Denver, Colorado, United States
- University of Colorado Anschutz Medical Campus, Pediatric Heart Lung Center, Department of Pediatrics, Aurora, Colorado, United States
| | - Taylor M. Nowlin
- University of Colorado Anschutz Medical Campus, Pediatric Heart Lung Center, Department of Pediatrics, Aurora, Colorado, United States
| | - Gregory J. Seedorf
- University of Colorado Anschutz Medical Campus, Pediatric Heart Lung Center, Department of Pediatrics, Aurora, Colorado, United States
| | - Steven H. Abman
- University of Colorado Anschutz Medical Campus, Pediatric Heart Lung Center, Department of Pediatrics, Aurora, Colorado, United States
| | - Douglas P. Shepherd
- University of Colorado Denver, Department of Physics, Denver, Colorado, United States
- University of Colorado Anschutz Medical Campus, Pediatric Heart Lung Center, Department of Pediatrics, Aurora, Colorado, United States
- Address all correspondence to: Douglas P. Shepherd, E-mail:
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136
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Smooth 2D manifold extraction from 3D image stack. Nat Commun 2017; 8:15554. [PMID: 28561033 PMCID: PMC5499208 DOI: 10.1038/ncomms15554] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 04/05/2017] [Indexed: 11/27/2022] Open
Abstract
Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy. Maximum Intensity Projection is a common tool to represent 3D biological imaging data in a 2D space, but it creates artefacts. Here the authors develop Smooth Manifold Extraction, an ImageJ/Fiji plugin, to preserve local spatial relationships when extracting the content of a 3D volume to a 2D space.
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137
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Wan Y, Otsuna H, Holman HA, Bagley B, Ito M, Lewis AK, Colasanto M, Kardon G, Ito K, Hansen C. FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis. BMC Bioinformatics 2017; 18:280. [PMID: 28549411 PMCID: PMC5446689 DOI: 10.1186/s12859-017-1694-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 05/18/2017] [Indexed: 12/05/2022] Open
Abstract
Background Image segmentation and registration techniques have enabled biologists to place large amounts of volume data from fluorescence microscopy, morphed three-dimensionally, onto a common spatial frame. Existing tools built on volume visualization pipelines for single channel or red-green-blue (RGB) channels have become inadequate for the new challenges of fluorescence microscopy. For a three-dimensional atlas of the insect nervous system, hundreds of volume channels are rendered simultaneously, whereas fluorescence intensity values from each channel need to be preserved for versatile adjustment and analysis. Although several existing tools have incorporated support of multichannel data using various strategies, the lack of a flexible design has made true many-channel visualization and analysis unavailable. The most common practice for many-channel volume data presentation is still converting and rendering pseudosurfaces, which are inaccurate for both qualitative and quantitative evaluations. Results Here, we present an alternative design strategy that accommodates the visualization and analysis of about 100 volume channels, each of which can be interactively adjusted, selected, and segmented using freehand tools. Our multichannel visualization includes a multilevel streaming pipeline plus a triple-buffer compositing technique. Our method also preserves original fluorescence intensity values on graphics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for interactive data analysis, such as freehand segmentation. We have implemented the design strategies as a thorough restructuring of our original tool, FluoRender. Conclusion The redesign of FluoRender not only maintains the existing multichannel capabilities for a greatly extended number of volume channels, but also enables new analysis functions for many-channel data from emerging biomedical-imaging techniques. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1694-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yong Wan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA.
| | - Hideo Otsuna
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Holly A Holman
- Department of Bioengineering, University of Utah, Salt Lake City, USA
| | - Brig Bagley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
| | - Masayoshi Ito
- Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo, Japan
| | - A Kelsey Lewis
- Department of Biology, University of Florida, Gainesville, USA
| | - Mary Colasanto
- Department of Human Genetics, University of Utah, Salt Lake City, USA
| | - Gabrielle Kardon
- Department of Human Genetics, University of Utah, Salt Lake City, USA
| | - Kei Ito
- Institute of Molecular and Cellular Biosciences, University of Tokyo, Tokyo, Japan
| | - Charles Hansen
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA
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138
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Fast assembling of neuron fragments in serial 3D sections. Brain Inform 2017; 4:183-186. [PMID: 28365869 PMCID: PMC5563299 DOI: 10.1007/s40708-017-0063-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 03/16/2017] [Indexed: 12/03/2022] Open
Abstract
Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.
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139
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Miller MA, Weissleder R. Imaging the pharmacology of nanomaterials by intravital microscopy: Toward understanding their biological behavior. Adv Drug Deliv Rev 2017; 113:61-86. [PMID: 27266447 PMCID: PMC5136524 DOI: 10.1016/j.addr.2016.05.023] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 05/25/2016] [Indexed: 12/15/2022]
Abstract
Therapeutic nanoparticles (NPs) can deliver cytotoxic chemotherapeutics and other drugs more safely and efficiently to patients; furthermore, selective delivery to target tissues can theoretically be accomplished actively through coating NPs with molecular ligands, and passively through exploiting physiological "enhanced permeability and retention" features. However, clinical trial results have been mixed in showing improved efficacy with drug nanoencapsulation, largely due to heterogeneous NP accumulation at target sites across patients. Thus, a clear need exists to better understand why many NP strategies fail in vivo and not result in significantly improved tumor uptake or therapeutic response. Multicolor in vivo confocal fluorescence imaging (intravital microscopy; IVM) enables integrated pharmacokinetic and pharmacodynamic (PK/PD) measurement at the single-cell level, and has helped answer key questions regarding the biological mechanisms of in vivo NP behavior. This review summarizes progress to date and also describes useful technical strategies for successful IVM experimentation.
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Affiliation(s)
- Miles A Miller
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, Boston, MA 02114, USA
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, Boston, MA 02114, USA; Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA.
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140
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Yackle K, Schwarz LA, Kam K, Sorokin JM, Huguenard JR, Feldman JL, Luo L, Krasnow MA. Breathing control center neurons that promote arousal in mice. Science 2017; 355:1411-1415. [PMID: 28360327 DOI: 10.1126/science.aai7984] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Accepted: 02/01/2017] [Indexed: 12/24/2022]
Abstract
Slow, controlled breathing has been used for centuries to promote mental calming, and it is used clinically to suppress excessive arousal such as panic attacks. However, the physiological and neural basis of the relationship between breathing and higher-order brain activity is unknown. We found a neuronal subpopulation in the mouse preBötzinger complex (preBötC), the primary breathing rhythm generator, which regulates the balance between calm and arousal behaviors. Conditional, bilateral genetic ablation of the ~175 Cdh9/Dbx1 double-positive preBötC neurons in adult mice left breathing intact but increased calm behaviors and decreased time in aroused states. These neurons project to, synapse on, and positively regulate noradrenergic neurons in the locus coeruleus, a brain center implicated in attention, arousal, and panic that projects throughout the brain.
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Affiliation(s)
- Kevin Yackle
- Howard Hughes Medical Institute, Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lindsay A Schwarz
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Kaiwen Kam
- Systems Neurobiology Laboratory, Department of Neurobiology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA.,Department of Cell Biology and Anatomy, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL 60064, USA
| | - Jordan M Sorokin
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - John R Huguenard
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
| | - Jack L Feldman
- Systems Neurobiology Laboratory, Department of Neurobiology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA
| | - Liqun Luo
- Howard Hughes Medical Institute, Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Mark A Krasnow
- Howard Hughes Medical Institute, Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA.
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141
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Šmít D, Fouquet C, Doulazmi M, Pincet F, Trembleau A, Zapotocky M. BFPTool: a software tool for analysis of Biomembrane Force Probe experiments. BMC BIOPHYSICS 2017; 10:2. [PMID: 28289540 PMCID: PMC5304404 DOI: 10.1186/s13628-016-0033-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2016] [Accepted: 12/22/2016] [Indexed: 01/31/2023]
Abstract
Background The Biomembrane Force Probe is an approachable experimental technique commonly used for single-molecule force spectroscopy and experiments on biological interfaces. The technique operates in the range of forces from 0.1 pN to 1000 pN. Experiments are typically repeated many times, conditions are often not optimal, the captured video can be unstable and lose focus; this makes efficient analysis challenging, while out-of-the-box non-proprietary solutions are not freely available. Results This dedicated tool was developed to integrate and simplify the image processing and analysis of videomicroscopy recordings from BFP experiments. A novel processing feature, allowing the tracking of the pipette, was incorporated to address a limitation of preceding methods. Emphasis was placed on versatility and comprehensible user interface implemented in a graphical form. Conclusions An integrated analytical tool was implemented to provide a faster, simpler and more convenient way to process and analyse BFP experiments. Electronic supplementary material The online version of this article (doi:10.1186/s13628-016-0033-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel Šmít
- Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, Prague, 14220 Czech Republic.,Institute of Biophysics and Informatics, First Faculty of Medicine, Charles University in Prague, Kateřinská 2, Prague, 12000 Czech Republic.,Sorbonne Université, UPMC Univ Paris 06, INSERM, CNRS, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS - IBPS), 9 Quai Saint Bernard, Paris, 75005 France
| | - Coralie Fouquet
- Sorbonne Université, UPMC Univ Paris 06, INSERM, CNRS, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS - IBPS), 9 Quai Saint Bernard, Paris, 75005 France
| | - Mohamed Doulazmi
- Sorbonne Université, UPMC Univ Paris 06, CNRS, Biological Adaptation and Ageing - Institut de Biologie Paris Seine (B2A - IBPS), 7 Quai Saint Bernard, Paris, 75005 France
| | - Frédéric Pincet
- Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University, Paris, France.,Université Paris Diderot Sorbonne Paris Cité, Paris, France.,Sorbonne Universités UPMC Univ Paris 06, CNRS, 24 rue Lhomond, Paris, 75005 France
| | - Alain Trembleau
- Sorbonne Université, UPMC Univ Paris 06, INSERM, CNRS, Neurosciences Paris Seine - Institut de Biologie Paris Seine (NPS - IBPS), 9 Quai Saint Bernard, Paris, 75005 France
| | - Martin Zapotocky
- Institute of Physiology, Czech Academy of Sciences, Vídeňská 1083, Prague, 14220 Czech Republic.,Institute of Biophysics and Informatics, First Faculty of Medicine, Charles University in Prague, Kateřinská 2, Prague, 12000 Czech Republic
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142
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Nketia TA, Sailem H, Rohde G, Machiraju R, Rittscher J. Analysis of live cell images: Methods, tools and opportunities. Methods 2017; 115:65-79. [DOI: 10.1016/j.ymeth.2017.02.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 02/20/2017] [Accepted: 02/21/2017] [Indexed: 01/19/2023] Open
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143
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Abstract
Understanding physical and chemical processes at an organismal scale is a fundamental goal in biology. While science is adept at explaining biological phenomena at both molecular and cellular levels, understanding how these processes translate to organismal functions remains a challenging problem. This issue is particularly significant for the nervous system where cell signaling and synaptic activities function in the context of broad neural networks. Recent progress in tissue clearing technologies lessens the barriers that previously prevented the study of large tissue samples while maintaining molecular and cellular resolution. While these new methods open vast opportunities and exciting new questions, the logistics of analyzing cellular processes in intact tissue have to be carefully considered. In this protocol, we outline a procedure to rapidly image intact brain tissue up to thousands of cubic millimeters. This experimental pipeline involves three steps: tissue clearing, tissue imaging, and data analysis. In an attempt to streamline the process for researchers entering this field, we address important considerations for each of these stages and describe an integrated solution to image intact biological tissues. Hopefully, this optimized protocol will lower the barrier of implementing high-resolution tissue imaging and facilitate the investigations of mesoscale questions at molecular and cellular resolution.
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144
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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons. Neuroinformatics 2016; 14:201-19. [PMID: 26701809 PMCID: PMC4823367 DOI: 10.1007/s12021-015-9287-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.
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145
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Lepeltier M, Appaix F, Liao YY, Dumur F, Marrot J, Le Bahers T, Andraud C, Monnereau C. Carbazole-Substituted Iridium Complex as a Solid State Emitter for Two-Photon Intravital Imaging. Inorg Chem 2016; 55:9586-9595. [DOI: 10.1021/acs.inorgchem.6b01253] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Marc Lepeltier
- Institut Lavoisier
de Versailles, UMR 8180 CNRS, Université de Versailles Saint-Quentin en Yvelines, 45 avenue des Etats-Unis, 78035 Cedex Versailles, France
| | - Florence Appaix
- Univ. Grenoble Alpes, Grenoble Institut
des Neurosciences, GIN, Inserm, U1216, F0-38000 Grenoble, France
| | - Yuan Yuan Liao
- Laboratoire de Chimie, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard, Université de Lyon, F69342 Lyon, France
| | - Frédéric Dumur
- Aix-Marseille Université, CNRS, Institut de Chimie Radicalaire ICR, UMR 7273, F-13397 Marseille, France
| | - Jérôme Marrot
- Institut Lavoisier
de Versailles, UMR 8180 CNRS, Université de Versailles Saint-Quentin en Yvelines, 45 avenue des Etats-Unis, 78035 Cedex Versailles, France
| | - Tangui Le Bahers
- Laboratoire de Chimie, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard, Université de Lyon, F69342 Lyon, France
| | - Chantal Andraud
- Laboratoire de Chimie, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard, Université de Lyon, F69342 Lyon, France
| | - Cyrille Monnereau
- Laboratoire de Chimie, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard, Université de Lyon, F69342 Lyon, France
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146
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Intrinsic Cornu Ammonis Area 1 Theta-Nested Gamma Oscillations Induced by Optogenetic Theta Frequency Stimulation. J Neurosci 2016; 36:4155-69. [PMID: 27076416 DOI: 10.1523/jneurosci.3150-15.2016] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 02/18/2016] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED Gamma oscillations (30-120 Hz) are thought to be important for various cognitive functions, including perception and working memory, and disruption of these oscillations has been implicated in brain disorders, such as schizophrenia and Alzheimer's disease. The cornu ammonis area 1 (CA1) of the hippocampus receives gamma frequency inputs from upstream regions (cornu ammonis area 3 and medial entorhinal cortex) and generates itself a faster gamma oscillation. The exact nature and origin of the intrinsic CA1 gamma oscillation is still under debate. Here, we expressed channel rhodopsin-2 under the CaMKIIα promoter in mice and prepared hippocampal slices to produce a model of intrinsic CA1 gamma oscillations. Sinusoidal optical stimulation of CA1 at theta frequency was found to induce robust theta-nested gamma oscillations with a temporal and spatial profile similar to CA1 gamma in vivo The results suggest the presence of a single gamma rhythm generator with a frequency range of 65-75 Hz at 32 °C. Pharmacological analysis found that the oscillations depended on both AMPA and GABAA receptors. Cell-attached and whole-cell recordings revealed that excitatory neuron firing slightly preceded interneuron firing within each gamma cycle, suggesting that this intrinsic CA1 gamma oscillation is generated with a pyramidal-interneuron circuit mechanism. SIGNIFICANCE STATEMENT This study demonstrates that the cornu ammonis area 1 (CA1) is capable of generating intrinsic gamma oscillations in response to theta input. This gamma generator is independent of activity in the upstream regions, highlighting that CA1 can produce its own gamma oscillation in addition to inheriting activity from the upstream regions. This supports the theory that gamma oscillations predominantly function to achieve local synchrony, and that a local gamma generated in each area conducts the signal to the downstream region.
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147
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Aghaallaei N, Gruhl F, Schaefer CQ, Wernet T, Weinhardt V, Centanin L, Loosli F, Baumbach T, Wittbrodt J. Identification, visualization and clonal analysis of intestinal stem cells in fish. Development 2016; 143:3470-3480. [PMID: 27578784 PMCID: PMC5087619 DOI: 10.1242/dev.134098] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 08/08/2016] [Indexed: 01/09/2023]
Abstract
Recently, a stochastic model of symmetrical stem cell division followed by neutral drift has been proposed for intestinal stem cells (ISCs), which has been suggested to represent the predominant mode of stem cell progression in mammals. In contrast, stem cells in the retina of teleost fish show an asymmetric division mode. To address whether the mode of stem cell division follows phylogenetic or ontogenetic routes, we analysed the entire gastrointestinal tract of the teleost medaka (Oryzias latipes). X-ray microcomputed tomography shows a correlation of 3D topography with the functional domains. Analysis of ISCs in proliferation assays and via genetically encoded lineage tracing highlights a stem cell niche in the furrow between the long intestinal folds that is functionally equivalent to mammalian intestinal crypts. Stem cells in this compartment are characterized by the expression of homologs of mammalian ISC markers – sox9, axin2 and lgr5 – emphasizing the evolutionary conservation of the Wnt pathway components in the stem cell niche of the intestine. The stochastic, sparse initial labelling of ISCs ultimately resulted in extended labelled or unlabelled domains originating from single stem cells in the furrow niche, contributing to both homeostasis and growth. Thus, different modes of stem cell division co-evolved within one organism, and in the absence of physical isolation in crypts, ISCs contribute to homeostatic growth. Summary: Adult medaka intestinal stem cells (ISCs) proliferate within a niche functionally equivalent to that in the mammal. Like mammalian ISCs, but unlike medaka retinal stem cells, their mode of division is largely symmetric.
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Affiliation(s)
- Narges Aghaallaei
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
| | - Franziska Gruhl
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
| | - Colin Q Schaefer
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
| | - Tobias Wernet
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany Laboratory for applications of synchrotron radiation, Karslruhe Institute for Technology (KIT), 76131 Karlsruhe, Germany
| | - Venera Weinhardt
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany Laboratory for applications of synchrotron radiation, Karslruhe Institute for Technology (KIT), 76131 Karlsruhe, Germany
| | - Lázaro Centanin
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
| | - Felix Loosli
- Laboratory for applications of synchrotron radiation, Karslruhe Institute for Technology (KIT), 76131 Karlsruhe, Germany
| | - Tilo Baumbach
- Laboratory for applications of synchrotron radiation, Karslruhe Institute for Technology (KIT), 76131 Karlsruhe, Germany
| | - Joachim Wittbrodt
- Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
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148
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Wan Y, Long F, Qu L, Xiao H, Hawrylycz M, Myers EW, Peng H. BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies. Neuroinformatics 2016; 13:487-99. [PMID: 26036213 DOI: 10.1007/s12021-015-9272-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.
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Affiliation(s)
- Yinan Wan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Fuhui Long
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Allen Institute for Brain Science, Seattle, WA, USA
| | - Lei Qu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Hang Xiao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Eugene W Myers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Hanchuan Peng
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA. .,Allen Institute for Brain Science, Seattle, WA, USA.
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149
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Pascal S, Denis-Quanquin S, Appaix F, Duperray A, Grichine A, Le Guennic B, Jacquemin D, Cuny J, Chi SH, Perry JW, van der Sanden B, Monnereau C, Andraud C, Maury O. Keto-polymethines: a versatile class of dyes with outstanding spectroscopic properties for in cellulo and in vivo two-photon microscopy imaging. Chem Sci 2016; 8:381-394. [PMID: 28451183 PMCID: PMC5365052 DOI: 10.1039/c6sc02488b] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 08/01/2016] [Indexed: 02/03/2023] Open
Abstract
The synthesis of keto-heptamethine derivatives has been expanded to various new symmetrical and asymmetrical structures, including an unprecedented di-anionic keto-polymethine. The spectroscopic behavior of these new dyes has been systematically and thoroughly investigated, revealing that the formation of hydrogen bond interactions with protic solvents is responsible for a dramatic enhancement of the fluorescence quantum yield in the far-red spectral region. The existence of these strong hydrogen-bond interactions was further confirmed by molecular dynamics simulations. These bis-dipolar polymethines exhibit large two-photon absorption (TPA) cross-sections (σ2 in GM) in the near-infrared, making them ideal candidates for NIR-to-NIR two-photon microscopy imaging applications. We demonstrate that the molecular engineering of the hydrophilic/hydrophobic balance enables targeting of different cellular components, such as cytoplasm or cell membranes. Addition of appropriate substituents provides the molecule with high-water-solubility, affording efficient two-photon probes for angiography.
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Affiliation(s)
- Simon Pascal
- ENS Lyon , Université de Lyon 1 , CNRS Laboratoire de chimie de l'ENS Lyon , UMR 5182 CNRS, 46 allée d'Italie , 69364 Lyon , France . ;
| | - Sandrine Denis-Quanquin
- ENS Lyon , Université de Lyon 1 , CNRS Laboratoire de chimie de l'ENS Lyon , UMR 5182 CNRS, 46 allée d'Italie , 69364 Lyon , France . ;
| | - Florence Appaix
- Univ. Grenoble Alpes , Grenoble Institut des Neurosciences , GIN, Inserm , U836 , F-38000 Grenoble , France
| | - Alain Duperray
- Inserm , Institut Albert Bonniot , U823 , F-38000 Grenoble , France.,Université Grenoble Alpes , IAB , F-38000 Grenoble , France
| | - Alexei Grichine
- Inserm , Institut Albert Bonniot , U823 , F-38000 Grenoble , France.,Université Grenoble Alpes , IAB , F-38000 Grenoble , France
| | - Boris Le Guennic
- Institut des Sciences Chimiques de Rennes , UMR 6226 CNRS , Université de Rennes 1 , 263 Avenue du Général Leclerc , 35042 Rennes Cedex , France
| | - Denis Jacquemin
- Laboratoire CEISAM , CNRS 6230 , Université; de Nantes , 2 Rue de la Houssiniére, BP 92208 , 44322 Nantes Cedex 3 , France.,Institut Universitaire de France , 103 Bvd Michelet , 75005 Paris Cedex 5 , France
| | - Jérôme Cuny
- Laboratoire de Chimie et Physique Quantiques (LCPQ) , Université de Toulouse III [UPS] and CNRS , 118 Route de Narbonne , 31062 Toulouse , France
| | - San-Hui Chi
- School of Chemistry and Biochemistry , Center for Organic Photonics and Electronics , Georgia Institute of Technology , 901 Atlantic Drive NW , Atlanta , GA 30332-0400 , USA
| | - Joseph W Perry
- School of Chemistry and Biochemistry , Center for Organic Photonics and Electronics , Georgia Institute of Technology , 901 Atlantic Drive NW , Atlanta , GA 30332-0400 , USA
| | - Boudewijn van der Sanden
- Univ. Grenoble Alpes , Grenoble Institut des Neurosciences , GIN, Inserm , U836 , F-38000 Grenoble , France
| | - Cyrille Monnereau
- ENS Lyon , Université de Lyon 1 , CNRS Laboratoire de chimie de l'ENS Lyon , UMR 5182 CNRS, 46 allée d'Italie , 69364 Lyon , France . ;
| | - Chantal Andraud
- ENS Lyon , Université de Lyon 1 , CNRS Laboratoire de chimie de l'ENS Lyon , UMR 5182 CNRS, 46 allée d'Italie , 69364 Lyon , France . ;
| | - Olivier Maury
- ENS Lyon , Université de Lyon 1 , CNRS Laboratoire de chimie de l'ENS Lyon , UMR 5182 CNRS, 46 allée d'Italie , 69364 Lyon , France . ;
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150
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Liu Z, Keller PJ. Emerging Imaging and Genomic Tools for Developmental Systems Biology. Dev Cell 2016; 36:597-610. [PMID: 27003934 DOI: 10.1016/j.devcel.2016.02.016] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 02/18/2016] [Accepted: 02/19/2016] [Indexed: 11/16/2022]
Abstract
Animal development is a complex and dynamic process orchestrated by exquisitely timed cell lineage commitment, divisions, migration, and morphological changes at the single-cell level. In the past decade, extensive genetic, stem cell, and genomic studies provided crucial insights into molecular underpinnings and the functional importance of genetic pathways governing various cellular differentiation processes. However, it is still largely unknown how the precise coordination of these pathways is achieved at the whole-organism level and how the highly regulated spatiotemporal choreography of development is established in turn. Here, we discuss the latest technological advances in imaging and single-cell genomics that hold great promise for advancing our understanding of this intricate process. We propose an integrated approach that combines such methods to quantitatively decipher in vivo cellular dynamic behaviors and their underlying molecular mechanisms at the systems level with single-cell, single-molecule resolution.
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Affiliation(s)
- Zhe Liu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Philipp J Keller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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