1
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Laubscher E, Wang X, Razin N, Dougherty T, Xu RJ, Ombelets L, Pao E, Graf W, Moffitt JR, Yue Y, Van Valen D. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. Cell Syst 2024; 15:475-482.e6. [PMID: 38754367 DOI: 10.1016/j.cels.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/05/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep-learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from multiplexed error-robust FISH (MERFISH), sequential fluorescence in situ hybridization (seqFISH), or in situ RNA sequencing (ISS) experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.
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Affiliation(s)
- Emily Laubscher
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA 91125, USA
| | - Xuefei Wang
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Nitzan Razin
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Tom Dougherty
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Rosalind J Xu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02115, USA
| | - Lincoln Ombelets
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA 91125, USA
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - William Graf
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yisong Yue
- Division of Computational and Mathematical Sciences, Caltech, Pasadena, CA 91125, USA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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2
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Laubscher E, Wang X(J, Razin N, Dougherty T, Xu RJ, Ombelets L, Pao E, Graf W, Moffitt JR, Yue Y, Van Valen D. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.03.556122. [PMID: 37732188 PMCID: PMC10508757 DOI: 10.1101/2023.09.03.556122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually-tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.
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Affiliation(s)
- Emily Laubscher
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | | | - Nitzan Razin
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Tom Dougherty
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Rosalind J. Xu
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - Lincoln Ombelets
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - William Graf
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Jeffrey R. Moffitt
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
| | - Yisong Yue
- Division of Computational and Mathematical Sciences, Caltech, Pasadena, CA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
- Howard Hughes Medical Institute, Chevy Chase, MD
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3
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Batnasan E, Kärkkäinen M, Koivukoski S, Sadeesh N, Tollis S, Ruusuvuori P, Scaravilli M, Latonen L. Platinum-based drugs induce phenotypic alterations in nucleoli and Cajal bodies in prostate cancer cells. Cancer Cell Int 2024; 24:29. [PMID: 38218884 PMCID: PMC10790272 DOI: 10.1186/s12935-023-03205-0] [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: 07/21/2023] [Accepted: 12/28/2023] [Indexed: 01/15/2024] Open
Abstract
PURPOSE Platinum-based drugs are cytotoxic drugs commonly used in cancer treatment. They cause DNA damage, effects of which on chromatin and cellular responses are relatively well described. Yet, the nuclear stress responses related to RNA processing are incompletely known and may be relevant for the heterogeneity with which cancer cells respond to these drugs. Here, we determine the type and extent of nuclear stress responses of prostate cancer cells to clinically relevant platinum drugs. METHODS We study nucleolar and Cajal body (CB) responses to cisplatin, carboplatin, and oxaliplatin with immunofluorescence-based methods in prostate cancer cells. We utilize organelle-specific markers NPM, Fibrillarin, Coilin, and SMN1, and study CB-regulatory proteins FUS and TDP-43 using siRNA-mediated downregulation. RESULTS Different types of prostate cancer cells have different sensitivities to platinum drugs. With equally cytotoxic doses, cisplatin, and oxaliplatin induce prominent nucleolar and CB stress responses while the nuclear stress phenotypes to carboplatin are milder. We find that Coilin is a stress-specific marker for platinum drug response heterogeneity. We also find that CB-associated, stress-responsive RNA binding proteins FUS and TDP-43 control Coilin and CB biology in prostate cancer cells and, further, that TDP-43 is associated with stress-responsive CBs in prostate cancer cells. CONCLUSION Our findings provide insight into the heterologous responses of prostate cancer cells to different platinum drug treatments and indicate Coilin and TDP-43 as stress mediators in the varied outcomes. These results help understand cancer drug responses at a cellular level and have implications in tackling heterogeneity in cancer treatment outcomes.
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Affiliation(s)
- Enkhzaya Batnasan
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | - Minttu Kärkkäinen
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | - Sonja Koivukoski
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | - Nithin Sadeesh
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | - Sylvain Tollis
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | | | - Mauro Scaravilli
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland
| | - Leena Latonen
- Institute of Biomedicine, University of Eastern Finland, 1627, 70211, Kuopio, Finland.
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4
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Su PR, You L, Beerens C, Bezstarosti K, Demmers J, Pabst M, Kanaar R, Hsu CC, Chien MP. Microscopy-based single-cell proteomic profiling reveals heterogeneity in DNA damage response dynamics. CELL REPORTS METHODS 2022; 2:100237. [PMID: 35784653 PMCID: PMC9243628 DOI: 10.1016/j.crmeth.2022.100237] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 04/03/2022] [Accepted: 05/23/2022] [Indexed: 11/01/2022]
Abstract
Single-cell proteomics has the potential to decipher tumor heterogeneity, and a method like single-cell proteomics by mass spectrometry (SCoPE-MS) allows profiling several tens of single cells for >1,000 proteins per cell. This method, however, cannot link the proteome of individual cells with phenotypes of interest. Here, we developed a microscopy-based functional single-cell proteomic-profiling technology, called FUNpro, to address this. FUNpro enables screening, identification, and isolation of single cells of interest in a real-time fashion, even if the phenotypes are dynamic or the cells of interest are rare. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified the PDS5A protein contributing to the abnormal DDR dynamics and helping the cells survive after IR.
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Affiliation(s)
- Pin-Rui Su
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
| | - Li You
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Cecile Beerens
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Karel Bezstarosti
- Proteomics Core Facility, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jeroen Demmers
- Proteomics Core Facility, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Martin Pabst
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Roland Kanaar
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
| | - Miao-Ping Chien
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Erasmus MC Cancer Institute, Rotterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
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5
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Prigent S, Valades-Cruz CA, Leconte L, Salamero J, Kervrann C. STracking: a free and open-source python library for particle tracking and analysis. Bioinformatics 2022; 38:3671-3673. [PMID: 35639941 DOI: 10.1093/bioinformatics/btac365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Analysis of intra and extra cellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking…) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a python library that allows combining algorithms into standardized particle tracking pipelines. AVAILABILITY AND IMPLEMENTATION STracking is available as a python library using "pip install" and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using "pip install". The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sylvain Prigent
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Cesar Augusto Valades-Cruz
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Ludovic Leconte
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Jean Salamero
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
| | - Charles Kervrann
- SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042, Rennes, France.,SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005, Paris, France
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6
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Lagache T, Hanson A, Pérez-Ortega JE, Fairhall A, Yuste R. Tracking calcium dynamics from individual neurons in behaving animals. PLoS Comput Biol 2021; 17:e1009432. [PMID: 34624016 PMCID: PMC8528277 DOI: 10.1371/journal.pcbi.1009432] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/20/2021] [Accepted: 09/08/2021] [Indexed: 12/03/2022] Open
Abstract
Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.
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Affiliation(s)
- Thibault Lagache
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
| | - Alison Hanson
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University, New York, New York, United States of America
| | - Jesús E Pérez-Ortega
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Adrienne Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, Washington, United States of America
- UW Computational Neuroscience Center, University of Washington, Seattle, Washington, United States of America
| | - Rafael Yuste
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
- Marine Biological Laboratory, Woods Hole, Massachusetts, United States of America
- Donostia International Physics Center, San Sebastian, Spain
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7
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Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020; 18:2312-2325. [PMID: 32994890 PMCID: PMC7494605 DOI: 10.1016/j.csbj.2020.08.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
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Affiliation(s)
- Erik Meijering
- School of Computer Science and Engineering & Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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8
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Deep-learning method for data association in particle tracking. Bioinformatics 2020; 36:4935-4941. [DOI: 10.1093/bioinformatics/btaa597] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 06/10/2020] [Accepted: 06/28/2020] [Indexed: 01/09/2023] Open
Abstract
Abstract
Motivation
Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data.
Results
Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts.
Availability and implementation
The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking.
Supplementary information
Supplementary data are available at Bioinformatics online.
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9
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Aaron J, Wait E, DeSantis M, Chew TL. Practical Considerations in Particle and Object Tracking and Analysis. ACTA ACUST UNITED AC 2019; 83:e88. [PMID: 31050869 DOI: 10.1002/cpcb.88] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The rapid advancement of live-cell imaging technologies has enabled biologists to generate high-dimensional data to follow biological movement at the microscopic level. Yet, the "perceived" ease of use of modern microscopes has led to challenges whereby sub-optimal data are commonly generated that cannot support quantitative tracking and analysis as a result of various ill-advised decisions made during image acquisition. Even optimally acquired images often require further optimization through digital processing before they can be analyzed. In writing this article, we presume our target audience to be biologists with a foundational understanding of digital image acquisition and processing, who are seeking to understand the essential steps for particle/object tracking experiments. It is with this targeted readership in mind that we review the basic principles of image-processing techniques as well as analysis strategies commonly used for tracking experiments. We conclude this technical survey with a discussion of how movement behavior can be mathematically modeled and described. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Jesse Aaron
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia
| | - Eric Wait
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia
| | - Michael DeSantis
- Light Microscopy Facility, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia
| | - Teng-Leong Chew
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia.,Light Microscopy Facility, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia
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10
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Gómez AI, Cruz M, López-Giménez JF. Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm. PLoS One 2019; 14:e0211330. [PMID: 30759168 PMCID: PMC6373910 DOI: 10.1371/journal.pone.0211330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/12/2019] [Indexed: 11/24/2022] Open
Abstract
Current drug discovery procedures require fast and effective quantification of the pharmacological response evoked in living cells by agonist compounds. In the case of G-protein coupled receptors (GPCRs), the efficacy of a particular drug to initiate the endocytosis process is related to the formation of endocytic vesicles or endosomes and their subsequent internalisation within intracellular compartments that can be observed with high spatial and temporal resolution by fluorescence microscopy techniques. Recently, an algorithm has been proposed to evaluate the pharmacological response by estimating the number of endosomes per cell on time series of images. However, the algorithm was limited by the dependence on some manually set parameters and in some cases the quality of the image does not allow a reliable detection of the endosomes. Here we propose a simple, fast and automated image analysis method—the Δm algorithm- to quantify a pharmacological response with data obtained from fluorescence microscopy experiments. This algorithm does not require individual object detection and computes the relative increment of the third order moment in fluorescence microscopy images after filtering with the Laplacian of Gaussian function. It was tested on simulations demonstrating its ability to discriminate different experimental situations according to the number and the fluorescence signal intensity of the simulated endosomes. Finally and in order to validate this methodology with real data, the algorithm was applied to several time-course experiments based on the endocytosis of the mu opioid receptor (MOP) initiated by different agonist compounds. Each drug displayed a different Δm sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters.
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Affiliation(s)
- Ana I. Gómez
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, Santander, Spain
- * E-mail:
| | - Marcos Cruz
- Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, Santander, Spain
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11
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Abstract
The study of intracellular dynamic processes is of fundamental importance for understanding a wide variety of diseases and developing effective drugs and therapies. Advanced fluorescence microscopy imaging systems nowadays allow the recording of virtually any type of process in space and time with super-resolved detail and with high sensitivity and specificity. The large volume and high information content of the resulting image data, and the desire to obtain objective, quantitative descriptions and biophysical models of the processes of interest, require a high level of automation in data analysis. Two key tasks in extracting biologically meaningful information about intracellular dynamics from image data are particle tracking and particle trajectory analysis. Here we present state-of-the-art software tools for these tasks and describe how to use them.
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12
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Todd DW, Philip RC, Niihori M, Ringle RA, Coyle KR, Zehri SF, Zabala L, Mudery JA, Francis RH, Rodriguez JJ, Jacob A. A Fully Automated High-Throughput Zebrafish Behavioral Ototoxicity Assay. Zebrafish 2017; 14:331-342. [PMID: 28520533 DOI: 10.1089/zeb.2016.1412] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Zebrafish animal models lend themselves to behavioral assays that can facilitate rapid screening of ototoxic, otoprotective, and otoregenerative drugs. Structurally similar to human inner ear hair cells, the mechanosensory hair cells on their lateral line allow the zebrafish to sense water flow and orient head-to-current in a behavior called rheotaxis. This rheotaxis behavior deteriorates in a dose-dependent manner with increased exposure to the ototoxin cisplatin, thereby establishing itself as an excellent biomarker for anatomic damage to lateral line hair cells. Building on work by our group and others, we have built a new, fully automated high-throughput behavioral assay system that uses automated image analysis techniques to quantify rheotaxis behavior. This novel system consists of a custom-designed swimming apparatus and imaging system consisting of network-controlled Raspberry Pi microcomputers capturing infrared video. Automated analysis techniques detect individual zebrafish, compute their orientation, and quantify the rheotaxis behavior of a zebrafish test population, producing a powerful, high-throughput behavioral assay. Using our fully automated biological assay to test a standardized ototoxic dose of cisplatin against varying doses of compounds that protect or regenerate hair cells may facilitate rapid translation of candidate drugs into preclinical mammalian models of hearing loss.
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Affiliation(s)
- Douglas W Todd
- 1 Department of Electrical and Computer Engineering, The University of Arizona , Tucson, Arizona
| | - Rohit C Philip
- 1 Department of Electrical and Computer Engineering, The University of Arizona , Tucson, Arizona
| | - Maki Niihori
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona.,3 The University of Arizona Cancer Center , Tucson, Arizona
| | - Ryan A Ringle
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona
| | - Kelsey R Coyle
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona
| | - Sobia F Zehri
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona
| | - Leanne Zabala
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona.,4 College of Medicine, The University of Arizona , Tucson, Arizona
| | - Jordan A Mudery
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona.,4 College of Medicine, The University of Arizona , Tucson, Arizona
| | - Ross H Francis
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona.,4 College of Medicine, The University of Arizona , Tucson, Arizona
| | - Jeffrey J Rodriguez
- 1 Department of Electrical and Computer Engineering, The University of Arizona , Tucson, Arizona
| | - Abraham Jacob
- 2 Department of Otolaryngology, The University of Arizona , Tucson, Arizona.,3 The University of Arizona Cancer Center , Tucson, Arizona.,5 BIO5 Institute, The University of Arizona , Tucson, Arizona.,6 Ear & Hearing, Center for Neurosciences , Tucson, Arizona
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13
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Shen H, Tauzin LJ, Baiyasi R, Wang W, Moringo N, Shuang B, Landes CF. Single Particle Tracking: From Theory to Biophysical Applications. Chem Rev 2017; 117:7331-7376. [PMID: 28520419 DOI: 10.1021/acs.chemrev.6b00815] [Citation(s) in RCA: 273] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
After three decades of developments, single particle tracking (SPT) has become a powerful tool to interrogate dynamics in a range of materials including live cells and novel catalytic supports because of its ability to reveal dynamics in the structure-function relationships underlying the heterogeneous nature of such systems. In this review, we summarize the algorithms behind, and practical applications of, SPT. We first cover the theoretical background including particle identification, localization, and trajectory reconstruction. General instrumentation and recent developments to achieve two- and three-dimensional subdiffraction localization and SPT are discussed. We then highlight some applications of SPT to study various biological and synthetic materials systems. Finally, we provide our perspective regarding several directions for future advancements in the theory and application of SPT.
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Affiliation(s)
- Hao Shen
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Lawrence J Tauzin
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Rashad Baiyasi
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Wenxiao Wang
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Nicholas Moringo
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Bo Shuang
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
| | - Christy F Landes
- Department of Chemistry and ‡Department of Electrical and Computer Engineering, §Smalley-Curl Institute, Rice University , Houston, Texas 77251, United States
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14
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Wortzel I, Koifman G, Rotter V, Seger R, Porat Z. High Throughput Analysis of Golgi Structure by Imaging Flow Cytometry. Sci Rep 2017; 7:788. [PMID: 28400563 PMCID: PMC5429768 DOI: 10.1038/s41598-017-00909-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/16/2017] [Indexed: 11/24/2022] Open
Abstract
The Golgi apparatus is a dynamic organelle, which regulates the vesicular trafficking. While cellular trafficking requires active changes of the Golgi membranes, these are not accompanied by changes in the general Golgi’s structure. However, cellular processes such as mitosis, apoptosis and migration require fragmentation of the Golgi complex. Currently, these changes are most commonly studied by basic immunofluorescence and quantified by manual and subjective classification of the Golgi structure in 100–500 stained cells. Several other high-throughput methods exist as well, but those are either complicated or do not provide enough morphological information. Therefore, a simple and informative high content methodology should be beneficial for the study of Golgi architecture. Here we describe the use of high-throughput imaging flow cytometry for quantification of Golgi fragmentation, which provides a simple way to analyze the changes in an automated, quantitative and non-biased manner. Furthermore, it provides a rapid and accurate way to analyze more than 50,000 cells per sample. Our results demonstrate that this method is robust and statistically powerful, thus, providing a much-needed analytical tool for future studies on Golgi dynamics, and can be adapted to other experimental systems.
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Affiliation(s)
- Inbal Wortzel
- Dept. of Biological Regulation, the Weizmann Institute of Science, Rehovot, Israel
| | - Gabriela Koifman
- Dept. Of Molecular Cell Biology, the Weizmann Institute of Science, Rehovot, Israel
| | - Varda Rotter
- Dept. Of Molecular Cell Biology, the Weizmann Institute of Science, Rehovot, Israel
| | - Rony Seger
- Dept. of Biological Regulation, the Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Porat
- Dept. of Life Sciences Core Facilities, the Weizmann Institute of Science, Rehovot, Israel.
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15
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Quantitative Co-Localization and Pattern Analysis of Endo-Lysosomal Cargo in Subcellular Image Cytometry and Validation on Synthetic Image Sets. Methods Mol Biol 2017; 1594:93-128. [PMID: 28456978 DOI: 10.1007/978-1-4939-6934-0_6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Late endosomes and lysosomes (LE/LYSs) play a central role in trafficking of endocytic cargo, secretion of exosomes, and hydrolysis of ingested proteins and lipids. Failure in such processes can lead to lysosomal storage disorders in which a particular metabolite accumulates within LE/LYSs. Analysis of endocytic trafficking relies heavily on quantitative fluorescence microscopy, but evaluation of the huge image data sets is challenging and demands computer-assisted statistical tools. Here, we describe how to use SpatTrack ( www.sdu.dk/bmb/spattrack ), an imaging toolbox, which we developed for quantification of the distribution and dynamics of endo-lysosomal cargo from fluorescence images of living cells. First, we explain how to analyze experimental images of endocytic processes in Niemann Pick C2 disease fibroblasts using SpatTrack. We demonstrate how to quantify the location of the sterol-binding protein NPC2 in LE/LYSs relative to cholesterol -rich lysosomal storage organelles (LSOs) stained with filipin. Second, we show how to simulate realistic vesicle patterns in the cell geometry using Markov Chain Monte Carlo and suitable inter-vesicle and cell-vesicle interaction potentials. Finally, we use such synthetic vesicle patterns as "ground truth" for validation of two-channel analysis tools in SpatTrack, revealing their high reliability. An improved version of SpatTrack for microscopy-based quantification of cargo transport through the endo-lysosomal system accompanies this protocol.
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16
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Svoboda D, Ulman V. MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:310-321. [PMID: 27623575 DOI: 10.1109/tmi.2016.2606545] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.
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17
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18
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Basset A, Boulanger J, Salamero J, Bouthemy P, Kervrann C. Adaptive Spot Detection With Optimal Scale Selection in Fluorescence Microscopy Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:4512-4527. [PMID: 26353357 DOI: 10.1109/tip.2015.2450996] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) automatically selects the optimal scale corresponding to the most frequent spot size in the image. Four criteria are proposed and compared to determine the optimal scale in a scale-space framework. Then, the segmentation stage amounts to thresholding the LoG of the intensity image. In contrast to other methods, the threshold is locally adapted given a probability of false alarm (PFA) specified by the user for the whole set of images to be processed. The local threshold is automatically derived from the PFA value and local image statistics estimated in a window whose size is not a critical parameter. We also propose a new data set for benchmarking, consisting of six collections of one hundred images each, which exploits backgrounds extracted from real microscopy images. We have carried out an extensive comparative evaluation on several data sets with ground-truth, which demonstrates that ATLAS outperforms existing methods. ATLAS does not need any fine parameter tuning and requires very low computation time. Convincing results are also reported on real total internal reflection fluorescence microscopy images.
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19
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Stoeger T, Battich N, Herrmann MD, Yakimovich Y, Pelkmans L. Computer vision for image-based transcriptomics. Methods 2015; 85:44-53. [DOI: 10.1016/j.ymeth.2015.05.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 04/13/2015] [Accepted: 05/17/2015] [Indexed: 12/14/2022] Open
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20
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Smal I, Meijering E. Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy. Med Image Anal 2015; 24:163-189. [PMID: 26176413 DOI: 10.1016/j.media.2015.06.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Revised: 04/29/2015] [Accepted: 06/17/2015] [Indexed: 02/08/2023]
Abstract
Biological studies of intracellular dynamic processes commonly require motion analysis of large numbers of particles in live-cell time-lapse fluorescence microscopy imaging data. Many particle tracking methods have been developed in the past years as a first step toward fully automating this task and enabling high-throughput data processing. Two crucial aspects of any particle tracking method are the detection of relevant particles in the image frames and their linking or association from frame to frame to reconstruct the trajectories. The performance of detection techniques as well as specific combinations of detection and linking techniques for particle tracking have been extensively evaluated in recent studies. Comprehensive evaluations of linking techniques per se, on the other hand, are lacking in the literature. Here we present the results of a quantitative comparison of data association techniques for solving the linking problem in biological particle tracking applications. Nine multiframe and two more traditional two-frame techniques are evaluated as a function of the level of missing and spurious detections in various scenarios. The results indicate that linking techniques are generally more negatively affected by missing detections than by spurious detections. If misdetections can be avoided, there appears to be no need to use sophisticated multiframe linking techniques. However, in the practically likely case of imperfect detections, the latter are a safer choice. Our study provides users and developers with novel information to select the right linking technique for their applications, given a detection technique of known quality.
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Affiliation(s)
- Ihor Smal
- Biomedical Imaging Group Rotterdam, Erasmus MC-University Medical Center Rotterdam, Departments of Medical Informatics and Radiology, P.O. Box 2040, Rotterdam 3000 CA, The Netherlands.
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Erasmus MC-University Medical Center Rotterdam, Departments of Medical Informatics and Radiology, P.O. Box 2040, Rotterdam 3000 CA, The Netherlands
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21
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Georgescu W, Osseiran A, Rojec M, Liu Y, Bombrun M, Tang J, Costes SV. Characterizing the DNA Damage Response by Cell Tracking Algorithms and Cell Features Classification Using High-Content Time-Lapse Analysis. PLoS One 2015; 10:e0129438. [PMID: 26107175 PMCID: PMC4479605 DOI: 10.1371/journal.pone.0129438] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 05/10/2015] [Indexed: 01/08/2023] Open
Abstract
Traditionally, the kinetics of DNA repair have been estimated using immunocytochemistry by labeling proteins involved in the DNA damage response (DDR) with fluorescent markers in a fixed cell assay. However, detailed knowledge of DDR dynamics across multiple cell generations cannot be obtained using a limited number of fixed cell time-points. Here we report on the dynamics of 53BP1 radiation induced foci (RIF) across multiple cell generations using live cell imaging of non-malignant human mammary epithelial cells (MCF10A) expressing histone H2B-GFP and the DNA repair protein 53BP1-mCherry. Using automatic extraction of RIF imaging features and linear programming techniques, we were able to characterize detailed RIF kinetics for 24 hours before and 24 hours after exposure to low and high doses of ionizing radiation. High-content-analysis at the single cell level over hundreds of cells allows us to quantify precisely the dose dependence of 53BP1 protein production, RIF nuclear localization and RIF movement after exposure to X-ray. Using elastic registration techniques based on the nuclear pattern of individual cells, we could describe the motion of individual RIF precisely within the nucleus. We show that DNA repair occurs in a limited number of large domains, within which multiple small RIFs form, merge and/or resolve with random motion following normal diffusion law. Large foci formation is shown to be mainly happening through the merging of smaller RIF rather than through growth of an individual focus. We estimate repair domain sizes of 7.5 to 11 µm2 with a maximum number of ~15 domains per MCF10A cell. This work also highlights DDR which are specific to doses larger than 1 Gy such as rapid 53BP1 protein increase in the nucleus and foci diffusion rates that are significantly faster than for spontaneous foci movement. We hypothesize that RIF merging reflects a "stressed" DNA repair process that has been taken outside physiological conditions when too many DSB occur at once. High doses of ionizing radiation lead to RIF merging into repair domains which in turn increases DSB proximity and misrepair. Such finding may therefore be critical to explain the supralinear dose dependence for chromosomal rearrangement and cell death measured after exposure to ionizing radiation.
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Affiliation(s)
- Walter Georgescu
- Lawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America
| | - Alma Osseiran
- Lawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America
| | - Maria Rojec
- Lawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America
| | - Yueyong Liu
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, United States of America
| | | | - Jonathan Tang
- Lawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America
| | - Sylvain V. Costes
- Lawrence Berkeley National Laboratory, Life Sciences Division, Berkeley, CA, United States of America
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22
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Štěpka K, Matula P, Matula P, Wörz S, Rohr K, Kozubek M. Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy. Cytometry A 2015; 87:759-72. [PMID: 26033916 DOI: 10.1002/cyto.a.22692] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 03/06/2015] [Accepted: 04/30/2015] [Indexed: 12/11/2022]
Abstract
Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.
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Affiliation(s)
- Karel Štěpka
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Pavel Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Stefan Wörz
- IPMB and BIOQUANT, Department of Bioinformatics and Functional Genomics, and DKFZ, University of Heidelberg, Heidelberg, Germany
| | - Karl Rohr
- IPMB and BIOQUANT, Department of Bioinformatics and Functional Genomics, and DKFZ, University of Heidelberg, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
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23
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Godinez WJ, Rohr K. Tracking multiple particles in fluorescence time-lapse microscopy images via probabilistic data association. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:415-432. [PMID: 25252280 DOI: 10.1109/tmi.2014.2359541] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to recalculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2-D and 3-D images as well as to real 2-D and 3-D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2-D and 3-D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.
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24
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Pécot T, Bouthemy P, Boulanger J, Chessel A, Bardin S, Salamero J, Kervrann C. Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:667-80. [PMID: 25531952 DOI: 10.1109/tip.2014.2380178] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Image analysis applied to fluorescence live cell microscopy has become a key tool in molecular biology since it enables to characterize biological processes in space and time at the subcellular level. In fluorescence microscopy imaging, the moving tagged structures of interest, such as vesicles, appear as bright spots over a static or nonstatic background. In this paper, we consider the problem of vesicle segmentation and time-varying background estimation at the cellular scale. The main idea is to formulate the joint segmentation-estimation problem in the general conditional random field framework. Furthermore, segmentation of vesicles and background estimation are alternatively performed by energy minimization using a min cut-max flow algorithm. The proposed approach relies on a detection measure computed from intensity contrasts between neighboring blocks in fluorescence microscopy images. This approach permits analysis of either 2D + time or 3D + time data. We demonstrate the performance of the so-called C-CRAFT through an experimental comparison with the state-of-the-art methods in fluorescence video-microscopy. We also use this method to characterize the spatial and temporal distribution of Rab6 transport carriers at the cell periphery for two different specific adhesion geometries.
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25
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Gupta A, Lloyd-Price J, Neeli-Venkata R, Oliveira SMD, Ribeiro AS. In vivo kinetics of segregation and polar retention of MS2-GFP-RNA complexes in Escherichia coli. Biophys J 2014; 106:1928-37. [PMID: 24806925 DOI: 10.1016/j.bpj.2014.03.035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 03/27/2014] [Accepted: 03/28/2014] [Indexed: 10/25/2022] Open
Abstract
The cytoplasm of Escherichia coli is a crowded, heterogeneous environment. From single cell live imaging, we investigated the spatial kinetics and heterogeneities of synthetic RNA-protein complexes. First, although their known tendency to accumulate at the cell poles does not appear to introduce asymmetries between older and newer cell poles within a cell lifetime, these emerge with cell divisions. This suggests strong polar retention of the complexes, which we verified in their history of positions and mean escape time from the poles. Next, we show that the polar retention relies on anisotropies in the displacement distribution in the region between midcell and poles, whereas the speed is homogeneous along the major cell axis. Afterward, we establish that these regions are at the border of the nucleoid and shift outward with cell growth, due to the nucleoid's replication. Overall, the spatiotemporal kinetics of the complexes, which is robust to suboptimal temperatures, suggests that nucleoid occlusion is a source of dynamic heterogeneities of macromolecules in E. coli that ultimately generate phenotypic differences between sister cells.
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Affiliation(s)
- Abhishekh Gupta
- Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Jason Lloyd-Price
- Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Ramakanth Neeli-Venkata
- Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Samuel M D Oliveira
- Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Andre S Ribeiro
- Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
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26
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Gupta A, Lloyd-Price J, Oliveira SMD, Yli-Harja O, Muthukrishnan AB, Ribeiro AS. Robustness of the division symmetry inEscherichia coliand functional consequences of symmetry breaking. Phys Biol 2014; 11:066005. [DOI: 10.1088/1478-3975/11/6/066005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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27
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Garmendia-Torres C, Skupin A, Michael SA, Ruusuvuori P, Kuwada NJ, Falconnet D, Cary GA, Hansen C, Wiggins PA, Dudley AM. Unidirectional P-body transport during the yeast cell cycle. PLoS One 2014; 9:e99428. [PMID: 24918601 PMCID: PMC4053424 DOI: 10.1371/journal.pone.0099428] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Accepted: 05/12/2014] [Indexed: 12/29/2022] Open
Abstract
P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained “hovering” near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions.
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Affiliation(s)
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, California, United States of America
| | - Sean A. Michael
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Pekka Ruusuvuori
- Tampere University of Technology, Pori, Finland
- BioMediTech, University of Tampere, Tampere, Finland
| | - Nathan J. Kuwada
- Physics and Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Didier Falconnet
- Centre for High-Throughput Biology, Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gregory A. Cary
- Institute for Systems Biology, Seattle, Washington, United States of America
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
| | - Carl Hansen
- Centre for High-Throughput Biology, Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Paul A. Wiggins
- Physics and Bioengineering, University of Washington, Seattle, Washington, United States of America
| | - Aimée M. Dudley
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
- Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America
- * E-mail:
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28
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Ruusuvuori P, Paavolainen L, Rutanen K, Mäki A, Huttunen H, Marjomäki V. Quantitative analysis of dynamic association in live biological fluorescent samples. PLoS One 2014; 9:e94245. [PMID: 24728133 PMCID: PMC3984138 DOI: 10.1371/journal.pone.0094245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 03/13/2014] [Indexed: 11/19/2022] Open
Abstract
Determining vesicle localization and association in live microscopy may be challenging due to non-simultaneous imaging of rapidly moving objects with two excitation channels. Besides errors due to movement of objects, imaging may also introduce shifting between the image channels, and traditional colocalization methods cannot handle such situations. Our approach to quantifying the association between tagged proteins is to use an object-based method where the exact match of object locations is not assumed. Point-pattern matching provides a measure of correspondence between two point-sets under various changes between the sets. Thus, it can be used for robust quantitative analysis of vesicle association between image channels. Results for a large set of synthetic images shows that the novel association method based on point-pattern matching demonstrates robust capability to detect association of closely located vesicles in live cell-microscopy where traditional colocalization methods fail to produce results. In addition, the method outperforms compared Iterated Closest Points registration method. Results for fixed and live experimental data shows the association method to perform comparably to traditional methods in colocalization studies for fixed cells and to perform favorably in association studies for live cells.
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Affiliation(s)
- Pekka Ruusuvuori
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- Department of Biological and Environmental Science/Nanoscience Center, University of Jyväskylä, Jyväskylä, Finland
| | - Lassi Paavolainen
- Department of Biological and Environmental Science/Nanoscience Center, University of Jyväskylä, Jyväskylä, Finland
- Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Kalle Rutanen
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- Department of Mathematics, Tampere University of Technology, Tampere, Finland
| | - Anita Mäki
- Department of Biological and Environmental Science/Nanoscience Center, University of Jyväskylä, Jyväskylä, Finland
| | - Heikki Huttunen
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Varpu Marjomäki
- Department of Biological and Environmental Science/Nanoscience Center, University of Jyväskylä, Jyväskylä, Finland
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29
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Cary GA, Yoon SH, Torres CG, Wang K, Hays M, Ludlow C, Goodlett DR, Dudley AM. Identification and characterization of a drug-sensitive strain enables puromycin-based translational assays in Saccharomyces cerevisiae. Yeast 2014; 31:167-78. [PMID: 24610064 DOI: 10.1002/yea.3007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Revised: 02/27/2014] [Accepted: 03/03/2014] [Indexed: 11/11/2022] Open
Abstract
Puromycin is an aminonucleoside antibiotic with structural similarity to aminoacyl tRNA. This structure allows the drug to bind the ribosomal A site and incorporate into nascent polypeptides, causing chain termination, ribosomal subunit dissociation and widespread translational arrest at high concentrations. In contrast, at sufficiently low concentrations, puromycin incorporates primarily at the C-terminus of proteins. While a number of techniques utilize puromycin incorporation as a tool for probing translational activity in vivo, these methods cannot be applied in yeasts that are insensitive to puromycin. Here, we describe a mutant strain of the yeast Saccharomyces cerevisiae that is sensitive to puromycin and characterize the cellular response to the drug. Puromycin inhibits the growth of yeast cells mutant for erg6∆, pdr1∆ and pdr3∆ (EPP) on both solid and liquid media. Puromycin also induces the aggregation of the cytoplasmic processing body component Edc3 in the mutant strain. We establish that puromycin is rapidly incorporated into yeast proteins and test the effects of puromycin on translation in vivo. This study establishes the EPP strain as a valuable tool for implementing puromycin-based assays in yeast, which will enable new avenues of inquiry into protein production and maturation.
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Affiliation(s)
- Gregory A Cary
- Institute for Systems Biology, Seattle, WA, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
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Chenouard N, Smal I, de Chaumont F, Maška M, Sbalzarini IF, Gong Y, Cardinale J, Carthel C, Coraluppi S, Winter M, Cohen AR, Godinez WJ, Rohr K, Kalaidzidis Y, Liang L, Duncan J, Shen H, Xu Y, Magnusson KEG, Jaldén J, Blau HM, Paul-Gilloteaux P, Roudot P, Kervrann C, Waharte F, Tinevez JY, Shorte SL, Willemse J, Celler K, van Wezel GP, Dan HW, Tsai YS, de Solórzano CO, Olivo-Marin JC, Meijering E. Objective comparison of particle tracking methods. Nat Methods 2014; 11:281-9. [PMID: 24441936 PMCID: PMC4131736 DOI: 10.1038/nmeth.2808] [Citation(s) in RCA: 468] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 12/11/2013] [Indexed: 01/27/2023]
Abstract
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.
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Affiliation(s)
- Nicolas Chenouard
- Institut Pasteur, Unité d'Analyse d'Images Quantitative, Centre National de la Recherche Scientifique Unité de Recherche Associée 2582, Paris, France
- Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- New York University Neuroscience Institute, New York University Medical Center, New York, New York USA
| | - Ihor Smal
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Fabrice de Chaumont
- Institut Pasteur, Unité d'Analyse d'Images Quantitative, Centre National de la Recherche Scientifique Unité de Recherche Associée 2582, Paris, France
| | - Martin Maška
- Center for Applied Medical Research, University of Navarra, Pamplona, Spain
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Ivo F Sbalzarini
- MOSAIC Group, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Yuanhao Gong
- MOSAIC Group, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Janick Cardinale
- MOSAIC Group, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | | | | | - Mark Winter
- Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania USA
| | - Andrew R Cohen
- Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania USA
| | - William J Godinez
- Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany
| | - Karl Rohr
- Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
- Division of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany
| | - Yannis Kalaidzidis
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia
| | - Liang Liang
- Department of Electrical Engineering, Yale University, New Haven, Connecticut USA
| | - James Duncan
- Department of Electrical Engineering, Yale University, New Haven, Connecticut USA
| | - Hongying Shen
- Department of Cell Biology, Yale University, New Haven, Connecticut USA
| | - Yingke Xu
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Klas E G Magnusson
- Department of Signal Processing, ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joakim Jaldén
- Department of Signal Processing, ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Helen M Blau
- Department of Microbiology and Immunology, Baxter Laboratory for Stem Cell Biology, Stanford University School of Medicine, Stanford, California USA
| | | | | | | | | | - Jean-Yves Tinevez
- Plateforme d'Imagerie Dynamique, Imagopole, Institut Pasteur, Paris, France
| | - Spencer L Shorte
- Plateforme d'Imagerie Dynamique, Imagopole, Institut Pasteur, Paris, France
| | - Joost Willemse
- Molecular Biotechnology Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Katherine Celler
- Molecular Biotechnology Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Gilles P van Wezel
- Molecular Biotechnology Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Han-Wei Dan
- Department of Biomedical Engineering, Chung Yuan Christian University, Chung Li City, Taiwan, China
| | - Yuh-Show Tsai
- Department of Biomedical Engineering, Chung Yuan Christian University, Chung Li City, Taiwan, China
| | | | - Jean-Christophe Olivo-Marin
- Institut Pasteur, Unité d'Analyse d'Images Quantitative, Centre National de la Recherche Scientifique Unité de Recherche Associée 2582, Paris, France
| | - Erik Meijering
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Rizk A, Paul G, Incardona P, Bugarski M, Mansouri M, Niemann A, Ziegler U, Berger P, Sbalzarini IF. Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nat Protoc 2014; 9:586-96. [DOI: 10.1038/nprot.2014.037] [Citation(s) in RCA: 173] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Annila T, Lihavainen E, Marques IJ, Williams DR, Yli-Harja O, Ribeiro A. ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis. BMC Bioinformatics 2013; 14 Suppl 10:S5. [PMID: 24267347 PMCID: PMC3750475 DOI: 10.1186/1471-2105-14-s10-s5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs. Methods We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface. Results We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression. Conclusions ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.
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Röding M, Deschout H, Martens T, Notelaers K, Hofkens J, Ameloot M, Braeckmans K, Särkkä A, Rudemo M. Automatic particle detection in microscopy using temporal correlations. Microsc Res Tech 2013; 76:997-1006. [PMID: 23857566 DOI: 10.1002/jemt.22260] [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: 06/24/2013] [Accepted: 06/29/2013] [Indexed: 11/11/2022]
Abstract
One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.
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Affiliation(s)
- Magnus Röding
- Department of Mathematical Statistics, Chalmers University of Technology and Gothenburg University, Gothenburg, Sweden
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Li Y, Ishitsuka Y, Hedde PN, Nienhaus GU. Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization. ACS NANO 2013; 7:5207-14. [PMID: 23647371 DOI: 10.1021/nn4009388] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
In localization-based super-resolution microscopy, individual fluorescent markers are stochastically photoactivated and subsequently localized within a series of camera frames, yielding a final image with a resolution far beyond the diffraction limit. Yet, before localization can be performed, the subregions within the frames where the individual molecules are present have to be identified-oftentimes in the presence of high background. In this work, we address the importance of reliable molecule identification for the quality of the final reconstructed super-resolution image. We present a fast and robust algorithm (a-livePALM) that vastly improves the molecule detection efficiency while minimizing false assignments that can lead to image artifacts.
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Affiliation(s)
- Yiming Li
- Institute of Applied Physics and Center for Functional Nanostructures (CFN), Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Strasse 1, 76131 Karlsruhe, Germany
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Differential protein partitioning within the herpesvirus tegument and envelope underlies a complex and variable virion architecture. Proc Natl Acad Sci U S A 2013; 110:E1613-20. [PMID: 23569236 DOI: 10.1073/pnas.1221896110] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The herpesvirus virion is a multilayered structure consisting of a DNA-filled capsid, tegument, and envelope. Detailed reconstructions of the capsid are possible based on its icosahedral symmetry, but the surrounding tegument and envelope layers lack regular architecture. To circumvent limitations of symmetry-based ultrastructural reconstruction methods, a fluorescence approach was developed using single-particle imaging combined with displacement measurements at nanoscale resolution. An analysis of 11 tegument and envelope proteins defined the composition and plasticity of symmetric and asymmetric elements of the virion architecture. The resulting virion protein map ascribes molecular composition to density profiles previously acquired by traditional ultrastructural methods, and provides a way forward to examine the dynamics of the virion architecture during infection.
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Abstract
Bioimaging software developed in a research setting often is not widely used by the scientific community. We suggest that, to maximize both the public's and researchers' investments, usability should be a more highly valued goal. We describe specific characteristics of usability toward which bioimaging software projects should aim.
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Affiliation(s)
- Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, U.S.A
| | - Lee Kamentsky
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, U.S.A
| | - Kevin W. Eliceiri
- Laboratory for Optical and Computational Instrumentation (LOCI), University of Wisconsin at Madison, Madison, Wisconsin, U.S.A
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Gold ES, Ramsey SA, Sartain MJ, Selinummi J, Podolsky I, Rodriguez DJ, Moritz RL, Aderem A. ATF3 protects against atherosclerosis by suppressing 25-hydroxycholesterol-induced lipid body formation. ACTA ACUST UNITED AC 2012; 209:807-17. [PMID: 22473958 PMCID: PMC3328364 DOI: 10.1084/jem.20111202] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The transcription factor ATF3 inhibits lipid body formation in macrophages during atherosclerosis in part by dampening the expression of cholesterol 25-hydroxylase. Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipid-loaded macrophages in the arterial wall. We demonstrate that macrophage lipid body formation can be induced by modified lipoproteins or by inflammatory Toll-like receptor agonists. We used an unbiased approach to study the overlap in these pathways to identify regulators that control foam cell formation and atherogenesis. An analysis method integrating epigenomic and transcriptomic datasets with a transcription factor (TF) binding site prediction algorithm suggested that the TF ATF3 may regulate macrophage foam cell formation. Indeed, we found that deletion of this TF results in increased lipid body accumulation, and that ATF3 directly regulates transcription of the gene encoding cholesterol 25-hydroxylase. We further showed that production of 25-hydroxycholesterol (25-HC) promotes macrophage foam cell formation. Finally, deletion of ATF3 in Apoe−/− mice led to in vivo increases in foam cell formation, aortic 25-HC levels, and disease progression. These results define a previously unknown role for ATF3 in controlling macrophage lipid metabolism and demonstrate that ATF3 is a key intersection point for lipid metabolic and inflammatory pathways in these cells.
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Motion analysis of live objects by super-resolution fluorescence microscopy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2011; 2012:859398. [PMID: 22162725 PMCID: PMC3227432 DOI: 10.1155/2012/859398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 09/26/2011] [Indexed: 11/21/2022]
Abstract
Motion analysis plays an important role in studing activities or behaviors of live objects in medicine, biotechnology, chemistry, physics, spectroscopy, nanotechnology, enzymology, and biological engineering. This paper briefly reviews the developments in this area mostly in the recent three years, especially for cellular analysis in fluorescence microscopy. The topic has received much attention with the increasing demands in biomedical applications. The tasks of motion analysis include detection and tracking of objects, as well as analysis of motion behavior, living activity, events, motion statistics, and so forth. In the last decades, hundreds of papers have been published in this research topic. They cover a wide area, such as investigation of cell, cancer, virus, sperm, microbe, karyogram, and so forth. These contributions are summarized in this review. Developed methods and practical examples are also introduced. The review is useful to people in the related field for easy referral of the state of the art.
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Bass GT, Ryall KA, Katikapalli A, Taylor BE, Dang ST, Acton ST, Saucerman JJ. Automated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy. J Mol Cell Cardiol 2011; 52:923-30. [PMID: 22142594 DOI: 10.1016/j.yjmcc.2011.11.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2011] [Revised: 10/08/2011] [Accepted: 11/13/2011] [Indexed: 11/24/2022]
Abstract
Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network's higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell-cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, β-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte-myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening.
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Affiliation(s)
- Gregory T Bass
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908-0759, USA
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Oheim M. Advances and challenges in high-throughput microscopy for live-cell subcellular imaging. Expert Opin Drug Discov 2011; 6:1299-315. [DOI: 10.1517/17460441.2011.637105] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Martin Oheim
- INSERM U603, CNRS UMR 8154, Université Paris Descartes, PRES Sorbonne Paris Cité, Laboratory of Neurophysiology and New Microscopies, F-75006 Paris, France ;
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