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Li Y, Lai C, Wang M, Wu J, Li Y, Peng H, Qu L. Automated segmentation and recognition of C. elegans whole-body cells. Bioinformatics 2024; 40:btae324. [PMID: 38775410 PMCID: PMC11139520 DOI: 10.1093/bioinformatics/btae324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 04/17/2024] [Accepted: 05/20/2024] [Indexed: 06/01/2024] Open
Abstract
MOTIVATION Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. RESULTS We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/reaneyli/ASR.
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Affiliation(s)
- Yuanyuan Li
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
| | - Chuxiao Lai
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
| | - Meng Wang
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
| | - Jun Wu
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
| | - Yongbin Li
- College of Life Sciences, Capital Normal University, Beijing 100048, China
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lei Qu
- Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230039, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230039, China
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2
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Lanza E, Lucente V, Nicoletti M, Schwartz S, Cavallo IF, Caprini D, Connor CW, Saifuddin MFA, Miller JM, L’Etoile ND, Folli V. See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons. PLoS One 2024; 19:e0300628. [PMID: 38517838 PMCID: PMC10959381 DOI: 10.1371/journal.pone.0300628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/29/2024] [Indexed: 03/24/2024] Open
Abstract
In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).
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Affiliation(s)
- Enrico Lanza
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Valeria Lucente
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
| | - Martina Nicoletti
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- Department of Engineering, Campus Bio-Medico University, Rome, Italy
| | - Silvia Schwartz
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Ilaria F. Cavallo
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
| | - Davide Caprini
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
| | - Christopher W. Connor
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Mashel Fatema A. Saifuddin
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Julia M. Miller
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Noelle D. L’Etoile
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Viola Folli
- Center for Life Nano- and Neuro-Science@Sapienza, Istituto Italiano di Tecnologia (IIT), Rome, Italy
- D-tails s.r.l., Rome, Italy
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3
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Toyoshima Y, Sato H, Nagata D, Kanamori M, Jang MS, Kuze K, Oe S, Teramoto T, Iwasaki Y, Yoshida R, Ishihara T, Iino Y. Ensemble dynamics and information flow deduction from whole-brain imaging data. PLoS Comput Biol 2024; 20:e1011848. [PMID: 38489379 PMCID: PMC10942262 DOI: 10.1371/journal.pcbi.1011848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/21/2024] [Indexed: 03/17/2024] Open
Abstract
The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hirofumi Sato
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Daiki Nagata
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koyo Kuze
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Suzu Oe
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Yuishi Iwasaki
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Nishi-ku, Fukuoka, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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4
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Wen C. Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals. Methods Mol Biol 2024; 2800:203-215. [PMID: 38709486 DOI: 10.1007/978-1-0716-3834-7_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, cells in living organisms often exhibit extensive and complex movements caused by organ deformation and whole-body motion. These movements pose a challenge in obtaining high-quality time-lapse cell images and tracking the intricate cell movements in the captured images. Recent advances in deep learning techniques provide powerful tools for detecting cells in low-quality images with densely packed cell populations, as well as estimating cell positions for cells undergoing large nonrigid movements. This chapter introduces the challenges of cell tracking in deforming organs and moving animals, outlines the solutions to these challenges, and presents a detailed protocol for data preparation, as well as for performing cell segmentation and tracking using the latest version of 3DeeCellTracker. This protocol is expected to enable researchers to gain deeper insights into organ dynamics and biological processes.
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Affiliation(s)
- Chentao Wen
- RIKEN Center for Biodynamic Research, Kobe, Japan.
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5
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Park CF, Barzegar-Keshteli M, Korchagina K, Delrocq A, Susoy V, Jones CL, Samuel ADT, Rahi SJ. Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation. Nat Methods 2024; 21:142-149. [PMID: 38052988 DOI: 10.1038/s41592-023-02096-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 10/20/2023] [Indexed: 12/07/2023]
Abstract
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce 'targeted augmentation', a method to automatically synthesize artificial annotations from a few manual annotations. Our method ('Targettrack') learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.
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Affiliation(s)
- Core Francisco Park
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Mahsa Barzegar-Keshteli
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Kseniia Korchagina
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ariane Delrocq
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vladislav Susoy
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Corinne L Jones
- Swiss Data Science Center, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Aravinthan D T Samuel
- Department of Physics and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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6
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Eom M, Han S, Park P, Kim G, Cho ES, Sim J, Lee KH, Kim S, Tian H, Böhm UL, Lowet E, Tseng HA, Choi J, Lucia SE, Ryu SH, Rózsa M, Chang S, Kim P, Han X, Piatkevich KD, Choi M, Kim CH, Cohen AE, Chang JB, Yoon YG. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat Methods 2023; 20:1581-1592. [PMID: 37723246 PMCID: PMC10555843 DOI: 10.1038/s41592-023-02005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 08/10/2023] [Indexed: 09/20/2023]
Abstract
Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.
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Affiliation(s)
- Minho Eom
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Seungjae Han
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Pojeong Park
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Gyuri Kim
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Eun-Seo Cho
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
| | - Jueun Sim
- Department of Materials Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Kang-Han Lee
- Department of Biology, Chungnam National University, Daejeon, Republic of Korea
| | - Seonghoon Kim
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - He Tian
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Urs L Böhm
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité University of Medicine Berlin, Berlin, Germany
| | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Jieun Choi
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Stephani Edwina Lucia
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Seung Hyun Ryu
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Republic of Korea
| | - Márton Rózsa
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Sunghoe Chang
- Department of Physiology and Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Pilhan Kim
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
- Graduate School of Nanoscience and Technology, KAIST, Daejeon, Republic of Korea
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Kiryl D Piatkevich
- Research Center for Industries of the Future and School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Myunghwan Choi
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Republic of Korea
| | - Cheol-Hee Kim
- Department of Biology, Chungnam National University, Daejeon, Republic of Korea
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Jae-Byum Chang
- Department of Materials Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Young-Gyu Yoon
- School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea.
- Department of Semiconductor System Engineering, KAIST, Daejeon, Republic of Korea.
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Pritz C, Itskovits E, Bokman E, Ruach R, Gritsenko V, Nelken T, Menasherof M, Azulay A, Zaslaver A. Principles for coding associative memories in a compact neural network. eLife 2023; 12:74434. [PMID: 37140557 PMCID: PMC10159626 DOI: 10.7554/elife.74434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/08/2023] [Indexed: 05/05/2023] Open
Abstract
A major goal in neuroscience is to elucidate the principles by which memories are stored in a neural network. Here, we have systematically studied how four types of associative memories (short- and long-term memories, each as positive and negative associations) are encoded within the compact neural network of Caenorhabditis elegans worms. Interestingly, sensory neurons were primarily involved in coding short-term, but not long-term, memories, and individual sensory neurons could be assigned to coding either the conditioned stimulus or the experience valence (or both). Moreover, when considering the collective activity of the sensory neurons, the specific training experiences could be decoded. Interneurons integrated the modulated sensory inputs and a simple linear combination model identified the experience-specific modulated communication routes. The widely distributed memory suggests that integrated network plasticity, rather than changes to individual neurons, underlies the fine behavioral plasticity. This comprehensive study reveals basic memory-coding principles and highlights the central roles of sensory neurons in memory formation.
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Affiliation(s)
- Christian Pritz
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eyal Itskovits
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eduard Bokman
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rotem Ruach
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Vladimir Gritsenko
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tal Nelken
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mai Menasherof
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aharon Azulay
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alon Zaslaver
- Department of Genetics, Silberman Institute for Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
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Morris TA, Eldeen S, Tran RDH, Grosberg A. A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture. BIOPHYSICS REVIEWS 2022; 3:041302. [PMID: 36407035 PMCID: PMC9667907 DOI: 10.1063/5.0057434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.
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Affiliation(s)
| | - Sarah Eldeen
- Center for Complex Biological Systems, University of California, Irvine, California 92697-2700, USA
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9
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Wu Y, Wu S, Wang X, Lang C, Zhang Q, Wen Q, Xu T. Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. PLoS Comput Biol 2022; 18:e1010594. [PMID: 36215325 PMCID: PMC9584436 DOI: 10.1371/journal.pcbi.1010594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 10/20/2022] [Accepted: 09/22/2022] [Indexed: 11/07/2022] Open
Abstract
Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C. elegans. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. With a small number of training samples, our bottom-up approach is able to process each volume—1024 × 1024 × 18 in voxels—in less than 1 second and achieves an accuracy of 91% in neuronal detection and above 80% in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors. An important question in neuroscience is to understand the relationship between brain dynamics and naturalistic behaviors when an animal is freely exploring its environment. In the last decade, it has become possible to genetically engineer animals whose neurons produce fluorescence reporters that change their brightness in response to brain activity. In small animals such as the nematode C. elegans, we can now record the fluorescence changes in and thereby infer neural activity from most neurons in the head of a worm, when the animal is freely moving. These neurons are densely packed in a small volume. Since the brain and body are moving and its shape undergoes significant deformation, a human expert, even after long hours of inspection, may still have difficulty to tell where the neurons are and who they are. We sought to develop an automatic method for rapidly detecting and tracking most of these neurons in a moving animal. To do this, we asked a human expert to annotate all head neurons—their locations and digital identities—across a small number of volumes. Then, we trained a computer to learn the locations and digital identities of these neurons across different imaging volumes. Our machine inference method is fast and accurate. While it takes a human expert several hours to complete a sequence of volumes, a machine can finish the task in a few minutes. We hope our method provides a better and more efficient engine for extracting knowledge from whole brain imaging datasets and animal behaviors.
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Affiliation(s)
- Yuxiang Wu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Shang Wu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Xin Wang
- John Hopcroft Center for Computer Science, School of electronic information and electrical engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chengtian Lang
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Quanshi Zhang
- John Hopcroft Center for Computer Science, School of electronic information and electrical engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Quan Wen
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
- * E-mail: (QW); (TX)
| | - Tianqi Xu
- Chinese Academy of Sciences Key Laboratory of Brain Function and Diseases, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
- * E-mail: (QW); (TX)
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10
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Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun 2022; 13:5165. [PMID: 36056020 PMCID: PMC9440141 DOI: 10.1038/s41467-022-32886-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50–70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors. Volumetric functional imaging is widely used for recording neuron activities in vivo for many experimental organisms. Here the authors report supervised deep-denoising methods for improved whole-brain imaging, large field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans.
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11
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Marzec M, Piórkowski A, Gertych A. Efficient automatic 3D segmentation of cell nuclei for high-content screening. BMC Bioinformatics 2022; 23:203. [PMID: 35641922 DOI: 10.1186/s12859-022-04737-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 05/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. RESULTS We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. CONCLUSIONS The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs.
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Affiliation(s)
- Mariusz Marzec
- Faculty of Science and Technology, Institute of Biomedical Engineering, University of Silesia, Bedzinska St. 39, 41-200, Sosnowiec, Poland.
| | - Adam Piórkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30, 30-059, Cracow, Poland
| | - Arkadiusz Gertych
- Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.,Faculty of Biomedical Engineering, Silesian University of Technology, Roosvelta 40, 41-800, Zabrze, Poland
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12
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Kang W, Ferruzzi J, Spatarelu CP, Han YL, Sharma Y, Koehler SA, Mitchel JA, Khan A, Butler JP, Roblyer D, Zaman MH, Park JA, Guo M, Chen Z, Pegoraro AF, Fredberg JJ. A novel jamming phase diagram links tumor invasion to non-equilibrium phase separation. iScience 2021; 24:103252. [PMID: 34755092 PMCID: PMC8564056 DOI: 10.1016/j.isci.2021.103252] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 09/14/2021] [Accepted: 10/07/2021] [Indexed: 12/31/2022] Open
Abstract
It is well established that the early malignant tumor invades surrounding extracellular matrix (ECM) in a manner that depends upon material properties of constituent cells, surrounding ECM, and their interactions. Recent studies have established the capacity of the invading tumor spheroids to evolve into coexistent solid-like, fluid-like, and gas-like phases. Using breast cancer cell lines invading into engineered ECM, here we show that the spheroid interior develops spatial and temporal heterogeneities in material phase which, depending upon cell type and matrix density, ultimately result in a variety of phase separation patterns at the invasive front. Using a computational approach, we further show that these patterns are captured by a novel jamming phase diagram. We suggest that non-equilibrium phase separation based upon jamming and unjamming transitions may provide a unifying physical picture to describe cellular migratory dynamics within, and invasion from, a tumor.
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Affiliation(s)
- Wenying Kang
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jacopo Ferruzzi
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | | | - Yu Long Han
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yasha Sharma
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Stephan A. Koehler
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jennifer A. Mitchel
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Adil Khan
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | - James P. Butler
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Darren Roblyer
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Muhammad H. Zaman
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Howard Hughes Medical Institute, Boston University, Boston, MA 02115, USA
| | - Jin-Ah Park
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ming Guo
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Zi Chen
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
| | | | - Jeffrey J. Fredberg
- Department of Environmental Science, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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13
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Emmons SW, Yemini E, Zimmer M. Methods for analyzing neuronal structure and activity in Caenorhabditis elegans. Genetics 2021; 218:6303616. [PMID: 34151952 DOI: 10.1093/genetics/iyab072] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/20/2021] [Indexed: 11/12/2022] Open
Abstract
The model research animal Caenorhabditis elegans has unique properties making it particularly advantageous for studies of the nervous system. The nervous system is composed of a stereotyped complement of neurons connected in a consistent manner. Here, we describe methods for studying nervous system structure and function. The transparency of the animal makes it possible to visualize and identify neurons in living animals with fluorescent probes. These methods have been recently enhanced for the efficient use of neuron-specific reporter genes. Because of its simple structure, for a number of years, C. elegans has been at the forefront of connectomic studies defining synaptic connectivity by electron microscopy. This field is burgeoning with new, more powerful techniques, and recommended up-to-date methods are here described that encourage the possibility of new work in C. elegans. Fluorescent probes for single synapses and synaptic connections have allowed verification of the EM reconstructions and for experimental approaches to synapse formation. Advances in microscopy and in fluorescent reporters sensitive to Ca2+ levels have opened the way to observing activity within single neurons across the entire nervous system.
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Affiliation(s)
- Scott W Emmons
- Department of Genetics and Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 1041, USA
| | - Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Manuel Zimmer
- Department of Neuroscience and Developmental Biology, University of Vienna, Vienna 1090, Austria and.,Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna 1030, Austria
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14
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Wu TC, Wang X, Li L, Bu Y, Umulis DM. Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification. Sci Rep 2021; 11:9847. [PMID: 33972575 PMCID: PMC8110989 DOI: 10.1038/s41598-021-88966-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 04/09/2021] [Indexed: 02/03/2023] Open
Abstract
Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.
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Affiliation(s)
- Tzu-Ching Wu
- grid.169077.e0000 0004 1937 2197Department of Agriculture and Biological Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - Xu Wang
- grid.169077.e0000 0004 1937 2197Department of Agriculture and Biological Engineering, Purdue University, West Lafayette, IN 47907 USA ,grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907 USA ,grid.508040.9Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510005 China
| | - Linlin Li
- grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - Ye Bu
- grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907 USA
| | - David M. Umulis
- grid.169077.e0000 0004 1937 2197Department of Agriculture and Biological Engineering, Purdue University, West Lafayette, IN 47907 USA ,grid.169077.e0000 0004 1937 2197Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907 USA
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15
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Wen C, Miura T, Voleti V, Yamaguchi K, Tsutsumi M, Yamamoto K, Otomo K, Fujie Y, Teramoto T, Ishihara T, Aoki K, Nemoto T, Hillman EMC, Kimura KD. 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images. eLife 2021; 10:e59187. [PMID: 33781383 PMCID: PMC8009680 DOI: 10.7554/elife.59187] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 02/23/2021] [Indexed: 12/12/2022] Open
Abstract
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.
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Affiliation(s)
- Chentao Wen
- Graduate School of Science, Nagoya City UniversityNagoyaJapan
| | - Takuya Miura
- Department of Biological Sciences, Graduate School of Science, Osaka UniversityToyonakaJapan
| | - Venkatakaushik Voleti
- Departments of Biomedical Engineering and Radiology and the Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Kazushi Yamaguchi
- Graduate School of Information Science and Technology, Hokkaido UniversitySapporoJapan
- National Institute for Physiological SciencesOkazakiJapan
| | - Motosuke Tsutsumi
- National Institute for Physiological SciencesOkazakiJapan
- Exploratory Research Center on Life and Living SystemsOkazakiJapan
| | - Kei Yamamoto
- National Institute for Basic Biology, National Institutes of Natural SciencesOkazakiJapan
- The Graduate School for Advanced StudyHayamaJapan
| | - Kohei Otomo
- National Institute for Physiological SciencesOkazakiJapan
- Exploratory Research Center on Life and Living SystemsOkazakiJapan
- The Graduate School for Advanced StudyHayamaJapan
| | - Yukako Fujie
- Department of Biological Sciences, Graduate School of Science, Osaka UniversityToyonakaJapan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Science, Kyushu UniversityFukuokaJapan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Science, Kyushu UniversityFukuokaJapan
| | - Kazuhiro Aoki
- Exploratory Research Center on Life and Living SystemsOkazakiJapan
- National Institute for Basic Biology, National Institutes of Natural SciencesOkazakiJapan
- The Graduate School for Advanced StudyHayamaJapan
| | - Tomomi Nemoto
- National Institute for Physiological SciencesOkazakiJapan
- Exploratory Research Center on Life and Living SystemsOkazakiJapan
- The Graduate School for Advanced StudyHayamaJapan
| | - Elizabeth MC Hillman
- Departments of Biomedical Engineering and Radiology and the Zuckerman Mind Brain Behavior Institute, Columbia UniversityNew YorkUnited States
| | - Koutarou D Kimura
- Graduate School of Science, Nagoya City UniversityNagoyaJapan
- Department of Biological Sciences, Graduate School of Science, Osaka UniversityToyonakaJapan
- RIKEN center for Advanced Intelligence ProjectTokyoJapan
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16
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Chaudhary S, Lee SA, Li Y, Patel DS, Lu H. Graphical-model framework for automated annotation of cell identities in dense cellular images. eLife 2021; 10:e60321. [PMID: 33625357 PMCID: PMC8032398 DOI: 10.7554/elife.60321] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/23/2021] [Indexed: 12/11/2022] Open
Abstract
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.
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Affiliation(s)
- Shivesh Chaudhary
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Sol Ah Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Yueyi Li
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
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17
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Kok RNU, Hebert L, Huelsz-Prince G, Goos YJ, Zheng X, Bozek K, Stephens GJ, Tans SJ, van Zon JS. OrganoidTracker: Efficient cell tracking using machine learning and manual error correction. PLoS One 2020; 15:e0240802. [PMID: 33091031 PMCID: PMC7580893 DOI: 10.1371/journal.pone.0240802] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.
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Affiliation(s)
| | - Laetitia Hebert
- Okinawa Institute of Science and Technology Graduate University (OIST), Onna-son, Okinawa, Japan
| | | | | | | | - Katarzyna Bozek
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Greg J. Stephens
- Okinawa Institute of Science and Technology Graduate University (OIST), Onna-son, Okinawa, Japan
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sander J. Tans
- AMOLF, Amsterdam, The Netherlands
- Bionanoscience Department, Kavli Institute of Nanoscience Delft, Delft University of Technology, Delft, The Netherlands
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18
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Piccinini F, Balassa T, Carbonaro A, Diosdi A, Toth T, Moshkov N, Tasnadi EA, Horvath P. Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates. Comput Struct Biotechnol J 2020; 18:1287-1300. [PMID: 32612752 PMCID: PMC7303562 DOI: 10.1016/j.csbj.2020.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.
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Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
| | - Antonella Carbonaro
- Department of Computer Science and Engineering, University of Bologna, Italy
| | - Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary
- National Research University Higher School of Economics, Moscow, Russia
| | - Ervin A. Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Single-Cell Technologies Ltd., Szeged, Hungary
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19
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Toyoshima Y, Wu S, Kanamori M, Sato H, Jang MS, Oe S, Murakami Y, Teramoto T, Park C, Iwasaki Y, Ishihara T, Yoshida R, Iino Y. Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biol 2020; 18:30. [PMID: 32188430 PMCID: PMC7081613 DOI: 10.1186/s12915-020-0745-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/29/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons. RESULTS Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging. CONCLUSION Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Stephen Wu
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hirofumi Sato
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Suzu Oe
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Yuko Murakami
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Chanhyun Park
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yuishi Iwasaki
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan.
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
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20
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Liu Y, Ng MK, Wu S. Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:435-448. [PMID: 29994480 DOI: 10.1109/tcbb.2018.2848904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, different domains may have different cluster structures. Due to rapid growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (including neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability.
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21
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Nürnberg E, Vitacolonna M, Klicks J, von Molitor E, Cesetti T, Keller F, Bruch R, Ertongur-Fauth T, Riedel K, Scholz P, Lau T, Schneider R, Meier J, Hafner M, Rudolf R. Routine Optical Clearing of 3D-Cell Cultures: Simplicity Forward. Front Mol Biosci 2020; 7:20. [PMID: 32154265 PMCID: PMC7046628 DOI: 10.3389/fmolb.2020.00020] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/04/2020] [Indexed: 12/11/2022] Open
Abstract
Three-dimensional cell cultures, such as spheroids and organoids, serve as increasingly important models in fundamental and applied research and start to be used for drug screening purposes. Optical tissue clearing procedures are employed to enhance visualization of fluorescence-stained organs, tissues, and three-dimensional cell cultures. To get a more systematic overview about the effects and applicability of optical tissue clearing on three-dimensional cell cultures, we compared six different clearing/embedding protocols on seven types of spheroid- and chip-based three-dimensional cell cultures of approximately 300 μm in size that were stained with nuclear dyes, immunofluorescence, cell trackers, and cyan fluorescent protein. Subsequent whole mount confocal microscopy and semi-automated image analysis were performed to quantify the effects. Quantitative analysis included fluorescence signal intensity and signal-to-noise ratio as a function of z-depth as well as segmentation and counting of nuclei and immunopositive cells. In general, these analyses revealed five key points, which largely confirmed current knowledge and were quantified in this study. First, there was a massive variability of effects of different clearing protocols on sample transparency and shrinkage as well as on dye quenching. Second, all tested clearing protocols worked more efficiently on samples prepared with one cell type than on co-cultures. Third, z-compensation was imperative to minimize variations in signal-to-noise ratio. Fourth, a combination of sample-inherent cell density, sample shrinkage, uniformity of signal-to-noise ratio, and image resolution had a strong impact on data segmentation, cell counts, and relative numbers of immunofluorescence-positive cells. Finally, considering all mentioned aspects and including a wish for simplicity and speed of protocols - in particular, for screening purposes - clearing with 88% Glycerol appeared to be the most promising option amongst the ones tested.
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Affiliation(s)
- Elina Nürnberg
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
- Zentralinstitut für Seelische Gesundheit, Department of Translational Brain Research, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mario Vitacolonna
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Julia Klicks
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Elena von Molitor
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Tiziana Cesetti
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Florian Keller
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Roman Bruch
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | | | | | | | - Thorsten Lau
- Zentralinstitut für Seelische Gesundheit, Department of Translational Brain Research, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Julia Meier
- TIP Oncology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Mathias Hafner
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Rüdiger Rudolf
- Institute of Molecular and Cell Biology, Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany
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22
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Dunn KW, Fu C, Ho DJ, Lee S, Han S, Salama P, Delp EJ. DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Sci Rep 2019; 9:18295. [PMID: 31797882 PMCID: PMC6892824 DOI: 10.1038/s41598-019-54244-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 11/08/2019] [Indexed: 12/22/2022] Open
Abstract
The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.
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Affiliation(s)
- Kenneth W Dunn
- Department of Medicine, Division of Nephrology Indiana University School of Medicine, 950 West Walnut St, R2-202, Indianapolis, IN, 46202, USA.
| | - Chichen Fu
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - David Joon Ho
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Soonam Lee
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shuo Han
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
| | - Edward J Delp
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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23
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Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat Methods 2019; 16:1323-1331. [DOI: 10.1038/s41592-019-0622-5] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/30/2019] [Indexed: 01/06/2023]
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24
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Ruszczycki B, Pels KK, Walczak A, Zamłyńska K, Such M, Szczepankiewicz AA, Hall MH, Magalska A, Magnowska M, Wolny A, Bokota G, Basu S, Pal A, Plewczynski D, Wilczyński GM. Three-Dimensional Segmentation and Reconstruction of Neuronal Nuclei in Confocal Microscopic Images. Front Neuroanat 2019; 13:81. [PMID: 31481881 PMCID: PMC6710455 DOI: 10.3389/fnana.2019.00081] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 07/31/2019] [Indexed: 12/31/2022] Open
Abstract
The detailed architectural examination of the neuronal nuclei in any brain region, using confocal microscopy, requires quantification of fluorescent signals in three-dimensional stacks of confocal images. An essential prerequisite to any quantification is the segmentation of the nuclei which are typically tightly packed in the tissue, the extreme being the hippocampal dentate gyrus (DG), in which nuclei frequently appear to overlap due to limitations in microscope resolution. Segmentation in DG is a challenging task due to the presence of a significant amount of image artifacts and densely packed nuclei. Accordingly, we established an algorithm based on continuous boundary tracing criterion aiming to reconstruct the nucleus surface and to separate the adjacent nuclei. The presented algorithm neither uses a pre-built nucleus model, nor performs image thresholding, which makes it robust against variations in image intensity and poor contrast. Further, the reconstructed surface is used to study morphology and spatial arrangement of the nuclear interior. The presented method is generally dedicated to segmentation of crowded, overlapping objects in 3D space. In particular, it allows us to study quantitatively the architecture of the neuronal nucleus using confocal-microscopic approach.
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Affiliation(s)
- Błażej Ruszczycki
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Agnieszka Walczak
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland
| | | | - Michał Such
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Center of New Technologies, University of Warsaw, Warsaw, Poland
| | | | - Małgorzata Hanna Hall
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Okinawa Institute of Science and Technology, Okinawa, Japan
| | - Adriana Magalska
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Marta Magnowska
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Artur Wolny
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Grzegorz Bokota
- Center of New Technologies, University of Warsaw, Warsaw, Poland
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Ayan Pal
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Dariusz Plewczynski
- Center of New Technologies, University of Warsaw, Warsaw, Poland.,Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
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25
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Iwanir S, Ruach R, Itskovits E, Pritz CO, Bokman E, Zaslaver A. Irrational behavior in C. elegans arises from asymmetric modulatory effects within single sensory neurons. Nat Commun 2019; 10:3202. [PMID: 31324786 PMCID: PMC6642097 DOI: 10.1038/s41467-019-11163-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 06/20/2019] [Indexed: 12/19/2022] Open
Abstract
C. elegans worms exhibit a natural chemotaxis towards food cues. This provides a potential platform to study the interactions between stimulus valence and innate behavioral preferences. Here we perform a comprehensive set of choice assays to measure worms' relative preference towards various attractants. Surprisingly, we find that when facing a combination of choices, worms' preferences do not always follow value-based hierarchy. In fact, the innate chemotaxis behavior in worms robustly violates key rationality paradigms of transitivity, independence of irrelevant alternatives and regularity. These violations arise due to asymmetric modulatory effects between the presented options. Functional analysis of the entire chemosensory system at a single-neuron resolution, coupled with analyses of mutants, defective in individual neurons, reveals that these asymmetric effects originate in specific sensory neurons.
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Affiliation(s)
- Shachar Iwanir
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Rotem Ruach
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Eyal Itskovits
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Christian O Pritz
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Eduard Bokman
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
| | - Alon Zaslaver
- Department of Genetics, Silberman Institute of Life Science, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.
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26
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Patel DS, Xu N, Lu H. Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans. Lab Anim (NY) 2019; 48:207-216. [PMID: 31217565 DOI: 10.1038/s41684-019-0326-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 05/17/2019] [Indexed: 11/09/2022]
Abstract
Deep phenotyping is an emerging conceptual paradigm and experimental approach aimed at measuring and linking many aspects of a phenotype to understand its underlying biology. To date, deep phenotyping has been applied mostly in cultured cells and used less in multicellular organisms. However, in the past decade, it has increasingly been recognized that deep phenotyping could lead to a better understanding of how genetics, environment and stochasticity affect the development, physiology and behavior of an organism. The nematode Caenorhabditis elegans is an invaluable model system for studying how genes affect a phenotypic trait, and new technologies have taken advantage of the worm's physical attributes to increase the throughput and informational content of experiments. Coupling of these technical advancements with computational and analytical tools has enabled a boom in deep-phenotyping studies of C. elegans. In this Review, we highlight how these new technologies and tools are digging into the biological origins of complex, multidimensional phenotypes.
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Affiliation(s)
- Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Nan Xu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.,The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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27
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Taylor MA, Vanwalleghem GC, Favre-Bulle IA, Scott EK. Diffuse light-sheet microscopy for stripe-free calcium imaging of neural populations. JOURNAL OF BIOPHOTONICS 2018; 11:e201800088. [PMID: 29920963 DOI: 10.1002/jbio.201800088] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 06/14/2018] [Indexed: 05/06/2023]
Abstract
Light-sheet microscopy is used extensively in developmental biology and neuroscience. One limitation of this approach is that absorption and scattering produces shadows in the illuminating light sheet, resulting in stripe artifacts. Here, we introduce diffuse light-sheet microscopes that use a line diffuser to randomize the light propagation within the image plane, allowing the light sheets to reform after obstacles. We incorporate diffuse light sheets in two existing configurations: selective plane illumination microscopy in which the sample is illuminated with a static sheet of light, and digitally scanned light sheet (DSLS) in which a thin Gaussian beam is scanned across the image plane during each acquisition. We compare diffuse light-sheet microscopes to their conventional counterparts for calcium imaging of neural activity in larval zebrafish. We show that stripe artifacts can cast deep shadows that conceal some neurons, and that the stripes can flicker, producing spurious signals that could be interpreted as biological activity. Diffuse light-sheets mitigate these problems, illuminating the blind spots produced by stripes and removing artifacts produced by the stripes' movements. The upgrade to diffuse light sheets is simple and inexpensive, especially in the case of DSLS, where it requires the addition of one optical element.
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Affiliation(s)
- Michael A Taylor
- School of Biomedical Sciences, The University of Queensland, St. Lucia, QLD, Australia
| | - Gilles C Vanwalleghem
- School of Biomedical Sciences, The University of Queensland, St. Lucia, QLD, Australia
| | - Itia A Favre-Bulle
- School of Biomedical Sciences, The University of Queensland, St. Lucia, QLD, Australia
| | - Ethan K Scott
- School of Biomedical Sciences, The University of Queensland, St. Lucia, QLD, Australia
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28
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Ravi B, Nassar LM, Kopchock RJ, Dhakal P, Scheetz M, Collins KM. Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans. J Vis Exp 2018. [PMID: 29443112 PMCID: PMC5912386 DOI: 10.3791/56911] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
It has become increasingly clear that neural circuit activity in behaving animals differs substantially from that seen in anesthetized or immobilized animals. Highly sensitive, genetically encoded fluorescent reporters of Ca2+ have revolutionized the recording of cell and synaptic activity using non-invasive optical approaches in behaving animals. When combined with genetic and optogenetic techniques, the molecular mechanisms that modulate cell and circuit activity during different behavior states can be identified. Here we describe methods for ratiometric Ca2+ imaging of single neurons in freely behaving Caenorhabditis elegans worms. We demonstrate a simple mounting technique that gently overlays worms growing on a standard Nematode Growth Media (NGM) agar block with a glass coverslip, permitting animals to be recorded at high-resolution during unrestricted movement and behavior. With this technique, we use the sensitive Ca2+ reporter GCaMP5 to record changes in intracellular Ca2+ in the serotonergic Hermaphrodite Specific Neurons (HSNs) as they drive egg-laying behavior. By co-expressing mCherry, a Ca2+-insensitive fluorescent protein, we can track the position of the HSN within ~ 1 µm and correct for fluctuations in fluorescence caused by changes in focus or movement. Simultaneous, infrared brightfield imaging allows for behavior recording and animal tracking using a motorized stage. By integrating these microscopic techniques and data streams, we can record Ca2+ activity in the C. elegans egg-laying circuit as it progresses between inactive and active behavior states over tens of minutes.
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Affiliation(s)
- Bhavya Ravi
- Neuroscience Program, University of Miami School of Medicine
| | - Layla M Nassar
- Neuroscience Program, University of Miami School of Medicine; Department of Biology, University of Miami
| | | | | | | | - Kevin M Collins
- Neuroscience Program, University of Miami School of Medicine; Department of Biology, University of Miami;
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29
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Nguyen JP, Linder AN, Plummer GS, Shaevitz JW, Leifer AM. Automatically tracking neurons in a moving and deforming brain. PLoS Comput Biol 2017; 13:e1005517. [PMID: 28545068 PMCID: PMC5436637 DOI: 10.1371/journal.pcbi.1005517] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 04/11/2017] [Indexed: 11/18/2022] Open
Abstract
Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.
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Affiliation(s)
- Jeffrey P. Nguyen
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
| | - Ashley N. Linder
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - George S. Plummer
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Joshua W. Shaevitz
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew M. Leifer
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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